From 4f2ddeb61a5cdb9896e8852f3748ada23cc3e937 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Mon, 5 Oct 2026 14:28:13 +0800
Subject: [PATCH 01/38] langid: LANGID role scaffolding

Add the language ID role: public header include/transcribe/langid.h
(info, label table, session lifecycle, run, result / candidate /
timings accessors), TRANSCRIBE_ROLE_LANGID, TRANSCRIBE_ERR_INPUT_TOO_SHORT
(21), ABI struct ids 19-23, Arch::langid ops table and its resolve_roles
check.

The dispatcher (src/transcribe-langid.cpp) owns everything that does not
depend on the family: label table validation, the allowed set, the crop
to the last max_audio_ms, softmax, allowed_mass, ranking and top-k.
Families only return logits.

Fixes carried from the langid.cpp review, each covered by
tests/langid_dispatch_unit.cpp against a fake arch:
- H1: max_audio_ms below 500 ms is rejected at session init, and the
  minimum is checked on the audio actually scored, after the crop.
- H2: only NULL means "all labels"; a non-NULL list with n_allowed <= 0,
  NULL with n_allowed > 0, or a NULL element is INVALID_ARG.
- H3: non-finite PCM is rejected up front; non-finite or miscounted
  logits fail the run instead of being ranked.
- H6: duplicate or empty codes and colliding / dangling aliases fail the
  load with TRANSCRIBE_ERR_GGUF.

No family serves the role yet. Bindings regenerate in a later commit, so
the generator / bindgen --check gates are red until then.
---
 include/transcribe.h            |  10 +
 include/transcribe/extensions.h |   1 +
 include/transcribe/langid.h     | 142 ++++++++++
 src/CMakeLists.txt              |   1 +
 src/transcribe-arch.cpp         |   5 +-
 src/transcribe-arch.h           |   2 +
 src/transcribe-langid.cpp       | 447 +++++++++++++++++++++++++++++
 src/transcribe-langid.h         |  71 +++++
 src/transcribe.cpp              |  23 ++
 tests/CMakeLists.txt            |   4 +-
 tests/api_smoke.c               |  36 +++
 tests/langid_dispatch_unit.cpp  | 486 ++++++++++++++++++++++++++++++++
 tests/role_resolve_unit.cpp     |  18 ++
 13 files changed, 1243 insertions(+), 3 deletions(-)
 create mode 100644 include/transcribe/langid.h
 create mode 100644 src/transcribe-langid.cpp
 create mode 100644 src/transcribe-langid.h
 create mode 100644 tests/langid_dispatch_unit.cpp

diff --git a/include/transcribe.h b/include/transcribe.h
index c36df7b75..ec016f3c4 100644
--- a/include/transcribe.h
+++ b/include/transcribe.h
@@ -315,6 +315,9 @@ typedef enum {
     TRANSCRIBE_ERR_OUTPUT_REPETITION      = 19,
     /* The model does not serve the role this call needs; see transcribe_model_roles(). */
     TRANSCRIBE_ERR_UNSUPPORTED_ROLE       = 20,
+    /* The audio is shorter than the role's minimum (e.g. language ID's
+     * transcribe_langid_info::min_audio_ms). Nothing was computed. */
+    TRANSCRIBE_ERR_INPUT_TOO_SHORT        = 21,
 } transcribe_status;
 
 /*
@@ -391,6 +394,12 @@ typedef enum {
     TRANSCRIBE_ABI_DIARIZE_INFO           = 16,
     TRANSCRIBE_ABI_DIARIZE_SESSION_PARAMS = 17,
     TRANSCRIBE_ABI_DIARIZE_PARAMS         = 18,
+    /* include/transcribe/langid.h */
+    TRANSCRIBE_ABI_LANGID_INFO            = 19,
+    TRANSCRIBE_ABI_LANGID_SESSION_PARAMS  = 20,
+    TRANSCRIBE_ABI_LANGID_PARAMS          = 21,
+    TRANSCRIBE_ABI_LANGID_RESULT          = 22,
+    TRANSCRIBE_ABI_LANGID_CANDIDATE       = 23,
 } transcribe_abi_struct;
 
 /* sizeof / alignof of the selected public struct, or 0 for an unknown id.
@@ -1362,6 +1371,7 @@ TRANSCRIBE_API void transcribe_capabilities_init(struct transcribe_capabilities
 typedef enum {
     TRANSCRIBE_ROLE_ASR     = 1u << 0,
     TRANSCRIBE_ROLE_DIARIZE = 1u << 1,
+    TRANSCRIBE_ROLE_LANGID  = 1u << 2,
 } transcribe_role;
 
 /*
diff --git a/include/transcribe/extensions.h b/include/transcribe/extensions.h
index 16c5684ad..f64cd75d6 100644
--- a/include/transcribe/extensions.h
+++ b/include/transcribe/extensions.h
@@ -15,6 +15,7 @@
 
 #include "transcribe.h"
 #include "transcribe/diarize.h"
+#include "transcribe/langid.h"
 #include "transcribe/moonshine_streaming.h"
 #include "transcribe/parakeet.h"
 #include "transcribe/sortformer.h"
diff --git a/include/transcribe/langid.h b/include/transcribe/langid.h
new file mode 100644
index 000000000..bd243682f
--- /dev/null
+++ b/include/transcribe/langid.h
@@ -0,0 +1,142 @@
+/*
+ * include/transcribe/langid.h - LANGID role: which language is spoken.
+ *
+ * Includes transcribe.h; safe to include in C or C++ TUs. For models whose
+ * product is a language decision (transcribe_model_roles() has
+ * TRANSCRIBE_ROLE_LANGID).
+ *
+ * Open a transcribe_langid_session on a loaded model, run it on a clip,
+ * then read the ranked candidates. Labels are the model's own codes ("en",
+ * "iw", "jw"); there is no canonicalization, so match a code against an ASR
+ * model's transcribe_capabilities::languages yourself. Usage guide:
+ * docs/langid.md. Threading and lifetime rules: docs/roles.md.
+ */
+
+#ifndef TRANSCRIBE_LANGID_H
+#define TRANSCRIBE_LANGID_H
+
+#include "transcribe.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct transcribe_langid_session;
+
+/* Static facts about a language ID model. */
+struct transcribe_langid_info {
+    uint64_t struct_size;
+    int32_t  sample_rate;  /* input PCM rate (16000) */
+    int32_t  n_labels;     /* label indices are [0, n_labels) */
+    int32_t  min_audio_ms; /* shorter scored audio is TRANSCRIBE_ERR_INPUT_TOO_SHORT */
+};
+
+struct transcribe_langid_session_params {
+    uint64_t struct_size;
+    int32_t  n_threads; /* 0 = library default */
+    /* Longer input is scored on its LAST max_audio_ms. 0 = 30000. A value
+     * below transcribe_langid_info::min_audio_ms is INVALID_ARG. */
+    int32_t  max_audio_ms;
+};
+
+struct transcribe_langid_params {
+    uint64_t             struct_size;
+    /* Restrict the decision to these labels (codes or aliases, borrowed for
+     * the call). NULL with n_allowed == 0 means every label; that is the
+     * only "all" form. A non-NULL list with n_allowed <= 0, a NULL list
+     * with n_allowed > 0, or a NULL element is INVALID_ARG. An unknown code
+     * is TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE. Duplicates count once. */
+    const char * const * allowed;
+    int32_t              n_allowed;
+    int32_t              top_k; /* keep the best top_k candidates; 0 = every allowed label */
+};
+
+/* Summary of the last run. */
+struct transcribe_langid_result {
+    uint64_t struct_size;
+    int32_t  n_candidates; /* rows readable via transcribe_langid_get_candidate */
+    int32_t  n_allowed;    /* labels in the allowed set (before top_k) */
+    /* Sum of p_unrestricted over the allowed set; 1 when unrestricted. A low
+     * value means the speech is probably outside the allowed set. */
+    float    allowed_mass;
+    int64_t  audio_ms; /* audio actually scored, after the crop */
+};
+
+/* One ranked label. code and name point at model-owned storage, valid until
+ * transcribe_model_free. */
+struct transcribe_langid_candidate {
+    uint64_t     struct_size;
+    int32_t      index; /* label index */
+    const char * code;
+    const char * name;
+    float        p;              /* softmax over the allowed set */
+    float        p_unrestricted; /* softmax over every label */
+    float        logit;
+};
+
+TRANSCRIBE_API void transcribe_langid_info_init(struct transcribe_langid_info * out);
+TRANSCRIBE_API void transcribe_langid_session_params_init(struct transcribe_langid_session_params * params);
+TRANSCRIBE_API void transcribe_langid_params_init(struct transcribe_langid_params * params);
+TRANSCRIBE_API void transcribe_langid_result_init(struct transcribe_langid_result * out);
+TRANSCRIBE_API void transcribe_langid_candidate_init(struct transcribe_langid_candidate * out);
+
+/* UNSUPPORTED_ROLE when the model does not serve TRANSCRIBE_ROLE_LANGID. */
+TRANSCRIBE_API transcribe_status transcribe_langid_get_info(const struct transcribe_model * model,
+                                                            struct transcribe_langid_info * out);
+
+/* The label table. Code and name of label i, or NULL when i is out of range
+ * or the model does not serve TRANSCRIBE_ROLE_LANGID; model-owned. */
+TRANSCRIBE_API const char * transcribe_langid_label_code(const struct transcribe_model * model, int32_t i);
+TRANSCRIBE_API const char * transcribe_langid_label_name(const struct transcribe_model * model, int32_t i);
+/* Index of a code or alias ("he" and "iw" name the same label), or -1. */
+TRANSCRIBE_API int32_t transcribe_langid_label_index(const struct transcribe_model * model, const char * code_or_alias);
+
+/* params may be NULL for defaults. UNSUPPORTED_ROLE when the model does not
+ * serve TRANSCRIBE_ROLE_LANGID. On failure *out_session is NULL. */
+TRANSCRIBE_API transcribe_status transcribe_langid_session_init(struct transcribe_model *                       model,
+                                                                const struct transcribe_langid_session_params * params,
+                                                                struct transcribe_langid_session ** out_session);
+
+/* NULL is a no-op. */
+TRANSCRIBE_API void transcribe_langid_session_free(struct transcribe_langid_session * session);
+
+/* Polled during a run; returning true stops it with TRANSCRIBE_ERR_ABORTED. */
+TRANSCRIBE_API void transcribe_langid_set_abort_callback(struct transcribe_langid_session * session,
+                                                         transcribe_abort_callback          cb,
+                                                         void *                             user_data);
+
+/*
+ * Identify the language of one clip: 16 kHz mono float32 PCM, every sample
+ * finite. params may be NULL for defaults. Input longer than the session's
+ * max_audio_ms is scored on its last max_audio_ms. Replaces the previous
+ * result. Malformed input (NULL pointers, n_samples <= 0, NaN / Inf, a bad
+ * allowed list, an unknown code, scored audio shorter than min_audio_ms)
+ * returns an error before the previous result is touched.
+ */
+TRANSCRIBE_API transcribe_status transcribe_langid_run(struct transcribe_langid_session *      session,
+                                                       const float *                           pcm,
+                                                       int                                     n_samples,
+                                                       const struct transcribe_langid_params * params);
+
+/*
+ * The last run's summary. Zeroed before any run and after a run that passed
+ * input validation but then failed (aborted, backend error).
+ */
+TRANSCRIBE_API transcribe_status transcribe_langid_get_result(const struct transcribe_langid_session * session,
+                                                              struct transcribe_langid_result *        out);
+
+/* Candidate i of the last run, ranked by p (descending; ties keep label
+ * order). An out-of-range index returns OK with a zeroed row (code NULL). */
+TRANSCRIBE_API transcribe_status transcribe_langid_get_candidate(const struct transcribe_langid_session * session,
+                                                                 int                                      i,
+                                                                 struct transcribe_langid_candidate *     out);
+
+/* load_ms plus the last run's stage times (mel_ms, encode_ms). */
+TRANSCRIBE_API transcribe_status transcribe_langid_get_timings(const struct transcribe_langid_session * session,
+                                                               struct transcribe_timings *              out);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* TRANSCRIBE_LANGID_H */
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 081473822..09903e680 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -14,6 +14,7 @@ add_library(transcribe
     transcribe.cpp
     transcribe-asr.cpp
     transcribe-diarize.cpp
+    transcribe-langid.cpp
     transcribe-loader.cpp
     transcribe-arch.cpp
     transcribe-meta.cpp
diff --git a/src/transcribe-arch.cpp b/src/transcribe-arch.cpp
index 0840408b4..b05e0f228 100644
--- a/src/transcribe-arch.cpp
+++ b/src/transcribe-arch.cpp
@@ -123,12 +123,13 @@ transcribe_status resolve_roles(transcribe_model * model) {
     const char * name        = arch.name != nullptr ? arch.name : "(unknown)";
     const bool   has_asr     = arch.init_context != nullptr && arch.run != nullptr;
     const bool   has_diarize = arch.diarize != nullptr;
+    const bool   has_langid  = arch.langid != nullptr;
 
     if (model->roles == 0 && has_asr) {
         model->roles = TRANSCRIBE_ROLE_ASR;
     }
 
-    const uint32_t known = TRANSCRIBE_ROLE_ASR | TRANSCRIBE_ROLE_DIARIZE;
+    const uint32_t known = TRANSCRIBE_ROLE_ASR | TRANSCRIBE_ROLE_DIARIZE | TRANSCRIBE_ROLE_LANGID;
     const char *   why   = nullptr;
     if (model->roles == 0) {
         why = "serves no role";
@@ -138,6 +139,8 @@ transcribe_status resolve_roles(transcribe_model * model) {
         why = "sets the ASR role without init_context / run hooks";
     } else if ((model->roles & TRANSCRIBE_ROLE_DIARIZE) != 0 && !has_diarize) {
         why = "sets the DIARIZE role without a diarize ops table";
+    } else if ((model->roles & TRANSCRIBE_ROLE_LANGID) != 0 && !has_langid) {
+        why = "sets the LANGID role without a langid ops table";
     }
     if (why != nullptr) {
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "transcribe_model_load_file: arch '%s' %s (roles 0x%x)", name, why,
diff --git a/src/transcribe-arch.h b/src/transcribe-arch.h
index 6a6e0e0a0..6d06d53c2 100644
--- a/src/transcribe-arch.h
+++ b/src/transcribe-arch.h
@@ -15,6 +15,7 @@ namespace transcribe {
 
 class Loader;
 struct DiarizeOps;
+struct LangidOps;
 
 // Per-family trait. Function pointers may be null when an entry point is
 // not yet implemented; the central dispatch converts null entries into
@@ -133,6 +134,7 @@ struct Arch {
 
     // Ops tables for the non-ASR roles; nullptr = role not implemented.
     const DiarizeOps * diarize;
+    const LangidOps *  langid;
 };
 
 // Look up an architecture by name. Returns nullptr if no registered
diff --git a/src/transcribe-langid.cpp b/src/transcribe-langid.cpp
new file mode 100644
index 000000000..4801fb837
--- /dev/null
+++ b/src/transcribe-langid.cpp
@@ -0,0 +1,447 @@
+// transcribe-langid.cpp - LANGID role C ABI (include/transcribe/langid.h):
+// label table, allowed set, crop, softmax, ranking and top-k.
+
+#include "transcribe-langid.h"
+
+#include "transcribe-abi.h"
+#include "transcribe-api-guard.h"
+#include "transcribe-arch.h"
+#include "transcribe-log.h"
+#include "transcribe-model.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <string_view>
+#include <utility>
+
+using transcribe::api_guard_status;
+using transcribe::api_guard_void;
+using transcribe::check_input_struct_size;
+using transcribe::check_struct_size;
+using transcribe::copy_out_prefix;
+using transcribe::LangidCandidateEntry;
+using transcribe::LangidLabels;
+
+namespace {
+
+constexpr size_t k_min_info_size           = TRANSCRIBE_FIELD_END(transcribe_langid_info, min_audio_ms);
+constexpr size_t k_min_session_params_size = TRANSCRIBE_FIELD_END(transcribe_langid_session_params, max_audio_ms);
+constexpr size_t k_min_params_size         = TRANSCRIBE_FIELD_END(transcribe_langid_params, top_k);
+constexpr size_t k_min_result_size         = TRANSCRIBE_FIELD_END(transcribe_langid_result, audio_ms);
+constexpr size_t k_min_candidate_size      = TRANSCRIBE_FIELD_END(transcribe_langid_candidate, logit);
+constexpr size_t k_min_timings_size        = TRANSCRIBE_FIELD_END(transcribe_timings, decode_ms);
+
+constexpr int64_t k_samples_per_ms = 16;  // 16 kHz
+
+const transcribe::LangidOps * langid_ops(const transcribe_model * model) {
+    return model != nullptr && (model->roles & TRANSCRIBE_ROLE_LANGID) != 0 ? model->arch->langid : nullptr;
+}
+
+const LangidLabels * langid_labels(const transcribe_model * model) {
+    const transcribe::LangidOps * ops = langid_ops(model);
+    return ops != nullptr ? &ops->labels(model) : nullptr;
+}
+
+int32_t label_index(const LangidLabels & labels, const char * code) {
+    if (code == nullptr) {
+        return -1;
+    }
+    const auto it = labels.index.find(std::string_view(code));
+    return it != labels.index.end() ? it->second : -1;
+}
+
+// Resolve params->allowed into a per-label mask. Rejects every malformed
+// form (H2): only NULL with n_allowed == 0 means "all".
+transcribe_status build_allowed_mask(const LangidLabels &             labels,
+                                     const transcribe_langid_params * params,
+                                     std::vector<uint8_t> &           mask,
+                                     int32_t &                        n_allowed) {
+    const size_t n_labels = labels.codes.size();
+    if (params->allowed == nullptr) {
+        if (params->n_allowed != 0) {
+            return TRANSCRIBE_ERR_INVALID_ARG;
+        }
+        mask.assign(n_labels, 1);
+        n_allowed = static_cast<int32_t>(n_labels);
+        return TRANSCRIBE_OK;
+    }
+    if (params->n_allowed <= 0) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    mask.assign(n_labels, 0);
+    for (int32_t i = 0; i < params->n_allowed; ++i) {
+        if (params->allowed[i] == nullptr) {
+            return TRANSCRIBE_ERR_INVALID_ARG;
+        }
+    }
+    n_allowed = 0;
+    for (int32_t i = 0; i < params->n_allowed; ++i) {
+        const int32_t idx = label_index(labels, params->allowed[i]);
+        if (idx < 0) {
+            transcribe::log_msg(TRANSCRIBE_LOG_LEVEL_WARN, "transcribe_langid_run: unknown language '%s'",
+                                params->allowed[i]);
+            return TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE;
+        }
+        if (mask[static_cast<size_t>(idx)] == 0) {
+            mask[static_cast<size_t>(idx)] = 1;
+            ++n_allowed;
+        }
+    }
+    return TRANSCRIBE_OK;
+}
+
+// Softmax over the entries with mask[i] != 0 (every entry when mask is
+// NULL); masked-out entries get 0. Sums in double.
+void masked_softmax(const std::vector<float> & logits, const uint8_t * mask, std::vector<float> & out) {
+    const size_t n  = logits.size();
+    float        mx = -INFINITY;
+    for (size_t i = 0; i < n; ++i) {
+        if (mask == nullptr || mask[i] != 0) {
+            mx = std::max(mx, logits[i]);
+        }
+    }
+    out.assign(n, 0.0f);
+    double sum = 0.0;
+    for (size_t i = 0; i < n; ++i) {
+        if (mask == nullptr || mask[i] != 0) {
+            const double e = std::exp(static_cast<double>(logits[i]) - static_cast<double>(mx));
+            out[i]         = static_cast<float>(e);
+            sum += e;
+        }
+    }
+    for (size_t i = 0; i < n; ++i) {
+        out[i] = static_cast<float>(static_cast<double>(out[i]) / sum);
+    }
+}
+
+}  // namespace
+
+transcribe_status transcribe::build_langid_labels(std::vector<std::string>         codes,
+                                                  std::vector<std::string>         names,
+                                                  const std::vector<std::string> & alias_specs,
+                                                  const char *                     tag,
+                                                  LangidLabels &                   out) {
+    auto fail = [tag](const char * what, const std::string & item) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: label table: %s '%s'", tag, what, item.c_str());
+        return TRANSCRIBE_ERR_GGUF;
+    };
+    if (codes.empty() || codes.size() != names.size()) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: label table: %zu codes vs %zu names", tag, codes.size(), names.size());
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (codes.size() > static_cast<size_t>(INT32_MAX)) {
+        return fail("too many labels", std::to_string(codes.size()));
+    }
+    LangidLabels staged;
+    for (size_t i = 0; i < codes.size(); ++i) {
+        if (codes[i].empty()) {
+            return fail("empty code at index", std::to_string(i));
+        }
+        if (!staged.index.emplace(codes[i], static_cast<int>(i)).second) {
+            return fail("duplicate code", codes[i]);
+        }
+    }
+    for (const std::string & spec : alias_specs) {
+        const size_t eq = spec.find('=');
+        if (eq == std::string::npos || eq == 0 || eq + 1 == spec.size()) {
+            return fail("malformed alias (want alias=code)", spec);
+        }
+        std::string alias  = spec.substr(0, eq);
+        std::string target = spec.substr(eq + 1);
+        const auto  it     = staged.index.find(target);
+        if (it == staged.index.end() || codes[static_cast<size_t>(it->second)] != target) {
+            return fail("alias names an unknown code", spec);
+        }
+        if (!staged.index.emplace(std::move(alias), it->second).second) {
+            return fail("alias collides with a code or another alias", spec);
+        }
+    }
+    staged.codes = std::move(codes);
+    staged.names = std::move(names);
+    out          = std::move(staged);
+    return TRANSCRIBE_OK;
+}
+
+extern "C" void transcribe_langid_info_init(struct transcribe_langid_info * p) {
+    transcribe::init_sized(p);
+}
+
+extern "C" void transcribe_langid_session_params_init(struct transcribe_langid_session_params * p) {
+    transcribe::init_sized(p);
+}
+
+extern "C" void transcribe_langid_params_init(struct transcribe_langid_params * p) {
+    transcribe::init_sized(p);
+}
+
+extern "C" void transcribe_langid_result_init(struct transcribe_langid_result * p) {
+    transcribe::init_sized(p);
+}
+
+extern "C" void transcribe_langid_candidate_init(struct transcribe_langid_candidate * p) {
+    transcribe::init_sized(p);
+}
+
+static transcribe_status langid_get_info_impl(const transcribe_model * model, transcribe_langid_info * out) {
+    if (model == nullptr || out == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    if (const auto st = check_struct_size(out->struct_size, k_min_info_size); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    const LangidLabels * labels = langid_labels(model);
+    if (labels == nullptr) {
+        return TRANSCRIBE_ERR_UNSUPPORTED_ROLE;
+    }
+    transcribe_langid_info staged{};
+    staged.struct_size  = out->struct_size;
+    staged.sample_rate  = 16000;
+    staged.n_labels     = static_cast<int32_t>(labels->codes.size());
+    staged.min_audio_ms = transcribe::k_langid_min_audio_ms;
+    copy_out_prefix(out, &staged, out->struct_size, sizeof(staged));
+    return TRANSCRIBE_OK;
+}
+
+static transcribe_status langid_session_init_impl(transcribe_model *                       model,
+                                                  const transcribe_langid_session_params * params,
+                                                  transcribe_langid_session **             out) {
+    if (out == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    *out = nullptr;
+    if (model == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    const transcribe::LangidOps * ops = langid_ops(model);
+    if (ops == nullptr) {
+        return TRANSCRIBE_ERR_UNSUPPORTED_ROLE;
+    }
+    transcribe_langid_session_params defaults;
+    transcribe_langid_session_params_init(&defaults);
+    if (params == nullptr) {
+        params = &defaults;
+    }
+    if (const auto st = check_input_struct_size(params->struct_size, k_min_session_params_size); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    if (params->n_threads < 0) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    // H1: a window below the minimum could never score anything.
+    const int32_t max_audio_ms =
+        params->max_audio_ms == 0 ? transcribe::k_langid_default_audio_ms : params->max_audio_ms;
+    if (max_audio_ms < transcribe::k_langid_min_audio_ms) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    *out                 = ops->new_session();
+    (*out)->model        = model;
+    (*out)->n_threads    = params->n_threads;
+    (*out)->max_audio_ms = max_audio_ms;
+    return TRANSCRIBE_OK;
+}
+
+static transcribe_status langid_run_impl(transcribe_langid_session *      session,
+                                         const float *                    pcm,
+                                         int                              n_samples,
+                                         const transcribe_langid_params * params) {
+    // Everything up to the commit point leaves the previous result intact.
+    if (session == nullptr || pcm == nullptr || n_samples <= 0 || !transcribe::pcm_is_finite(pcm, n_samples)) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    transcribe_langid_params defaults;
+    transcribe_langid_params_init(&defaults);
+    if (params == nullptr) {
+        params = &defaults;
+    }
+    if (const auto st = check_input_struct_size(params->struct_size, k_min_params_size); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    if (params->top_k < 0) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    const transcribe::LangidOps * ops    = langid_ops(session->model);
+    const LangidLabels &          labels = ops->labels(session->model);
+    std::vector<uint8_t>          mask;
+    int32_t                       n_allowed = 0;
+    if (const auto st = build_allowed_mask(labels, params, mask, n_allowed); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    // Score the last max_audio_ms; the minimum applies to what is scored (H1).
+    const int64_t max_samples = static_cast<int64_t>(session->max_audio_ms) * k_samples_per_ms;
+    const int64_t n_used      = std::min<int64_t>(n_samples, max_samples);
+    if (n_used < static_cast<int64_t>(transcribe::k_langid_min_audio_ms) * k_samples_per_ms) {
+        return TRANSCRIBE_ERR_INPUT_TOO_SHORT;
+    }
+    const float * pcm_used = pcm + (n_samples - n_used);
+
+    session->candidates.clear();
+    session->n_allowed    = 0;
+    session->allowed_mass = 0.0f;
+    session->audio_ms     = 0;
+    session->t_mel_us     = 0;
+    session->t_encode_us  = 0;
+    session->t_decode_us  = 0;
+    transcribe::ScratchReleaseGuard release{ session, true };
+
+    std::vector<float> logits;
+    if (const auto st = ops->run(session, pcm_used, static_cast<int>(n_used), logits); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    if (logits.size() != labels.codes.size()) {
+        transcribe::log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "transcribe_langid_run: %zu logits for %zu labels",
+                            logits.size(), labels.codes.size());
+        return TRANSCRIBE_ERR_BACKEND;
+    }
+    for (const float v : logits) {
+        if (!std::isfinite(v)) {
+            transcribe::log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "transcribe_langid_run: non-finite logit");
+            return TRANSCRIBE_ERR_BACKEND;
+        }
+    }
+
+    const bool         restricted = n_allowed != static_cast<int32_t>(labels.codes.size());
+    std::vector<float> p_open;
+    std::vector<float> p_allowed;
+    masked_softmax(logits, nullptr, p_open);
+    masked_softmax(logits, mask.data(), p_allowed);
+    double mass = 0.0;
+    for (size_t i = 0; i < logits.size(); ++i) {
+        if (mask[i] != 0) {
+            mass += p_open[i];
+        }
+    }
+
+    std::vector<LangidCandidateEntry> ranked;
+    ranked.reserve(static_cast<size_t>(n_allowed));
+    for (size_t i = 0; i < logits.size(); ++i) {
+        if (mask[i] != 0) {
+            ranked.push_back({ static_cast<int32_t>(i), p_allowed[i], p_open[i], logits[i] });
+        }
+    }
+    std::stable_sort(ranked.begin(), ranked.end(),
+                     [](const LangidCandidateEntry & a, const LangidCandidateEntry & b) { return a.p > b.p; });
+    if (params->top_k > 0 && static_cast<size_t>(params->top_k) < ranked.size()) {
+        ranked.resize(static_cast<size_t>(params->top_k));
+    }
+
+    session->candidates.swap(ranked);
+    session->n_allowed    = n_allowed;
+    session->allowed_mass = restricted ? static_cast<float>(mass) : 1.0f;
+    session->audio_ms     = n_used / k_samples_per_ms;
+    return TRANSCRIBE_OK;
+}
+
+extern "C" const char * transcribe_langid_label_code(const struct transcribe_model * model, int32_t i) {
+    const LangidLabels * labels = langid_labels(model);
+    if (labels == nullptr || i < 0 || static_cast<size_t>(i) >= labels->codes.size()) {
+        return nullptr;
+    }
+    return labels->codes[static_cast<size_t>(i)].c_str();
+}
+
+extern "C" const char * transcribe_langid_label_name(const struct transcribe_model * model, int32_t i) {
+    const LangidLabels * labels = langid_labels(model);
+    if (labels == nullptr || i < 0 || static_cast<size_t>(i) >= labels->names.size()) {
+        return nullptr;
+    }
+    return labels->names[static_cast<size_t>(i)].c_str();
+}
+
+extern "C" void transcribe_langid_set_abort_callback(struct transcribe_langid_session * session,
+                                                     transcribe_abort_callback          cb,
+                                                     void *                             user_data) {
+    if (session != nullptr) {
+        session->abort_cb       = cb;
+        session->abort_userdata = user_data;
+    }
+}
+
+extern "C" transcribe_status transcribe_langid_get_result(const struct transcribe_langid_session * session,
+                                                          struct transcribe_langid_result *        out) {
+    if (session == nullptr || out == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    if (const auto st = check_struct_size(out->struct_size, k_min_result_size); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    transcribe_langid_result staged{};
+    staged.struct_size  = out->struct_size;
+    staged.n_candidates = static_cast<int32_t>(session->candidates.size());
+    staged.n_allowed    = session->n_allowed;
+    staged.allowed_mass = session->allowed_mass;
+    staged.audio_ms     = session->audio_ms;
+    copy_out_prefix(out, &staged, out->struct_size, sizeof(staged));
+    return TRANSCRIBE_OK;
+}
+
+extern "C" transcribe_status transcribe_langid_get_candidate(const struct transcribe_langid_session * session,
+                                                             int                                      i,
+                                                             struct transcribe_langid_candidate *     out) {
+    if (out == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    if (const auto st = check_struct_size(out->struct_size, k_min_candidate_size); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    transcribe_langid_candidate staged{};
+    staged.struct_size = out->struct_size;
+    if (session != nullptr && i >= 0 && static_cast<size_t>(i) < session->candidates.size()) {
+        const LangidCandidateEntry & c = session->candidates[static_cast<size_t>(i)];
+        staged.index                   = c.index;
+        staged.code                    = transcribe_langid_label_code(session->model, c.index);
+        staged.name                    = transcribe_langid_label_name(session->model, c.index);
+        staged.p                       = c.p;
+        staged.p_unrestricted          = c.p_unrestricted;
+        staged.logit                   = c.logit;
+    }
+    copy_out_prefix(out, &staged, out->struct_size, sizeof(staged));
+    return TRANSCRIBE_OK;
+}
+
+extern "C" transcribe_status transcribe_langid_get_timings(const struct transcribe_langid_session * session,
+                                                           struct transcribe_timings *              out) {
+    if (session == nullptr || out == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    if (const auto st = check_struct_size(out->struct_size, k_min_timings_size); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    transcribe::copy_out_timings(session->model->t_load_us, session->t_mel_us, session->t_encode_us,
+                                 session->t_decode_us, out);
+    return TRANSCRIBE_OK;
+}
+
+// C ABI forwarders for the entry points that allocate, compute, transfer
+// ownership, or build a lookup key; the ones above are nothrow by
+// construction.
+
+extern "C" transcribe_status transcribe_langid_get_info(const struct transcribe_model * model,
+                                                        struct transcribe_langid_info * out) {
+    return api_guard_status("transcribe_langid_get_info", [&] { return langid_get_info_impl(model, out); });
+}
+
+extern "C" int32_t transcribe_langid_label_index(const struct transcribe_model * model, const char * code_or_alias) {
+    return transcribe::api_guard_value("transcribe_langid_label_index", int32_t{ -1 }, [&] {
+        const LangidLabels * labels = langid_labels(model);
+        return labels != nullptr ? label_index(*labels, code_or_alias) : int32_t{ -1 };
+    });
+}
+
+extern "C" transcribe_status transcribe_langid_session_init(struct transcribe_model *                       model,
+                                                            const struct transcribe_langid_session_params * params,
+                                                            struct transcribe_langid_session **             out) {
+    return api_guard_status("transcribe_langid_session_init",
+                            [&] { return langid_session_init_impl(model, params, out); });
+}
+
+extern "C" void transcribe_langid_session_free(struct transcribe_langid_session * session) {
+    api_guard_void("transcribe_langid_session_free", [&] { delete session; });
+}
+
+extern "C" transcribe_status transcribe_langid_run(struct transcribe_langid_session *      session,
+                                                   const float *                           pcm,
+                                                   int                                     n_samples,
+                                                   const struct transcribe_langid_params * params) {
+    return api_guard_status("transcribe_langid_run", [&] { return langid_run_impl(session, pcm, n_samples, params); });
+}
diff --git a/src/transcribe-langid.h b/src/transcribe-langid.h
new file mode 100644
index 000000000..e6a0e2ccd
--- /dev/null
+++ b/src/transcribe-langid.h
@@ -0,0 +1,71 @@
+// transcribe-langid.h - internal LANGID role surface: the session base, the
+// per-arch ops table, and the shared label table.
+//
+// Families compute logits over their label table; the role dispatcher
+// (transcribe-langid.cpp) crops the input, applies the allowed set, and
+// owns the softmax, ranking and top-k.
+
+#pragma once
+
+#include "transcribe-session-core.h"
+#include "transcribe/langid.h"
+
+#include <cstdint>
+#include <functional>
+#include <map>
+#include <string>
+#include <vector>
+
+namespace transcribe {
+
+struct LangidCandidateEntry {
+    int32_t index          = 0;
+    float   p              = 0.0f;
+    float   p_unrestricted = 0.0f;
+    float   logit          = 0.0f;
+};
+
+// A model's labels. Built once at load by build_langid_labels, immutable after.
+struct LangidLabels {
+    std::vector<std::string>                codes;
+    std::vector<std::string>                names;
+    std::map<std::string, int, std::less<>> index;  // every code and alias -> label index
+};
+
+// Build a label table from parallel code / name lists and "alias=code"
+// specs. Returns TRANSCRIBE_ERR_GGUF (and logs `tag`) when the lists are
+// empty or differ in length, a code is empty or repeated, or an alias is
+// malformed, repeated, collides with a code, or names an unknown code.
+transcribe_status build_langid_labels(std::vector<std::string>         codes,
+                                      std::vector<std::string>         names,
+                                      const std::vector<std::string> & alias_specs,
+                                      const char *                     tag,
+                                      LangidLabels &                   out);
+
+// Scored-audio bounds every LANGID model shares.
+constexpr int32_t k_langid_min_audio_ms     = 500;
+constexpr int32_t k_langid_default_audio_ms = 30000;
+
+struct LangidOps {
+    const LangidLabels & (*labels)(const transcribe_model * model);
+    transcribe_langid_session * (*new_session)();
+    // Score already validated, already cropped PCM; fill one logit per label.
+    // Poll session->poll_abort() and return TRANSCRIBE_ERR_ABORTED when it
+    // fires. Set session->t_mel_us / t_encode_us.
+    transcribe_status (*run)(transcribe_langid_session * session,
+                             const float *               pcm,
+                             int                         n_samples,
+                             std::vector<float> &        logits);
+};
+
+}  // namespace transcribe
+
+struct transcribe_langid_session : transcribe::SessionCore {
+    int32_t max_audio_ms = transcribe::k_langid_default_audio_ms;
+
+    // Last successful run's result; empty / zero otherwise.
+    std::vector<transcribe::LangidCandidateEntry> candidates;
+    int32_t                                       n_allowed    = 0;
+    float                                         allowed_mass = 0.0f;
+    int64_t                                       audio_ms     = 0;
+};
diff --git a/src/transcribe.cpp b/src/transcribe.cpp
index 940a9004f..f38a428fb 100644
--- a/src/transcribe.cpp
+++ b/src/transcribe.cpp
@@ -29,6 +29,7 @@
 #include "transcribe-model.h"
 #include "transcribe-path.h"
 #include "transcribe/diarize.h"
+#include "transcribe/langid.h"
 
 #if defined(TRANSCRIBE_GGML_BACKEND_DL) && defined(_WIN32)
 #    ifndef WIN32_LEAN_AND_MEAN
@@ -112,6 +113,8 @@ extern "C" const char * transcribe_status_string(int status) {
             return "output repetition: decode stopped when the output began repeating itself";
         case TRANSCRIBE_ERR_UNSUPPORTED_ROLE:
             return "model does not serve the requested role";
+        case TRANSCRIBE_ERR_INPUT_TOO_SHORT:
+            return "input audio too short";
         default:
             return "unknown status";
     }
@@ -178,6 +181,16 @@ extern "C" size_t transcribe_abi_struct_size(transcribe_abi_struct which) {
             return sizeof(struct transcribe_diarize_session_params);
         case TRANSCRIBE_ABI_DIARIZE_PARAMS:
             return sizeof(struct transcribe_diarize_params);
+        case TRANSCRIBE_ABI_LANGID_INFO:
+            return sizeof(struct transcribe_langid_info);
+        case TRANSCRIBE_ABI_LANGID_SESSION_PARAMS:
+            return sizeof(struct transcribe_langid_session_params);
+        case TRANSCRIBE_ABI_LANGID_PARAMS:
+            return sizeof(struct transcribe_langid_params);
+        case TRANSCRIBE_ABI_LANGID_RESULT:
+            return sizeof(struct transcribe_langid_result);
+        case TRANSCRIBE_ABI_LANGID_CANDIDATE:
+            return sizeof(struct transcribe_langid_candidate);
     }
     return 0;  // unknown id: "cannot verify", never a real size
 }
@@ -222,6 +235,16 @@ extern "C" size_t transcribe_abi_struct_align(transcribe_abi_struct which) {
             return alignof(struct transcribe_diarize_session_params);
         case TRANSCRIBE_ABI_DIARIZE_PARAMS:
             return alignof(struct transcribe_diarize_params);
+        case TRANSCRIBE_ABI_LANGID_INFO:
+            return alignof(struct transcribe_langid_info);
+        case TRANSCRIBE_ABI_LANGID_SESSION_PARAMS:
+            return alignof(struct transcribe_langid_session_params);
+        case TRANSCRIBE_ABI_LANGID_PARAMS:
+            return alignof(struct transcribe_langid_params);
+        case TRANSCRIBE_ABI_LANGID_RESULT:
+            return alignof(struct transcribe_langid_result);
+        case TRANSCRIBE_ABI_LANGID_CANDIDATE:
+            return alignof(struct transcribe_langid_candidate);
     }
     return 0;
 }
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 2b60c01d0..980bf8b23 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -343,10 +343,10 @@ add_test(NAME transcribe_run_dispatch_unit
 
 # -----------------------------------------------------------------------------
 # Dispatcher units against fake arches: roles, host thread pool, non-finite
-# input, throwing stream hooks, DIARIZE role
+# input, throwing stream hooks, DIARIZE and LANGID roles
 # -----------------------------------------------------------------------------
 
-foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize_dispatch)
+foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize_dispatch langid_dispatch)
     add_executable(transcribe_${_t}_unit ${_t}_unit.cpp)
     target_link_libraries(transcribe_${_t}_unit PRIVATE transcribe)
     target_include_directories(transcribe_${_t}_unit PRIVATE ${CMAKE_SOURCE_DIR}/src)
diff --git a/tests/api_smoke.c b/tests/api_smoke.c
index dbdb6dca0..a2afe6bd2 100644
--- a/tests/api_smoke.c
+++ b/tests/api_smoke.c
@@ -78,6 +78,7 @@ static void test_status_string(void) {
         TRANSCRIBE_ERR_OUTPUT_TRUNCATED,
         TRANSCRIBE_ERR_OUTPUT_REPETITION,
         TRANSCRIBE_ERR_UNSUPPORTED_ROLE,
+        TRANSCRIBE_ERR_INPUT_TOO_SHORT,
     };
     for (size_t i = 0; i < sizeof(all) / sizeof(all[0]); ++i) {
         const char * s = transcribe_status_string(all[i]);
@@ -527,6 +528,41 @@ static void test_model_introspection_null(void) {
 
     CHECK(transcribe_model_roles(NULL) == 0);
 
+    /* LANGID role entry points are NULL-safe. */
+    {
+        struct transcribe_langid_info info;
+        transcribe_langid_info_init(&info);
+        CHECK(info.struct_size == sizeof(info));
+        CHECK(transcribe_langid_get_info(NULL, &info) == TRANSCRIBE_ERR_INVALID_ARG);
+        CHECK(transcribe_langid_label_code(NULL, 0) == NULL);
+        CHECK(transcribe_langid_label_name(NULL, 0) == NULL);
+        CHECK(transcribe_langid_label_index(NULL, "en") == -1);
+
+        struct transcribe_langid_session * ls = (struct transcribe_langid_session *) 0x1;
+        CHECK(transcribe_langid_session_init(NULL, NULL, &ls) == TRANSCRIBE_ERR_INVALID_ARG);
+        CHECK(ls == NULL);
+        transcribe_langid_session_free(NULL);
+        transcribe_langid_set_abort_callback(NULL, NULL, NULL);
+
+        const float pcm[1] = { 0.0f };
+        CHECK(transcribe_langid_run(NULL, pcm, 1, NULL) == TRANSCRIBE_ERR_INVALID_ARG);
+
+        struct transcribe_langid_result r;
+        transcribe_langid_result_init(&r);
+        CHECK(transcribe_langid_get_result(NULL, &r) == TRANSCRIBE_ERR_INVALID_ARG);
+        struct transcribe_langid_candidate c;
+        transcribe_langid_candidate_init(&c);
+        CHECK(transcribe_langid_get_candidate(NULL, 0, &c) == TRANSCRIBE_OK);
+        CHECK(c.code == NULL);
+        struct transcribe_timings tm;
+        transcribe_timings_init(&tm);
+        CHECK(transcribe_langid_get_timings(NULL, &tm) == TRANSCRIBE_ERR_INVALID_ARG);
+
+        CHECK(transcribe_abi_struct_size(TRANSCRIBE_ABI_LANGID_CANDIDATE) ==
+              sizeof(struct transcribe_langid_candidate));
+        CHECK(transcribe_abi_struct_align(TRANSCRIBE_ABI_LANGID_RESULT) != 0);
+    }
+
     /* The feature probe is also NULL-safe and returns false for every
      * known feature value plus any out-of-range enum. */
     CHECK(transcribe_model_supports(NULL, TRANSCRIBE_FEATURE_INITIAL_PROMPT) == false);
diff --git a/tests/langid_dispatch_unit.cpp b/tests/langid_dispatch_unit.cpp
new file mode 100644
index 000000000..5902f5346
--- /dev/null
+++ b/tests/langid_dispatch_unit.cpp
@@ -0,0 +1,486 @@
+// langid_dispatch_unit.cpp - LANGID role dispatcher (transcribe-langid.cpp)
+// against a fake arch: role checks, label table validation (H6), the
+// allowed set (H2), crop and minimum length (H1), non-finite input and
+// logits (H3), softmax / ranking / top-k, accessors.
+
+#include "transcribe-arch.h"
+#include "transcribe-langid.h"
+#include "transcribe-model.h"
+#include "transcribe.h"
+#include "transcribe/langid.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <limits>
+#include <string>
+#include <vector>
+
+namespace {
+
+int g_failures = 0;
+
+#define CHECK(cond)                                                              \
+    do {                                                                         \
+        if (!(cond)) {                                                           \
+            std::fprintf(stderr, "FAIL %s:%d: %s\n", __FILE__, __LINE__, #cond); \
+            ++g_failures;                                                        \
+        }                                                                        \
+    } while (0)
+
+bool near(double a, double b, double tol = 1e-6) {
+    return std::fabs(a - b) <= tol;
+}
+
+transcribe::LangidLabels g_labels;  // aa..ee, alias xx=aa
+
+int                g_run_calls   = 0;
+int                g_last_n      = 0;
+const float *      g_last_pcm    = nullptr;
+transcribe_status  g_run_status  = TRANSCRIBE_OK;
+std::vector<float> g_logits      = { 1.0f, 3.0f, 2.0f, 0.0f, -1.0f };
+bool               g_check_abort = false;
+
+const transcribe::LangidLabels & fake_labels(const transcribe_model *) {
+    return g_labels;
+}
+
+transcribe_langid_session * fake_new_session() {
+    return new transcribe_langid_session();
+}
+
+transcribe_status fake_run(transcribe_langid_session * s, const float * pcm, int n, std::vector<float> & logits) {
+    ++g_run_calls;
+    g_last_n   = n;
+    g_last_pcm = pcm;
+    if (g_check_abort && s->poll_abort()) {
+        return TRANSCRIBE_ERR_ABORTED;
+    }
+    if (g_run_status != TRANSCRIBE_OK) {
+        return g_run_status;
+    }
+    s->t_mel_us    = 1000;
+    s->t_encode_us = 2000;
+    logits         = g_logits;
+    return TRANSCRIBE_OK;
+}
+
+const transcribe::LangidOps k_ops = { fake_labels, fake_new_session, fake_run };
+
+const transcribe::Arch k_arch = {
+    /* .name             = */ "fake-langid",
+    /* .load             = */ nullptr,
+    /* .init_context     = */ nullptr,
+    /* .run              = */ nullptr,
+    /* .run_batch        = */ nullptr,
+    /* .stream_validate  = */ nullptr,
+    /* .stream_begin     = */ nullptr,
+    /* .stream_feed      = */ nullptr,
+    /* .stream_finalize  = */ nullptr,
+    /* .stream_reset     = */ nullptr,
+    /* .accepts_ext_kind = */ nullptr,
+    /* .run_validate     = */ nullptr,
+    /* .diarize          = */ nullptr,
+    /* .langid           = */ &k_ops,
+};
+
+void reset_fake() {
+    g_run_calls   = 0;
+    g_last_n      = 0;
+    g_last_pcm    = nullptr;
+    g_run_status  = TRANSCRIBE_OK;
+    g_logits      = { 1.0f, 3.0f, 2.0f, 0.0f, -1.0f };
+    g_check_abort = false;
+}
+
+struct Fixture {
+    transcribe_model            model;
+    transcribe_langid_session * session = nullptr;
+    std::vector<float>          pcm     = std::vector<float>(16000, 0.0f);  // 1 s
+
+    explicit Fixture(int32_t max_audio_ms = 0) {
+        reset_fake();
+        model.arch  = &k_arch;
+        model.roles = TRANSCRIBE_ROLE_LANGID;
+        transcribe_langid_session_params sp;
+        transcribe_langid_session_params_init(&sp);
+        sp.max_audio_ms = max_audio_ms;
+        CHECK(transcribe_langid_session_init(&model, &sp, &session) == TRANSCRIBE_OK);
+    }
+
+    ~Fixture() { transcribe_langid_session_free(session); }
+
+    transcribe_status run(const transcribe_langid_params * p = nullptr) {
+        return transcribe_langid_run(session, pcm.data(), static_cast<int>(pcm.size()), p);
+    }
+
+    transcribe_langid_result result() const {
+        transcribe_langid_result r;
+        transcribe_langid_result_init(&r);
+        CHECK(transcribe_langid_get_result(session, &r) == TRANSCRIBE_OK);
+        return r;
+    }
+
+    transcribe_langid_candidate candidate(int i) const {
+        transcribe_langid_candidate c;
+        transcribe_langid_candidate_init(&c);
+        CHECK(transcribe_langid_get_candidate(session, i, &c) == TRANSCRIBE_OK);
+        return c;
+    }
+};
+
+transcribe_status labels_from(std::vector<std::string> codes,
+                              std::vector<std::string> names,
+                              std::vector<std::string> aliases) {
+    transcribe::LangidLabels out;
+    return transcribe::build_langid_labels(std::move(codes), std::move(names), aliases, "test", out);
+}
+
+// H6: malformed label metadata fails the load instead of being last-wins.
+void test_label_table() {
+    const std::vector<std::string> codes = { "aa", "bb" };
+    const std::vector<std::string> names = { "A", "B" };
+    CHECK(labels_from(codes, names, {}) == TRANSCRIBE_OK);
+    CHECK(labels_from(codes, names, { "xx=aa", "yy=bb" }) == TRANSCRIBE_OK);
+
+    CHECK(labels_from({}, {}, {}) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from(codes, { "A" }, {}) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from({ "aa", "" }, names, {}) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from({ "aa", "aa" }, names, {}) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from(codes, names, { "xx" }) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from(codes, names, { "=aa" }) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from(codes, names, { "xx=" }) == TRANSCRIBE_ERR_GGUF);
+    CHECK(labels_from(codes, names, { "xx=zz" }) == TRANSCRIBE_ERR_GGUF);           // unknown target
+    CHECK(labels_from(codes, names, { "bb=aa" }) == TRANSCRIBE_ERR_GGUF);           // alias remaps a code
+    CHECK(labels_from(codes, names, { "xx=aa", "xx=bb" }) == TRANSCRIBE_ERR_GGUF);  // alias repeated
+    CHECK(labels_from(codes, names, { "xx=aa", "yy=xx" }) == TRANSCRIBE_ERR_GGUF);  // alias of an alias
+
+    transcribe::LangidLabels out;
+    CHECK(transcribe::build_langid_labels({ "aa" }, { "A" }, { "xx=aa" }, "test", out) == TRANSCRIBE_OK);
+    CHECK(out.codes.size() == 1 && out.index.size() == 2 && out.index.at("xx") == 0);
+}
+
+void test_role_checks_and_labels() {
+    reset_fake();
+    transcribe_model model;
+    model.arch  = &k_arch;
+    model.roles = TRANSCRIBE_ROLE_ASR;  // no LANGID bit
+
+    transcribe_langid_session * s = reinterpret_cast<transcribe_langid_session *>(0x1);
+    CHECK(transcribe_langid_session_init(&model, nullptr, &s) == TRANSCRIBE_ERR_UNSUPPORTED_ROLE);
+    CHECK(s == nullptr);
+    transcribe_langid_info info;
+    transcribe_langid_info_init(&info);
+    CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_ERR_UNSUPPORTED_ROLE);
+    CHECK(transcribe_langid_label_code(&model, 0) == nullptr);
+    CHECK(transcribe_langid_label_name(&model, 0) == nullptr);
+    CHECK(transcribe_langid_label_index(&model, "aa") == -1);
+
+    model.roles = TRANSCRIBE_ROLE_LANGID;
+    CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_OK);
+    CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500);
+    CHECK(transcribe_langid_get_info(nullptr, &info) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(transcribe_langid_get_info(&model, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    info.struct_size = 0;
+    CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
+
+    CHECK(std::strcmp(transcribe_langid_label_code(&model, 1), "bb") == 0);
+    CHECK(std::strcmp(transcribe_langid_label_name(&model, 4), "Ee") == 0);
+    CHECK(transcribe_langid_label_code(&model, 5) == nullptr);
+    CHECK(transcribe_langid_label_code(&model, -1) == nullptr);
+    CHECK(transcribe_langid_label_code(nullptr, 0) == nullptr);
+    CHECK(transcribe_langid_label_index(&model, "cc") == 2);
+    CHECK(transcribe_langid_label_index(&model, "xx") == 0);
+    CHECK(transcribe_langid_label_index(&model, "zz") == -1);
+    CHECK(transcribe_langid_label_index(&model, "") == -1);
+    CHECK(transcribe_langid_label_index(&model, nullptr) == -1);
+    CHECK(transcribe_langid_label_index(nullptr, "aa") == -1);
+
+    transcribe_langid_session_params sp;
+    transcribe_langid_session_params_init(&sp);
+    sp.n_threads = -1;
+    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(s == nullptr);
+    sp.n_threads    = 2;
+    sp.max_audio_ms = 499;  // H1: a window below the minimum
+    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(s == nullptr);
+    sp.max_audio_ms = -1;
+    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
+    sp.struct_size = 0;
+    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
+    CHECK(s == nullptr);
+    transcribe_langid_session_params_init(&sp);
+    sp.max_audio_ms = 500;
+    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_OK);
+    CHECK(s != nullptr);
+    transcribe_langid_session_free(s);
+    CHECK(transcribe_langid_session_init(nullptr, nullptr, &s) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(transcribe_langid_session_init(&model, nullptr, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    transcribe_langid_session_free(nullptr);
+}
+
+void test_accessors_before_run() {
+    Fixture                  f;
+    transcribe_langid_result r = f.result();
+    CHECK(r.n_candidates == 0 && r.n_allowed == 0 && r.allowed_mass == 0.0f && r.audio_ms == 0);
+    transcribe_langid_candidate c = f.candidate(0);
+    CHECK(c.code == nullptr && c.name == nullptr && c.index == 0);
+
+    CHECK(transcribe_langid_get_result(f.session, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(transcribe_langid_get_result(nullptr, &r) == TRANSCRIBE_ERR_INVALID_ARG);
+    r.struct_size = 0;
+    CHECK(transcribe_langid_get_result(f.session, &r) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
+    CHECK(transcribe_langid_get_candidate(f.session, 0, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    c.struct_size = 0;
+    CHECK(transcribe_langid_get_candidate(f.session, 0, &c) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
+    transcribe_langid_candidate_init(&c);
+    CHECK(transcribe_langid_get_candidate(nullptr, 0, &c) == TRANSCRIBE_OK);
+    CHECK(c.code == nullptr);
+
+    transcribe_timings tm;
+    transcribe_timings_init(&tm);
+    CHECK(transcribe_langid_get_timings(f.session, &tm) == TRANSCRIBE_OK);
+    CHECK(tm.mel_ms == 0.0f && tm.encode_ms == 0.0f);
+    CHECK(transcribe_langid_get_timings(nullptr, &tm) == TRANSCRIBE_ERR_INVALID_ARG);
+}
+
+void test_unrestricted_ranking() {
+    Fixture f;
+    CHECK(f.run() == TRANSCRIBE_OK);
+    CHECK(g_run_calls == 1 && g_last_n == 16000 && g_last_pcm == f.pcm.data());
+
+    const transcribe_langid_result r = f.result();
+    CHECK(r.n_candidates == 5 && r.n_allowed == 5 && r.allowed_mass == 1.0f && r.audio_ms == 1000);
+
+    // Logits {1, 3, 2, 0, -1} rank bb, cc, aa, dd, ee.
+    const int    want[] = { 1, 2, 0, 3, 4 };
+    double       sum    = 0.0;
+    const double denom  = std::exp(1.0) + std::exp(3.0) + std::exp(2.0) + std::exp(0.0) + std::exp(-1.0);
+    for (int i = 0; i < 5; ++i) {
+        const transcribe_langid_candidate c = f.candidate(i);
+        CHECK(c.index == want[i]);
+        CHECK(c.code != nullptr && std::strcmp(c.code, g_labels.codes[want[i]].c_str()) == 0);
+        CHECK(c.name != nullptr && std::strcmp(c.name, g_labels.names[want[i]].c_str()) == 0);
+        CHECK(c.p == c.p_unrestricted);
+        CHECK(c.logit == g_logits[want[i]]);
+        CHECK(near(c.p, std::exp(static_cast<double>(g_logits[want[i]])) / denom));
+        sum += c.p;
+    }
+    CHECK(near(sum, 1.0));
+    CHECK(f.candidate(5).code == nullptr);  // out of range: zeroed row
+
+    transcribe_timings tm;
+    transcribe_timings_init(&tm);
+    CHECK(transcribe_langid_get_timings(f.session, &tm) == TRANSCRIBE_OK);
+    CHECK(tm.mel_ms == 1.0f && tm.encode_ms == 2.0f);
+
+    // top_k truncates the rows but not n_allowed.
+    transcribe_langid_params p;
+    transcribe_langid_params_init(&p);
+    p.top_k = 2;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.result().n_candidates == 2 && f.result().n_allowed == 5);
+    CHECK(f.candidate(1).index == 2 && f.candidate(2).code == nullptr);
+    p.top_k = 9;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.result().n_candidates == 5);
+
+    // Ties keep label order.
+    g_logits = { 0.5f, 0.5f, 0.5f, 0.5f, 0.5f };
+    CHECK(f.run() == TRANSCRIBE_OK);
+    for (int i = 0; i < 5; ++i) {
+        CHECK(f.candidate(i).index == i);
+    }
+}
+
+// H2: only NULL means "all"; every other malformed allowed list is rejected.
+void test_allowed_set() {
+    Fixture                  f;
+    transcribe_langid_params p;
+    transcribe_langid_params_init(&p);
+
+    const char * dd_bb[] = { "dd", "bb" };
+    p.allowed            = dd_bb;
+    p.n_allowed          = 2;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    transcribe_langid_result r = f.result();
+    CHECK(r.n_candidates == 2 && r.n_allowed == 2);
+    const double e_bb = std::exp(3.0);
+    const double e_dd = std::exp(0.0);
+    const double all  = std::exp(1.0) + e_bb + std::exp(2.0) + e_dd + std::exp(-1.0);
+    CHECK(near(r.allowed_mass, (e_bb + e_dd) / all));
+    const transcribe_langid_candidate c0 = f.candidate(0);
+    const transcribe_langid_candidate c1 = f.candidate(1);
+    CHECK(c0.index == 1 && c1.index == 3);
+    CHECK(near(c0.p, e_bb / (e_bb + e_dd)) && near(c1.p, e_dd / (e_bb + e_dd)));
+    CHECK(near(c0.p_unrestricted, e_bb / all));
+    CHECK(near(c0.p + c1.p, 1.0));
+
+    const char * one[] = { "cc" };
+    p.allowed          = one;
+    p.n_allowed        = 1;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.result().n_allowed == 1 && f.candidate(0).index == 2 && f.candidate(0).p == 1.0f);
+
+    const char * alias[] = { "xx" };
+    p.allowed            = alias;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.candidate(0).index == 0 && std::strcmp(f.candidate(0).code, "aa") == 0);
+
+    const char * dup[] = { "bb", "bb", "xx", "aa" };
+    p.allowed          = dup;
+    p.n_allowed        = 4;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.result().n_allowed == 2 && f.result().n_candidates == 2);
+
+    const char * every[] = { "aa", "bb", "cc", "dd", "ee" };
+    p.allowed            = every;
+    p.n_allowed          = 5;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.result().n_allowed == 5 && f.result().allowed_mass == 1.0f);
+    CHECK(f.candidate(0).p == f.candidate(0).p_unrestricted);
+
+    // Rejections never reach the family and keep the last result.
+    reset_fake();
+    p.n_allowed = -1;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
+    p.n_allowed = 0;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
+    p.allowed   = nullptr;
+    p.n_allowed = 2;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
+    const char * with_null[] = { "aa", nullptr };
+    p.allowed                = with_null;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
+    const char * unknown[] = { "aa", "zz" };
+    p.allowed              = unknown;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE);
+    CHECK(g_run_calls == 0);
+    CHECK(f.result().n_allowed == 5);
+}
+
+// H1: the crop keeps the last max_audio_ms, and the minimum applies to the
+// audio actually scored.
+void test_crop_and_minimum() {
+    {
+        Fixture f(500);
+        CHECK(f.run() == TRANSCRIBE_OK);  // 1 s scored as its last 500 ms
+        CHECK(g_last_n == 8000 && g_last_pcm == f.pcm.data() + 8000);
+        CHECK(f.result().audio_ms == 500);
+    }
+    {
+        Fixture            f;
+        std::vector<float> pcm(8000, 0.0f);  // exactly 500 ms
+        CHECK(transcribe_langid_run(f.session, pcm.data(), 8000, nullptr) == TRANSCRIBE_OK);
+        CHECK(f.result().audio_ms == 500);
+        g_run_calls = 0;
+        CHECK(transcribe_langid_run(f.session, pcm.data(), 7999, nullptr) == TRANSCRIBE_ERR_INPUT_TOO_SHORT);
+        CHECK(g_run_calls == 0);
+        CHECK(f.result().audio_ms == 500);  // previous result kept
+    }
+    {
+        Fixture            f(1000);
+        std::vector<float> pcm(16000 * 45, 0.0f);
+        CHECK(transcribe_langid_run(f.session, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+        CHECK(g_last_n == 16000 && g_last_pcm == pcm.data() + pcm.size() - 16000);
+        CHECK(f.result().audio_ms == 1000);
+    }
+    {
+        Fixture            f;  // default window 30 s
+        std::vector<float> pcm(16000 * 45, 0.0f);
+        CHECK(transcribe_langid_run(f.session, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+        CHECK(f.result().audio_ms == 30000);
+    }
+}
+
+// H3: non-finite PCM is rejected up front; non-finite or miscounted logits
+// fail the run instead of ranking garbage.
+void test_non_finite() {
+    Fixture f;
+    CHECK(f.run() == TRANSCRIBE_OK);
+    g_run_calls = 0;
+
+    std::vector<float> bad(16000, 0.0f);
+    bad[3] = std::numeric_limits<float>::quiet_NaN();
+    CHECK(transcribe_langid_run(f.session, bad.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    bad[3] = std::numeric_limits<float>::infinity();
+    CHECK(transcribe_langid_run(f.session, bad.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    std::vector<float> all_nan(16000, std::numeric_limits<float>::quiet_NaN());
+    CHECK(transcribe_langid_run(f.session, all_nan.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(g_run_calls == 0);
+    CHECK(f.result().n_candidates == 5);
+
+    g_logits = { 1.0f, std::numeric_limits<float>::quiet_NaN(), 0.0f, 0.0f, 0.0f };
+    CHECK(f.run() == TRANSCRIBE_ERR_BACKEND);
+    CHECK(f.result().n_candidates == 0 && f.result().allowed_mass == 0.0f);
+    g_logits = { 1.0f, -std::numeric_limits<float>::infinity(), 0.0f, 0.0f, 0.0f };
+    CHECK(f.run() == TRANSCRIBE_ERR_BACKEND);
+    g_logits = { 1.0f, 2.0f };
+    CHECK(f.run() == TRANSCRIBE_ERR_BACKEND);
+}
+
+void test_bad_args_and_failures() {
+    Fixture f;
+    CHECK(f.run() == TRANSCRIBE_OK);
+    g_run_calls = 0;
+
+    CHECK(transcribe_langid_run(nullptr, f.pcm.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(transcribe_langid_run(f.session, nullptr, 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(transcribe_langid_run(f.session, f.pcm.data(), 0, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(transcribe_langid_run(f.session, f.pcm.data(), -5, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+    transcribe_langid_params p;
+    transcribe_langid_params_init(&p);
+    p.top_k = -1;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
+    p.top_k       = 0;
+    p.struct_size = 0;
+    CHECK(f.run(&p) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
+    CHECK(g_run_calls == 0);
+    CHECK(f.result().n_candidates == 5);
+
+    // A family failure after the commit point clears the result.
+    g_run_status = TRANSCRIBE_ERR_OOM;
+    CHECK(f.run() == TRANSCRIBE_ERR_OOM);
+    CHECK(f.result().n_candidates == 0 && f.result().audio_ms == 0);
+    g_run_status = TRANSCRIBE_OK;
+
+    // The abort callback reaches the family.
+    CHECK(f.run() == TRANSCRIBE_OK);
+    g_check_abort = true;
+    transcribe_langid_set_abort_callback(f.session, [](void *) { return true; }, nullptr);
+    CHECK(f.run() == TRANSCRIBE_ERR_ABORTED);
+    CHECK(f.result().n_candidates == 0);
+    transcribe_langid_set_abort_callback(f.session, nullptr, nullptr);
+    CHECK(f.run() == TRANSCRIBE_OK);
+    transcribe_langid_set_abort_callback(nullptr, nullptr, nullptr);
+}
+
+}  // namespace
+
+int main() {
+    transcribe_log_set(nullptr, nullptr);
+
+    if (transcribe::build_langid_labels({ "aa", "bb", "cc", "dd", "ee" }, { "Aa", "Bb", "Cc", "Dd", "Ee" }, { "xx=aa" },
+                                        "test", g_labels) != TRANSCRIBE_OK) {
+        std::fprintf(stderr, "FAIL: fixture label table\n");
+        return EXIT_FAILURE;
+    }
+
+    test_label_table();
+    test_role_checks_and_labels();
+    test_accessors_before_run();
+    test_unrestricted_ranking();
+    test_allowed_set();
+    test_crop_and_minimum();
+    test_non_finite();
+    test_bad_args_and_failures();
+
+    if (g_failures != 0) {
+        std::fprintf(stderr, "%d failure(s)\n", g_failures);
+        return EXIT_FAILURE;
+    }
+    std::printf("langid_dispatch_unit: ok\n");
+    return EXIT_SUCCESS;
+}
diff --git a/tests/role_resolve_unit.cpp b/tests/role_resolve_unit.cpp
index e39917b19..85ef5ca9f 100644
--- a/tests/role_resolve_unit.cpp
+++ b/tests/role_resolve_unit.cpp
@@ -2,6 +2,7 @@
 
 #include "transcribe-arch.h"
 #include "transcribe-diarize.h"
+#include "transcribe-langid.h"
 #include "transcribe-model.h"
 #include "transcribe.h"
 
@@ -59,9 +60,21 @@ transcribe::Arch make_diarize_arch() {
 
 const transcribe::Arch k_diarize_arch = make_diarize_arch();
 
+const transcribe::LangidOps k_langid_ops = {};
+
+transcribe::Arch make_langid_arch() {
+    transcribe::Arch a = {};
+    a.name             = "fake_langid";
+    a.langid           = &k_langid_ops;
+    return a;
+}
+
+const transcribe::Arch k_langid_arch = make_langid_arch();
+
 void test_resolve_roles() {
     constexpr uint32_t ASR = TRANSCRIBE_ROLE_ASR;
     constexpr uint32_t DIA = TRANSCRIBE_ROLE_DIARIZE;
+    constexpr uint32_t LID = TRANSCRIBE_ROLE_LANGID;
 
     struct Case {
         const transcribe::Arch * arch;
@@ -81,6 +94,11 @@ void test_resolve_roles() {
         { &k_asr_arch,      DIA,              TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
         { &k_asr_arch,      ASR | DIA,        TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
         { &k_diarize_arch,  ASR | DIA,        TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
+        { &k_langid_arch,   LID,              TRANSCRIBE_OK,                  LID },
+        { &k_langid_arch,   0,                TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
+        { &k_asr_arch,      LID,              TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   }, // no langid ops table
+        { &k_diarize_arch,  DIA | LID,        TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
+        { &k_langid_arch,   ASR | LID,        TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
         { &k_asr_arch,      ASR | (1u << 31), TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   }, // unknown bit
         { nullptr,          0,                TRANSCRIBE_ERR_NOT_IMPLEMENTED, 0   },
     };

From 76eb1cf5566681f6842f8fa38fedab9aa7783809 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Mon, 5 Oct 2026 14:30:23 +0800
Subject: [PATCH 02/38] langid: ecapa_tdnn family (VoxLingua107 ECAPA-TDNN) on
 the LANGID role

Port SpeechBrain's VoxLingua107 ECAPA-TDNN from handy-computer/langid.cpp
(96af3a5) into src/arch/ecapa_tdnn/, serving TRANSCRIBE_ROLE_LANGID.

- Family-local SpeechBrain log-mel front end (periodic Hamming,
  10*log10, 80 dB top-db, per-bin mean), now on run_on_threads; the
  graph helpers live in graph.cpp. The embed path is dropped.
- GGUF contract moves to the shared namespaces: stt.frontend.*,
  stt.ecapa_tdnn.*, stt.langid.labels.*, stt.variant, uint32 scalars.
  Tensor names follow the transcribe-quantize rules (.weight / .bias,
  .conv. for k>1 kernels, frontend.mel_filterbank), so the quant policy
  needs no ECAPA entries. Metadata-derived dimensions are bounded.
- load() builds the label table while the model is still RAII-owned
  (H4); a duplicate label fails the load (H6, fixture-tested, 0 leaks).
- scripts/convert-ecapa_tdnn.py writes F32 only, from the HF cache; its
  tensors are byte-identical to langid.cpp's GGUF. F16 / Q8_0 come from
  transcribe-quantize (FAMILY_PRESETS: F16, Q8_0).
- Reference env, SpeechBrain dumper and NumPy reference, golden manifest
  and tolerances (carried over: every C++ stage tensor is bit-identical
  to langid.cpp's on CPU / 1 thread). validate.py gains an ecapa_tdnn
  branch: encoder-only reference stage, and a top-1 label gate instead of
  a transcript (cases set language: null so no hint reaches the model).
- transcribe-cli routes LANGID models to a new driver (--allow, --top,
  --max-audio-ms) with stable `language:` / `candidate:` lines.
- Tests: toy-fixture smoke (public API; registered above the shared
  build cut, so it also runs against shared libtranscribe), front-end
  unit, real-model smoke (TRANSCRIBE_ECAPA_TDNN_GGUF), CLI smoke. The
  fixture-generation block in tests/CMakeLists.txt moves above that cut.
- Ten FLEURS sample clips (CC-BY-4.0) and the intake JSON; the intake
  schema and normalize alias table learn hamming_periodic,
  sentence_mean, encoder-classifier and accuracy.

validate.py all --family ecapa_tdnn: 8/8 cases, 17/17 tensors in
tolerance, top-1 agrees with SpeechBrain on every case.
---
 docs/porting/families/_intake-schema.json     |   10 +-
 examples/cli/CMakeLists.txt                   |    1 +
 examples/cli/cli.h                            |   17 +-
 examples/cli/langid.cpp                       |  101 ++
 examples/cli/main.cpp                         |   44 +-
 .../lang-id-voxlingua107-ecapa/intake.json    |  154 +++
 samples/README.md                             |   30 +
 samples/fleurs-de.wav                         |  Bin 0 -> 357164 bytes
 samples/fleurs-en.wav                         |  Bin 0 -> 337964 bytes
 samples/fleurs-es.wav                         |  Bin 0 -> 311084 bytes
 samples/fleurs-fr.wav                         |  Bin 0 -> 326444 bytes
 samples/fleurs-id.wav                         |  Bin 0 -> 274604 bytes
 samples/fleurs-ja.wav                         |  Bin 0 -> 334124 bytes
 samples/fleurs-ru.wav                         |  Bin 0 -> 253484 bytes
 samples/fleurs-zh.wav                         |  Bin 0 -> 332204 bytes
 samples/long-45s.wav                          |  Bin 0 -> 1673004 bytes
 samples/short-800ms.wav                       |  Bin 0 -> 25644 bytes
 scripts/convert-ecapa_tdnn.py                 |  934 ++++++++++++++
 .../dump_reference_ecapa_tdnn_speechbrain.py  |  609 +++++++++
 scripts/ecapa_numpy_reference.py              |  168 +++
 scripts/envs/ecapa_tdnn/pyproject.toml        |   31 +
 scripts/envs/ecapa_tdnn/uv.lock               | 1096 +++++++++++++++++
 scripts/lib/ecapa_numpy.py                    |  398 ++++++
 scripts/lib/gguf_common.py                    |    6 +-
 scripts/lib/quant_policy.py                   |    6 +
 scripts/lib/test_quant_policy_sync.py         |    6 +
 scripts/validate.py                           |   58 +-
 src/CMakeLists.txt                            |    4 +
 src/arch/ecapa_tdnn/ecapa_tdnn.h              |   75 ++
 src/arch/ecapa_tdnn/graph.cpp                 |  376 ++++++
 src/arch/ecapa_tdnn/graph.h                   |   69 ++
 src/arch/ecapa_tdnn/mel.cpp                   |  364 ++++++
 src/arch/ecapa_tdnn/mel.h                     |  171 +++
 src/arch/ecapa_tdnn/model.cpp                 |  472 +++++++
 src/arch/ecapa_tdnn/weights.cpp               |  407 ++++++
 src/arch/ecapa_tdnn/weights.h                 |  217 ++++
 src/transcribe-arch.cpp                       |    6 +-
 tests/CMakeLists.txt                          |  144 ++-
 tests/cli_langid_smoke.cmake                  |   67 +
 tests/ecapa_tdnn_mel_unit.cpp                 |  449 +++++++
 tests/ecapa_tdnn_real_smoke.cpp               |  138 +++
 tests/ecapa_tdnn_smoke.cpp                    |  259 ++++
 tests/fixtures/make_gguf_fixtures.py          |  146 ++-
 .../lang-id-voxlingua107-ecapa.manifest.json  |   52 +
 tests/tolerances/ecapa_tdnn.json              |  131 ++
 45 files changed, 7154 insertions(+), 62 deletions(-)
 create mode 100644 examples/cli/langid.cpp
 create mode 100644 reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json
 create mode 100644 samples/fleurs-de.wav
 create mode 100644 samples/fleurs-en.wav
 create mode 100644 samples/fleurs-es.wav
 create mode 100644 samples/fleurs-fr.wav
 create mode 100644 samples/fleurs-id.wav
 create mode 100644 samples/fleurs-ja.wav
 create mode 100644 samples/fleurs-ru.wav
 create mode 100644 samples/fleurs-zh.wav
 create mode 100644 samples/long-45s.wav
 create mode 100644 samples/short-800ms.wav
 create mode 100644 scripts/convert-ecapa_tdnn.py
 create mode 100644 scripts/dump_reference_ecapa_tdnn_speechbrain.py
 create mode 100755 scripts/ecapa_numpy_reference.py
 create mode 100644 scripts/envs/ecapa_tdnn/pyproject.toml
 create mode 100644 scripts/envs/ecapa_tdnn/uv.lock
 create mode 100644 scripts/lib/ecapa_numpy.py
 create mode 100644 src/arch/ecapa_tdnn/ecapa_tdnn.h
 create mode 100644 src/arch/ecapa_tdnn/graph.cpp
 create mode 100644 src/arch/ecapa_tdnn/graph.h
 create mode 100644 src/arch/ecapa_tdnn/mel.cpp
 create mode 100644 src/arch/ecapa_tdnn/mel.h
 create mode 100644 src/arch/ecapa_tdnn/model.cpp
 create mode 100644 src/arch/ecapa_tdnn/weights.cpp
 create mode 100644 src/arch/ecapa_tdnn/weights.h
 create mode 100644 tests/cli_langid_smoke.cmake
 create mode 100644 tests/ecapa_tdnn_mel_unit.cpp
 create mode 100644 tests/ecapa_tdnn_real_smoke.cpp
 create mode 100644 tests/ecapa_tdnn_smoke.cpp
 create mode 100644 tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json
 create mode 100644 tests/tolerances/ecapa_tdnn.json

diff --git a/docs/porting/families/_intake-schema.json b/docs/porting/families/_intake-schema.json
index 7d7d33ae7..6e80cb879 100644
--- a/docs/porting/families/_intake-schema.json
+++ b/docs/porting/families/_intake-schema.json
@@ -85,7 +85,7 @@
           "type": "array",
           "items": {
             "type": "string",
-            "enum": ["encoder-transducer", "encoder-decoder", "audio-llm", "encoder-ctc", "encoder-diarizer"]
+            "enum": ["encoder-transducer", "encoder-decoder", "audio-llm", "encoder-ctc", "encoder-diarizer", "encoder-classifier"]
           },
           "description": "Heuristic matches against known patterns. Human selects one in architecture_pattern."
         },
@@ -147,12 +147,12 @@
         "fft_size": {"type": ["integer", "null"]},
         "window": {
           "type": ["string", "null"],
-          "enum": ["hann_periodic", "hann_symmetric", "hamming", "blackman", null],
+          "enum": ["hann_periodic", "hann_symmetric", "hamming", "hamming_periodic", "blackman", null],
           "description": "Window type. 'hann_periodic' and 'hann_symmetric' are a classic mismatch source."
         },
         "normalization": {
           "type": ["string", "null"],
-          "enum": ["per_feature", "global", "per_utterance", "none", null]
+          "enum": ["per_feature", "global", "per_utterance", "sentence_mean", "none", null]
         },
         "preemphasis": {
           "type": ["number", "null"],
@@ -257,7 +257,7 @@
           },
           "metric": {
             "type": "string",
-            "enum": ["wer", "cer", "bleu", "other"]
+            "enum": ["wer", "cer", "bleu", "accuracy", "other"]
           },
           "score": {
             "type": ["number", "null"],
@@ -288,7 +288,7 @@
     },
     "architecture_pattern": {
       "type": ["string", "null"],
-      "enum": ["encoder-transducer", "encoder-decoder", "audio-llm", "encoder-ctc", "encoder-diarizer", null]
+      "enum": ["encoder-transducer", "encoder-decoder", "audio-llm", "encoder-ctc", "encoder-diarizer", "encoder-classifier", null]
     },
     "known_risks": {
       "type": "array",
diff --git a/examples/cli/CMakeLists.txt b/examples/cli/CMakeLists.txt
index 07a681522..43eff1e6f 100644
--- a/examples/cli/CMakeLists.txt
+++ b/examples/cli/CMakeLists.txt
@@ -8,6 +8,7 @@ add_executable(transcribe-cli
     main.cpp
     asr.cpp
     diarize.cpp
+    langid.cpp
 )
 
 target_link_libraries(transcribe-cli
diff --git a/examples/cli/cli.h b/examples/cli/cli.h
index fbe4b5b50..0b02f4e50 100644
--- a/examples/cli/cli.h
+++ b/examples/cli/cli.h
@@ -1,7 +1,7 @@
 // cli.h - shared declarations for the transcribe-cli example.
 //
 // main.cpp parses arguments, owns process setup (log sink, output file), and
-// routes each model to its role's driver (asr.cpp, diarize.cpp). Shared
+// routes each model to its role's driver (asr.cpp, diarize.cpp, langid.cpp). Shared
 // helpers live in namespace transcribe_cli next to the WAV loader in
 // examples/common.
 
@@ -103,6 +103,14 @@ struct cli_args {
     // explicit K. Silently ignored by families without
     // supports_spec_decode. Set by --spec-k-drafts N.
     int spec_k_drafts        = -1;
+
+    // LANGID role (language ID models). --allow restricts the decision to
+    // these labels (codes or aliases); --top keeps the best N candidates
+    // (0 = every allowed label); --max-audio-ms scores the last N ms
+    // (0 = library default, 30000).
+    std::vector<std::string> langid_allow;
+    int                      langid_top_k        = 0;
+    int                      langid_max_audio_ms = 0;
 };
 
 // -o/--output: write `text` (newline-terminated) to `output` if non-null.
@@ -129,4 +137,11 @@ int run_diarize_file(const cli_args &           args,
                      double                     duration_s,
                      std::ofstream *            output);
 
+// With -o, writes the candidate lines.
+int run_langid_file(const cli_args &           args,
+                    transcribe_model *         model,
+                    const std::vector<float> & pcm,
+                    double                     duration_s,
+                    std::ofstream *            output);
+
 }  // namespace transcribe_cli
diff --git a/examples/cli/langid.cpp b/examples/cli/langid.cpp
new file mode 100644
index 000000000..713199a51
--- /dev/null
+++ b/examples/cli/langid.cpp
@@ -0,0 +1,101 @@
+// langid.cpp - transcribe-cli LANGID driver: ranked language candidates for
+// one file through include/transcribe/langid.h.
+//
+// Output lines are stable (scripts/validate.py parses `language:`):
+//   language: <code> index=<i> p=<p>
+//   candidate: <rank> <code> index=<i> p=<p> p_unrestricted=<p> logit=<z>
+
+#include "transcribe/langid.h"
+
+#include "cli.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <fstream>
+#include <string>
+#include <vector>
+
+int transcribe_cli::run_langid_file(const cli_args &           args,
+                                    transcribe_model *         model,
+                                    const std::vector<float> & pcm,
+                                    double                     duration_s,
+                                    std::ofstream *            output) {
+    if (args.stream_chunk_ms > 0) {
+        std::fprintf(stderr, "stream: the language ID path has no streaming entry point; drop --stream-chunk-ms\n");
+        transcribe_model_free(model);
+        return EXIT_FAILURE;
+    }
+
+    transcribe_langid_session_params sp;
+    transcribe_langid_session_params_init(&sp);
+    sp.n_threads                        = args.n_threads;
+    sp.max_audio_ms                     = args.langid_max_audio_ms;
+    transcribe_langid_session * session = nullptr;
+    transcribe_status           st      = transcribe_langid_session_init(model, &sp, &session);
+    if (st != TRANSCRIBE_OK) {
+        std::fprintf(stderr, "langid session init: %s\n", transcribe_status_string(st));
+        transcribe_model_free(model);
+        return EXIT_FAILURE;
+    }
+
+    std::vector<const char *> allowed;
+    for (const std::string & code : args.langid_allow) {
+        allowed.push_back(code.c_str());
+    }
+    transcribe_langid_params lp;
+    transcribe_langid_params_init(&lp);
+    lp.allowed   = allowed.empty() ? nullptr : allowed.data();
+    lp.n_allowed = static_cast<int32_t>(allowed.size());
+    lp.top_k     = args.langid_top_k;
+
+    // --repeat N runs transcribe_langid_run() N times for steady-state perf
+    // measurements.
+    for (int r = 0; r < args.repeat; ++r) {
+        st = transcribe_langid_run(session, pcm.data(), static_cast<int>(pcm.size()), &lp);
+        if (st != TRANSCRIBE_OK) {
+            break;
+        }
+    }
+    std::printf("run: %s\n", transcribe_status_string(st));
+    bool output_ok = true;
+    if (st == TRANSCRIBE_OK) {
+        transcribe_langid_result res;
+        transcribe_langid_result_init(&res);
+        transcribe_langid_get_result(session, &res);
+
+        std::string lines;
+        for (int i = 0; i < res.n_candidates; ++i) {
+            transcribe_langid_candidate c;
+            transcribe_langid_candidate_init(&c);
+            transcribe_langid_get_candidate(session, i, &c);
+            if (i == 0) {
+                std::printf("language: %s index=%d p=%.6f\n", c.code, c.index, static_cast<double>(c.p));
+                std::printf("  name:         %s\n", c.name);
+                std::printf("  allowed_mass: %.6f (%d allowed)\n", static_cast<double>(res.allowed_mass),
+                            res.n_allowed);
+                std::printf("  audio_ms:     %lld\n", static_cast<long long>(res.audio_ms));
+            }
+            char line[160];
+            std::snprintf(line, sizeof(line), "candidate: %d %s index=%d p=%.6f p_unrestricted=%.6f logit=%.4f\n",
+                          i + 1, c.code, c.index, static_cast<double>(c.p), static_cast<double>(c.p_unrestricted),
+                          static_cast<double>(c.logit));
+            std::printf("  %s", line);
+            lines += line;
+        }
+        output_ok = write_output_file(output, args.output_path, lines.c_str());
+    }
+
+    transcribe_timings tm;
+    transcribe_timings_init(&tm);
+    transcribe_langid_get_timings(session, &tm);
+    const double total_ms = tm.mel_ms + tm.encode_ms;
+    if (total_ms > 0.0 && duration_s > 0.0) {
+        std::printf("  realtime:   %.0fx (%.1f ms for %.1f s; mel %.1f ms, encode %.1f ms)\n",
+                    duration_s * 1000.0 / total_ms, total_ms, duration_s, static_cast<double>(tm.mel_ms),
+                    static_cast<double>(tm.encode_ms));
+    }
+
+    transcribe_langid_session_free(session);
+    transcribe_model_free(model);
+    return st == TRANSCRIBE_OK && output_ok ? EXIT_SUCCESS : EXIT_FAILURE;
+}
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 0d5f4c0b9..5bea58e3b 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -83,6 +83,9 @@ void print_usage(const char * argv0) {
                  "  --diarize             (moss/granite-plus) speaker attribution: segments carry\n"
                  "                        speaker ids; granite-plus requests its speaker task\n"
                  "  --no-diarize          disable speaker attribution (the library default)\n"
+                 "  --allow CODES         (language ID) comma-separated labels to choose from\n"
+                 "  --top N               (language ID) print the best N candidates (0 = all)\n"
+                 "  --max-audio-ms N      (language ID) score the last N ms (0 = 30000)\n"
                  "  --raw-tokens          keep <|...|> control tokens in output text\n"
                  "  --stream-chunk-ms N   single-file: drive the streaming API by feeding\n"
                  "                        N-ms PCM slices; requires model to advertise\n"
@@ -418,6 +421,41 @@ bool parse_args(int argc, char ** argv, cli_args & out) {
         } else if (a == "--no-pnc") {
             out.canary_pnc     = false;
             out.canary_pnc_set = true;
+        } else if (a == "--allow") {
+            const char * v = take_value(a.c_str());
+            if (!v) {
+                return false;
+            }
+            const std::string list = v;
+            size_t            pos  = 0;
+            while (pos <= list.size()) {
+                const size_t comma = list.find(',', pos);
+                const size_t end   = comma == std::string::npos ? list.size() : comma;
+                if (end > pos) {
+                    out.langid_allow.push_back(list.substr(pos, end - pos));
+                }
+                pos = end + 1;
+            }
+            if (out.langid_allow.empty()) {
+                std::fprintf(stderr, "error: --allow needs at least one label\n");
+                return false;
+            }
+        } else if (a == "--top") {
+            const char * v = take_value(a.c_str());
+            if (!v) {
+                return false;
+            }
+            out.langid_top_k = std::atoi(v);
+            if (out.langid_top_k < 0) {
+                std::fprintf(stderr, "error: --top must be >= 0\n");
+                return false;
+            }
+        } else if (a == "--max-audio-ms") {
+            const char * v = take_value(a.c_str());
+            if (!v) {
+                return false;
+            }
+            out.langid_max_audio_ms = std::atoi(v);
         } else if (a == "--diarize") {
             out.diarize     = true;
             out.diarize_set = true;
@@ -583,9 +621,13 @@ int run_file(const cli_args & args, std::ofstream * output) {
         std::printf("  license:    %s\n", lic);
     }
 
-    if ((transcribe_model_roles(model) & TRANSCRIBE_ROLE_ASR) != 0) {
+    const uint32_t roles = transcribe_model_roles(model);
+    if ((roles & TRANSCRIBE_ROLE_ASR) != 0) {
         return transcribe_cli::run_asr_file(args, model, pcm, duration_s, output);
     }
+    if ((roles & TRANSCRIBE_ROLE_LANGID) != 0) {
+        return transcribe_cli::run_langid_file(args, model, pcm, duration_s, output);
+    }
     return transcribe_cli::run_diarize_file(args, model, pcm, duration_s, output);
 }
 
diff --git a/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json b/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json
new file mode 100644
index 000000000..52e67d2df
--- /dev/null
+++ b/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json
@@ -0,0 +1,154 @@
+{
+  "schema_version": "transcribe-intake-v1",
+  "family": "ecapa_tdnn",
+  "hf_repo": "speechbrain/lang-id-voxlingua107-ecapa",
+  "hf_revision": "0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9",
+  "sources": {
+    "config": {
+      "kind": "hf_file",
+      "path": "hyperparams.yaml",
+      "status": "found",
+      "detail": "SpeechBrain hyperparams.yaml (no HF config.json). Instantiates lobes.features.Fbank, lobes.models.ECAPA_TDNN.ECAPA_TDNN and lobes.models.Xvector.Classifier by class name; only channels / kernel_sizes / dilations / attention_channels / lin_neurons and four Fbank fields are explicit, the rest are speechbrain 1.1.1 defaults (asserted against the live modules by scripts/convert-ecapa_tdnn.py)."
+    },
+    "preprocessor": {
+      "kind": "hf_file",
+      "path": "hyperparams.yaml::compute_features",
+      "status": "found",
+      "detail": "Fbank(n_mels=60, left_frames=0, right_frames=0, deltas=False) + InputNormalization(norm_type=sentence, std_norm=False)."
+    },
+    "tokenizer_config": {
+      "kind": "hf_file",
+      "path": "tokenizer_config.json",
+      "status": "missing"
+    },
+    "tokenizer_json": {
+      "kind": "hf_file",
+      "path": "tokenizer.json",
+      "status": "missing"
+    },
+    "generation_config": {
+      "kind": "hf_file",
+      "path": "generation_config.json",
+      "status": "missing"
+    },
+    "safetensors_metadata": {
+      "kind": "hf_api",
+      "path": "HfApi.get_safetensors_metadata",
+      "status": "missing",
+      "detail": "header-only floating dtype distribution; no tensor payloads downloaded"
+    },
+    "reference_modeling_code": {
+      "kind": "reference_code",
+      "path": "speechbrain==1.1.1 speechbrain.inference.classifiers.EncoderClassifier",
+      "status": "found",
+      "detail": "The only published implementation; ported from handy-computer/langid.cpp@96af3a5 where the C++ was first validated against it."
+    }
+  },
+  "variants": [
+    {
+      "name": "lang-id-voxlingua107-ecapa",
+      "memory_gb": 0.1,
+      "files": [
+        "hyperparams.yaml",
+        "embedding_model.ckpt",
+        "classifier.ckpt",
+        "label_encoder.txt"
+      ]
+    }
+  ],
+  "config": {
+    "architecture_candidates": [
+      "encoder-classifier"
+    ],
+    "key_fields": {
+      "channels": [
+        1024,
+        1024,
+        1024,
+        1024,
+        3072
+      ],
+      "kernel_sizes": [
+        5,
+        3,
+        3,
+        3,
+        1
+      ],
+      "dilations": [
+        1,
+        2,
+        3,
+        4,
+        1
+      ],
+      "res2net_scale": 8,
+      "se_channels": 128,
+      "attention_channels": 128,
+      "lin_neurons": 256,
+      "classifier_hidden": 512,
+      "n_labels": 107
+    }
+  },
+  "dtype": {
+    "expected": "float32",
+    "source": "weights_header",
+    "evidence": "embedding_model.ckpt / classifier.ckpt are fp32 PyTorch state dicts (21,058,432 + 188,011 params); the converter asserts every parameter is torch.float32.",
+    "details": {
+      "config_declared": null,
+      "header_distribution": {
+        "float32": 21246443
+      }
+    }
+  },
+  "frontend": {
+    "sample_rate": 16000,
+    "n_mels": 60,
+    "hop_length": 160,
+    "fft_size": 400,
+    "window": "hamming_periodic",
+    "normalization": "sentence_mean",
+    "preemphasis": null,
+    "dither": null,
+    "center": true,
+    "padding_mode": "constant",
+    "mel_filterbank_norm": null
+  },
+  "tokenizer": {
+    "type": "other",
+    "vocab_size": 0,
+    "special_tokens": {},
+    "has_language_tokens": false,
+    "vocab_sha256": null
+  },
+  "capabilities": {
+    "languages": [],
+    "language_detection": false,
+    "translation": false,
+    "timestamps": [],
+    "streaming": false,
+    "speaker_diarization": false
+  },
+  "upstream_benchmarks": [
+    {
+      "dataset": "VoxLingua107 dev",
+      "language": null,
+      "metric": "accuracy",
+      "score": 93.3,
+      "score_unit": "percent",
+      "source": "model card (6.7% error rate on the VoxLingua107 development set)",
+      "notes": "Language ID role: top-1 accuracy over 107 labels. Not a WER figure."
+    }
+  ],
+  "reference_framework": "author_repo_speechbrain",
+  "reference_rationale": "SpeechBrain is the only implementation of this checkpoint: hyperparams.yaml instantiates SpeechBrain classes by name and there is no Transformers shim. Pinned to speechbrain 1.1.1 / torch 2.13.0, the versions langid.cpp's tolerances were measured against.",
+  "architecture_pattern": "encoder-classifier",
+  "known_risks": [
+    "Role is LANGID, not ASR: acceptance is FLEURS decision parity vs SpeechBrain (12000 decisions) plus top-1 accuracy, not WER on LibriSpeech.",
+    "capabilities.language_detection / stt.capability.lang_detect mean ASR auto-detect and stay false; language ID is the model's role (transcribe_model_roles), not an ASR capability.",
+    "SpeechBrain front end (periodic Hamming, 10*log10, 80 dB top-db, per-bin mean) is not expressible by transcribe-mel; it is family-local (src/arch/ecapa_tdnn/mel.cpp).",
+    "Labels use VoxLingua107's legacy codes (iw, jw, tl, no); modern codes are stored as aliases. Codes are not canonicalized against ASR models' language lists.",
+    "Closed-set classifier: out-of-set speech still gets a confident label; callers use allowed_mass / restrict the allowed set."
+  ],
+  "intake_gaps": []
+}
diff --git a/samples/README.md b/samples/README.md
index d3fd433f2..c76bea08e 100644
--- a/samples/README.md
+++ b/samples/README.md
@@ -40,6 +40,36 @@ and is recorded here so each clip can be traced back.
 Regenerate the source pool, not the clips themselves, with
 `uv run scripts/wer/ingest.py fleurs <lang>`.
 
+## Language ID clips (FLEURS)
+
+Fixtures for the ecapa_tdnn (LANGID) family, carried over byte-for-byte from
+langid.cpp (`handy-computer/langid.cpp@96af3a5`, `scripts/make_samples_fleurs.py`)
+so its measured parity and tolerances still apply. Rule for the eight
+`fleurs-<code>` clips: the first test-split utterance of 4-12 s, in parquet
+order, that the SpeechBrain reference (`speechbrain/lang-id-voxlingua107-ecapa`
+@ `0253049a`) classifies as the expected VoxLingua107 code. They back
+`tests/golden/ecapa_tdnn/`, `transcribe_ecapa_tdnn_real_smoke` and
+`transcribe_cli_langid_smoke`.
+
+Source: [google/fleurs](https://huggingface.co/datasets/google/fleurs), test
+split, licensed **CC-BY-4.0**.
+
+| file | duration | config | FLEURS id |
+| --- | ---: | --- | --- |
+| `fleurs-en.wav` | 10.56 s | `en_us` | 1904 |
+| `fleurs-de.wav` | 11.16 s | `de_de` | 1738 |
+| `fleurs-fr.wav` | 10.20 s | `fr_fr` | 1987 |
+| `fleurs-es.wav` | 9.72 s | `es_419` | 1764 |
+| `fleurs-ja.wav` | 10.44 s | `ja_jp` | 1828 |
+| `fleurs-zh.wav` | 10.38 s | `cmn_hans_cn` | 1906 |
+| `fleurs-ru.wav` | 7.92 s | `ru_ru` | 1917 |
+| `fleurs-id.wav` | 8.58 s | `id_id` | 1909 |
+| `short-800ms.wav` | 0.80 s | `en_us` | 1904 (first 800 ms) |
+| `long-45s.wav` | 52.28 s | `en_us` | 1904, 1675, 1950, 1728, 1972, 1938 (concatenated) |
+
+`long-45s.wav` is longer than its name says; it only has to exceed the 30 s
+language ID window, and the name is kept so it matches langid.cpp's records.
+
 ## Everything else
 
 These predate this file and arrived inside unrelated commits, so their source
diff --git a/samples/fleurs-de.wav b/samples/fleurs-de.wav
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diff --git a/scripts/convert-ecapa_tdnn.py b/scripts/convert-ecapa_tdnn.py
new file mode 100644
index 000000000..dd632f93b
--- /dev/null
+++ b/scripts/convert-ecapa_tdnn.py
@@ -0,0 +1,934 @@
+#!/usr/bin/env python3
+"""
+convert-ecapa_tdnn.py - convert SpeechBrain's VoxLingua107 ECAPA-TDNN
+language-ID checkpoint into the F32 reference GGUF that transcribe.cpp's
+ecapa_tdnn family (LANGID role) loads. F16 / Q8_0 come from
+tools/transcribe-quantize, never from here.
+
+Source format:
+    SpeechBrain checkpoint directory (HF repo or local), with:
+
+      hyperparams.yaml       instantiates lobes.features.Fbank,
+                             lobes.models.ECAPA_TDNN.ECAPA_TDNN and
+                             lobes.models.Xvector.Classifier by name
+      embedding_model.ckpt   ECAPA-TDNN encoder (21,058,432 params)
+      classifier.ckpt        Xvector.Classifier head (188,011 params)
+      label_encoder.txt      107 `'<code>: <name>' => <index>` lines
+
+There is no Transformers shim for this checkpoint: SpeechBrain is the only
+implementation. So this converter LOADS THE MODEL THROUGH SPEECHBRAIN
+(exactly the way `scripts/dump_reference_ecapa_tdnn_speechbrain.py` does,
+including `overrides={"pretrained_path": ...}` — without it SpeechBrain
+silently re-fetches the .ckpt files from the HF `main` branch and the
+pinned revision is a lie) and reads weights off the live module tree. The
+checkpoint resolves through scripts/lib/hf_source.py (HF hub cache, or the
+$TRANSCRIBE_MODELS_DIR mirror when set).
+
+Loading through SpeechBrain also lets the converter assert the
+architecture against the *instantiated modules and tensor shapes* rather
+than against `hyperparams.yaml`: the yaml carries only `input_size`,
+`channels`, `kernel_sizes`, `dilations`, `attention_channels`,
+`lin_neurons` and four front-end fields. Everything else (res2net_scale, se_channels, global_context, padding mode, STFT
+geometry, top_db, amin, ...) is a SpeechBrain *default* of the pinned
+version, so the yaml cannot confirm it.
+
+Four exact rewrites happen here; the NumPy reference
+`scripts/lib/ecapa_numpy.py` proves each of them numerically:
+
+  1. Every TDNN BatchNorm becomes a `scale`/`shift` vector pair. It cannot
+     be folded into its conv because SpeechBrain's TDNNBlock is
+     conv -> ReLU -> BN.
+  2. The three BNs that feed a linear map with no activation between
+     (`asp_bn -> fc`, `cls.bn0 -> cls.l1`, `cls.bn1 -> cls.out`) are folded
+     FORWARD into that linear map and disappear.
+  3. `asp.tdnn`'s 9216-wide input weight is split into `x`/`mean`/`std`
+     weights, one per member of SpeechBrain's `cat([x, mean, std])`.
+  4. `mfa`'s 3072-wide input weight is split into three weights, one per
+     SERes2Net block output.
+
+Layout and naming conventions (the loader contract). Names follow the
+shared tools/transcribe-quantize rules, so the quant policy needs no
+ECAPA-specific entries:
+  * k>1 kernels are `*.conv.weight`, stored TAP-MAJOR: numpy `[K, OC, IC]`
+    (= `torch_w.permute(2, 0, 1)`), i.e. ggml `ne = [IC, OC, K]`, so each
+    tap is a contiguous `[IC, OC]` matmul operand. `.conv.` keeps them in
+    the quantizer's Conv bucket (F32 / F16 only).
+  * 1x1 kernels are 2-D `*.weight`: numpy `[OC, IC]`, ggml `ne = [IC, OC]`
+    (ordinary matmul operands; the quantizer's Linear bucket).
+  * `frontend.mel_filterbank` is mel-major: numpy `[60, 201]` (=
+    SpeechBrain's `[201, 60]` fbank matrix transposed), ggml
+    `ne = [201, 60]`, so each filter's 201 weights are contiguous.
+  * Biases (`*.bias`) and BN `*.bn.scale` / `*.bn.shift` are 1-D, F32.
+
+CLI (writes models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf):
+
+    uv run --project scripts/envs/ecapa_tdnn \
+      scripts/convert-ecapa_tdnn.py speechbrain/lang-id-voxlingua107-ecapa
+
+Quantize with tools/transcribe-quantize afterwards.
+
+Single-file, top-to-bottom — no hidden helpers.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Any
+
+# XET-backed transfers occasionally stall on this checkpoint's small .ckpt
+# files; the plain HTTP path is fast enough for 21 M parameters. Set before
+# huggingface_hub is imported (indirectly, via speechbrain) so it takes.
+os.environ.setdefault("HF_HUB_DISABLE_XET", "1")
+
+import numpy as np
+import torch
+from gguf import GGMLQuantizationType, LlamaFileType
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+SCRIPTS_DIR = REPO_ROOT / "scripts"
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+from lib.gguf_common import (  # noqa: E402
+    add_general_identity,
+    canonicalize_normalize,
+    encode_for_gguf,
+    gguf_name,
+    gguf_writer,
+    slug_from_repo_id,
+)
+from lib.hf_source import looks_like_repo_id, resolve_model_dir  # noqa: E402
+
+DEFAULT_REPO = "speechbrain/lang-id-voxlingua107-ecapa"
+DEFAULT_REVISION = "0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9"
+DEFAULT_VARIANT = "lang-id-voxlingua107-ecapa"
+
+ARCH = "ecapa_tdnn"
+FORMAT_VERSION = 1
+
+# Architecture constants. Every one of these is ASSERTED against the live
+# module tree below, never assumed — see `assert_architecture`.
+SAMPLE_RATE = 16000
+N_FFT = 400
+WIN = 400
+HOP = 160
+N_STFT = N_FFT // 2 + 1  # 201
+N_MELS = 60
+LOG_FLOOR = 1e-10
+TOP_DB = 80.0
+
+CHANNELS = [1024, 1024, 1024, 1024, 3072]
+KERNEL_SIZES = [5, 3, 3, 3, 1]
+DILATIONS = [1, 2, 3, 4, 1]
+RES2NET_SCALE = 8
+SE_CHANNELS = 128
+ATTENTION_CHANNELS = 128
+ASP_EPS = 1e-12
+EMB_DIM = 256
+HIDDEN = 512
+N_LABELS = 107
+LEAKY_SLOPE = 0.01
+BN_EPS = 1e-5
+
+# Measured on the pinned checkpoint.
+N_PARAMS_EMBEDDING = 21_058_432
+N_PARAMS_CLASSIFIER = 188_011
+
+# `he`/`jv`/`fil`/`nb` are the modern ISO codes users type; the VoxLingua107
+# label set predates them and uses the legacy forms. Stored as metadata so
+# the LANGID role resolves them without a per-family table.
+LABEL_ALIASES = ["he=iw", "jv=jw", "fil=tl", "nb=no"]
+
+
+# ---------------------------------------------------------------------------
+# Checkpoint resolution
+# ---------------------------------------------------------------------------
+
+
+def resolve_checkpoint_dir(model_arg: str, revision: str | None) -> Path:
+    """Return a local SpeechBrain checkpoint directory (HF cache or local)."""
+    p = Path(model_arg).expanduser()
+    if not p.is_dir() and not looks_like_repo_id(model_arg):
+        raise SystemExit(
+            f"error: {model_arg!r} is neither an existing directory nor an "
+            f"org/name Hugging Face repo id"
+        )
+    src = resolve_model_dir(model_arg, revision)
+    needed = ["hyperparams.yaml", "embedding_model.ckpt",
+              "classifier.ckpt", "label_encoder.txt"]
+    missing = [f for f in needed if not (src / f).exists()]
+    if missing:
+        raise SystemExit(f"error: {src} is missing {missing}")
+    return src
+
+
+def load_reference(src: Path, device: str = "cpu"):
+    """Build the SpeechBrain `EncoderClassifier`, in eval mode, fp32.
+
+    `overrides={"pretrained_path": <local dir>}` is the load-bearing line:
+    `hyperparams.yaml` sets `pretrained_path` to the HF repo id and
+    `Pretrainer.collect_files` splits every `paths[...]` entry into
+    (source, filename), so without the override the .ckpt files are
+    re-fetched from the HF `main` branch and `--revision` means nothing.
+
+    `savedir != source` because SpeechBrain symlinks the collected
+    loadables into savedir; aiming it at the snapshot would symlink files
+    onto themselves.
+    """
+    from speechbrain.inference.classifiers import EncoderClassifier
+
+    torch.manual_seed(0)
+    torch.set_num_threads(1)
+
+    savedir = REPO_ROOT / "build" / "speechbrain" / ARCH
+    savedir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Loading EncoderClassifier: source={src} savedir={savedir}")
+    clf = EncoderClassifier.from_hparams(
+        source=str(src),
+        savedir=str(savedir),
+        overrides={"pretrained_path": str(src)},
+        run_opts={"device": device},
+    )
+    clf.mods.eval()
+    for p in clf.mods.parameters():
+        p.requires_grad_(False)
+
+    training = [n for n, m in clf.mods.named_modules() if m.training]
+    if training:
+        raise SystemExit(f"error: modules still in training mode: {training[:5]}")
+
+    dtypes = {str(p.dtype) for p in clf.mods.parameters()}
+    if dtypes != {"torch.float32"}:
+        raise SystemExit(f"error: expected an fp32 model, got dtypes {dtypes}")
+
+    return clf
+
+
+# ---------------------------------------------------------------------------
+# Architecture assertions
+# ---------------------------------------------------------------------------
+
+
+class ArchError(SystemExit):
+    """Raised (as exit 2) when the checkpoint is not the model we expect."""
+
+    def __init__(self, msg: str) -> None:
+        super().__init__(2)
+        self.msg = msg
+
+
+_checks: list[str] = []
+
+
+def check(cond: bool, what: str, got: Any = None) -> None:
+    """Assert one architectural fact, recording it for the summary."""
+    if not cond:
+        detail = f" (got {got!r})" if got is not None else ""
+        print(f"\nERROR: architecture mismatch: {what}{detail}", file=sys.stderr)
+        print("This converter only handles SpeechBrain's VoxLingua107 "
+              "ECAPA-TDNN as pinned in scripts/envs/ecapa_tdnn.", file=sys.stderr)
+        raise ArchError(what)
+    _checks.append(what)
+
+
+def bn_of(module) -> torch.nn.BatchNorm1d:
+    """Resolve a SpeechBrain BatchNorm1d wrapper to the torch module.
+
+    The wrapper depth is NOT uniform in this checkpoint: TDNN blocks and the
+    classifier reach the torch layer at `<block>.norm.norm`, while
+    `embedding_model.asp_bn` is itself the wrapper and its torch layer is
+    `asp_bn.norm`. Walking to the first real `torch.nn.BatchNorm1d` instead
+    of hardcoding a depth is what stops a one-level slip from silently
+    producing plausible-looking garbage.
+    """
+    m = module
+    for _ in range(4):
+        if isinstance(m, torch.nn.BatchNorm1d):
+            return m
+        if hasattr(m, "norm"):
+            m = m.norm
+            continue
+        break
+    raise ArchError(f"no torch BatchNorm1d under {type(module).__name__}")
+
+
+def assert_conv(conv, oc: int, ic: int, k: int, d: int, label: str) -> None:
+    """Assert one SpeechBrain Conv1d: shape, dilation, padding, bias."""
+    w = conv.conv.weight
+    check(tuple(w.shape) == (oc, ic, k), f"{label}.weight is [{oc}, {ic}, {k}]",
+          tuple(w.shape))
+    check(conv.conv.bias is not None, f"{label} has a bias")
+    check(int(conv.dilation) == d, f"{label}.dilation == {d}", conv.dilation)
+    check(conv.padding == "same", f"{label}.padding == 'same'", conv.padding)
+    check(conv.padding_mode == "reflect", f"{label}.padding_mode == 'reflect'",
+          conv.padding_mode)
+    check(int(conv.conv.groups) == 1, f"{label}.groups == 1", conv.conv.groups)
+
+
+def assert_tdnn_block(blk, oc: int, ic: int, k: int, d: int, label: str) -> None:
+    """conv -> ReLU -> BN(eps 1e-5) -> Dropout1d(p=0) (identity in eval)."""
+    assert_conv(blk.conv, oc, ic, k, d, f"{label}.conv")
+    check(isinstance(blk.activation, torch.nn.ReLU),
+          f"{label}.activation is ReLU", type(blk.activation).__name__)
+    bn = bn_of(blk.norm)
+    check(int(bn.num_features) == oc, f"{label}.norm has {oc} features",
+          bn.num_features)
+    check(abs(float(bn.eps) - BN_EPS) < 1e-12, f"{label}.norm.eps == 1e-5", bn.eps)
+    check(bn.running_mean is not None and bn.running_var is not None,
+          f"{label}.norm carries running statistics")
+    # SpeechBrain 1.1.1 added a Dropout1d child (oracle report D3). At p=0 in
+    # eval mode it is an exact identity, so the C++ graph ignores it — but a
+    # nonzero p would mean this checkpoint is not the one we modelled.
+    check(float(blk.dropout.p) == 0.0, f"{label}.dropout.p == 0", blk.dropout.p)
+
+
+def assert_architecture(clf) -> dict[str, Any]:
+    """Verify every hyperparameter the C++ graph assumes against live modules.
+
+    Returns the label table (codes/names) as a side effect of validating it.
+    """
+    em = clf.mods.embedding_model
+    cl = clf.mods.classifier
+    fe = clf.mods.compute_features
+    mvn = clf.mods.mean_var_norm
+
+    # ---- front end -------------------------------------------------------
+    st = fe.compute_STFT
+    check(int(st.n_fft) == N_FFT, "Fbank STFT n_fft == 400", st.n_fft)
+    check(int(st.win_length) == WIN, "Fbank STFT win_length == 400 samples "
+          "(25 ms)", st.win_length)
+    check(int(st.hop_length) == HOP, "Fbank STFT hop_length == 160 samples "
+          "(10 ms)", st.hop_length)
+    check(int(st.sample_rate) == SAMPLE_RATE, "Fbank sample_rate == 16000",
+          st.sample_rate)
+    check(bool(st.center) is True, "Fbank STFT center == True", st.center)
+    check(st.pad_mode == "constant", "Fbank STFT pad_mode == 'constant' "
+          "(zero pad 200 each side)", st.pad_mode)
+    check(bool(st.onesided) is True, "Fbank STFT onesided == True", st.onesided)
+    check(bool(st.normalized_stft) is False, "Fbank STFT normalized == False",
+          st.normalized_stft)
+    check(torch.equal(st.window.to(torch.float32).cpu(),
+                      torch.hamming_window(WIN)),
+          "Fbank STFT window == torch.hamming_window(400) (periodic)")
+
+    fb = fe.compute_fbanks
+    check(int(fb.n_mels) == N_MELS, "Filterbank n_mels == 60", fb.n_mels)
+    check(fb.filter_shape == "triangular", "Filterbank filter_shape == "
+          "'triangular'", fb.filter_shape)
+    check(float(fb.f_min) == 0.0, "Filterbank f_min == 0", fb.f_min)
+    check(float(fb.f_max) == 8000.0, "Filterbank f_max == 8000", fb.f_max)
+    check(int(fb.n_stft) == N_STFT, "Filterbank n_stft == 201", fb.n_stft)
+    check(int(fb.power_spectrogram) == 2, "Filterbank power_spectrogram == 2 "
+          "(multiplier 10)", fb.power_spectrogram)
+    check(int(fb.multiplier) == 10, "Filterbank dB multiplier == 10",
+          fb.multiplier)
+    check(float(fb.amin) == LOG_FLOOR, "Filterbank amin == 1e-10", fb.amin)
+    check(float(fb.ref_value) == 1.0, "Filterbank ref_value == 1.0 "
+          "(db_multiplier 0, no reference term)", fb.ref_value)
+    check(float(fb.db_multiplier) == 0.0, "Filterbank db_multiplier == 0",
+          fb.db_multiplier)
+    check(float(fb.top_db) == TOP_DB, "Filterbank top_db == 80", fb.top_db)
+    check(bool(fb.log_mel) is True, "Filterbank log_mel == True", fb.log_mel)
+    check(bool(fb.freeze) is True, "Filterbank freeze == True (filters are "
+          "constants, not parameters)", fb.freeze)
+    check(float(fb.param_rand_factor) == 0.0,
+          "Filterbank param_rand_factor == 0", fb.param_rand_factor)
+    check(bool(fe.deltas) is False, "Fbank deltas == False", fe.deltas)
+    check(bool(fe.context) is False, "Fbank context window == False", fe.context)
+
+    check(mvn.norm_type == "sentence", "InputNormalization norm_type == "
+          "'sentence'", mvn.norm_type)
+    check(bool(mvn.std_norm) is False, "InputNormalization std_norm == False "
+          "(mean subtraction only)", mvn.std_norm)
+    check(int(mvn.length_dim) == 1, "InputNormalization length_dim == 1 "
+          "(per-bin mean over time)", mvn.length_dim)
+    check(bool(mvn.avoid_padding_norm) is False,
+          "InputNormalization avoid_padding_norm == False", mvn.avoid_padding_norm)
+
+    # ---- embedding network ----------------------------------------------
+    check(len(em.blocks) == 4, "ECAPA_TDNN has 4 blocks (1 TDNN + 3 SERes2Net)",
+          len(em.blocks))
+
+    assert_tdnn_block(em.blocks[0], CHANNELS[0], N_MELS, KERNEL_SIZES[0],
+                      DILATIONS[0], "blocks.0")
+
+    for i in (1, 2, 3):
+        b = em.blocks[i]
+        pfx = f"blocks.{i}"
+        check(b.shortcut is None, f"{pfx} has no shortcut projection "
+              "(in_channels == out_channels)")
+        assert_tdnn_block(b.tdnn1, CHANNELS[i], CHANNELS[i - 1], 1, 1,
+                          f"{pfx}.tdnn1")
+        r2 = b.res2net_block
+        check(int(r2.scale) == RES2NET_SCALE, f"{pfx}.res2net scale == 8",
+              r2.scale)
+        check(len(r2.blocks) == RES2NET_SCALE - 1,
+              f"{pfx}.res2net has 7 sub-blocks", len(r2.blocks))
+        width = CHANNELS[i] // RES2NET_SCALE
+        check(width == 128, f"{pfx}.res2net chunk width == 128", width)
+        for j in range(RES2NET_SCALE - 1):
+            assert_tdnn_block(r2.blocks[j], width, width, KERNEL_SIZES[i],
+                              DILATIONS[i], f"{pfx}.res2net.{j}")
+        assert_tdnn_block(b.tdnn2, CHANNELS[i], CHANNELS[i], 1, 1,
+                          f"{pfx}.tdnn2")
+        se = b.se_block
+        assert_conv(se.conv1, SE_CHANNELS, CHANNELS[i], 1, 1, f"{pfx}.se.conv1")
+        assert_conv(se.conv2, CHANNELS[i], SE_CHANNELS, 1, 1, f"{pfx}.se.conv2")
+        check(isinstance(se.relu, torch.nn.ReLU), f"{pfx}.se activation is ReLU",
+              type(se.relu).__name__)
+        check(isinstance(se.sigmoid, torch.nn.Sigmoid),
+              f"{pfx}.se gate is Sigmoid", type(se.sigmoid).__name__)
+
+    mfa_in = CHANNELS[3] * 3
+    assert_tdnn_block(em.mfa, CHANNELS[4], mfa_in, KERNEL_SIZES[4],
+                      DILATIONS[4], "mfa")
+
+    asp = em.asp
+    check(bool(asp.global_context) is True, "asp global_context == True",
+          asp.global_context)
+    check(float(asp.eps) == ASP_EPS, "asp eps == 1e-12", asp.eps)
+    assert_tdnn_block(asp.tdnn, ATTENTION_CHANNELS, CHANNELS[4] * 3, 1, 1,
+                      "asp.tdnn")
+    check(isinstance(asp.tanh, torch.nn.Tanh), "asp gate activation is Tanh",
+          type(asp.tanh).__name__)
+    assert_conv(asp.conv, CHANNELS[4], ATTENTION_CHANNELS, 1, 1, "asp.conv")
+
+    asp_bn = bn_of(em.asp_bn)
+    check(int(asp_bn.num_features) == CHANNELS[4] * 2,
+          "asp_bn is BatchNorm1d(6144)", asp_bn.num_features)
+    check(abs(float(asp_bn.eps) - BN_EPS) < 1e-12, "asp_bn.eps == 1e-5",
+          asp_bn.eps)
+    assert_conv(em.fc, EMB_DIM, CHANNELS[4] * 2, 1, 1, "fc")
+
+    # ---- classifier ------------------------------------------------------
+    check(isinstance(cl.act, torch.nn.LeakyReLU),
+          "classifier.act is LeakyReLU", type(cl.act).__name__)
+    check(float(cl.act.negative_slope) == LEAKY_SLOPE,
+          "classifier.act slope == 0.01", cl.act.negative_slope)
+    bn0 = bn_of(cl.norm)
+    check(int(bn0.num_features) == EMB_DIM, "classifier bn0 is BatchNorm1d(256)",
+          bn0.num_features)
+    check(abs(float(bn0.eps) - BN_EPS) < 1e-12, "classifier bn0.eps == 1e-5",
+          bn0.eps)
+    check(len(cl.DNN) == 1, "classifier has 1 linear block", len(cl.DNN))
+    blk = cl.DNN.block_0
+    check(tuple(blk.linear.w.weight.shape) == (HIDDEN, EMB_DIM),
+          "cls.l1 weight is [512, 256]", tuple(blk.linear.w.weight.shape))
+    check(isinstance(blk.act, torch.nn.LeakyReLU),
+          "classifier hidden activation is LeakyReLU", type(blk.act).__name__)
+    check(float(blk.act.negative_slope) == LEAKY_SLOPE,
+          "classifier hidden slope == 0.01", blk.act.negative_slope)
+    bn1 = bn_of(blk.norm)
+    check(int(bn1.num_features) == HIDDEN, "classifier bn1 is BatchNorm1d(512)",
+          bn1.num_features)
+    check(abs(float(bn1.eps) - BN_EPS) < 1e-12, "classifier bn1.eps == 1e-5",
+          bn1.eps)
+    check(tuple(cl.out.w.weight.shape) == (N_LABELS, HIDDEN),
+          "cls.out weight is [107, 512]", tuple(cl.out.w.weight.shape))
+
+    # ---- parameter counts ------------------------------------------------
+    n_emb = sum(int(p.numel()) for p in em.parameters())
+    n_cls = sum(int(p.numel()) for p in cl.parameters())
+    check(n_emb == N_PARAMS_EMBEDDING,
+          f"embedding network has {N_PARAMS_EMBEDDING:,} parameters", n_emb)
+    check(n_cls == N_PARAMS_CLASSIFIER,
+          f"classifier has {N_PARAMS_CLASSIFIER:,} parameters", n_cls)
+
+    # ---- labels ----------------------------------------------------------
+    ind2lab = clf.hparams.label_encoder.ind2lab
+    check(len(ind2lab) == N_LABELS, f"label encoder has {N_LABELS} labels",
+          len(ind2lab))
+    codes: list[str] = []
+    names: list[str] = []
+    for i in range(N_LABELS):
+        raw = str(ind2lab[i])
+        if ": " not in raw:
+            raise ArchError(f"label {i} ({raw!r}) has no '<code>: <name>' form")
+        code, name = raw.split(": ", 1)
+        codes.append(code.strip())
+        names.append(name.strip())
+    check(len(set(codes)) == N_LABELS, "all 107 label codes are distinct",
+          len(set(codes)))
+    for alias in LABEL_ALIASES:
+        a, c = alias.split("=", 1)
+        check(c in codes, f"alias target {c!r} is in the label set")
+        check(a not in codes, f"alias name {a!r} is NOT already a label code")
+
+    print(f"Architecture verified: {len(_checks)} checks passed.")
+    print(f"  front end  : STFT {N_FFT}/{WIN}/{HOP} periodic-hamming, center, "
+          f"zero pad; {N_MELS} triangular mels 0-8000 Hz; "
+          f"10*log10(max(x,{LOG_FLOOR:g})); top_db {TOP_DB:g}; sentence-mean norm")
+    print(f"  encoder    : channels {CHANNELS} kernels {KERNEL_SIZES} "
+          f"dilations {DILATIONS}; res2net scale {RES2NET_SCALE} "
+          f"({RES2NET_SCALE - 1} blocks of {CHANNELS[1] // RES2NET_SCALE}->"
+          f"{CHANNELS[1] // RES2NET_SCALE} k={KERNEL_SIZES[1]}); "
+          f"SE {SE_CHANNELS}; attention {ATTENTION_CHANNELS}; "
+          f"global_context True; asp eps {ASP_EPS:g}; embedding {EMB_DIM}")
+    print(f"  classifier : LeakyReLU({LEAKY_SLOPE}) -> BN(256) -> "
+          f"Linear(256->{HIDDEN}) -> LeakyReLU -> BN({HIDDEN}) -> "
+          f"Linear({HIDDEN}->{N_LABELS}) -> log_softmax")
+    print(f"  every conv : padding='same', padding_mode='reflect', groups=1, "
+          f"bias=True; every BN eps={BN_EPS:g}")
+    print(f"  parameters : embedding {n_emb:,} + classifier {n_cls:,} = "
+          f"{n_emb + n_cls:,}")
+    print(f"  labels     : {N_LABELS} codes, aliases {LABEL_ALIASES}")
+
+    return {"codes": codes, "names": names}
+
+
+# ---------------------------------------------------------------------------
+# Filterbank capture
+# ---------------------------------------------------------------------------
+
+
+def capture_fbank_matrix(clf) -> np.ndarray:
+    """Return the exact [201, 60] fbank matrix the forward pass builds.
+
+    SpeechBrain 1.1.1's `Filterbank` has no `fbank_matrix` attribute or
+    buffer. The matrix is constructed
+    inside `forward()` by `_create_fbank_matrix(f_central_mat, band_mat)`.
+
+    So we wrap that method, push 0.1 s of silence through the real Fbank,
+    and keep what it returned. This is stronger than recomputing the
+    triangle geometry in float64: a rebuild matches to only ~1.2e-05
+    because `f_central` / `band` are fp32 buffers, whereas the capture is
+    bit-identical to what the reference used.
+    """
+    fbanks = clf.mods.compute_features.compute_fbanks
+    original = fbanks._create_fbank_matrix
+    captured: dict[str, Any] = {"calls": 0}
+
+    def capture(f_central_mat, band_mat):
+        m = original(f_central_mat, band_mat)
+        captured.setdefault("value", m)
+        captured["calls"] += 1
+        return m
+
+    fbanks._create_fbank_matrix = capture
+    try:
+        with torch.inference_mode():
+            clf.mods.compute_features(torch.zeros(1, SAMPLE_RATE // 10))
+    finally:
+        fbanks._create_fbank_matrix = original
+
+    if captured["calls"] != 1:
+        raise ArchError(f"_create_fbank_matrix fired {captured['calls']} times, "
+                        "expected 1")
+    m = captured["value"]
+    check(tuple(m.shape) == (N_STFT, N_MELS),
+          f"fbank matrix is [{N_STFT}, {N_MELS}]", tuple(m.shape))
+    return np.ascontiguousarray(
+        m.detach().to(dtype=torch.float32, device="cpu").numpy())
+
+
+def crosscheck_filters(filters_201x60: np.ndarray) -> float | None:
+    """Diff the captured matrix against the T2 reference dump, if present.
+
+    `build/validate/.../ref/fe.filters.f32` was written by the reference
+    dumper from the same `_create_fbank_matrix` call during a real
+    classification, so the difference must be exactly 0. Anything in the
+    1e-05 range means this converter recomputed the geometry instead of
+    capturing it (a float64 rebuild differs by about 1.16e-05).
+    """
+    ref = (REPO_ROOT / "build" / "validate" / ARCH / DEFAULT_VARIANT /
+           "fleurs-en" / "ref" / "fe.filters.f32")
+    if not ref.exists():
+        print(f"  fbank cross-check: SKIPPED ({ref} not found; run "
+              f"`uv run scripts/validate.py ref --family {ARCH}` first)")
+        return None
+    a = np.fromfile(ref, dtype=np.float32)
+    if a.size != N_STFT * N_MELS:
+        raise ArchError(f"{ref} has {a.size} elements, expected "
+                        f"{N_STFT * N_MELS}")
+    diff = float(np.max(np.abs(a.reshape(N_STFT, N_MELS) - filters_201x60)))
+    status = "exact" if diff == 0.0 else ("ok" if diff < 1e-6 else "SUSPICIOUS")
+    print(f"  fbank cross-check vs reference dump: max|diff| = {diff:.3e} "
+          f"[{status}]")
+    if diff >= 1e-6:
+        raise ArchError(
+            f"captured fbank matrix differs from the reference dump by "
+            f"{diff:.3e}; it should be bit-identical (same _create_fbank_matrix "
+            f"call). A ~1e-05 diff means the geometry was recomputed instead "
+            f"of captured.")
+    return diff
+
+
+# ---------------------------------------------------------------------------
+# Weight extraction / conversion-time algebra
+# ---------------------------------------------------------------------------
+
+
+def w2d(conv) -> np.ndarray:
+    """1x1 SpeechBrain Conv1d weight `[OC, IC, 1]` -> numpy `[OC, IC]` (f64)."""
+    w = conv.conv.weight.detach().to(torch.float64).cpu().numpy()
+    if w.shape[2] != 1:
+        raise ArchError(f"expected a 1x1 kernel, got K={w.shape[2]}")
+    return np.ascontiguousarray(w[:, :, 0])
+
+
+def w3d(conv) -> np.ndarray:
+    """k>1 Conv1d weight `[OC, IC, K]` -> TAP-MAJOR numpy `[K, OC, IC]` (f64).
+
+    ggml sees `ne = [IC, OC, K]`, so tap `t` is the contiguous `[IC, OC]`
+    matrix the graph multiplies against a `t*dilation`-shifted view of the
+    reflect-padded activation.
+    """
+    w = conv.conv.weight.detach().to(torch.float64).cpu().numpy()
+    return np.ascontiguousarray(np.transpose(w, (2, 0, 1)))
+
+
+def bias(conv) -> np.ndarray:
+    return np.ascontiguousarray(
+        conv.conv.bias.detach().to(torch.float64).cpu().numpy())
+
+
+def bn_affine(module) -> tuple[np.ndarray, np.ndarray]:
+    """BatchNorm -> (scale, shift) with `y = x * scale + shift`.
+
+    `scale = gamma / sqrt(var + eps)`, `shift = beta - mean * scale`,
+    computed in float64. This is NOT folded into the preceding conv: a
+    TDNNBlock is conv -> ReLU -> BN, and the ReLU makes a backward fold
+    impossible.
+    """
+    bn = bn_of(module)
+    g = bn.weight.detach().to(torch.float64).cpu().numpy()
+    b = bn.bias.detach().to(torch.float64).cpu().numpy()
+    m = bn.running_mean.detach().to(torch.float64).cpu().numpy()
+    v = bn.running_var.detach().to(torch.float64).cpu().numpy()
+    scale = g / np.sqrt(v + float(bn.eps))
+    shift = b - m * scale
+    return np.ascontiguousarray(scale), np.ascontiguousarray(shift)
+
+
+def fold_bn_into_linear(module, w: np.ndarray, b: np.ndarray
+                        ) -> tuple[np.ndarray, np.ndarray]:
+    """Fold a BatchNorm FORWARD into the linear map that consumes it.
+
+    `y = W(x * s + t) + b = (W * diag(s)) x + (W t + b)`. Valid only when
+    nothing sits between the BN and the linear map — true for
+    `asp_bn -> fc`, `cls.bn0 -> cls.l1` and `cls.bn1 -> cls.out`. Note the
+    classifier's BNs come AFTER their LeakyReLU, so the activation is still
+    applied before each folded linear at run time; only the BN disappears.
+    """
+    s, t = bn_affine(module)
+    if w.shape[1] != s.size:
+        raise ArchError(f"fold shape mismatch: W is {w.shape}, BN has {s.size} "
+                        "features")
+    return (np.ascontiguousarray(w * s[None, :]),
+            np.ascontiguousarray(w @ t + b))
+
+
+# ---------------------------------------------------------------------------
+# GGUF writing
+# ---------------------------------------------------------------------------
+
+
+def ggml_ne(shape: tuple[int, ...]) -> list[int]:
+    """numpy shape -> ggml ne (fastest axis first)."""
+    return list(reversed(shape))
+
+
+def sha256_file(path: Path) -> str:
+    h = hashlib.sha256()
+    with path.open("rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def convert(clf, out_path: Path, *, variant: str, repo_id: str,
+            revision: str, filters_201x60: np.ndarray,
+            labels: dict[str, list[str]]) -> dict[str, Any]:
+    em = clf.mods.embedding_model
+    cl = clf.mods.classifier
+
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    writer = gguf_writer(str(out_path), ARCH)
+
+    # ----- general.* ------------------------------------------------------
+    add_general_identity(
+        writer,
+        name=repo_id,
+        basename=slug_from_repo_id(repo_id),
+        size_label="21M",
+        file_type=int(LlamaFileType.ALL_F32),
+        license="apache-2.0",
+        license_name="Apache License 2.0",
+        license_link="https://www.apache.org/licenses/LICENSE-2.0",
+        author="SpeechBrain",
+        organization="speechbrain",
+        repo_url=f"https://huggingface.co/{repo_id}",
+        source_url=f"https://huggingface.co/{repo_id}",
+        languages=labels["codes"],
+        tags=["language-identification", "ecapa-tdnn", "voxlingua107",
+              "speechbrain"],
+        description=(
+            "SpeechBrain ECAPA-TDNN spoken-language identification trained "
+            "on VoxLingua107 (107 languages). Converted from "
+            f"{repo_id} for transcribe.cpp."
+        ),
+    )
+    # `general.source.commit` has no helper in gguf-py; write it plainly so
+    # every artifact records the exact upstream snapshot it came from.
+    writer.add_string("general.source.commit", revision)
+
+    # ----- stt.* ----------------------------------------------------------
+    writer.add_string("stt.variant", variant)
+
+    # SpeechBrain Fbank + InputNormalization: zero-padded centered STFT,
+    # periodic Hamming, power spectrum, mel, 10*log10(max(p, log_clamp_min)),
+    # top_db floor over the clip, per-bin mean subtraction.
+    writer.add_string("stt.frontend.type", "speechbrain_fbank")
+    writer.add_uint32("stt.frontend.sample_rate", SAMPLE_RATE)
+    writer.add_uint32("stt.frontend.n_fft", N_FFT)
+    writer.add_uint32("stt.frontend.hop_length", HOP)
+    writer.add_uint32("stt.frontend.win_length", WIN)
+    writer.add_uint32("stt.frontend.num_mels", N_MELS)
+    writer.add_string("stt.frontend.window", "hamming_periodic")
+    writer.add_string("stt.frontend.pad_mode", "constant")
+    writer.add_float32("stt.frontend.log_clamp_min", LOG_FLOOR)
+    writer.add_float32("stt.frontend.top_db", TOP_DB)
+    writer.add_string("stt.frontend.normalize", canonicalize_normalize("sentence_mean"))
+
+    writer.add_uint32("stt.ecapa_tdnn.format_version", FORMAT_VERSION)
+    writer.add_array("stt.ecapa_tdnn.channels", CHANNELS)
+    writer.add_array("stt.ecapa_tdnn.kernel_sizes", KERNEL_SIZES)
+    writer.add_array("stt.ecapa_tdnn.dilations", DILATIONS)
+    writer.add_uint32("stt.ecapa_tdnn.res2net_scale", RES2NET_SCALE)
+    writer.add_uint32("stt.ecapa_tdnn.se_channels", SE_CHANNELS)
+    writer.add_uint32("stt.ecapa_tdnn.attention_channels", ATTENTION_CHANNELS)
+    writer.add_float32("stt.ecapa_tdnn.asp_eps", ASP_EPS)
+    writer.add_uint32("stt.ecapa_tdnn.embedding_dim", EMB_DIM)
+    writer.add_uint32("stt.ecapa_tdnn.classifier_hidden", HIDDEN)
+    writer.add_float32("stt.ecapa_tdnn.classifier_leaky_slope", LEAKY_SLOPE)
+
+    writer.add_array("stt.langid.labels.codes", labels["codes"])
+    writer.add_array("stt.langid.labels.names", labels["names"])
+    writer.add_array("stt.langid.labels.aliases", LABEL_ALIASES)
+
+    # ----- tensors --------------------------------------------------------
+    rows: list[tuple[str, str, str, str, int]] = []
+    total_bytes = 0
+
+    def add(name: str, arr: np.ndarray) -> None:
+        nonlocal total_bytes
+        a = np.ascontiguousarray(arr.astype(np.float32))
+        t = GGMLQuantizationType.F32
+        encoded, raw_dtype = encode_for_gguf(a, t)
+        writer.add_tensor(name, encoded, raw_dtype=raw_dtype)
+        nbytes = int(encoded.nbytes)
+        rows.append((name, str(list(a.shape)), str(ggml_ne(a.shape)),
+                     t.name, nbytes))
+        total_bytes += nbytes
+
+    # Front-end filterbank, mel-major so each filter's 201 taps are
+    # contiguous: numpy [60, 201] -> ggml ne = [201, 60].
+    add("frontend.mel_filterbank", filters_201x60.T)
+
+    # blocks.0: the only k>1 conv outside the res2net stacks.
+    b0 = em.blocks[0]
+    add("blk.0.conv.weight", w3d(b0.conv))
+    add("blk.0.conv.bias", bias(b0.conv))
+    s, t = bn_affine(b0.norm)
+    add("blk.0.bn.scale", s)
+    add("blk.0.bn.shift", t)
+
+    for i in (1, 2, 3):
+        b = em.blocks[i]
+        p = f"blk.{i}"
+
+        add(f"{p}.tdnn1.weight", w2d(b.tdnn1.conv))
+        add(f"{p}.tdnn1.bias", bias(b.tdnn1.conv))
+        s, t = bn_affine(b.tdnn1.norm)
+        add(f"{p}.tdnn1.bn.scale", s)
+        add(f"{p}.tdnn1.bn.shift", t)
+
+        # res2.{j} operates on chunk j+1 of the 8 channel chunks: chunk 0 is
+        # passed through untouched and each later chunk is summed with the
+        # PREVIOUS sub-block's output before being convolved.
+        for j in range(RES2NET_SCALE - 1):
+            sub = b.res2net_block.blocks[j]
+            add(f"{p}.res2.{j}.conv.weight", w3d(sub.conv))
+            add(f"{p}.res2.{j}.conv.bias", bias(sub.conv))
+            s, t = bn_affine(sub.norm)
+            add(f"{p}.res2.{j}.bn.scale", s)
+            add(f"{p}.res2.{j}.bn.shift", t)
+
+        add(f"{p}.tdnn2.weight", w2d(b.tdnn2.conv))
+        add(f"{p}.tdnn2.bias", bias(b.tdnn2.conv))
+        s, t = bn_affine(b.tdnn2.norm)
+        add(f"{p}.tdnn2.bn.scale", s)
+        add(f"{p}.tdnn2.bn.shift", t)
+
+        add(f"{p}.se.c1.weight", w2d(b.se_block.conv1))
+        add(f"{p}.se.c1.bias", bias(b.se_block.conv1))
+        add(f"{p}.se.c2.weight", w2d(b.se_block.conv2))
+        add(f"{p}.se.c2.bias", bias(b.se_block.conv2))
+
+    # MFA split: SpeechBrain concatenates blocks.1/2/3 along channels before a
+    # 1x1 conv, so the [3072, 3072] weight is three [3072, 1024] blocks in
+    # block order. Summing three matmuls costs the same FLOPs and skips the
+    # concat copy.
+    mfa_w = w2d(em.mfa.conv)
+    for k, name in enumerate(("mfa.w1.weight", "mfa.w2.weight", "mfa.w3.weight")):
+        add(name, mfa_w[:, k * CHANNELS[3]:(k + 1) * CHANNELS[3]])
+    add("mfa.bias", bias(em.mfa.conv))
+    s, t = bn_affine(em.mfa.norm)
+    add("mfa.bn.scale", s)
+    add("mfa.bn.shift", t)
+
+    # ASP split: the attention TDNN consumes cat([x, mean, std]) (that exact
+    # order — see AttentiveStatisticsPooling.forward), so its [128, 9216]
+    # weight is three [128, 3072] blocks. mean/std are constant over time, so
+    # the graph folds wm@mean + ws@std + b into one [128] vector.
+    asp_w = w2d(em.asp.tdnn.conv)
+    for k, name in enumerate(("asp.tdnn.x.weight", "asp.tdnn.mean.weight", "asp.tdnn.std.weight")):
+        add(name, asp_w[:, k * CHANNELS[4]:(k + 1) * CHANNELS[4]])
+    add("asp.tdnn.bias", bias(em.asp.tdnn.conv))
+    s, t = bn_affine(em.asp.tdnn.norm)
+    add("asp.tdnn.bn.scale", s)
+    add("asp.tdnn.bn.shift", t)
+
+    add("asp.attn.weight", w2d(em.asp.conv))
+    add("asp.attn.bias", bias(em.asp.conv))
+
+    # asp_bn folds forward into fc (nothing between them).
+    fc_w, fc_b = fold_bn_into_linear(em.asp_bn, w2d(em.fc), bias(em.fc))
+    add("fc.weight", fc_w)
+    add("fc.bias", fc_b)
+
+    # cls.bn0 folds into cls.l1, cls.bn1 into cls.out. The LeakyReLUs sit
+    # BEFORE each BN, so they survive in the graph: the run-time chain is
+    # LeakyReLU -> cls.l1 -> LeakyReLU -> cls.out.
+    l1 = cl.DNN.block_0.linear.w
+    l1_w = l1.weight.detach().to(torch.float64).cpu().numpy()
+    l1_b = l1.bias.detach().to(torch.float64).cpu().numpy()
+    l1_w, l1_b = fold_bn_into_linear(cl.norm, l1_w, l1_b)
+    add("cls.l1.weight", l1_w)
+    add("cls.l1.bias", l1_b)
+
+    out = cl.out.w
+    out_w = out.weight.detach().to(torch.float64).cpu().numpy()
+    out_b = out.bias.detach().to(torch.float64).cpu().numpy()
+    out_w, out_b = fold_bn_into_linear(cl.DNN.block_0.norm, out_w, out_b)
+    add("cls.out.weight", out_w)
+    add("cls.out.bias", out_b)
+
+    # 1 filterbank + 4 blk.0 + 3*40 SERes2Net + 6 mfa + 6 asp.tdnn
+    # + 2 asp.attn + 2 fc + 2 cls.l1 + 2 cls.out
+    expected = 1 + 4 + 3 * 40 + 6 + 6 + 2 + 2 + 2 + 2
+    if len(rows) != expected:
+        raise ArchError(f"tensor count mismatch: emitted {len(rows)}, "
+                        f"expected {expected}")
+
+    # ----- table ----------------------------------------------------------
+    w_name = max(len(r[0]) for r in rows)
+    w_np = max(len(r[1]) for r in rows)
+    w_ne = max(len(r[2]) for r in rows)
+    print(f"\n{'tensor'.ljust(w_name)}  {'numpy'.ljust(w_np)}  "
+          f"{'ggml ne'.ljust(w_ne)}  {'dtype':6}  {'bytes':>12}")
+    print("-" * (w_name + w_np + w_ne + 6 + 12 + 8))
+    for name, npshape, ne, dt, nb in rows:
+        print(f"{name.ljust(w_name)}  {npshape.ljust(w_np)}  {ne.ljust(w_ne)}  "
+              f"{dt:6}  {nb:>12,}")
+    print("-" * (w_name + w_np + w_ne + 6 + 12 + 8))
+    print(f"{len(rows)} tensors, {total_bytes:,} bytes of tensor data "
+          f"({total_bytes / (1024 * 1024):.2f} MiB)")
+
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file()
+    writer.close()
+
+    size = out_path.stat().st_size
+    digest = sha256_file(out_path)
+    print(f"Wrote {out_path} ({size:,} bytes, {size / (1024 * 1024):.2f} MiB)")
+    print(f"sha256: {digest}")
+
+    return {
+        "family": ARCH,
+        "variant": variant,
+        "refdtype": "F32",
+        "gguf_path": str(out_path.relative_to(REPO_ROOT))
+        if out_path.is_relative_to(REPO_ROOT) else str(out_path),
+        "gguf_bytes": int(size),
+        "gguf_sha256": digest,
+        "source_hf_repo": repo_id,
+        "source_hf_revision": revision,
+        "converter_script": "scripts/convert-ecapa_tdnn.py",
+        "n_tensors": len(rows),
+        "tensor_data_bytes": int(total_bytes),
+    }
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description="Convert SpeechBrain's VoxLingua107 ECAPA-TDNN checkpoint "
+                    "to the F32 reference GGUF.")
+    p.add_argument("model", nargs="?", default=DEFAULT_REPO,
+                   help=f"HF repo id or local checkpoint dir "
+                        f"(default: {DEFAULT_REPO})")
+    p.add_argument("out_path", type=Path, nargs="?",
+                   help="Output .gguf path (derived from the repo id when "
+                        "omitted)")
+    p.add_argument("--revision", default=DEFAULT_REVISION,
+                   help="HF revision (commit SHA) to pin the download to")
+    p.add_argument("--variant", default=DEFAULT_VARIANT,
+                   help=f"stt.variant string (default: {DEFAULT_VARIANT})")
+    p.add_argument("--repo-id", default=None,
+                   help="HF repo id used for the output slug and metadata "
+                        "when converting from a local path")
+    args = p.parse_args(argv)
+
+    repo_id = args.repo_id or (args.model if looks_like_repo_id(args.model)
+                               else None)
+    if repo_id is None:
+        print("error: cannot infer the source repo id from a local path; "
+              "pass --repo-id", file=sys.stderr)
+        return 2
+
+    src = resolve_checkpoint_dir(args.model, args.revision)
+    slug = slug_from_repo_id(repo_id)
+    out_path = args.out_path or (REPO_ROOT / "models" / slug /
+                                 gguf_name(slug, "F32"))
+
+    clf = load_reference(src)
+    labels = assert_architecture(clf)
+    filters = capture_fbank_matrix(clf)
+    crosscheck_filters(filters)
+
+    import speechbrain
+
+    report = convert(clf, out_path, variant=args.variant,
+                     repo_id=repo_id, revision=args.revision,
+                     filters_201x60=filters, labels=labels)
+    report["speechbrain_version"] = speechbrain.__version__
+    report["torch_version"] = torch.__version__
+
+    rep_dir = REPO_ROOT / "reports" / "convert"
+    rep_dir.mkdir(parents=True, exist_ok=True)
+    rep_path = rep_dir / f"{slug}-F32.json"
+    rep_path.write_text(json.dumps(report, indent=2) + "\n")
+    print(f"Wrote {rep_path}")
+    return 0
+
+
+if __name__ == "__main__":
+    try:
+        raise SystemExit(main())
+    except ArchError as e:
+        print(f"error: {e.msg}", file=sys.stderr)
+        raise
diff --git a/scripts/dump_reference_ecapa_tdnn_speechbrain.py b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
new file mode 100644
index 000000000..6792f3e9c
--- /dev/null
+++ b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
@@ -0,0 +1,609 @@
+#!/usr/bin/env python3
+"""
+dump_reference_ecapa_tdnn_speechbrain.py - generate SpeechBrain
+VoxLingua107 ECAPA-TDNN reference tensors.
+
+SpeechBrain is the only published implementation of this checkpoint: the
+`hyperparams.yaml` in the HF repo instantiates SpeechBrain classes
+(`lobes.features.Fbank`, `lobes.models.ECAPA_TDNN.ECAPA_TDNN`,
+`lobes.models.Xvector.Classifier`) and the model card's inference example
+is `EncoderClassifier.from_hparams`. There is no Transformers shim.
+
+Usage:
+
+    uv run --project scripts/envs/ecapa_tdnn \
+      scripts/dump_reference_ecapa_tdnn_speechbrain.py encoder \
+      --model speechbrain/lang-id-voxlingua107-ecapa \
+      --audio samples/fleurs-en.wav \
+      --out build/validate/ecapa_tdnn/voxlingua107/fleurs-en/ref
+
+Writes:
+    <name>.f32        raw little-endian float32, row-major
+    <name>.json       per-tensor sidecar via shared scripts.lib.ref_dump
+    prediction.json   behavioural artifact (top-1 label + top-5 log-probs)
+
+There is a single stage, `encoder`: language ID is one forward pass, so
+the front end, the ECAPA-TDNN embedding network and the classifier head
+are all dumped from one `classify_batch` call. Running the model twice
+would fire every hook twice; the script asserts each hook fired exactly
+once.
+
+Architecture summary:
+
+    audio (16 kHz mono)
+      -> Fbank: STFT(n_fft=400, win=400, hop=160, hamming periodic,
+                     center, constant pad) -> power spectrum [T, 201]
+                60 triangular mel filters -> 10*log10 -> 80 dB floor
+      -> InputNormalization(norm_type="sentence", std_norm=False)
+                     => fe.mel [T, 60]
+      -> ECAPA_TDNN: blocks.0 TDNN(60->1024, k=5)
+                     blocks.1..3 SERes2Net(1024, k=3, d=2/3/4)
+                     mfa TDNN(3072->3072, k=1) over concat(blocks.1..3)
+                     asp AttentiveStatisticsPooling(3072, attn=128)
+                     asp_bn -> fc(6144->256)   => enc.emb [256]
+      -> Xvector.Classifier: LeakyReLU -> BN -> Linear(256->512)
+                     -> LeakyReLU -> BN -> Linear(512->107)
+                     -> log_softmax             => cls.log_probs [107]
+
+Dump points (the contract with src/arch/ecapa_tdnn/graph.h, in that exact
+order and with those exact names). Every activation is written TIME-MAJOR `[T, C]`:
+PyTorch carries ECAPA activations as `[B, C, T]`, while the C++ debug
+dumper writes ggml `ne=[C, T]` as row-major `[T, C]`. Squeezing the batch
+dim and transposing here is what makes the two directories comparable.
+
+    fe.mel                  [T, 60]     mean_var_norm output
+    enc.blk.0.out           [T, 1024]
+    enc.blk.1.tdnn1.out     [T, 1024]   SERes2Net sub-steps, for bisecting
+    enc.blk.1.res2.out      [T, 1024]
+    enc.blk.1.se.out        [T, 1024]   before the residual add
+    enc.blk.{1,2,3}.out     [T, 1024]
+    enc.mfa.out             [T, 3072]
+    enc.asp.attn_logits     [T, 3072]   asp.conv (asp.attn.weight) output, pre-softmax
+    enc.asp.out             [6144]
+    enc.emb                 [256]       fc output
+    cls.hidden              [512]       DNN.block_0.act output (pre-BN1)
+    cls.logits_raw          [107]       the gate
+    cls.log_probs           [107]       informational
+
+Plus two run-invariant front-end tables, dumped so the converter and the
+C++ mel unit test can be checked against SpeechBrain directly:
+
+    fe.window               [400]       STFT.window (periodic Hamming)
+    fe.filters              [201, 60]   the fbank matrix used this forward
+
+These two are NOT part of the tolerance contract: the C++ runtime does
+not dump them (the window is recomputed and the filterbank is read from
+the GGUF), so compare_tensors reports them MISSING-left without failing.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+SCRIPTS_DIR = REPO_ROOT / "scripts"
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+from scripts.lib.ref_dump import write_tensor  # noqa: E402
+from lib.gguf_common import slug_from_repo_id  # noqa: E402
+from lib.hf_source import looks_like_repo_id, resolve_model_dir  # noqa: E402
+
+DEFAULT_REPO = "speechbrain/lang-id-voxlingua107-ecapa"
+DEFAULT_REVISION = "0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9"
+
+SAMPLE_RATE = 16000
+HOP = 160
+N_FFT = 400
+WIN = 400
+N_STFT = N_FFT // 2 + 1  # 201
+N_MELS = 60
+EMB_DIM = 256
+N_LABELS = 107
+HIDDEN = 512
+
+
+# ---------------------------------------------------------------------------
+# Torch setup
+# ---------------------------------------------------------------------------
+
+
+def configure_torch(args: argparse.Namespace) -> None:
+    import torch
+
+    torch.manual_seed(0)
+    if args.torch_threads > 0:
+        torch.set_num_threads(args.torch_threads)
+        try:
+            torch.set_num_interop_threads(1)
+        except RuntimeError:
+            # Already set by an earlier call in this process; harmless.
+            pass
+    try:
+        torch.use_deterministic_algorithms(True, warn_only=True)
+    except TypeError:
+        torch.use_deterministic_algorithms(True)
+
+
+# ---------------------------------------------------------------------------
+# Shape conversion
+# ---------------------------------------------------------------------------
+
+
+def act_time_major(t, channels: int, *, name: str) -> np.ndarray:
+    """`[1, C, T]` torch activation -> `[T, C]` float32 numpy.
+
+    The channel count is asserted rather than inferred. A bare transpose
+    would silently succeed on a tensor that is already `[1, T, C]`, and the
+    resulting dump would look plausible while being the transpose of what
+    the C++ side writes.
+    """
+    import torch
+
+    if not isinstance(t, torch.Tensor):
+        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
+    if t.dim() != 3 or t.shape[0] != 1:
+        raise ValueError(f"{name}: expected [1, C, T], got {tuple(t.shape)}")
+    if t.shape[1] != channels:
+        raise ValueError(
+            f"{name}: expected {channels} channels on dim 1, "
+            f"got {tuple(t.shape)}"
+        )
+    a = t.detach().to(dtype=torch.float32, device="cpu")[0].transpose(0, 1).numpy()
+    return np.ascontiguousarray(a, dtype=np.float32)
+
+
+def feat_time_major(t, channels: int, *, name: str) -> np.ndarray:
+    """`[1, T, C]` torch feature tensor -> `[T, C]` float32 numpy.
+
+    SpeechBrain's front end is already time-major (`[B, T, n_mels]`), so
+    this only drops the batch dim — but it asserts the channel count for
+    the same reason `act_time_major` does.
+    """
+    import torch
+
+    if not isinstance(t, torch.Tensor):
+        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
+    if t.dim() != 3 or t.shape[0] != 1:
+        raise ValueError(f"{name}: expected [1, T, C], got {tuple(t.shape)}")
+    if t.shape[2] != channels:
+        raise ValueError(
+            f"{name}: expected {channels} channels on dim 2, "
+            f"got {tuple(t.shape)}"
+        )
+    a = t.detach().to(dtype=torch.float32, device="cpu")[0].numpy()
+    return np.ascontiguousarray(a, dtype=np.float32)
+
+
+def vec(t, n: int, *, name: str) -> np.ndarray:
+    """Any torch tensor with exactly `n` elements -> 1-D float32 numpy."""
+    import torch
+
+    if not isinstance(t, torch.Tensor):
+        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
+    if t.numel() != n:
+        raise ValueError(f"{name}: expected {n} elements, got {t.numel()} "
+                         f"(shape {tuple(t.shape)})")
+    a = t.detach().to(dtype=torch.float32, device="cpu").reshape(-1).numpy()
+    return np.ascontiguousarray(a, dtype=np.float32)
+
+
+def mat(t, shape: tuple[int, int], *, name: str) -> np.ndarray:
+    """Torch matrix -> 2-D float32 numpy, with an exact shape assertion."""
+    import torch
+
+    if not isinstance(t, torch.Tensor):
+        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
+    if tuple(t.shape) != shape:
+        raise ValueError(f"{name}: expected shape {shape}, got {tuple(t.shape)}")
+    a = t.detach().to(dtype=torch.float32, device="cpu").numpy()
+    return np.ascontiguousarray(a, dtype=np.float32)
+
+
+# ---------------------------------------------------------------------------
+# Hooks
+# ---------------------------------------------------------------------------
+
+
+class CaptureHook:
+    """Record the output a module emits during forward, and count firings.
+
+    The count is checked after the pass: a hook that fired zero times means
+    the module was not on the path we think it was, and a hook that fired
+    twice means the model ran more than once (which would make the dump
+    reflect the second call). Both are silent-wrong-number bugs otherwise.
+    """
+
+    def __init__(self, name: str) -> None:
+        self.name = name
+        self.value = None
+        self.calls = 0
+
+    def __call__(self, module, inputs, output) -> None:
+        self.calls += 1
+        if isinstance(output, tuple):
+            output = output[0]
+        self.value = output
+
+
+def hook(module, name: str, registry: dict[str, CaptureHook], handles: list):
+    h = CaptureHook(name)
+    handles.append(module.register_forward_hook(h))
+    registry[name] = h
+    return h
+
+
+# ---------------------------------------------------------------------------
+# Model loading
+# ---------------------------------------------------------------------------
+
+
+def resolve_checkpoint_dir(model_arg: str, revision: str | None) -> Path:
+    """Return a local SpeechBrain checkpoint directory (HF cache or local)."""
+    p = Path(model_arg).expanduser()
+    if not p.is_dir() and not looks_like_repo_id(model_arg):
+        raise SystemExit(
+            f"error: --model {model_arg!r} is neither an existing directory "
+            f"nor an org/name Hugging Face repo id"
+        )
+    return resolve_model_dir(model_arg, revision)
+
+
+def load_reference(args: argparse.Namespace):
+    """Build the SpeechBrain `EncoderClassifier` for this checkpoint.
+
+    Two things here are load-bearing:
+
+    1. `overrides={"pretrained_path": <local dir>}`. The shipped
+       `hyperparams.yaml` sets `pretrained_path:
+       speechbrain/lang-id-voxlingua107-ecapa`, and `Pretrainer.collect_files`
+       splits each `paths[...]` entry into (source, filename) — so without
+       the override SpeechBrain re-fetches the `.ckpt` files from the HF
+       `main` branch, ignoring our pinned revision entirely.
+    2. `savedir != source`. SpeechBrain symlinks the collected loadables
+       into `savedir`; pointing it at the snapshot directory makes it
+       symlink files onto themselves.
+    """
+    import torch
+    from speechbrain.inference.classifiers import EncoderClassifier
+
+    src = resolve_checkpoint_dir(args.model, args.revision)
+    savedir = REPO_ROOT / "build" / "speechbrain" / src.parent.name
+    savedir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Loading EncoderClassifier: source={src} savedir={savedir} "
+          f"device={args.device}")
+    clf = EncoderClassifier.from_hparams(
+        source=str(src),
+        savedir=str(savedir),
+        overrides={"pretrained_path": str(src)},
+        run_opts={"device": args.device},
+    )
+
+    # Force eval + fp32 + no grad ourselves rather than trusting run_opts.
+    # A BatchNorm left in train mode would use this batch's statistics
+    # (batch of one!) instead of the running stats, and every tensor from
+    # that layer onward would be plausible-looking garbage.
+    clf.mods.eval()
+    for p in clf.mods.parameters():
+        p.requires_grad_(False)
+
+    training = [n for n, m in clf.mods.named_modules() if m.training]
+    if training:
+        raise SystemExit(f"error: modules still in training mode: {training[:5]}")
+
+    n_bn = 0
+    for name, m in clf.mods.named_modules():
+        if isinstance(m, torch.nn.modules.batchnorm._BatchNorm):
+            n_bn += 1
+            if m.running_mean is None or m.running_var is None:
+                raise SystemExit(
+                    f"error: BatchNorm {name} has no running statistics; "
+                    "the checkpoint did not load as expected"
+                )
+    print(f"eval mode ok; {n_bn} BatchNorm layers carry running statistics")
+
+    dtypes = {str(p.dtype) for p in clf.mods.parameters()}
+    if dtypes != {"torch.float32"}:
+        raise SystemExit(f"error: expected an fp32 model, got dtypes {dtypes}")
+
+    return clf, src
+
+
+# ---------------------------------------------------------------------------
+# Audio
+# ---------------------------------------------------------------------------
+
+
+def load_audio(audio_path: Path) -> np.ndarray:
+    import soundfile as sf
+
+    pcm, sr = sf.read(str(audio_path), dtype="float32", always_2d=False)
+    if pcm.ndim > 1:
+        pcm = pcm.mean(axis=1)
+    if int(sr) != SAMPLE_RATE:
+        raise SystemExit(
+            f"error: {audio_path} is {sr} Hz; the model front end is "
+            f"{SAMPLE_RATE} Hz only. Resample before dumping."
+        )
+    return np.ascontiguousarray(pcm, dtype=np.float32)
+
+
+def split_label(label: str) -> tuple[str, str]:
+    """`'zh: Chinese'` -> `('zh', 'Chinese')`.
+
+    The label encoder stores the code and the English name in one string.
+    A label without the separator is passed through as its own code with an
+    empty name rather than crashing, so a future label-set change surfaces
+    as odd metadata instead of a traceback mid-dump.
+    """
+    if ": " in label:
+        code, name = label.split(": ", 1)
+        return code.strip(), name.strip()
+    return label.strip(), ""
+
+
+# ---------------------------------------------------------------------------
+# encoder stage
+# ---------------------------------------------------------------------------
+
+
+def cmd_encoder(args: argparse.Namespace) -> int:
+    import torch
+    import speechbrain
+
+    configure_torch(args)
+    clf, src_dir = load_reference(args)
+
+    audio_path = Path(args.audio).expanduser().resolve()
+    out_dir = Path(args.out).expanduser().resolve()
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    pcm = load_audio(audio_path)
+    n_samples = int(pcm.size)
+    expected_T = n_samples // HOP + 1
+    print(f"audio: {audio_path.name} samples={n_samples} "
+          f"({n_samples / SAMPLE_RATE:.3f} s) expected_frames={expected_T}")
+
+    device = torch.device(args.device)
+    wav = torch.from_numpy(pcm).to(device).unsqueeze(0)          # [1, N]
+    # `wav_lens` is RELATIVE (fraction of the padded batch max), not a
+    # sample count. Batch of one, unpadded -> 1.0. Passing n_samples here
+    # would make InputNormalization and the ASP/SE masks compute over
+    # round(n_samples * T) frames, i.e. the wrong span, silently.
+    wav_lens = torch.ones(1, device=device)
+
+    fe = clf.mods.compute_features
+    em = clf.mods.embedding_model
+    cl = clf.mods.classifier
+
+    handles: list = []
+    hooks: dict[str, CaptureHook] = {}
+
+    hook(clf.mods.mean_var_norm, "fe.mel", hooks, handles)
+    hook(em.blocks[0], "enc.blk.0.out", hooks, handles)
+    hook(em.blocks[1].tdnn1, "enc.blk.1.tdnn1.out", hooks, handles)
+    hook(em.blocks[1].res2net_block, "enc.blk.1.res2.out", hooks, handles)
+    hook(em.blocks[1].se_block, "enc.blk.1.se.out", hooks, handles)
+    hook(em.blocks[1], "enc.blk.1.out", hooks, handles)
+    hook(em.blocks[2], "enc.blk.2.out", hooks, handles)
+    hook(em.blocks[3], "enc.blk.3.out", hooks, handles)
+    hook(em.mfa, "enc.mfa.out", hooks, handles)
+    hook(em.asp.conv, "enc.asp.attn_logits", hooks, handles)
+    hook(em.asp, "enc.asp.out", hooks, handles)
+    hook(em.fc, "enc.emb", hooks, handles)
+    hook(cl.DNN.block_0.act, "cls.hidden", hooks, handles)
+    hook(cl.out, "cls.logits_raw", hooks, handles)
+    hook(cl.softmax, "cls.log_probs", hooks, handles)
+
+    # SpeechBrain 1.1.1's Filterbank builds its [201, 60] matrix inside
+    # forward() from `f_central` / `band`; there is no `fbank_matrix`
+    # attribute or buffer to read (notes/PLAN.md section 1 assumes one — see the
+    # oracle report). Wrapping the real method captures exactly the matrix
+    # this forward pass used, which is stronger than recomputing it.
+    fbanks = fe.compute_fbanks
+    captured_filters: dict[str, Any] = {}
+    original_create = fbanks._create_fbank_matrix
+
+    def capture_create(f_central_mat, band_mat):
+        m = original_create(f_central_mat, band_mat)
+        captured_filters.setdefault("value", m)
+        captured_filters["calls"] = captured_filters.get("calls", 0) + 1
+        return m
+
+    fbanks._create_fbank_matrix = capture_create
+
+    try:
+        with torch.inference_mode():
+            out_prob, score, index, text_lab = clf.classify_batch(wav, wav_lens)
+    finally:
+        fbanks._create_fbank_matrix = original_create
+        for h in handles:
+            h.remove()
+
+    # Every hook must have fired exactly once. Zero means the module was
+    # not on the forward path; more than one means the model ran twice and
+    # the dump would reflect the last call.
+    bad = {n: h.calls for n, h in hooks.items() if h.calls != 1}
+    if bad:
+        raise SystemExit(f"error: unexpected hook call counts: {bad}")
+    if captured_filters.get("calls") != 1:
+        raise SystemExit(
+            f"error: Filterbank._create_fbank_matrix fired "
+            f"{captured_filters.get('calls')} times, expected 1"
+        )
+
+    T = int(hooks["fe.mel"].value.shape[1])
+    if T != expected_T:
+        raise SystemExit(
+            f"error: frame count mismatch: fe.mel has T={T}, but "
+            f"floor({n_samples}/{HOP}) + 1 = {expected_T}. The C++ front end "
+            f"derives T from this formula; a mismatch means the STFT "
+            f"framing assumption in notes/PLAN.md section 1.1 is wrong."
+        )
+    print(f"frames: T={T} == floor({n_samples}/{HOP}) + 1  [ok]")
+
+    source: dict[str, Any] = {
+        "kind": "ecapa-tdnn-speechbrain",
+        "framework": "speechbrain",
+        "framework_version": speechbrain.__version__,
+        "torch_version": torch.__version__,
+        "model": args.model,
+        "revision": args.revision,
+        "checkpoint_dir": str(src_dir),
+        "device": args.device,
+        "torch_threads": args.torch_threads,
+        "model_dtype": "f32",
+        "audio": audio_path.name,
+        "n_samples": n_samples,
+        "sample_rate": SAMPLE_RATE,
+        "n_frames": T,
+        "n_mels": N_MELS,
+    }
+
+    def dump(name: str, array: np.ndarray, stage: str) -> None:
+        print(f"  {name}: shape={array.shape} min={array.min():.4e} "
+              f"max={array.max():.4e} mean={array.mean():.6e}")
+        write_tensor(name, array, stage=stage, source=source, out_dir=out_dir)
+
+    # ---- front end -------------------------------------------------------
+    dump("fe.mel", feat_time_major(hooks["fe.mel"].value, N_MELS, name="fe.mel"),
+         "frontend")
+
+    # ---- encoder ---------------------------------------------------------
+    for name, channels in (
+        ("enc.blk.0.out", 1024),
+        ("enc.blk.1.tdnn1.out", 1024),
+        ("enc.blk.1.res2.out", 1024),
+        ("enc.blk.1.se.out", 1024),
+        ("enc.blk.1.out", 1024),
+        ("enc.blk.2.out", 1024),
+        ("enc.blk.3.out", 1024),
+        ("enc.mfa.out", 3072),
+        ("enc.asp.attn_logits", 3072),
+    ):
+        dump(name, act_time_major(hooks[name].value, channels, name=name), "encoder")
+
+    dump("enc.asp.out", vec(hooks["enc.asp.out"].value, 6144, name="enc.asp.out"),
+         "encoder")
+    dump("enc.emb", vec(hooks["enc.emb"].value, EMB_DIM, name="enc.emb"), "encoder")
+
+    # ---- classifier ------------------------------------------------------
+    dump("cls.hidden", vec(hooks["cls.hidden"].value, HIDDEN, name="cls.hidden"),
+         "classifier")
+    dump("cls.logits_raw",
+         vec(hooks["cls.logits_raw"].value, N_LABELS, name="cls.logits_raw"),
+         "classifier")
+    log_probs = vec(hooks["cls.log_probs"].value, N_LABELS, name="cls.log_probs")
+    dump("cls.log_probs", log_probs, "classifier")
+
+    # The hooked softmax output must be the tensor classify_batch returned;
+    # if it is not, the hook is on the wrong module.
+    returned = out_prob.detach().to(dtype=torch.float32, device="cpu").reshape(-1).numpy()
+    if not np.array_equal(returned, log_probs):
+        raise SystemExit(
+            "error: cls.log_probs hook does not match classify_batch's out_prob"
+        )
+
+    # ---- run-invariant front-end tables ---------------------------------
+    window = fe.compute_STFT.window
+    expected_window = torch.hamming_window(WIN)
+    if not torch.equal(window.to(torch.float32).cpu(), expected_window):
+        raise SystemExit(
+            "error: STFT window is not torch.hamming_window(400); notes/PLAN.md "
+            "section 1.1 (periodic Hamming) does not describe this checkpoint"
+        )
+    dump("fe.window", vec(window, WIN, name="fe.window"), "frontend")
+
+    filters = captured_filters["value"]
+    dump("fe.filters", mat(filters, (N_STFT, N_MELS), name="fe.filters"), "frontend")
+
+    # ---- prediction ------------------------------------------------------
+    label_index = int(index.item())
+    label_encoder = clf.hparams.label_encoder
+    ind2lab = label_encoder.ind2lab
+    if len(ind2lab) != N_LABELS:
+        raise SystemExit(
+            f"error: label encoder has {len(ind2lab)} labels, expected {N_LABELS}"
+        )
+    code, name = split_label(str(ind2lab[label_index]))
+    decoded = text_lab[0] if isinstance(text_lab, list) else str(text_lab)
+    if str(decoded) != str(ind2lab[label_index]):
+        raise SystemExit(
+            f"error: label_encoder.decode_torch gave {decoded!r} but "
+            f"ind2lab[{label_index}] is {ind2lab[label_index]!r}"
+        )
+
+    order = np.argsort(-log_probs)[:5]
+    top5 = []
+    for i in order:
+        c, n = split_label(str(ind2lab[int(i)]))
+        top5.append({
+            "index": int(i),
+            "code": c,
+            "name": n,
+            "log_prob": float(log_probs[int(i)]),
+        })
+
+    prediction = {
+        "label_index": label_index,
+        "code": code,
+        "name": name,
+        "log_prob": float(log_probs[label_index]),
+        "top5": top5,
+        "source": {**source, "score": float(score.item())},
+    }
+    (out_dir / "prediction.json").write_text(json.dumps(prediction, indent=2) + "\n")
+    print(f"prediction: {code} ({name}) log_prob={prediction['log_prob']:.6f} "
+          f"prob={math.exp(prediction['log_prob']):.4f}")
+    print("top5: " + ", ".join(f"{e['code']}={e['log_prob']:.3f}" for e in top5))
+
+    return 0
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def add_common_args(p: argparse.ArgumentParser) -> None:
+    p.add_argument("--model", default=DEFAULT_REPO,
+                   help=f"HF repo id or local checkpoint dir (default: {DEFAULT_REPO})")
+    p.add_argument("--revision", default=DEFAULT_REVISION,
+                   help="HF revision (commit SHA) to pin the download to. "
+                        "Ignored when --model is a local directory.")
+    p.add_argument("--audio", required=True, help="16 kHz mono WAV path")
+    p.add_argument("--out", required=True, help="Output directory for dumps")
+    p.add_argument("--device", default="cpu", help="torch device (default: cpu)")
+    p.add_argument("--torch-threads", type=int, default=1,
+                   help="torch.set_num_threads (0 = unchanged)")
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description="SpeechBrain VoxLingua107 ECAPA-TDNN reference dumper.")
+    sub = p.add_subparsers(dest="cmd", required=True)
+
+    ep = sub.add_parser(
+        "encoder",
+        help="Run the full forward pass; dump front end, encoder, classifier "
+             "and prediction.json")
+    add_common_args(ep)
+    ep.set_defaults(func=cmd_encoder)
+
+    args = p.parse_args(argv)
+    return args.func(args)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/ecapa_numpy_reference.py b/scripts/ecapa_numpy_reference.py
new file mode 100755
index 000000000..01feb5368
--- /dev/null
+++ b/scripts/ecapa_numpy_reference.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "numpy>=1.26",
+#     "gguf>=0.10",
+#     "soundfile>=0.12",
+# ]
+# ///
+"""
+ecapa_numpy_reference.py - dump the NumPy forward pass (scripts/lib/
+ecapa_numpy.py) in the same on-disk format as the SpeechBrain reference
+dumper and the C++ debug dumper.
+
+This is the middle term of a three-way comparison:
+
+    SpeechBrain  <--compare_tensors-->  NumPy  <--compare_tensors-->  C++
+      (the truth)                     (the GGUF)                 (the ship)
+
+The NumPy side reads ONLY the GGUF, so a NumPy-vs-SpeechBrain pass proves
+the converter's algebra (BN as affine, the three forward BN folds, the
+ASP/MFA weight splits, tap-major kernels), and a C++-vs-NumPy diff after
+that isolates graph bugs from conversion bugs.
+
+It deliberately runs on plain `uv run` rather than the pinned SpeechBrain
+env: nothing here needs torch, and being able to re-run the reference
+forward pass without a 2 GB dependency tree is worth a PEP-723 header.
+
+Usage:
+
+    uv run scripts/ecapa_numpy_reference.py \
+      --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
+      --audio samples/fleurs-en.wav \
+      --out build/validate/ecapa_tdnn/voxlingua107/fleurs-en/numpy
+
+    uv run scripts/compare_tensors.py \
+      build/validate/ecapa_tdnn/voxlingua107/fleurs-en/numpy \
+      build/validate/ecapa_tdnn/voxlingua107/fleurs-en/ref \
+      --tolerances tests/tolerances/ecapa_tdnn.json
+
+Writes `<name>.f32` + `<name>.json` for every stage tensor in the dump contract, plus `prediction.json` in the same shape the SpeechBrain dumper
+writes so validate.py can compare `label_index` on either pair.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+SCRIPTS_DIR = REPO_ROOT / "scripts"
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+from lib.ecapa_numpy import EcapaNumpy  # noqa: E402
+from lib.ref_dump import write_tensor  # noqa: E402
+
+# Which coarse stage each contract tensor belongs to, for the sidecar's
+# `stage` field. Same grouping the SpeechBrain dumper uses so the two sets
+# of sidecars are directly comparable.
+STAGES = {
+    "fe.mel": "frontend",
+    "enc.blk.0.out": "encoder",
+    "enc.blk.1.tdnn1.out": "encoder",
+    "enc.blk.1.res2.out": "encoder",
+    "enc.blk.1.se.out": "encoder",
+    "enc.blk.1.out": "encoder",
+    "enc.blk.2.out": "encoder",
+    "enc.blk.3.out": "encoder",
+    "enc.mfa.out": "encoder",
+    "enc.asp.attn_logits": "encoder",
+    "enc.asp.out": "encoder",
+    "enc.emb": "encoder",
+    "cls.hidden": "classifier",
+    "cls.logits_raw": "classifier",
+    "cls.log_probs": "classifier",
+}
+
+
+def load_audio(path: Path, sample_rate: int) -> np.ndarray:
+    pcm, sr = sf.read(str(path), dtype="float32", always_2d=False)
+    if pcm.ndim > 1:
+        pcm = pcm.mean(axis=1)
+    if int(sr) != sample_rate:
+        raise SystemExit(
+            f"error: {path} is {sr} Hz; this model's front end is "
+            f"{sample_rate} Hz only. Resample before dumping.")
+    return np.ascontiguousarray(pcm, dtype=np.float32)
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description="Dump the NumPy ECAPA-TDNN forward pass from a GGUF.")
+    p.add_argument("--gguf", required=True, help="ecapa_tdnn GGUF path")
+    p.add_argument("--audio", required=True, help="16 kHz mono WAV path")
+    p.add_argument("--out", required=True, help="output directory for dumps")
+    args = p.parse_args(argv)
+
+    gguf_path = Path(args.gguf).expanduser().resolve()
+    audio_path = Path(args.audio).expanduser().resolve()
+    out_dir = Path(args.out).expanduser().resolve()
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    model = EcapaNumpy(gguf_path)
+    pcm = load_audio(audio_path, model.sample_rate)
+    n_samples = int(pcm.size)
+    expected_T = n_samples // model.hop + 1
+    print(f"audio: {audio_path.name} samples={n_samples} "
+          f"({n_samples / model.sample_rate:.3f} s) frames={expected_T}")
+
+    result = model.forward(pcm)
+
+    T = int(result["fe.mel"].shape[0])
+    if T != expected_T:
+        raise SystemExit(
+            f"error: fe.mel has T={T} but floor({n_samples}/{model.hop}) + 1 "
+            f"= {expected_T}")
+
+    # The dtype mix actually used for this run. Recorded per dump so an
+    # F16/Q8_0 comparison is never mistaken for an F32 parity number.
+    quant_kinds = sorted(set(model.tensor_types.values()))
+    source = {
+        "kind": "ecapa-tdnn-numpy",
+        "framework": "numpy",
+        "framework_version": np.__version__,
+        "gguf": str(gguf_path),
+        "gguf_file_type": int(model.meta.get("general.file_type", -1)),
+        "gguf_tensor_types": quant_kinds,
+        "variant": model.meta.get("stt.variant"),
+        "source_hf_repo": model.meta.get("general.name"),
+        "source_hf_revision": model.meta.get("general.source.commit"),
+        "audio": audio_path.name,
+        "n_samples": n_samples,
+        "sample_rate": model.sample_rate,
+        "n_frames": T,
+        "n_mels": model.n_mels,
+        "compute_dtype": "f32",
+    }
+
+    missing = [n for n in STAGES if n not in result]
+    if missing:
+        raise SystemExit(f"error: forward() did not return {missing}")
+
+    for name, stage in STAGES.items():
+        arr = np.ascontiguousarray(result[name], dtype=np.float32)
+        print(f"  {name}: shape={arr.shape} min={arr.min():.4e} "
+              f"max={arr.max():.4e} mean={arr.mean():.6e}")
+        write_tensor(name, arr, stage=stage, source=source, out_dir=out_dir)
+
+    pred = dict(result["prediction"])
+    pred["source"] = source
+    (out_dir / "prediction.json").write_text(json.dumps(pred, indent=2) + "\n")
+    print(f"prediction: {pred['code']} ({pred['name']}) "
+          f"log_prob={pred['log_prob']:.6f} "
+          f"prob={math.exp(pred['log_prob']):.4f}")
+    print("top5: " + ", ".join(f"{e['code']}={e['log_prob']:.3f}"
+                               for e in pred["top5"]))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/envs/ecapa_tdnn/pyproject.toml b/scripts/envs/ecapa_tdnn/pyproject.toml
new file mode 100644
index 000000000..2711774de
--- /dev/null
+++ b/scripts/envs/ecapa_tdnn/pyproject.toml
@@ -0,0 +1,31 @@
+[project]
+name = "transcribe-ecapa_tdnn-env"
+version = "0.1.0"
+description = "Python env for ECAPA-TDNN VoxLingua107 reference dumps and conversion (SpeechBrain)"
+requires-python = ">=3.11,<3.13"
+dependencies = [
+    # Exact pin. The VoxLingua107 checkpoint ships as a SpeechBrain
+    # hyperparams.yaml that instantiates SpeechBrain classes by name, so the
+    # module tree the reference dumper hooks (`embedding_model.blocks[1].se_block`,
+    # `classifier.DNN.block_0.act`, `compute_features.compute_fbanks`) is
+    # version-specific, and so is the arithmetic inside them.
+    # tests/tolerances/ecapa_tdnn.json was measured against THIS version
+    # (recorded as reference.revision in
+    # tests/golden/ecapa_tdnn/voxlingua107.manifest.json); a silent bump can
+    # move both the hook points and the numbers.
+    "speechbrain==1.1.1",
+    # CPU torch. The parity regime is fp32 on CPU with one thread
+    # (notes/PLAN.md section 5); no CUDA/Metal build is needed or wanted here.
+    "torch==2.13.0",
+    "torchaudio==2.11.0",
+    "numpy>=1.26",
+    "soundfile>=0.12",
+    "huggingface-hub>=0.30",
+    # Used by the converter (T3), not by the dumper.
+    "gguf>=0.10",
+    "pyyaml>=6.0",
+    # FLEURS lives in the local HF hub cache as parquet; the fixture builder
+    # reads it with pyarrow rather than pulling in `datasets`.
+    "pyarrow>=15",
+    "pandas>=2.0",
+]
diff --git a/scripts/envs/ecapa_tdnn/uv.lock b/scripts/envs/ecapa_tdnn/uv.lock
new file mode 100644
index 000000000..298534cb0
--- /dev/null
+++ b/scripts/envs/ecapa_tdnn/uv.lock
@@ -0,0 +1,1096 @@
+version = 1
+requires-python = ">=3.11, <3.13"
+resolution-markers = [
+    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'emscripten'",
+    "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "anyio"
+version = "4.14.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions" },
+]
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+
+[[package]]
+name = "transcribe-ecapa-tdnn-env"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "gguf" },
+    { name = "huggingface-hub" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "pandas" },
+    { name = "pyarrow" },
+    { name = "pyyaml" },
+    { name = "soundfile" },
+    { name = "speechbrain" },
+    { name = "torch" },
+    { name = "torchaudio" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "gguf", specifier = ">=0.10" },
+    { name = "huggingface-hub", specifier = ">=0.30" },
+    { name = "numpy", specifier = ">=1.26" },
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+    { name = "pyyaml", specifier = ">=6.0" },
+    { name = "soundfile", specifier = ">=0.12" },
+    { name = "speechbrain", specifier = "==1.1.1" },
+    { name = "torch", specifier = "==2.13.0" },
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diff --git a/scripts/lib/ecapa_numpy.py b/scripts/lib/ecapa_numpy.py
new file mode 100644
index 000000000..61d49df15
--- /dev/null
+++ b/scripts/lib/ecapa_numpy.py
@@ -0,0 +1,398 @@
+"""ecapa_numpy.py - the readable specification of the ecapa_tdnn forward pass.
+
+A complete ECAPA-TDNN language-ID forward pass in NumPy, driven entirely by
+an ecapa_tdnn GGUF: weights, layout, and every hyperparameter come out of the
+file, nothing is hardcoded from the checkpoint. It exists for three reasons:
+
+  1. It proves the converter's four conversion-time rewrites (the C++ graph — BN as affine, forward BN folds, ASP split, MFA split) are
+     exact: its dumps are compared against the SpeechBrain reference dumps
+     with the same tolerance file the C++ has to meet.
+  2. It is the executable version of the C++ graph, in ~300 lines
+     a reviewer can read top to bottom, for when the ggml graph disagrees
+     with SpeechBrain and someone has to work out which one is wrong.
+  3. It bisects failures: every stage tensor in the C++ graph is
+     returned, so a C++ mismatch can be localised to a layer without
+     rebuilding anything.
+
+Deliberate numerical choices:
+
+  * Everything after the FFT is float32, accumulated with NumPy's float32
+    `@`. float64 would hide exactly the accumulation-order differences the
+    C++ is being measured for. `np.fft.rfft` always computes in float64
+    (NumPy has no float32 FFT), so the power spectrum is cast straight back
+    down to float32 — that is the only place this file is wider than the
+    reference, and it is the reason `fe.mel` is the loosest tensor in the
+    tolerance file.
+  * The conv is written the way the C++ graph does it:
+    reflect-pad the time axis once, then sum K matmuls against shifted
+    slices, one per kernel tap. Not `np.convolve`, not im2col — the point is
+    to mirror the graph, not to be fast.
+  * Activations are carried TIME-MAJOR `[T, C]`. That is what both dumpers
+    write to disk (C++ ggml `ne = [C, T]` lands row-major as `[T, C]`), so
+    no dump-time transpose can silently paper over a layout error.
+
+Usage:
+
+    from lib.ecapa_numpy import EcapaNumpy
+    model = EcapaNumpy("models/.../lang-id-voxlingua107-ecapa-F32.gguf")
+    out = model.forward(pcm_float32_16k)
+    out["cls.logits_raw"]      # [107]
+    out["prediction"]["code"]  # e.g. "en"
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import gguf
+from gguf import GGMLQuantizationType, GGUFValueType
+
+
+class EcapaNumpy:
+    """A GGUF-driven ECAPA-TDNN language identifier, in NumPy."""
+
+    def __init__(self, gguf_path: str | Path) -> None:
+        self.path = Path(gguf_path)
+        reader = gguf.GGUFReader(str(self.path), "r")
+
+        # ---- metadata ----------------------------------------------------
+        self.meta: dict[str, Any] = {}
+        for name, field in reader.fields.items():
+            if not field.types:
+                continue
+            if field.types[0] == GGUFValueType.ARRAY:
+                self.meta[name] = field.contents()
+            else:
+                self.meta[name] = field.contents()
+
+        arch = self.meta.get("general.architecture")
+        if arch != "ecapa_tdnn":
+            raise ValueError(
+                f"{self.path}: general.architecture is {arch!r}, expected "
+                "'ecapa_tdnn'")
+        fmt = int(self.meta.get("stt.ecapa_tdnn.format_version", -1))
+        if fmt != 1:
+            raise ValueError(
+                f"{self.path}: stt.ecapa_tdnn.format_version is {fmt}, expected 1")
+
+        # ---- tensors -----------------------------------------------------
+        # Everything is dequantised up front to float32 in its numpy shape
+        # (= reversed ggml ne). F16 widens by cast; Q8_0 goes through
+        # gguf.quants.dequantize, which is the same block-scale arithmetic
+        # ggml's vec_dot does, so an F16/Q8_0 run here is a fair proxy for
+        # the C++ run on the same file.
+        self.tensors: dict[str, np.ndarray] = {}
+        self.tensor_types: dict[str, str] = {}
+        for t in reader.tensors:
+            data = t.data
+            if t.tensor_type == GGMLQuantizationType.F32:
+                arr = np.asarray(data, dtype=np.float32)
+            elif t.tensor_type == GGMLQuantizationType.F16:
+                arr = np.asarray(data).astype(np.float32)
+            else:
+                arr = gguf.quants.dequantize(
+                    np.asarray(data), t.tensor_type).astype(np.float32)
+            self.tensors[t.name] = np.ascontiguousarray(arr)
+            self.tensor_types[t.name] = t.tensor_type.name
+
+        # ---- shape / hyperparameter cache --------------------------------
+        self.sample_rate = int(self.meta["stt.frontend.sample_rate"])
+        self.n_fft = int(self.meta["stt.frontend.n_fft"])
+        self.hop = int(self.meta["stt.frontend.hop_length"])
+        self.win = int(self.meta["stt.frontend.win_length"])
+        self.n_mels = int(self.meta["stt.frontend.num_mels"])
+        self.log_floor = float(self.meta["stt.frontend.log_clamp_min"])
+        self.top_db = float(self.meta["stt.frontend.top_db"])
+        self.window_kind = str(self.meta["stt.frontend.window"])
+        self.normalize = str(self.meta["stt.frontend.normalize"])
+
+        self.channels = [int(c) for c in self.meta["stt.ecapa_tdnn.channels"]]
+        self.kernel_sizes = [int(k) for k in
+                             self.meta["stt.ecapa_tdnn.kernel_sizes"]]
+        self.dilations = [int(d) for d in self.meta["stt.ecapa_tdnn.dilations"]]
+        self.res2net_scale = int(self.meta["stt.ecapa_tdnn.res2net_scale"])
+        self.asp_eps = float(self.meta["stt.ecapa_tdnn.asp_eps"])
+        self.embedding_dim = int(self.meta["stt.ecapa_tdnn.embedding_dim"])
+        self.leaky_slope = float(self.meta["stt.ecapa_tdnn.classifier_leaky_slope"])
+
+        self.codes = list(self.meta["stt.langid.labels.codes"])
+        self.names = list(self.meta["stt.langid.labels.names"])
+        self.aliases = {a: c for a, c in
+                        (s.split("=", 1)
+                         for s in self.meta["stt.langid.labels.aliases"])}
+
+        if self.window_kind != "hamming_periodic":
+            raise ValueError(f"unsupported window {self.window_kind!r}")
+        if self.normalize != "sentence_mean":
+            raise ValueError(f"unsupported normalize {self.normalize!r}")
+
+        # Periodic Hamming: w[n] = 0.54 - 0.46*cos(2*pi*n/N), n = 0..N-1.
+        # NOT the symmetric variant (which divides by N-1) — the two differ
+        # by 5.5e-03 at the edges, far above any tolerance here.
+        n = np.arange(self.win, dtype=np.float64)
+        self.window = (0.54 - 0.46 * np.cos(2.0 * np.pi * n / self.win)
+                       ).astype(np.float32)
+
+        # [60, 201] mel-major, straight from SpeechBrain's Filterbank.
+        self.filters = self.tensors["frontend.mel_filterbank"]
+        if self.filters.shape != (self.n_mels, self.n_fft // 2 + 1):
+            raise ValueError(
+                f"frontend.mel_filterbank is {self.filters.shape}, expected "
+                f"{(self.n_mels, self.n_fft // 2 + 1)}")
+
+    # -- small helpers -----------------------------------------------------
+
+    def t(self, name: str) -> np.ndarray:
+        try:
+            return self.tensors[name]
+        except KeyError:
+            raise KeyError(f"{self.path.name}: missing tensor {name!r}") from None
+
+    @staticmethod
+    def _relu(x: np.ndarray) -> np.ndarray:
+        return np.maximum(x, np.float32(0.0))
+
+    def _leaky(self, x: np.ndarray) -> np.ndarray:
+        return np.where(x >= 0, x, x * np.float32(self.leaky_slope)
+                        ).astype(np.float32)
+
+    @staticmethod
+    def _linear(x: np.ndarray, w: np.ndarray, b: np.ndarray) -> np.ndarray:
+        """`[T, IC] @ [OC, IC]^T + [OC]` -> `[T, OC]`, all float32.
+
+        This is the 1x1 convolution: with channel-innermost activations a
+        pointwise conv is exactly a matmul, which is why the GGUF stores 1x1
+        kernels as plain 2-D `[OC, IC]` matrices.
+        """
+        return (x @ w.T + b).astype(np.float32)
+
+    @staticmethod
+    def _bn(x: np.ndarray, scale: np.ndarray, shift: np.ndarray) -> np.ndarray:
+        """`y = x * scale + shift`, the converter's affine form of BatchNorm.
+
+        Broadcasts over time. The scale/shift pair already absorbed
+        gamma/beta/running_mean/running_var/eps at conversion time
+        (notes/DECISIONS.md D08 explains why this cannot fold into the conv).
+        """
+        return (x * scale + shift).astype(np.float32)
+
+    def _conv_taps(self, x: np.ndarray, w: np.ndarray, b: np.ndarray,
+                   dilation: int) -> np.ndarray:
+        """Dilated conv as a sum of per-tap matmuls, mirroring the ggml graph.
+
+        `w` is tap-major `[K, OC, IC]`. SpeechBrain uses `padding="same"` with
+        `padding_mode="reflect"`, i.e. `p = dilation*(K-1)/2` reflected frames
+        on each side, so the output keeps T frames. One `np.pad(mode=
+        "reflect")` here stands in for the C++'s single `ggml_get_rows`
+        gather with a precomputed mirror index; the arithmetic is identical.
+        """
+        k = w.shape[0]
+        if k == 1:
+            return self._linear(x, w[0], b)
+        p = dilation * (k - 1) // 2
+        if x.shape[0] <= p:
+            raise ValueError(
+                f"need more than {p} frames for a k={k} d={dilation} conv, "
+                f"got {x.shape[0]}")
+        xp = np.pad(x, ((p, p), (0, 0)), mode="reflect")
+        T = x.shape[0]
+        y = np.zeros((T, w.shape[1]), dtype=np.float32)
+        for tap in range(k):
+            off = tap * dilation
+            y += xp[off:off + T] @ w[tap].T
+        return (y + b).astype(np.float32)
+
+    def _tdnn(self, x: np.ndarray, prefix: str, dilation: int,
+              tap_major: bool) -> np.ndarray:
+        """SpeechBrain TDNNBlock: conv -> ReLU -> BN.
+
+        (The Dropout1d(p=0) fourth child is an exact identity in eval mode
+        and carries no weights, so it is absent here and from the GGUF.)
+        """
+        stem = f"{prefix}.conv" if tap_major else prefix
+        w = self.t(f"{stem}.weight")
+        b = self.t(f"{stem}.bias")
+        y = (self._conv_taps(x, w, b, dilation) if tap_major
+             else self._linear(x, w, b))
+        y = self._relu(y)
+        return self._bn(y, self.t(f"{prefix}.bn.scale"),
+                        self.t(f"{prefix}.bn.shift"))
+
+    # -- front end ---------------------------------------------------------
+
+    def mel(self, pcm: np.ndarray) -> np.ndarray:
+        """16 kHz mono float32 -> `[T, 60]` sentence-mean-normalised log-mel.
+
+        Matches SpeechBrain's `Fbank` + `InputNormalization(norm_type=
+        "sentence", std_norm=False)` exactly: centred STFT with 200 samples
+        of ZERO padding each side (`pad_mode="constant"`, not reflect — the
+        reflect padding in this model is inside the convolutions), periodic
+        Hamming window, power spectrum, 60 triangular mel filters,
+        `10*log10(max(x, 1e-10))`, an 80 dB floor taken over time AND
+        frequency of the whole utterance, then per-bin mean subtraction.
+        """
+        pcm = np.ascontiguousarray(pcm, dtype=np.float32)
+        n = pcm.size
+        T = n // self.hop + 1
+        pad = self.n_fft // 2
+        xp = np.pad(pcm, (pad, pad), mode="constant")
+
+        idx = np.arange(self.win)[None, :] + (np.arange(T) * self.hop)[:, None]
+        frames = xp[idx] * self.window                       # [T, 400] f32
+
+        spec = np.fft.rfft(frames, n=self.n_fft, axis=-1)    # f64, always
+        # Power spectrum re^2 + im^2 (SpeechBrain's spectral_magnitude with
+        # power=1 over a squared-and-summed real/imag pair). Back to f32
+        # immediately so the rest of the front end matches the reference's
+        # precision.
+        power = (spec.real ** 2 + spec.imag ** 2).astype(np.float32)
+
+        mel = (power @ self.filters.T).astype(np.float32)    # [T, 60]
+        db = (10.0 * np.log10(np.maximum(mel, np.float32(self.log_floor)))
+              ).astype(np.float32)
+        # ref_value = 1.0 so db_multiplier = log10(1) = 0: no reference term.
+        db = np.maximum(db, db.max() - np.float32(self.top_db))
+        return (db - db.mean(axis=0, keepdims=True)).astype(np.float32)
+
+    # -- forward -----------------------------------------------------------
+
+    def forward(self, pcm: np.ndarray) -> dict[str, Any]:
+        """Run the whole model; return every the C++ graph stage tensor."""
+        out: dict[str, Any] = {}
+
+        x = self.mel(pcm)                                    # [T, 60]
+        out["fe.mel"] = x
+
+        # blocks.0: TDNN 60 -> 1024, k=5, d=1 (reflect pad 2).
+        x = self._tdnn(x, "blk.0", self.dilations[0], tap_major=True)
+        out["enc.blk.0.out"] = x
+
+        # blocks.1..3: SERes2Net, in == out channels so the residual is
+        # the identity (no shortcut projection in this checkpoint).
+        block_outs: list[np.ndarray] = []
+        for i in (1, 2, 3):
+            p = f"blk.{i}"
+            residual = x
+
+            x = self._tdnn(x, f"{p}.tdnn1", 1, tap_major=False)
+            if i == 1:
+                out["enc.blk.1.tdnn1.out"] = x
+
+            # Res2Net, scale 8: split into 8 chunks of 128 channels.
+            #   y0 = x0                       (passed through untouched)
+            #   y1 = B0(x1)
+            #   yi = B(i-1)(xi + y(i-1))      i = 2..7
+            # The addend is the PREVIOUS sub-block's output, which is what
+            # makes this a cascade rather than 7 independent branches.
+            chunks = np.split(x, self.res2net_scale, axis=1)
+            ys: list[np.ndarray] = [chunks[0]]
+            y_prev = chunks[0]
+            for j in range(1, self.res2net_scale):
+                inp = chunks[j] if j == 1 else (chunks[j] + y_prev
+                                                ).astype(np.float32)
+                y_prev = self._tdnn(inp, f"{p}.res2.{j - 1}",
+                                    self.dilations[i], tap_major=True)
+                ys.append(y_prev)
+            x = np.ascontiguousarray(np.concatenate(ys, axis=1))
+            if i == 1:
+                out["enc.blk.1.res2.out"] = x
+
+            x = self._tdnn(x, f"{p}.tdnn2", 1, tap_major=False)
+
+            # Squeeze-and-excitation: mean over time, two 1x1 convs, sigmoid
+            # gate, per-channel rescale.
+            s = x.mean(axis=0).astype(np.float32)
+            s = self._relu(self.t(f"{p}.se.c1.weight") @ s + self.t(f"{p}.se.c1.bias"))
+            s = 1.0 / (1.0 + np.exp(-(self.t(f"{p}.se.c2.weight") @ s
+                                      + self.t(f"{p}.se.c2.bias"))))
+            x = (x * s.astype(np.float32)).astype(np.float32)
+            if i == 1:
+                out["enc.blk.1.se.out"] = x
+
+            x = (x + residual).astype(np.float32)
+            out[f"enc.blk.{i}.out"] = x
+            block_outs.append(x)
+
+        # MFA. SpeechBrain concatenates blocks.1/2/3 and runs one 1x1 conv;
+        # the converter split that weight into three blocks, so the concat
+        # copy disappears and this is three matmuls summed. Same FLOPs, same
+        # result.
+        y = (block_outs[0] @ self.t("mfa.w1.weight").T
+             + block_outs[1] @ self.t("mfa.w2.weight").T
+             + block_outs[2] @ self.t("mfa.w3.weight").T
+             + self.t("mfa.bias")).astype(np.float32)
+        y = self._bn(self._relu(y), self.t("mfa.bn.scale"),
+                     self.t("mfa.bn.shift"))
+        out["enc.mfa.out"] = y
+
+        # Attentive statistics pooling with global context.
+        eps = np.float32(self.asp_eps)
+        mean = y.mean(axis=0).astype(np.float32)
+        var = ((y - mean) ** 2).mean(axis=0).astype(np.float32)
+        std = np.sqrt(np.maximum(var, eps)).astype(np.float32)
+
+        # cat([x, mean, std]) -> 1x1 conv, split at conversion time into
+        # wx (time-varying) and wm/ws (constant over T), so the mean/std
+        # contribution collapses into the bias vector.
+        const = (self.t("asp.tdnn.mean.weight") @ mean + self.t("asp.tdnn.std.weight") @ std
+                 + self.t("asp.tdnn.bias")).astype(np.float32)
+        a = (y @ self.t("asp.tdnn.x.weight").T + const).astype(np.float32)
+        a = self._bn(self._relu(a), self.t("asp.tdnn.bn.scale"),
+                     self.t("asp.tdnn.bn.shift"))
+        a = np.tanh(a).astype(np.float32)
+        logits = self._linear(a, self.t("asp.attn.weight"), self.t("asp.attn.bias"))
+        out["enc.asp.attn_logits"] = logits                  # [T, 3072]
+
+        # Softmax over TIME, per channel. (SpeechBrain masks padded frames
+        # to -inf first; with a single unpadded utterance the mask is all
+        # ones and the masked_fill is a no-op.)
+        z = logits - logits.max(axis=0, keepdims=True)
+        e = np.exp(z).astype(np.float32)
+        attn = (e / e.sum(axis=0, keepdims=True)).astype(np.float32)
+
+        mu = (attn * y).sum(axis=0).astype(np.float32)
+        sigma = np.sqrt(np.maximum((attn * (y - mu) ** 2).sum(axis=0), eps)
+                        ).astype(np.float32)
+        pooled = np.concatenate([mu, sigma]).astype(np.float32)  # [6144]
+        out["enc.asp.out"] = pooled
+
+        # asp_bn is folded into fc, so this single matmul is BN + linear.
+        emb = (self.t("fc.weight") @ pooled + self.t("fc.bias")).astype(np.float32)
+        out["enc.emb"] = emb                                 # [256]
+
+        # Classifier. cls.bn0 is folded into cls.l1 and cls.bn1 into
+        # cls.out, but both LeakyReLUs come BEFORE their BN in SpeechBrain,
+        # so they survive: leaky -> l1 -> leaky -> out.
+        h = self._leaky(emb)
+        h = (self.t("cls.l1.weight") @ h + self.t("cls.l1.bias")).astype(np.float32)
+        hidden = self._leaky(h)
+        out["cls.hidden"] = hidden                           # [512]
+
+        z = (self.t("cls.out.weight") @ hidden + self.t("cls.out.bias")
+             ).astype(np.float32)
+        out["cls.logits_raw"] = z                            # [107]
+
+        m = z.max()
+        log_probs = (z - m - np.log(np.exp(z - m).sum())).astype(np.float32)
+        out["cls.log_probs"] = log_probs
+
+        order = np.argsort(-log_probs)
+        best = int(order[0])
+        out["prediction"] = {
+            "label_index": best,
+            "code": self.codes[best],
+            "name": self.names[best],
+            "log_prob": float(log_probs[best]),
+            "top5": [
+                {
+                    "index": int(i),
+                    "code": self.codes[int(i)],
+                    "name": self.names[int(i)],
+                    "log_prob": float(log_probs[int(i)]),
+                }
+                for i in order[:5]
+            ],
+        }
+        return out
diff --git a/scripts/lib/gguf_common.py b/scripts/lib/gguf_common.py
index 98091f6f3..3ad5f0e8c 100644
--- a/scripts/lib/gguf_common.py
+++ b/scripts/lib/gguf_common.py
@@ -265,12 +265,16 @@ def encode_for_gguf(
     "all_features":     "global",
     "global":           "global",
     "per_utterance":    "per_utterance",
+    # SpeechBrain InputNormalization(norm_type="sentence", std_norm=False):
+    # per-bin mean subtraction only (ecapa_tdnn).
+    "sentence_mean":    "sentence_mean",
 }
 
 
 def canonicalize_normalize(raw) -> str:
     """Map a reference-framework normalize value to the canonical enum
-    in our intake schema (per_feature / global / per_utterance / none).
+    in our intake schema (per_feature / global / per_utterance /
+    sentence_mean / none).
     Unknown values raise — Stage 3 should fail loudly rather than emit
     a value the C++ loader will not recognise."""
     key = raw if raw is None else str(raw)
diff --git a/scripts/lib/quant_policy.py b/scripts/lib/quant_policy.py
index 472a3eef0..72f4e5956 100644
--- a/scripts/lib/quant_policy.py
+++ b/scripts/lib/quant_policy.py
@@ -48,6 +48,12 @@
     # either). K tiers also save little (Q8_0 139MB -> Q4_K_M 92MB) and
     # are slower than Q8_0 on CPU. Ship only the near-reference tiers.
     "sortformer": ("F16", "Q8_0"),
+    # ecapa_tdnn (language ID): a 21M-parameter model whose decision is an
+    # argmax over 107 logits; Q8_0 already costs ~7 points of agreement with
+    # the reference on FLEURS for langid.cpp's Python-written file (re-measure
+    # on the transcribe-quantize output), and the K tiers would
+    # save a few MB at most. Ship only the near-reference tiers.
+    "ecapa_tdnn": ("F16", "Q8_0"),
 }
 
 
diff --git a/scripts/lib/test_quant_policy_sync.py b/scripts/lib/test_quant_policy_sync.py
index 2b6cda9c6..ddfc51648 100644
--- a/scripts/lib/test_quant_policy_sync.py
+++ b/scripts/lib/test_quant_policy_sync.py
@@ -85,6 +85,8 @@
     "dec.pos_emb.weight",                   # whisper decoder pos_emb
     "frontend.mel_filterbank",              # mel frontend buffer
     "frontend.window",                      # window frontend buffer
+    "blk.1.tdnn1.bn.scale",                 # ecapa_tdnn folded BN affine (.bn.)
+    "blk.1.res2.0.conv.bias",               # ecapa_tdnn conv bias (.bias)
 ]
 
 # Conv bucket: 2D / depthwise / 1x1 pointwise conv kernels. The loader has no
@@ -104,6 +106,7 @@
     "enc.blocks.3.conv.pointwise2.weight",  # conformer 1x1 pointwise
     "enc.pre_encode.conv.0.weight",         # pre-encode subsampling conv
     "enc.blocks.3.conv.depthwise.weight",   # conformer depthwise conv
+    "blk.1.res2.0.conv.weight",             # ecapa_tdnn tap-major k>1 kernel
 ]
 
 # Linear / Embed: ggml_mul_mat operands and the decoder token embedding.
@@ -128,6 +131,9 @@
     "enc.blocks.3.attn.rel_pos_emb.weight",
     "enc.blocks.3.attn.kv.weight",          # granite5_ctc fused K|V projection
     "enc.ctc_proj.weight",                  # granite5_ctc tied CTC head
+    "blk.1.tdnn1.weight",                   # ecapa_tdnn 1x1 TDNN (a matmul operand)
+    "asp.attn.weight",                      # ecapa_tdnn ASP attention 1x1 (NOT .conv.)
+    "cls.out.weight",                       # ecapa_tdnn classifier head
 ]
 
 # KNOWN DRIFT — policy.cpp::classify_tensor places these in the Norm (F32) or
diff --git a/scripts/validate.py b/scripts/validate.py
index cbd9fc717..5ecdef171 100644
--- a/scripts/validate.py
+++ b/scripts/validate.py
@@ -309,6 +309,16 @@ def parse_cli_transcript(output: str) -> str | None:
     return None
 
 
+def parse_cli_language(output: str) -> dict[str, Any] | None:
+    """The LANGID CLI's `language: <code> index=<i> p=<p>` line."""
+    for line in output.splitlines():
+        if line.startswith("language: "):
+            parts = line[len("language: "):].split()
+            fields = dict(p.split("=", 1) for p in parts[1:] if "=" in p)
+            return {"code": parts[0], "label_index": int(fields["index"]), "p": float(fields["p"])}
+    return None
+
+
 def write_cpp_transcript(
     out_dir: Path,
     *,
@@ -379,7 +389,7 @@ def cmd_ref(args: argparse.Namespace) -> int:
         # family's dumper accepts --revision. The dumper itself ignores
         # --revision when --model resolves to a local directory.
         hf_revision = (manifest.get("source_model") or {}).get("hf_revision")
-        if hf_revision and args.family in ("qwen3_asr", "granite_nar", "granite5_ctc"):
+        if hf_revision and args.family in ("qwen3_asr", "granite_nar", "granite5_ctc", "ecapa_tdnn"):
             common_args += ["--revision", str(hf_revision)]
 
         # Forward any manifest-declared dumper args verbatim. Used today
@@ -411,8 +421,14 @@ def cmd_ref(args: argparse.Namespace) -> int:
         # (--preset) and the C++ side (TRANSCRIBE_SORTFORMER_STREAM_PRESET in
         # cmd_cpp); unset -> the checkpoint-shipped cfg (single chunk on the
         # short oracle, i.e. diar.probs == diar.preds_offline).
+        #
+        # ecapa_tdnn (language ID) is one forward pass: the `encoder`
+        # subcommand dumps the front end, encoder, classifier and
+        # prediction.json from a single classify_batch call.
         if args.family == "sortformer":
             stages = ["encoder", "diarize"]
+        elif args.family == "ecapa_tdnn":
+            stages = ["encoder"]
         else:
             stages = ["encoder", "decode"]
         sf_preset = os.environ.get("VALIDATE_SORTFORMER_PRESET")
@@ -550,6 +566,13 @@ def cmd_cpp(args: argparse.Namespace) -> int:
         transcript = parse_cli_transcript(result.stdout or "")
         if transcript is None and "speaker segments:" in (result.stdout or ""):
             continue  # a diarizer has no transcript
+        prediction = parse_cli_language(result.stdout or "") if transcript is None else None
+        if prediction is not None:
+            # A language ID model: its behavioural artifact is the top-1
+            # label, compared against the reference's prediction.json.
+            (out_dir / "prediction.json").write_text(json.dumps(prediction, indent=2) + "\n")
+            print(f"  wrote {out_dir / 'prediction.json'}", file=sys.stderr)
+            continue
         if transcript is None:
             raise SystemExit(
                 f"error: cpp dump [{args.family}/{case_name}] did not emit a transcript line"
@@ -648,6 +671,33 @@ def cmd_compare(args: argparse.Namespace) -> int:
         # emits speaker segments, not `text`). Its behavioral artifact is the
         # diar.probs tensor, gated above; the `diarize` stage's segment lines
         # are informational only, so skip the text-transcript comparison.
+        # Language ID: the gate is the top-1 label index, C++ vs reference.
+        # The case's expected_language is informational (the reference itself
+        # may miss it); it is reported, never gated.
+        ref_prediction = ref_dir / "prediction.json"
+        if ref_prediction.exists():
+            cpp_prediction = cpp_dir / "prediction.json"
+            ref_pred = json.loads(ref_prediction.read_text())
+            if not cpp_prediction.exists():
+                print(f"FAIL prediction: missing C++ artifact: {cpp_prediction}", file=sys.stderr)
+                all_passed = False
+                transcript_results.append({"case": case_name, "match": False,
+                                           "reason": "missing C++ prediction artifact"})
+                continue
+            cpp_pred = json.loads(cpp_prediction.read_text())
+            match = int(cpp_pred["label_index"]) == int(ref_pred["label_index"])
+            expected = case.get("expected_language") if isinstance(case, dict) else None
+            transcript_results.append({"case": case_name, "match": match,
+                                       "reference": ref_pred.get("code"), "cpp": cpp_pred.get("code"),
+                                       "expected": expected, "mode": "label"})
+            if not match:
+                print(f"\nFAIL prediction mismatch: reference {ref_pred.get('code')!r} "
+                      f"vs c++ {cpp_pred.get('code')!r}")
+                all_passed = False
+            else:
+                note = "" if expected in (None, cpp_pred.get("code")) else f" (expected {expected!r})"
+                print(f"\n  Prediction: ok {cpp_pred.get('code')!r}{note}")
+
         ref_transcript = ref_dir / "transcript.json"
         if ref_transcript.exists() and args.family != "sortformer":
             transcript_compare = case_transcript_compare(manifest, case)
@@ -860,7 +910,11 @@ def write_report_bundle(
         for tr in transcript_results:
             summary.append(f"### {tr['case']}")
             summary.append("")
-            if tr["match"]:
+            if tr["match"] and tr.get("mode") == "label":
+                summary.append(f"- Match: **yes**")
+                summary.append(f"- Mode: `label` (top-1, C++ vs reference)")
+                summary.append(f"- label: `{tr.get('cpp', '')}` (expected `{tr.get('expected')}`)")
+            elif tr["match"]:
                 summary.append(f"- Match: **yes**")
                 summary.append(f"- Mode: `{tr.get('mode', 'exact')}`")
                 summary.append(f"- text: `{tr.get('cpp', '')!r}`")
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 09903e680..b4307e6cb 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -67,6 +67,10 @@ add_library(transcribe
     arch/sortformer/model.cpp
     arch/sortformer/stream.cpp
     arch/sortformer/weights.cpp
+    arch/ecapa_tdnn/graph.cpp
+    arch/ecapa_tdnn/mel.cpp
+    arch/ecapa_tdnn/model.cpp
+    arch/ecapa_tdnn/weights.cpp
     arch/voxtral/model.cpp
     arch/voxtral/capabilities.cpp
     arch/voxtral/weights.cpp
diff --git a/src/arch/ecapa_tdnn/ecapa_tdnn.h b/src/arch/ecapa_tdnn/ecapa_tdnn.h
new file mode 100644
index 000000000..18bac9b5e
--- /dev/null
+++ b/src/arch/ecapa_tdnn/ecapa_tdnn.h
@@ -0,0 +1,75 @@
+// arch/ecapa_tdnn/ecapa_tdnn.h - ECAPA-TDNN family model and session types.
+//
+// INTERNAL to src/arch/ecapa_tdnn/. Defines the concrete classes deriving
+// from transcribe_model / transcribe_langid_session, plus the family's Arch
+// instance.
+//
+// The family is SpeechBrain's ECAPA-TDNN embedder with the VoxLingua107
+// classifier head on top, serving the LANGID role. One model owns the
+// weights, the GGUF metadata context, the backend buffer, the label table,
+// and a shared (const-after-construction) log-mel front end; one session
+// owns per-call host scratch plus the ggml scheduler inherited from
+// SessionCore.
+
+#pragma once
+
+#include "mel.h"
+#include "transcribe-backend.h"
+#include "transcribe-langid.h"
+#include "transcribe-model.h"
+#include "weights.h"
+
+#include <cstdint>
+#include <memory>
+#include <vector>
+
+struct ggml_context;
+struct ggml_backend_buffer;
+typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+
+namespace transcribe {
+struct Arch;
+}
+
+namespace transcribe::ecapa_tdnn {
+
+struct Model final : public transcribe_model {
+    HParams hparams;
+    Weights weights;
+
+    // GGUF metadata context: owns the ggml_tensor structs the weight slots
+    // above point at. Their data lives in backend_buffer.
+    ggml_context *        ctx_meta       = nullptr;
+    ggml_backend_buffer_t backend_buffer = nullptr;
+    BackendPlan           plan;
+
+    // Built at load from stt.langid.labels.*; immutable after.
+    LangidLabels labels;
+
+    // Front end, built once at load from stt.frontend.* plus the
+    // `frontend.mel_filterbank` tensor. const after construction, so every
+    // session shares it.
+    std::unique_ptr<MelFrontend> mel;
+
+    Model() = default;
+    ~Model() override;
+};
+
+struct Session final : public transcribe_langid_session {
+    // Host scratch, reused across calls.
+    std::vector<float>   mel_buf;                // [T * n_mels], frame-major
+    std::vector<int32_t> idx_buf[kNumSeBlocks];  // reflect indices, [T + 2p]
+
+    // Metadata arena for the per-call graph build, handed to ggml_init as
+    // mem_buffer so a per-call ggml_init does not malloc / free it on every
+    // run. Outlives compute_ctx's use: ggml_free never touches a borrowed
+    // mem_buffer.
+    std::vector<uint8_t> graph_arena;
+
+    // One-shot TRANSCRIBE_ECAPA_GRAPH_STATS reporting latch (see model.cpp).
+    bool logged_graph_stats = false;
+};
+
+extern const Arch arch;
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/graph.cpp b/src/arch/ecapa_tdnn/graph.cpp
new file mode 100644
index 000000000..175e61c2d
--- /dev/null
+++ b/src/arch/ecapa_tdnn/graph.cpp
@@ -0,0 +1,376 @@
+// arch/ecapa_tdnn/graph.cpp - the ECAPA-TDNN forward graph.
+//
+// Mirrors speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN.forward followed by
+// Xvector.Classifier, with the four conversion-time rewrites already applied
+// (scripts/convert-ecapa_tdnn.py): BatchNorm as an affine scale/shift, the three
+// activation-free BatchNorms folded forward into the following linear map,
+// and the ASP / MFA input concatenations split into per-operand weight
+// blocks.
+//
+// Layout: every activation is ggml ne = [C, T]. Reductions over time need
+// ggml's ne[0], so they run on a contiguous transposed copy [T, C] and the
+// result is reshaped back. The three SE transposes and two ASP transposes
+// per pass are negligible next to the 1x1 convolutions.
+//
+// Helper contract (named / linear / bn_affine / reflect_rows / conv_taps):
+// an activation is ggml `ne = [C, T]`, CHANNEL-INNERMOST, frames are rows.
+// That makes
+//   - a 1x1 convolution a plain `ggml_mul_mat(w[IC, OC], x[IC, T])`,
+//   - a bias / BatchNorm vector `[C]` a free broadcast over T,
+//   - reflect padding a single `ggml_get_rows` over the frame axis,
+//   - a dilated k>1 convolution a sum of matmuls against row-slices of the
+//     padded activation, with no im2col and no transposes.
+// Time-axis reductions (means, softmax, weighted sums) act on ggml's ne[0],
+// so the caller transposes to `[T, C]` first.
+//
+//   mel [n_mels, T]
+//     -> blk.0        TDNNBlock  n_mels -> C, k=K0, reflect pad
+//     -> blk.1..3     SERes2Net  C -> C, dilation 2 / 3 / 4
+//     -> mfa          w1@x1 + w2@x2 + w3@x3 + b, ReLU, BN     [3C, T]
+//     -> asp          attentive statistics pooling            [6C]
+//     -> fc           6C -> emb (asp_bn folded in)            [emb]
+//     -> classifier   LeakyReLU, emb -> hid, LeakyReLU, hid -> n_labels
+
+#include "graph.h"
+
+#include "ecapa_tdnn.h"
+#include "ggml.h"
+#include "transcribe-debug.h"
+#include "transcribe-log.h"
+
+#include <cfloat>
+#include <cstddef>
+#include <cstdio>
+#include <string>
+
+namespace transcribe::ecapa_tdnn {
+
+namespace {
+
+// Name a tensor and return it; NULL-safe.
+ggml_tensor * named(ggml_tensor * t, const char * name) {
+    if (t != nullptr && name != nullptr) {
+        ggml_set_name(t, name);
+    }
+    return t;
+}
+
+// y = W x (+ b): w is ne = [IC, OC], x is ne = [IC, T] or [IC]; b may be null.
+ggml_tensor * linear(ggml_context * ctx, ggml_tensor * w, ggml_tensor * x, ggml_tensor * b) {
+    ggml_tensor * y = ggml_mul_mat(ctx, w, x);
+    if (w->type == GGML_TYPE_F16) {
+        // F32 accumulation for F16 weights: the reductions here are 1024 and
+        // 3072 wide and the parity regime is measured with an F32
+        // accumulator, so never let a backend pick the F16 one.
+        ggml_prec_set_acc(y, GGML_PREC_F32);
+    }
+    if (b != nullptr) {
+        y = ggml_add(ctx, y, b);
+    }
+    return y;
+}
+
+// BatchNorm stored as an affine map: y = x * scale + shift, both ne = [C].
+ggml_tensor * bn_affine(ggml_context * ctx, ggml_tensor * x, ggml_tensor * scale, ggml_tensor * shift) {
+    ggml_tensor * y = ggml_mul(ctx, x, scale);
+    y               = ggml_add(ctx, y, shift);
+    return y;
+}
+
+// Reflect padding along the frame axis as one gather. idx is I32 of length
+// T + 2p holding [p, ..., 1, 0, 1, ..., T-1, T-2, ..., T-1-p] (torch "reflect",
+// edge excluded); the result ne = [C, T + 2p] is contiguous. Requires T > p.
+ggml_tensor * reflect_rows(ggml_context * ctx, ggml_tensor * x, ggml_tensor * idx) {
+    return ggml_get_rows(ctx, x, idx);
+}
+
+// Dilated 1-D conv with "same" padding as a sum over taps: xpad is the
+// reflect-padded ne = [IC, T + 2p] (p = dilation * (K - 1) / 2), w the
+// tap-major ne = [IC, OC, K]. y[t] = sum_k w_k . xpad[t + k*dilation], which
+// is torch's Conv1d(padding="same", padding_mode="reflect"). ne = [OC, T].
+ggml_tensor * conv_taps(ggml_context * ctx, ggml_tensor * xpad, ggml_tensor * w, ggml_tensor * b, int dilation, int T) {
+    const int64_t IC = w->ne[0];
+    const int64_t K  = w->ne[2];
+
+    ggml_tensor * acc = nullptr;
+    for (int64_t k = 0; k < K; ++k) {
+        // Tap k's operand: rows [k*d, k*d + T) of the padded activation. The
+        // row stride is xpad->nb[1] and the offset is a whole number of rows,
+        // so the view is contiguous and usable as a matmul src1 everywhere.
+        ggml_tensor * v  = ggml_view_2d(ctx, xpad, IC, T, xpad->nb[1], static_cast<size_t>(k * dilation) * xpad->nb[1]);
+        // Tap k's kernel: the contiguous [IC, OC] slice at ne[2] == k.
+        ggml_tensor * wk = ggml_view_2d(ctx, w, IC, w->ne[1], w->nb[1], static_cast<size_t>(k) * w->nb[2]);
+
+        ggml_tensor * y = ggml_mul_mat(ctx, wk, v);
+        if (w->type == GGML_TYPE_F16) {
+            ggml_prec_set_acc(y, GGML_PREC_F32);
+        }
+        acc = (acc == nullptr) ? y : ggml_add(ctx, acc, y);
+    }
+
+    if (b != nullptr) {
+        acc = ggml_add(ctx, acc, b);
+    }
+    return acc;
+}
+
+// Nodes reserved in the cgraph. The real graph is 566 nodes for the published
+// configuration and is independent of T (TRANSCRIBE_ECAPA_GRAPH_STATS
+// reports it).
+constexpr size_t kGraphSize = 2048;
+
+// Name a tensor, mark it as a graph output so the scheduler cannot reuse its
+// buffer before the post-compute dump pass reads it (a no-op unless
+// TRANSCRIBE_DUMP_DIR is set), and stash the pointer.
+void mark_dump(ggml_tensor *& slot, ggml_tensor * t, const char * name) {
+    named(t, name);
+    debug::mark_tensor_for_dump(t);
+    slot = t;
+}
+
+// One TDNNBlock with k = 1: 1x1 conv -> ReLU -> BatchNorm.
+ggml_tensor * tdnn_1x1(ggml_context * ctx, const TdnnLayer & l, ggml_tensor * x) {
+    ggml_tensor * y = linear(ctx, l.w, x, l.b);
+    y               = ggml_relu(ctx, y);
+    return bn_affine(ctx, y, l.bn.scale, l.bn.shift);
+}
+
+// One TDNNBlock with k > 1 over an already reflect-padded input:
+// dilated conv -> ReLU -> BatchNorm.
+ggml_tensor * tdnn_conv(ggml_context *   ctx,
+                        ggml_tensor *    xpad,
+                        ggml_tensor *    w,
+                        ggml_tensor *    b,
+                        const BnAffine & bn,
+                        int              dilation,
+                        int              T) {
+    ggml_tensor * y = conv_taps(ctx, xpad, w, b, dilation, T);
+    y               = ggml_relu(ctx, y);
+    return bn_affine(ctx, y, bn.scale, bn.shift);
+}
+
+// Mean over the time axis of x ne = [C, T], returned as ne = [C].
+ggml_tensor * mean_over_time(ggml_context * ctx, ggml_tensor * x) {
+    ggml_tensor * xT = ggml_cont(ctx, ggml_transpose(ctx, x));  // [T, C]
+    return ggml_reshape_1d(ctx, ggml_mean(ctx, xT), x->ne[0]);
+}
+
+// SpeechBrain Res2NetBlock with scale S: split the C channels into S chunks
+// of C/S, pass the first through unchanged, and run each remaining chunk
+// through its own dilated conv after adding the previous chunk's output.
+//
+//   y0 = x0
+//   y1 = B0(x1)
+//   yi = B(i-1)(xi + y(i-1))    i = 2 .. S-1
+//   out = concat(y0 .. y(S-1))
+//
+// Chunk views are strided (stride C, width C/S), so each needs a ggml_cont
+// before it can serve as a matmul operand.
+ggml_tensor * res2net(ggml_context *         ctx,
+                      const SeRes2NetBlock & blk,
+                      ggml_tensor *          h,
+                      ggml_tensor *          idx,
+                      int                    dilation,
+                      int                    T,
+                      int                    chunk) {
+    const size_t chunk_bytes = static_cast<size_t>(chunk) * ggml_element_size(h);
+
+    ggml_tensor * acc  = nullptr;  // concatenated output so far
+    ggml_tensor * prev = nullptr;  // y(i-1)
+
+    for (int i = 0; i < kRes2NetScale; ++i) {
+        ggml_tensor * ci =
+            ggml_cont(ctx, ggml_view_2d(ctx, h, chunk, T, h->nb[1], static_cast<size_t>(i) * chunk_bytes));
+
+        ggml_tensor * yi = nullptr;
+        if (i == 0) {
+            yi = ci;
+        } else {
+            ggml_tensor *   in = (i == 1) ? ci : ggml_add(ctx, ci, prev);
+            const Res2Sub & s  = blk.res2[i - 1];
+            yi                 = tdnn_conv(ctx, reflect_rows(ctx, in, idx), s.w, s.b, s.bn, dilation, T);
+        }
+
+        prev = yi;
+        acc  = (acc == nullptr) ? yi : ggml_concat(ctx, acc, yi, 0);
+    }
+
+    return acc;
+}
+
+// SpeechBrain SEBlock: s = mean_T(x) -> 1x1 -> ReLU -> 1x1 -> sigmoid, then
+// x * s broadcast over T.
+ggml_tensor * se_block(ggml_context * ctx, const SeBlock & se, ggml_tensor * x) {
+    ggml_tensor * s = mean_over_time(ctx, x);
+    s               = ggml_relu(ctx, linear(ctx, se.c1_w, s, se.c1_b));
+    s               = ggml_sigmoid(ctx, linear(ctx, se.c2_w, s, se.c2_b));
+    return ggml_mul(ctx, x, s);
+}
+
+}  // namespace
+
+GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
+    GraphBuild gb{};
+
+    const HParams & hp = model.hparams;
+    const Weights & w  = model.weights;
+
+    if (ctx == nullptr || T <= 0) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "ecapa_tdnn graph: invalid arg (ctx=%p, T=%d)", static_cast<void *>(ctx),
+                T);
+        return gb;
+    }
+
+    const int C     = hp.c_block();
+    const int Cm    = hp.c_mfa();
+    const int chunk = hp.c_chunk();
+
+    // reflect_rows needs T > p so the mirrored indices stay in range. The
+    // 500 ms LANGID minimum gives T >= 51 frames; the largest p here is 4.
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        if (T <= hp.pad(i + 1)) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "ecapa_tdnn graph: T=%d is too short for reflect padding %d", T,
+                    hp.pad(i + 1));
+            return gb;
+        }
+    }
+
+    gb.graph = ggml_new_graph_custom(ctx, kGraphSize, /*grads=*/false);
+    if (gb.graph == nullptr) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "ecapa_tdnn graph: ggml_new_graph_custom failed");
+        return gb;
+    }
+
+    // ---- inputs ----------------------------------------------------------
+    gb.mel_in = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.mel_n_mels, T);
+    named(gb.mel_in, "fe.mel.in");
+    ggml_set_input(gb.mel_in);
+
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        char name[32];
+        std::snprintf(name, sizeof(name), "reflect.idx.%d", i);
+        gb.idx[i] = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, T + 2 * hp.pad(i + 1));
+        named(gb.idx[i], name);
+        ggml_set_input(gb.idx[i]);
+    }
+
+    // ---- stage 0: TDNNBlock n_mels -> C ----------------------------------
+    // read_hparams guarantees pad(0) == pad(1), so idx[0] is the right gather.
+    ggml_tensor * x =
+        tdnn_conv(ctx, reflect_rows(ctx, gb.mel_in, gb.idx[0]), w.blk0_w, w.blk0_b, w.blk0_bn, hp.dilations[0], T);
+    mark_dump(gb.dumps.blk0_out, x, "enc.blk.0.out");
+
+    // ---- stages 1..3: SERes2Net ------------------------------------------
+    ggml_tensor * blk_out[kNumSeBlocks] = { nullptr, nullptr, nullptr };
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        const SeRes2NetBlock & blk = w.blocks[i];
+        const int              d   = hp.dilations[static_cast<size_t>(i + 1)];
+
+        ggml_tensor * residual = x;
+
+        ggml_tensor * h = tdnn_1x1(ctx, blk.tdnn1, x);
+        if (i == 0) {
+            mark_dump(gb.dumps.blk1_tdnn1_out, h, "enc.blk.1.tdnn1.out");
+        }
+
+        h = res2net(ctx, blk, h, gb.idx[i], d, T, chunk);
+        if (i == 0) {
+            mark_dump(gb.dumps.blk1_res2_out, h, "enc.blk.1.res2.out");
+        }
+
+        h = tdnn_1x1(ctx, blk.tdnn2, h);
+
+        h = se_block(ctx, blk.se, h);
+        if (i == 0) {
+            mark_dump(gb.dumps.blk1_se_out, h, "enc.blk.1.se.out");
+        }
+
+        x = ggml_add(ctx, h, residual);
+
+        char name[32];
+        std::snprintf(name, sizeof(name), "enc.blk.%d.out", i + 1);
+        mark_dump(gb.dumps.blk_out[i], x, name);
+        blk_out[i] = x;
+    }
+
+    // ---- MFA: 1x1 over concat(blk1, blk2, blk3), split into three blocks --
+    ggml_tensor * m = nullptr;
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        ggml_tensor * part = ggml_mul_mat(ctx, w.mfa_w[i], blk_out[i]);
+        if (w.mfa_w[i]->type == GGML_TYPE_F16) {
+            ggml_prec_set_acc(part, GGML_PREC_F32);
+        }
+        m = (m == nullptr) ? part : ggml_add(ctx, m, part);
+    }
+    m = ggml_add(ctx, m, w.mfa_b);
+    m = ggml_relu(ctx, m);
+    m = bn_affine(ctx, m, w.mfa_bn.scale, w.mfa_bn.shift);
+    mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
+
+    // ---- attentive statistics pooling ------------------------------------
+    //
+    // mean / std are the utterance statistics over T with the uniform
+    // (unpadded) weighting SpeechBrain uses when wav_lens is all-ones; the
+    // variance is the biased 1/T one, and the clamp before the sqrt is
+    // SpeechBrain's `.clamp(eps)` and must not be dropped.
+    ggml_tensor * mT   = ggml_cont(ctx, ggml_transpose(ctx, m));  // [T, Cm]
+    ggml_tensor * mean = ggml_mean(ctx, mT);                      // [1, Cm]
+    ggml_tensor * var  = ggml_mean(ctx, ggml_sqr(ctx, ggml_sub(ctx, mT, mean)));
+    ggml_tensor * sd   = ggml_sqrt(ctx, ggml_clamp(ctx, var, hp.asp_eps, FLT_MAX));
+
+    ggml_tensor * mean1 = ggml_reshape_1d(ctx, mean, Cm);
+    ggml_tensor * sd1   = ggml_reshape_1d(ctx, sd, Cm);
+
+    // The context term wm@mean + ws@std + b is a [att] vector broadcast over
+    // T, so the 3*Cm -> att convolution costs one Cm x T matmul instead of
+    // three.
+    ggml_tensor * cvec = ggml_add(ctx, linear(ctx, w.asp_wm, mean1, nullptr), linear(ctx, w.asp_ws, sd1, nullptr));
+    cvec               = ggml_add(ctx, cvec, w.asp_b);
+
+    ggml_tensor * a = ggml_add(ctx, linear(ctx, w.asp_wx, m, nullptr), cvec);  // [att, T]
+    a               = ggml_relu(ctx, a);
+    a               = bn_affine(ctx, a, w.asp_bn.scale, w.asp_bn.shift);
+    a               = ggml_tanh(ctx, a);
+
+    ggml_tensor * al = linear(ctx, w.asp_attn_w, a, w.asp_attn_b);  // [Cm, T]
+    // Marked BEFORE the transpose: ggml ne = [Cm, T] lands on disk as
+    // [T, Cm], the orientation the reference dumps.
+    mark_dump(gb.dumps.asp_attn_logits, al, "enc.asp.attn_logits");
+
+    ggml_tensor * alT  = ggml_cont(ctx, ggml_transpose(ctx, al));     // [T, Cm]
+    ggml_tensor * attn = ggml_soft_max(ctx, alT);                     // over ne[0] = T
+
+    ggml_tensor * mu  = ggml_sum_rows(ctx, ggml_mul(ctx, attn, mT));  // [1, Cm]
+    ggml_tensor * dev = ggml_sqr(ctx, ggml_sub(ctx, mT, mu));
+    ggml_tensor * sig = ggml_sum_rows(ctx, ggml_mul(ctx, attn, dev));
+    sig               = ggml_sqrt(ctx, ggml_clamp(ctx, sig, hp.asp_eps, FLT_MAX));
+
+    ggml_tensor * pooled =
+        ggml_concat(ctx, ggml_reshape_1d(ctx, mu, Cm), ggml_reshape_1d(ctx, sig, Cm), /*dim=*/0);  // [2*Cm]
+    mark_dump(gb.dumps.asp_out, pooled, "enc.asp.out");
+
+    // ---- embedding head (asp_bn folded into fc) ---------------------------
+    ggml_tensor * emb = linear(ctx, w.fc_w, pooled, w.fc_b);
+    mark_dump(gb.dumps.emb, emb, "enc.emb");
+
+    // ---- classifier -------------------------------------------------------
+    // LeakyReLU comes FIRST, before the BatchNorm that is folded into the
+    // following linear map; getting that order wrong is invisible until the
+    // logits are compared.
+    ggml_tensor * e = ggml_leaky_relu(ctx, emb, hp.leaky_slope, /*inplace=*/false);
+    ggml_tensor * hcls =
+        ggml_leaky_relu(ctx, linear(ctx, w.cls_l1_w, e, w.cls_l1_b), hp.leaky_slope, /*inplace=*/false);
+    mark_dump(gb.dumps.cls_hidden, hcls, "cls.hidden");
+
+    ggml_tensor * z = linear(ctx, w.cls_out_w, hcls, w.cls_out_b);
+    mark_dump(gb.dumps.cls_logits, z, "cls.logits_raw");
+    gb.logits = z;
+
+    ggml_set_output(gb.logits);
+    ggml_build_forward_expand(gb.graph, gb.logits);
+
+    (void) C;
+    return gb;
+}
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/graph.h b/src/arch/ecapa_tdnn/graph.h
new file mode 100644
index 000000000..d0d4db339
--- /dev/null
+++ b/src/arch/ecapa_tdnn/graph.h
@@ -0,0 +1,69 @@
+// arch/ecapa_tdnn/graph.h - ECAPA-TDNN forward graph builder.
+//
+// INTERNAL to src/arch/ecapa_tdnn/. The LANGID ops table's run hook builds
+// and computes this graph once per call.
+//
+// Every activation is ggml `ne = [C, T]` (channel-innermost); see the
+// helper notes at the top of graph.cpp. Topology is independent of T, so the
+// scheduler's allocator settles after the first call.
+
+#pragma once
+
+#include "ggml.h"
+#include "weights.h"
+
+struct ggml_context;
+struct ggml_cgraph;
+struct ggml_tensor;
+
+namespace transcribe::ecapa_tdnn {
+
+struct Model;
+
+// Named intermediates for the numerical-parity harness. Each is a borrowed
+// pointer into the compute context and is already marked as a graph output
+// when TRANSCRIBE_DUMP_DIR is set (otherwise the scheduler may reuse its
+// buffer). The dump names are the contract with the reference dumper
+// (scripts/dump_reference_ecapa_tdnn_speechbrain.py); each tensor's ggml `ne`
+// is reversed on disk, so `ne = [C, T]` lands as the reference's `[T, C]`.
+struct Dumps {
+    ggml_tensor * blk0_out              = nullptr;  // enc.blk.0.out          [C, T]
+    ggml_tensor * blk1_tdnn1_out        = nullptr;  // enc.blk.1.tdnn1.out    [C, T]
+    ggml_tensor * blk1_res2_out         = nullptr;  // enc.blk.1.res2.out     [C, T]
+    ggml_tensor * blk1_se_out           = nullptr;  // enc.blk.1.se.out       [C, T]
+    ggml_tensor * blk_out[kNumSeBlocks] = { nullptr, nullptr, nullptr };
+    // enc.blk.{1,2,3}.out    [C, T]
+    ggml_tensor * mfa_out               = nullptr;  // enc.mfa.out            [Cm, T]
+    // asp.attn's output BEFORE the transpose: ggml ne = [Cm, T] lands on disk
+    // as [T, Cm], which is the orientation the reference dumps. Marking the
+    // transposed copy instead would be a silent SHAPE mismatch.
+    ggml_tensor * asp_attn_logits       = nullptr;  // enc.asp.attn_logits    [Cm, T]
+    ggml_tensor * asp_out               = nullptr;  // enc.asp.out            [2*Cm]
+    ggml_tensor * emb                   = nullptr;  // enc.emb                [emb]
+    ggml_tensor * cls_hidden            = nullptr;  // cls.hidden             [hid]
+    ggml_tensor * cls_logits            = nullptr;  // cls.logits_raw         [n_labels]
+};
+
+struct GraphBuild {
+    // Log-mel input, ne = [n_mels, T] f32. The caller uploads the front end's
+    // frame-major buffer verbatim (frame t's n_mels values contiguous).
+    ggml_tensor * mel_in = nullptr;
+
+    // Reflect-padding gather indices, one per SERes2Net block, I32
+    // ne = [T + 2*pad(i+1)]. Stage 0 reuses idx[0] (read_hparams enforces
+    // pad(0) == pad(1)).
+    ggml_tensor * idx[kNumSeBlocks] = { nullptr, nullptr, nullptr };
+
+    // Output.
+    ggml_tensor * logits = nullptr;  // ne = [n_labels]
+
+    Dumps dumps{};
+
+    ggml_cgraph * graph = nullptr;
+};
+
+// Build the full forward graph for T frames into `ctx` (a fresh no_alloc
+// context). Returns a build with `graph == nullptr` on invalid input.
+GraphBuild build_graph(ggml_context * ctx, const Model & model, int T);
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/mel.cpp b/src/arch/ecapa_tdnn/mel.cpp
new file mode 100644
index 000000000..922f40479
--- /dev/null
+++ b/src/arch/ecapa_tdnn/mel.cpp
@@ -0,0 +1,364 @@
+// arch/ecapa_tdnn/mel.cpp - SpeechBrain log-mel front end.
+//
+// See mel.h for the SpeechBrain pipeline this reproduces and the
+// per-step numerical constants. Implementation notes:
+//
+//   * The FFT is the mixed-radix fp32 routine from transcribe-mel.cpp
+//     (itself lifted from whisper.cpp). n_fft = 400 = 2^4 * 5^2, so it recurses four
+//     radix-2 levels down to 25-point naive DFT leaves. There is no
+//     power-of-two path here: this front end is non-pow2 only.
+//   * Accumulation policy: the window and the FFT run in fp32 (matching
+//     torch's fp32 STFT); the mel matmul, the log, the top-dB max and the
+//     per-bin mean all accumulate in fp64 and round once at storage. That
+//     is strictly more accurate than SpeechBrain's fp32 matmul, which is
+//     the direction we want — the residual is far inside the fe.mel
+//     tolerance.
+//   * Output is frame-major [T, n_mels]. Every frame is written by
+//     exactly one thread into its own slot, so the result is bit-
+//     identical for any thread count.
+
+#include "mel.h"
+
+#include "transcribe-batch-util.h"
+#include "transcribe-log.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <cstring>
+#include <limits>
+#include <vector>
+
+#ifndef M_PI
+#    define M_PI 3.14159265358979323846
+#endif
+
+namespace transcribe::ecapa_tdnn {
+
+namespace {
+
+// Naive O(N^2) DFT leaf for the mixed-radix path. Used when N hits an odd
+// factor during recursive halving (N=400 -> 200 -> 100 -> 50 -> 25 odd ->
+// leaf). Uses the per-frontend fp32 LUT (lut_size divisible by N by
+// construction).
+void dft_naive_f32(const float * in, int N, const float * cos_lut, const float * sin_lut, int lut_size, float * out) {
+    const int stride = lut_size / N;
+    for (int k = 0; k < N; ++k) {
+        float re = 0.0f;
+        float im = 0.0f;
+        for (int n = 0; n < N; ++n) {
+            const int idx = (k * n * stride) % lut_size;
+            re += in[n] * cos_lut[idx];
+            im -= in[n] * sin_lut[idx];
+        }
+        out[2 * k]     = re;
+        out[2 * k + 1] = im;
+    }
+}
+
+// Cooley-Tukey mixed-radix FFT. Recursively halves while N is even and
+// falls back to the naive DFT on odd leaves.
+//
+// Buffer contract:
+//   in    : 2*N floats. First N hold the real input; second N are used as
+//           scratch for the even/odd split at this level.
+//   out   : 8*N floats. The top-level result lands in the first 2*N;
+//           recursion uses the remainder as intermediate output.
+void mixed_radix_fft_f32(float * in, int N, const float * cos_lut, const float * sin_lut, int lut_size, float * out) {
+    if (N == 1) {
+        out[0] = in[0];
+        out[1] = 0.0f;
+        return;
+    }
+    if (N & 1) {
+        dft_naive_f32(in, N, cos_lut, sin_lut, lut_size, out);
+        return;
+    }
+    const int half_N = N / 2;
+    float *   even   = in + N;
+    for (int i = 0; i < half_N; ++i) {
+        even[i] = in[2 * i];
+    }
+    float * even_fft = out + 2 * N;
+    mixed_radix_fft_f32(even, half_N, cos_lut, sin_lut, lut_size, even_fft);
+
+    float * odd = even;
+    for (int i = 0; i < half_N; ++i) {
+        odd[i] = in[2 * i + 1];
+    }
+    float * odd_fft = even_fft + N;
+    mixed_radix_fft_f32(odd, half_N, cos_lut, sin_lut, lut_size, odd_fft);
+
+    const int step = lut_size / N;
+    for (int k = 0; k < half_N; ++k) {
+        const int   idx           = k * step;
+        const float w_re          = cos_lut[idx];
+        const float w_im          = -sin_lut[idx];
+        const float re_odd        = odd_fft[2 * k];
+        const float im_odd        = odd_fft[2 * k + 1];
+        out[2 * k]                = even_fft[2 * k] + w_re * re_odd - w_im * im_odd;
+        out[2 * k + 1]            = even_fft[2 * k + 1] + w_re * im_odd + w_im * re_odd;
+        out[2 * (k + half_N)]     = even_fft[2 * k] - w_re * re_odd + w_im * im_odd;
+        out[2 * (k + half_N) + 1] = even_fft[2 * k + 1] - w_re * im_odd - w_im * re_odd;
+    }
+}
+
+// Twiddle LUT shared by the constructor and the test hook: computed in
+// fp64 for accuracy, stored fp32 to match the FFT precision. lut_size ==
+// n_fft, so every recursion level divides it exactly.
+void build_twiddle_lut(int n_fft, std::vector<float> & cos_lut, std::vector<float> & sin_lut) {
+    cos_lut.resize(static_cast<size_t>(n_fft));
+    sin_lut.resize(static_cast<size_t>(n_fft));
+    for (int i = 0; i < n_fft; ++i) {
+        const double theta              = 2.0 * M_PI * i / static_cast<double>(n_fft);
+        cos_lut[static_cast<size_t>(i)] = static_cast<float>(std::cos(theta));
+        sin_lut[static_cast<size_t>(i)] = static_cast<float>(std::sin(theta));
+    }
+}
+
+}  // namespace
+
+// ---------- MelFrontend ----------
+
+MelFrontend::MelFrontend(const MelConfig & cfg) : cfg_(cfg) {
+    auto reject = [this](const char * field, const char * why) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: invalid config field '%s': %s", field, why);
+        status_ = TRANSCRIBE_ERR_GGUF;
+    };
+
+    if (cfg_.n_fft <= 0 || (cfg_.n_fft & 1) != 0) {
+        // The mixed-radix FFT halves while N is even; an odd n_fft would
+        // degenerate to a single O(N^2) leaf, and n_fft/2 padding would
+        // no longer be exact.
+        reject("n_fft", "must be a positive even integer");
+    }
+    if (cfg_.n_mels <= 0) {
+        reject("n_mels", "must be positive");
+    }
+    if (cfg_.hop_length <= 0) {
+        reject("hop_length", "must be positive");
+    }
+    if (cfg_.sample_rate <= 0) {
+        reject("sample_rate", "must be positive");
+    }
+    if (cfg_.win_length != cfg_.n_fft) {
+        // SpeechBrain passes win_length == n_fft, so there is no
+        // sub-window zero-pad placement to get wrong. Reject rather than
+        // silently guess where the window sits inside the FFT buffer.
+        reject("win_length", "must equal n_fft");
+    }
+    if (cfg_.window_type != "hamming_periodic") {
+        reject("window_type", "only \"hamming_periodic\" is implemented");
+    }
+    if (cfg_.pad_mode != "constant") {
+        reject("pad_mode", "only \"constant\" is implemented");
+    }
+    if (cfg_.normalize != "sentence_mean" && cfg_.normalize != "none") {
+        reject("normalize", "only \"sentence_mean\" and \"none\" are implemented");
+    }
+
+    if (status_ != TRANSCRIBE_OK) {
+        return;
+    }
+
+    n_freq_ = cfg_.n_fft / 2 + 1;
+
+    if (cfg_.filterbank.size() != static_cast<size_t>(cfg_.n_mels) * static_cast<size_t>(n_freq_)) {
+        // The filterbank is checkpoint-provided (`frontend.mel_filterbank`);
+        // SpeechBrain's triangle geometry is never rebuilt here.
+        reject("filterbank", "must hold exactly n_mels * (n_fft/2 + 1) weights");
+        return;
+    }
+
+    // Periodic Hamming: w[n] = 0.54 - 0.46*cos(2*pi*n/N), N = n_fft.
+    // torch.hamming_window defaults to periodic=True. Computed in fp64,
+    // stored fp32 (the STFT runs fp32).
+    window_.resize(static_cast<size_t>(cfg_.n_fft));
+    for (int n = 0; n < cfg_.n_fft; ++n) {
+        const double v                  = 0.54 - 0.46 * std::cos(2.0 * M_PI * n / static_cast<double>(cfg_.n_fft));
+        window_[static_cast<size_t>(n)] = static_cast<float>(v);
+    }
+
+    build_twiddle_lut(cfg_.n_fft, cos_lut_, sin_lut_);
+}
+
+int MelFrontend::n_frames_for(int64_t n_samples) const {
+    if (n_samples < 0 || cfg_.hop_length <= 0) {
+        return 0;
+    }
+    // torch center=True: the signal is padded by n_fft/2 on both sides and
+    // framed at every hop, giving floor(n / hop) + 1 frames. The trailing
+    // frames run into the zero pad; SpeechBrain keeps them.
+    return static_cast<int>(n_samples / static_cast<int64_t>(cfg_.hop_length)) + 1;
+}
+
+transcribe_status MelFrontend::compute(const float *        pcm,
+                                       int64_t              n_samples,
+                                       std::vector<float> & out,
+                                       int &                out_n_frames,
+                                       int                  n_threads) const {
+    if (status_ != TRANSCRIBE_OK) {
+        // Constructor already logged the offending field.
+        return status_;
+    }
+    if (pcm == nullptr) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: pcm is NULL");
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    if (n_samples < 1) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: n_samples = %lld, need at least 1",
+                static_cast<long long>(n_samples));
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+
+    const int n_fft   = cfg_.n_fft;
+    const int hop     = cfg_.hop_length;
+    const int n_mels  = cfg_.n_mels;
+    const int n_freq  = n_freq_;
+    const int pad     = n_fft / 2;
+    const int n_frame = n_frames_for(n_samples);
+
+    // ---- 1. Zero pad (torch center=True, pad_mode="constant") ----
+    //
+    // Frame t reads padded[t*hop .. t*hop + n_fft). The last frame starts
+    // at (n_frame-1)*hop = floor(n/hop)*hop <= n, so it ends at most at
+    // n + n_fft = n + 2*pad — exactly the buffer size, with no slack. The
+    // check below turns an off-by-one into an error instead of a heap
+    // over-read.
+    const size_t padded_len = static_cast<size_t>(n_samples) + 2 * static_cast<size_t>(pad);
+    if (static_cast<size_t>(n_frame - 1) * static_cast<size_t>(hop) + static_cast<size_t>(n_fft) > padded_len) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: internal framing overflow (n_frames=%d, n_samples=%lld)", n_frame,
+                static_cast<long long>(n_samples));
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+
+    std::vector<float> padded(padded_len, 0.0f);
+    std::memcpy(padded.data() + pad, pcm, static_cast<size_t>(n_samples) * sizeof(float));
+
+    // ---- 2. STFT -> power -> mel -> log, per frame ----
+    //
+    // Output frame-major: out[t * n_mels + m]. Each thread owns a disjoint
+    // set of frames (strided assignment keeps the load balanced), so the
+    // result does not depend on the thread count.
+    out.assign(static_cast<size_t>(n_frame) * static_cast<size_t>(n_mels), 0.0f);
+
+    const double log_floor = static_cast<double>(cfg_.log_floor);
+
+    int stft_threads = n_threads;
+    if (stft_threads <= 0) {
+        stft_threads = default_n_threads();
+    }
+    if (stft_threads > n_frame) {
+        stft_threads = std::max(1, n_frame);
+    }
+
+    auto worker = [&](int tid) {
+        std::vector<float> fft_in(2 * static_cast<size_t>(n_fft), 0.0f);
+        std::vector<float> fft_out(8 * static_cast<size_t>(n_fft), 0.0f);
+        std::vector<float> power(static_cast<size_t>(n_freq), 0.0f);
+
+        for (int t = tid; t < n_frame; t += stft_threads) {
+            const size_t start = static_cast<size_t>(t) * static_cast<size_t>(hop);
+            for (int n = 0; n < n_fft; ++n) {
+                fft_in[static_cast<size_t>(n)] =
+                    padded[start + static_cast<size_t>(n)] * window_[static_cast<size_t>(n)];
+            }
+            mixed_radix_fft_f32(fft_in.data(), n_fft, cos_lut_.data(), sin_lut_.data(),
+                                static_cast<int>(cos_lut_.size()), fft_out.data());
+            for (int k = 0; k < n_freq; ++k) {
+                const float re                = fft_out[2 * static_cast<size_t>(k)];
+                const float im                = fft_out[2 * static_cast<size_t>(k) + 1];
+                power[static_cast<size_t>(k)] = re * re + im * im;
+            }
+
+            float * dst = out.data() + static_cast<size_t>(t) * static_cast<size_t>(n_mels);
+            for (int m = 0; m < n_mels; ++m) {
+                const float * fb_row = cfg_.filterbank.data() + static_cast<size_t>(m) * static_cast<size_t>(n_freq);
+                double        sum    = 0.0;
+                for (int k = 0; k < n_freq; ++k) {
+                    sum += static_cast<double>(fb_row[k]) * static_cast<double>(power[static_cast<size_t>(k)]);
+                }
+                // SpeechBrain Filterbank.log_mel: 10*log10(max(x, amin))
+                // with amin = 1e-10. ref_value is 1.0, so the usual
+                // "- 10*log10(ref)" term is exactly zero.
+                if (sum < log_floor) {
+                    sum = log_floor;
+                }
+                dst[m] = static_cast<float>(10.0 * std::log10(sum));
+            }
+        }
+    };
+    run_on_threads(stft_threads, worker);
+
+    const size_t total = out.size();
+
+    // ---- 3. Top-dB floor ----
+    //
+    // Per utterance over time AND frequency, applied BEFORE the mean
+    // normalisation (SpeechBrain clamps inside Filterbank, and
+    // InputNormalization runs afterwards in compute_features).
+    if (cfg_.top_db > 0.0f) {
+        double max_all = -std::numeric_limits<double>::infinity();
+        for (size_t i = 0; i < total; ++i) {
+            const double v = static_cast<double>(out[i]);
+            if (v > max_all) {
+                max_all = v;
+            }
+        }
+        const double floor_val = max_all - static_cast<double>(cfg_.top_db);
+        for (size_t i = 0; i < total; ++i) {
+            if (static_cast<double>(out[i]) < floor_val) {
+                out[i] = static_cast<float>(floor_val);
+            }
+        }
+    }
+
+    // ---- 4. Sentence-mean normalisation ----
+    //
+    // InputNormalization(norm_type="sentence", std_norm=False): subtract
+    // each mel bin's mean over ALL frames. fp64 accumulator, one rounding
+    // at storage.
+    if (cfg_.normalize == "sentence_mean") {
+        std::vector<double> mean(static_cast<size_t>(n_mels), 0.0);
+        for (int t = 0; t < n_frame; ++t) {
+            const float * row = out.data() + static_cast<size_t>(t) * static_cast<size_t>(n_mels);
+            for (int m = 0; m < n_mels; ++m) {
+                mean[static_cast<size_t>(m)] += static_cast<double>(row[m]);
+            }
+        }
+        for (int m = 0; m < n_mels; ++m) {
+            mean[static_cast<size_t>(m)] /= static_cast<double>(n_frame);
+        }
+        for (int t = 0; t < n_frame; ++t) {
+            float * row = out.data() + static_cast<size_t>(t) * static_cast<size_t>(n_mels);
+            for (int m = 0; m < n_mels; ++m) {
+                row[m] = static_cast<float>(static_cast<double>(row[m]) - mean[static_cast<size_t>(m)]);
+            }
+        }
+    }
+
+    out_n_frames = n_frame;
+    return TRANSCRIBE_OK;
+}
+
+// --- test hook ---------------------------------------------------------
+
+void mel_test_fft(const float * in400, float * out_re201, float * out_im201) {
+    constexpr int      kN = 400;
+    std::vector<float> cos_lut;
+    std::vector<float> sin_lut;
+    build_twiddle_lut(kN, cos_lut, sin_lut);
+
+    std::vector<float> fft_in(2 * static_cast<size_t>(kN), 0.0f);
+    std::vector<float> fft_out(8 * static_cast<size_t>(kN), 0.0f);
+    std::memcpy(fft_in.data(), in400, static_cast<size_t>(kN) * sizeof(float));
+
+    mixed_radix_fft_f32(fft_in.data(), kN, cos_lut.data(), sin_lut.data(), kN, fft_out.data());
+
+    for (int k = 0; k <= kN / 2; ++k) {
+        out_re201[k] = fft_out[2 * static_cast<size_t>(k)];
+        out_im201[k] = fft_out[2 * static_cast<size_t>(k) + 1];
+    }
+}
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/mel.h b/src/arch/ecapa_tdnn/mel.h
new file mode 100644
index 000000000..e24cca04d
--- /dev/null
+++ b/src/arch/ecapa_tdnn/mel.h
@@ -0,0 +1,171 @@
+// arch/ecapa_tdnn/mel.h - SpeechBrain log-mel front end (60-bin,
+// sentence-mean normalised) for the ecapa_tdnn family.
+//
+// INTERNAL to src/arch/ecapa_tdnn/. Pure CPU, no ggml dependency:
+// audio in, std::vector<float> mel out; the family graph builder copies
+// the result into a ggml_tensor at the backend boundary.
+//
+// Reproduces SpeechBrain's Fbank pipeline (speechbrain/processing/
+// features.py: STFT -> spectral_magnitude(power=1, log=False) ->
+// Filterbank -> InputNormalization(norm_type="sentence", std_norm=False))
+// as configured by speechbrain/lang-id-voxlingua107-ecapa:
+//
+//   * torch.stft(n_fft=400, hop=160, win_length=400, center=True,
+//     pad_mode="constant", onesided=True) -> 201 bins, T = n/160 + 1.
+//   * Window: torch.hamming_window(400) — PERIODIC,
+//     w[n] = 0.54 - 0.46*cos(2*pi*n/400).
+//   * Power spectrum re^2 + im^2 (SpeechBrain's spectral_magnitude with
+//     power=1 on the squared-modulus tensor).
+//   * Mel: 60 SpeechBrain triangles, applied as a plain [60, 201] matmul.
+//     The filterbank is NOT rebuilt here — its triangle geometry is
+//     SpeechBrain-specific and is dumped by the converter into the GGUF
+//     tensor `frontend.mel_filterbank`. MelConfig::filterbank is required.
+//   * Log: 10*log10(max(x, 1e-10)) (Filterbank.log_mel, amin=1e-10,
+//     ref_value=1.0 so the reference term vanishes).
+//   * Top-dB floor: per utterance over time AND frequency,
+//     x = max(x, max(x) - 80).
+//   * Normalisation: subtract each mel bin's mean over all frames.
+//
+// No pre-emphasis, no dither, no deltas: SpeechBrain's compute_features
+// for this checkpoint applies none of them.
+//
+// Construction is one-shot: the constructor validates the config and
+// precomputes the window and the FFT twiddle LUT. compute() is then a
+// pure function of (config, audio) and is const / thread-safe.
+
+#pragma once
+
+#include "transcribe.h"
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+namespace transcribe::ecapa_tdnn {
+
+// Front-end configuration. Mirrors the stt.frontend.* GGUF metadata the
+// loader reads; the defaults are the real VoxLingua107 values, so a
+// default-constructed MelConfig plus the checkpoint's filterbank gives a
+// working extractor.
+struct MelConfig {
+    int sample_rate = 16000;
+    int n_mels      = 60;
+    int n_fft       = 400;
+    int win_length  = 400;  // must equal n_fft; SpeechBrain uses no sub-window pad
+    int hop_length  = 160;
+
+    // STFT window shape. Only "hamming_periodic" is implemented:
+    // torch.hamming_window(N) with the default periodic=True,
+    // w[n] = 0.54 - 0.46*cos(2*pi*n/N). Any other value is rejected.
+    std::string window_type = "hamming_periodic";
+
+    // STFT input padding. Only "constant" is implemented: torch's
+    // center=True with pad_mode="constant", i.e. n_fft/2 zeros on each
+    // side. Any other value is rejected.
+    std::string pad_mode = "constant";
+
+    // Log floor: 10*log10(max(power, log_floor)).
+    float log_floor = 1e-10f;
+
+    // Per-utterance dynamic-range floor applied over time and frequency:
+    // x = max(x, max_all(x) - top_db). <= 0 disables it.
+    float top_db = 80.0f;
+
+    // Normalisation mode:
+    //   "sentence_mean" — subtract each mel bin's mean over all frames
+    //                     (SpeechBrain InputNormalization, norm_type=
+    //                     "sentence", std_norm=False). Default.
+    //   "none"          — emit the log-mel as-is.
+    std::string normalize = "sentence_mean";
+
+    // Mel filterbank [n_mels * n_freq] row-major MEL-MAJOR (each filter's
+    // n_freq weights contiguous), n_freq = n_fft/2 + 1. REQUIRED: the
+    // front end never rebuilds it, because SpeechBrain's triangle
+    // geometry (symmetric filters whose half-width is the distance to
+    // the left neighbour) is not the librosa/Slaney formula. Supplied by
+    // the loader from the GGUF tensor `frontend.mel_filterbank`.
+    std::vector<float> filterbank;
+};
+
+// Pure C++ log-mel extractor. Construct once, call compute() any number
+// of times. Thread-safety: const after construction; multiple threads may
+// call compute() concurrently.
+class MelFrontend {
+  public:
+    explicit MelFrontend(const MelConfig & cfg);
+
+    // TRANSCRIBE_OK when the config was accepted, else TRANSCRIBE_ERR_GGUF
+    // (the offending field is named in a log message). compute() returns
+    // the same status without touching the audio.
+    transcribe_status status() const { return status_; }
+
+    // Frame count for a given audio length, before calling compute().
+    // Matches torch center=True: floor(n_samples / hop_length) + 1. No
+    // trailing-frame drop — the trailing frames see the zero pad, exactly
+    // as torch does, and SpeechBrain keeps them.
+    int n_frames_for(int64_t n_samples) const;
+
+    // Run the full pipeline. pcm is 16 kHz mono float32 in [-1, 1].
+    //
+    // out is resized to n_frames * n_mels and written FRAME-MAJOR: frame
+    // t's n_mels values are contiguous at out[t * n_mels]. That is
+    // exactly ggml ne = [n_mels, T] and the reference dump layout
+    // [T, 60], so the graph builder can memcpy it and the dumper can
+    // write it without a transpose.
+    //
+    // n_threads controls STFT parallelism: 0 means default_n_threads()
+    // (capped at 8), further capped at n_frames. The result is
+    // bit-identical for any thread count — every frame is computed
+    // independently into its own output slot, and the log / top-dB /
+    // mean passes are single-threaded.
+    //
+    // Returns:
+    //   TRANSCRIBE_OK               normal success.
+    //   TRANSCRIBE_ERR_GGUF         bad config (see status()).
+    //   TRANSCRIBE_ERR_INVALID_ARG  pcm is NULL or n_samples < 1. The
+    //                               500 ms minimum is enforced by the
+    //                               LANGID role dispatcher, not here.
+    transcribe_status compute(const float *        pcm,
+                              int64_t              n_samples,
+                              std::vector<float> & out,
+                              int &                out_n_frames,
+                              int                  n_threads = 0) const;
+
+    // Read-only accessors for unit tests and the parity tool.
+    //
+    // window() and n_freq() are the CONSTRUCTED state and are only valid
+    // when status() == TRANSCRIBE_OK: a rejected config leaves the window
+    // empty and n_freq() at 0, while config() still returns the caller's
+    // struct verbatim. Check status() before trusting either.
+    const std::vector<float> & window() const { return window_; }
+
+    const MelConfig & config() const { return cfg_; }
+
+    int n_freq() const { return n_freq_; }
+
+  private:
+    MelConfig         cfg_;
+    transcribe_status status_ = TRANSCRIBE_OK;
+    int               n_freq_ = 0;  // n_fft/2 + 1
+
+    std::vector<float> window_;     // [n_fft], periodic Hamming, fp32
+
+    // cos/sin LUT for the mixed-radix FFT, indexed by 2*pi*i / n_fft.
+    // Sized to n_fft so every recursion level N (n_fft -> n_fft/2 -> ...
+    // -> odd leaf) divides the LUT exactly and lookups never fall back to
+    // live std::cos / std::sin. Stored fp32 to match the FFT precision
+    // and avoid a per-load cast in the inner butterfly.
+    std::vector<float> cos_lut_;
+    std::vector<float> sin_lut_;
+};
+
+// --- test hook ---------------------------------------------------------
+//
+// Run the production mixed-radix FFT over a 400-sample real input,
+// building the twiddle LUT exactly as MelFrontend's constructor does, and
+// write the 201 one-sided bins. Exists so tests/ecapa_tdnn_mel_unit.cpp can compare
+// the FFT against a naive O(N^2) fp64 DFT without including the .cpp or
+// running the whole pipeline. Not used by the runtime.
+void mel_test_fft(const float * in400, float * out_re201, float * out_im201);
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
new file mode 100644
index 000000000..26753a8b0
--- /dev/null
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -0,0 +1,472 @@
+// arch/ecapa_tdnn/model.cpp - ECAPA-TDNN family handler (LANGID role).
+//
+// load() reads the hyperparameters and label table, builds the tensor
+// catalogue, uploads the weights to the chosen backend, and constructs the
+// shared log-mel front end from the GGUF's own filterbank. The LANGID run
+// hook drives the front end host-side, builds and computes the graph in
+// graph.cpp, and hands one logit per label back to the role dispatcher
+// (transcribe-langid.cpp), which owns the softmax / ranking.
+
+#include "ecapa_tdnn.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#include "ggml.h"
+#include "gguf.h"
+#include "graph.h"
+#include "transcribe-arch.h"
+#include "transcribe-backend.h"
+#include "transcribe-batch-util.h"
+#include "transcribe-debug.h"
+#include "transcribe-env.h"
+#include "transcribe-load-common.h"
+#include "transcribe-loader.h"
+#include "transcribe-log.h"
+#include "transcribe-meta.h"
+#include "weights.h"
+
+#include <algorithm>
+#include <chrono>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace transcribe::ecapa_tdnn {
+
+namespace {
+
+constexpr const char * kTag = "ecapa_tdnn";
+
+// stt.variant is optional in the GGUF; this is the family's only shipped
+// checkpoint and the default when the key is absent.
+constexpr const char k_default_variant[] = "lang-id-voxlingua107-ecapa";
+
+// Nodes reserved in the scheduler. Must be >= the graph's node count; see
+// graph.cpp's kGraphSize. The built graph is 566 nodes and is independent of
+// T, so this is ~3.5x headroom.
+constexpr size_t k_sched_graph_size = 2048;
+
+// Metadata arena for one per-call graph build. The published configuration
+// uses 0.36 MiB of it (TRANSCRIBE_ECAPA_GRAPH_STATS reports the figure; the
+// graph is independent of T); the rest is headroom so a future graph change
+// cannot silently truncate.
+constexpr size_t k_compute_ctx_bytes = 4u * 1024u * 1024u;
+
+int64_t now_us() {
+    using clock = std::chrono::steady_clock;
+    return std::chrono::duration_cast<std::chrono::microseconds>(clock::now().time_since_epoch()).count();
+}
+
+// Frees the gguf_context on every exit path of load().
+struct GgufGuard {
+    gguf_context * ctx = nullptr;
+
+    GgufGuard() = default;
+
+    ~GgufGuard() {
+        if (ctx != nullptr) {
+            gguf_free(ctx);
+        }
+    }
+
+    GgufGuard(const GgufGuard &)             = delete;
+    GgufGuard & operator=(const GgufGuard &) = delete;
+};
+
+// Reflect-padding gather indices for a [C, T] activation:
+//   [p, p-1, ..., 1, 0, 1, ..., T-1, T-2, ..., T-1-p]
+// i.e. torch's "reflect" mode, which mirrors WITHOUT repeating the edge
+// frame. Requires T > p, which the 500 ms audio minimum guarantees.
+void build_reflect_indices(int T, int p, std::vector<int32_t> & out) {
+    out.resize(static_cast<size_t>(T) + 2 * static_cast<size_t>(p));
+    for (int j = 0; j < p; ++j) {
+        out[static_cast<size_t>(j)] = p - j;
+    }
+    for (int t = 0; t < T; ++t) {
+        out[static_cast<size_t>(p + t)] = t;
+    }
+    for (int j = 0; j < p; ++j) {
+        out[static_cast<size_t>(p + T + j)] = T - 2 - j;
+    }
+}
+
+// Read stt.langid.labels.* into a validated label table (H6). Aliases are
+// optional; a present-but-mistyped key is a converter bug.
+transcribe_status read_labels(const gguf_context * gguf, LangidLabels & out) {
+    std::vector<std::string> codes;
+    std::vector<std::string> names;
+    std::vector<std::string> aliases;
+    if (read_string_array_kv(gguf, "stt.langid.labels.codes", codes) != KvResult::Ok ||
+        read_string_array_kv(gguf, "stt.langid.labels.names", names) != KvResult::Ok) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.langid.labels.codes / names missing or not string arrays", kTag);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (read_string_array_kv(gguf, "stt.langid.labels.aliases", aliases) == KvResult::BadType) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.langid.labels.aliases is not a string array", kTag);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    return build_langid_labels(std::move(codes), std::move(names), aliases, kTag, out);
+}
+
+// ---------------------------------------------------------------------------
+// load
+// ---------------------------------------------------------------------------
+
+transcribe_status load(Loader & loader, const transcribe_model_load_params * params, transcribe_model ** out_model) {
+    const int64_t t_load_start = now_us();
+
+    // Owned by RAII until the very end, so a failure (or a throw) anywhere
+    // below, label construction included, frees everything built so far (H4).
+    auto m     = std::make_unique<Model>();
+    m->arch    = &arch;
+    m->variant = loader.variant().empty() ? k_default_variant : loader.variant();
+
+    if (auto st = read_hparams(loader.gguf(), m->hparams); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    const HParams & hp = m->hparams;
+
+    if (auto st = read_labels(loader.gguf(), m->labels); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    if (static_cast<int32_t>(m->labels.codes.size()) != hp.n_labels) {
+        return TRANSCRIBE_ERR_GGUF;  // unreachable: both read the same KV
+    }
+
+    // ---- front end ------------------------------------------------------
+    //
+    // The filterbank is SpeechBrain's own triangle geometry and is never
+    // rebuilt here. It is stored ne = [n_freq, n_mels], so the flat host
+    // vector is already mel-major, which is what MelConfig wants.
+    {
+        const size_t expected = static_cast<size_t>(hp.mel_n_mels) * static_cast<size_t>(hp.n_freq());
+
+        MelConfig cfg;
+        cfg.sample_rate = hp.sample_rate;
+        cfg.n_mels      = hp.mel_n_mels;
+        cfg.n_fft       = hp.mel_n_fft;
+        cfg.win_length  = hp.mel_win;
+        cfg.hop_length  = hp.mel_hop;
+        cfg.window_type = hp.mel_window;
+        cfg.pad_mode    = hp.mel_pad_mode;
+        cfg.log_floor   = hp.mel_log_floor;
+        cfg.top_db      = hp.mel_top_db;
+        cfg.normalize   = hp.mel_normalize;
+
+        const auto rr = load_common::read_f32_tensor_checked(loader.gguf(), loader.path(), "frontend.mel_filterbank",
+                                                             expected, kTag, cfg.filterbank);
+        if (rr != load_common::ReadF32Result::Ok) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
+                    "%s: frontend.mel_filterbank missing or unreadable (expected %zu f32 values)", kTag, expected);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+
+        m->mel = std::make_unique<MelFrontend>(cfg);
+        if (m->mel->status() != TRANSCRIBE_OK) {
+            return m->mel->status();
+        }
+    }
+
+    // ---- weights ---------------------------------------------------------
+    //
+    // Reopen the file with no_alloc so ctx_meta holds tensor structs whose
+    // data pointers are filled in by the backend allocation below.
+    GgufGuard guard;
+    {
+        gguf_init_params init_params{};
+        init_params.no_alloc = true;
+        init_params.ctx      = &m->ctx_meta;
+        guard.ctx            = gguf_init_from_file(loader.path().c_str(), init_params);
+        if (guard.ctx == nullptr) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: failed to reopen \"%s\" for tensor data", kTag,
+                    loader.path().c_str());
+            return TRANSCRIBE_ERR_GGUF;
+        }
+    }
+
+    if (auto st = build_weights(m->ctx_meta, hp, m->weights); st != TRANSCRIBE_OK) {
+        return st;
+    }
+
+    const transcribe_backend_request backend_req = params != nullptr ? params->backend : TRANSCRIBE_BACKEND_AUTO;
+    if (auto st = load_common::init_backends(backend_req, params != nullptr ? params->device : nullptr, kTag, m->plan);
+        st != TRANSCRIBE_OK) {
+        return st;
+    }
+    m->backend         = ggml_backend_name(m->plan.primary);
+    m->primary_backend = m->plan.primary;
+
+    m->backend_buffer = ggml_backend_alloc_ctx_tensors(m->ctx_meta, m->plan.primary);
+    if (m->backend_buffer == nullptr) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: ggml_backend_alloc_ctx_tensors failed", kTag);
+        return TRANSCRIBE_ERR_OOM;
+    }
+    ggml_backend_buffer_set_usage(m->backend_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+
+    if (auto st = load_common::stream_tensor_data(loader.path(), guard.ctx, m->ctx_meta, kTag); st != TRANSCRIBE_OK) {
+        return st;
+    }
+
+    m->roles = TRANSCRIBE_ROLE_LANGID;
+    // The abort callback is honored (transcribe_langid_set_abort_callback).
+    set_feature(m.get(), TRANSCRIBE_FEATURE_CANCELLATION, true);
+    m->t_load_us = now_us() - t_load_start;
+    *out_model   = m.release();
+    return TRANSCRIBE_OK;
+}
+
+// ---------------------------------------------------------------------------
+// forward pass
+// ---------------------------------------------------------------------------
+//
+// On success the returned GraphBuild's tensors are live: they belong to
+// s.compute_ctx, which stays alive until the dispatcher releases the scratch,
+// and their data belongs to the scheduler's allocation for the graph that was
+// just computed. The caller reads outputs with ggml_backend_tensor_get.
+transcribe_status forward(Session & s, const Model & m, const float * pcm, int n_samples, GraphBuild & gb_out) {
+    debug::init();
+
+    const HParams & hp        = m.hparams;
+    const int       n_threads = s.n_threads > 0 ? s.n_threads : default_n_threads();
+
+    // ---- front end (host side) -------------------------------------------
+    const int64_t t_mel_start = now_us();
+    int           T           = 0;
+    if (auto st = m.mel->compute(pcm, n_samples, s.mel_buf, T, n_threads); st != TRANSCRIBE_OK) {
+        return st;
+    }
+    s.t_mel_us = now_us() - t_mel_start;
+
+    if (T <= 0) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: front end produced no frames for %d samples", kTag, n_samples);
+        return TRANSCRIBE_ERR_INPUT_TOO_SHORT;
+    }
+    if (s.poll_abort()) {
+        return TRANSCRIBE_ERR_ABORTED;
+    }
+
+    if (debug::enabled()) {
+        const long long shape[2] = { T, hp.mel_n_mels };
+        debug::dump_host_f32("fe.mel", s.mel_buf.data(), static_cast<long long>(s.mel_buf.size()), shape, 2,
+                             "frontend");
+    }
+
+    // ---- reflect-padding indices ------------------------------------------
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        build_reflect_indices(T, hp.pad(i + 1), s.idx_buf[i]);
+    }
+
+    // ---- graph -------------------------------------------------------------
+    //
+    // A fresh no_alloc metadata context per call: the previous build's tensor
+    // structs are dead the moment the scheduler is reset.
+    if (s.compute_ctx != nullptr) {
+        ggml_free(s.compute_ctx);
+        s.compute_ctx = nullptr;
+    }
+    {
+        if (s.graph_arena.size() != k_compute_ctx_bytes) {
+            s.graph_arena.resize(k_compute_ctx_bytes);
+        }
+        ggml_init_params init_params{};
+        init_params.mem_size   = s.graph_arena.size();
+        init_params.mem_buffer = s.graph_arena.data();
+        init_params.no_alloc   = true;
+        s.compute_ctx          = ggml_init(init_params);
+        if (s.compute_ctx == nullptr) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: compute context allocation failed", kTag);
+            return TRANSCRIBE_ERR_OOM;
+        }
+    }
+
+    GraphBuild gb = build_graph(s.compute_ctx, m, T);
+    if (gb.graph == nullptr || gb.logits == nullptr) {
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    if (s.sched == nullptr) {
+        // Cast away const for the scheduler list only: the scheduler borrows
+        // the model's backends and never mutates the model.
+        auto & backends = const_cast<std::vector<ggml_backend_t> &>(m.plan.scheduler_list);
+        s.sched         = ggml_backend_sched_new(backends.data(), nullptr, static_cast<int>(backends.size()),
+                                                 k_sched_graph_size, /*parallel=*/false, /*op_offload=*/true);
+        if (s.sched == nullptr) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: scheduler allocation failed", kTag);
+            return TRANSCRIBE_ERR_OOM;
+        }
+    }
+    configure_sched_n_threads(s.sched, n_threads);
+
+    ggml_backend_sched_reset(s.sched);
+    if (!ggml_backend_sched_alloc_graph(s.sched, gb.graph)) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: graph allocation failed", kTag);
+        return TRANSCRIBE_ERR_OOM;
+    }
+
+    // ---- graph stats (TRANSCRIBE_ECAPA_GRAPH_STATS=1) ------------------------
+    //
+    // One line per session, after the first allocation: the node count, how
+    // much of the metadata arena the build used, the number of scheduler
+    // splits (more than one means an op fell back to another backend), and
+    // each backend's compute buffer.
+    if (!s.logged_graph_stats && env::flag("TRANSCRIBE_ECAPA_GRAPH_STATS")) {
+        s.logged_graph_stats = true;
+        log_msg(TRANSCRIBE_LOG_LEVEL_DEBUG, "%s: graph T=%d nodes=%d arena=%.2f/%.2f MiB splits=%d threads=%d", kTag, T,
+                ggml_graph_n_nodes(gb.graph), static_cast<double>(ggml_used_mem(s.compute_ctx)) / (1024.0 * 1024.0),
+                static_cast<double>(s.graph_arena.size()) / (1024.0 * 1024.0), ggml_backend_sched_get_n_splits(s.sched),
+                n_threads);
+        for (ggml_backend_t be : m.plan.scheduler_list) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_DEBUG, "%s:   sched buffer [%s] %.2f MiB", kTag, ggml_backend_name(be),
+                    static_cast<double>(ggml_backend_sched_get_buffer_size(s.sched, be)) / (1024.0 * 1024.0));
+        }
+    }
+
+    // ---- inputs -------------------------------------------------------------
+    // The front end writes frame-major [T, n_mels], byte-identical to ggml
+    // ne = [n_mels, T].
+    ggml_backend_tensor_set(gb.mel_in, s.mel_buf.data(), 0, s.mel_buf.size() * sizeof(float));
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        ggml_backend_tensor_set(gb.idx[i], s.idx_buf[i].data(), 0, s.idx_buf[i].size() * sizeof(int32_t));
+    }
+
+    // ---- compute -------------------------------------------------------------
+    const int64_t t_enc_start = now_us();
+    if (const ggml_status gs = ggml_backend_sched_graph_compute(s.sched, gb.graph); gs != GGML_STATUS_SUCCESS) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: graph compute failed (%d)", kTag, static_cast<int>(gs));
+        return TRANSCRIBE_ERR_BACKEND;
+    }
+    s.t_encode_us = now_us() - t_enc_start;
+
+    gb_out = gb;
+    return TRANSCRIBE_OK;
+}
+
+// Write the contract's stage tensors. No-op unless TRANSCRIBE_DUMP_DIR is set.
+void dump_stages(const GraphBuild & gb) {
+    if (!debug::enabled()) {
+        return;
+    }
+
+    auto try_dump = [](const char * name, ggml_tensor * t, const char * stage) {
+        if (t != nullptr) {
+            debug::dump_tensor(name, t, stage);
+        }
+    };
+
+    try_dump("enc.blk.0.out", gb.dumps.blk0_out, "encoder");
+    try_dump("enc.blk.1.tdnn1.out", gb.dumps.blk1_tdnn1_out, "encoder");
+    try_dump("enc.blk.1.res2.out", gb.dumps.blk1_res2_out, "encoder");
+    try_dump("enc.blk.1.se.out", gb.dumps.blk1_se_out, "encoder");
+    try_dump("enc.blk.1.out", gb.dumps.blk_out[0], "encoder");
+    try_dump("enc.blk.2.out", gb.dumps.blk_out[1], "encoder");
+    try_dump("enc.blk.3.out", gb.dumps.blk_out[2], "encoder");
+    try_dump("enc.mfa.out", gb.dumps.mfa_out, "encoder");
+    try_dump("enc.asp.attn_logits", gb.dumps.asp_attn_logits, "encoder");
+    try_dump("enc.asp.out", gb.dumps.asp_out, "encoder");
+    try_dump("enc.emb", gb.dumps.emb, "encoder");
+    try_dump("cls.hidden", gb.dumps.cls_hidden, "classifier");
+    try_dump("cls.logits_raw", gb.dumps.cls_logits, "classifier");
+}
+
+// SpeechBrain's classifier ends in log_softmax; the role needs raw logits, so
+// the log-probabilities are recomputed here purely for the validation dump.
+// Stable form: z - (max + log(sum(exp(z - max)))).
+void dump_log_probs(const std::vector<float> & logits) {
+    if (!debug::enabled() || logits.empty()) {
+        return;
+    }
+
+    const float max_logit = *std::max_element(logits.begin(), logits.end());
+    double      sum       = 0.0;
+    for (const float v : logits) {
+        sum += std::exp(static_cast<double>(v - max_logit));
+    }
+    const double log_z = static_cast<double>(max_logit) + std::log(sum);
+
+    std::vector<float> lp(logits.size());
+    for (size_t i = 0; i < logits.size(); ++i) {
+        lp[i] = static_cast<float>(static_cast<double>(logits[i]) - log_z);
+    }
+
+    const long long shape[1] = { static_cast<long long>(lp.size()) };
+    debug::dump_host_f32("cls.log_probs", lp.data(), static_cast<long long>(lp.size()), shape, 1, "classifier");
+}
+
+// ---------------------------------------------------------------------------
+// LANGID ops
+// ---------------------------------------------------------------------------
+
+const LangidLabels & langid_labels(const transcribe_model * model) {
+    return static_cast<const Model *>(model)->labels;
+}
+
+transcribe_langid_session * langid_new_session() {
+    return new Session();
+}
+
+transcribe_status langid_run(transcribe_langid_session * session,
+                             const float *               pcm,
+                             int                         n_samples,
+                             std::vector<float> &        logits) {
+    auto &       s = static_cast<Session &>(*session);
+    const auto & m = static_cast<const Model &>(*session->model);
+    if (s.poll_abort()) {
+        return TRANSCRIBE_ERR_ABORTED;
+    }
+
+    GraphBuild gb{};
+    if (auto st = forward(s, m, pcm, n_samples, gb); st != TRANSCRIBE_OK) {
+        return st;
+    }
+
+    logits.assign(static_cast<size_t>(m.hparams.n_labels), 0.0f);
+    ggml_backend_tensor_get(gb.logits, logits.data(), 0, logits.size() * sizeof(float));
+
+    dump_stages(gb);
+    dump_log_probs(logits);
+    return TRANSCRIBE_OK;
+}
+
+const LangidOps k_langid_ops = { langid_labels, langid_new_session, langid_run };
+
+}  // namespace
+
+// ---------------------------------------------------------------------------
+// Destructor, registry entry
+// ---------------------------------------------------------------------------
+
+Model::~Model() {
+    mel.reset();
+    if (ctx_meta != nullptr) {
+        ggml_free(ctx_meta);
+    }
+    if (backend_buffer != nullptr) {
+        safe_buffer_free(backend_buffer);
+    }
+    for (auto it = plan.scheduler_list.rbegin(); it != plan.scheduler_list.rend(); ++it) {
+        safe_backend_free(*it);
+    }
+    plan.scheduler_list.clear();
+    plan.primary = nullptr;
+}
+
+const Arch arch = {
+    /* .name             = */ "ecapa_tdnn",
+    /* .load             = */ load,
+    /* .init_context     = */ nullptr,
+    /* .run              = */ nullptr,
+    /* .run_batch        = */ nullptr,
+    /* .stream_validate  = */ nullptr,
+    /* .stream_begin     = */ nullptr,
+    /* .stream_feed      = */ nullptr,
+    /* .stream_finalize  = */ nullptr,
+    /* .stream_reset     = */ nullptr,
+    /* .accepts_ext_kind = */ nullptr,
+    /* .run_validate     = */ nullptr,
+    /* .diarize          = */ nullptr,
+    /* .langid           = */ &k_langid_ops,
+};
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/weights.cpp b/src/arch/ecapa_tdnn/weights.cpp
new file mode 100644
index 000000000..30bf28e0f
--- /dev/null
+++ b/src/arch/ecapa_tdnn/weights.cpp
@@ -0,0 +1,407 @@
+// arch/ecapa_tdnn/weights.cpp - read_hparams + build_weights.
+//
+// read_hparams is deliberately strict: every stt.ecapa_tdnn.* shape the graph
+// builder assumes is checked here, so a converter change that the graph
+// cannot express fails at load with a named key instead of producing silently
+// wrong logits. Every dimension is also bounded, so the products the loader
+// and graph derive from them (tensor sizes, frame counts) cannot overflow.
+
+#include "weights.h"
+
+#include "ggml.h"
+#include "gguf.h"
+#include "transcribe-log.h"
+#include "transcribe-meta.h"
+#include "transcribe-weights-util.h"
+
+#include <cmath>
+#include <cstdio>
+#include <initializer_list>
+#include <string>
+
+namespace transcribe::ecapa_tdnn {
+
+namespace {
+
+// Upper bounds on metadata-derived dimensions. Far above any real ECAPA
+// (C = 1024, n_fft = 400, 107 labels) and small enough that every product
+// the loader and graph form fits comfortably in int64 / size_t.
+constexpr int32_t kMaxChannels = 1 << 16;
+constexpr int32_t kMaxKernel   = 63;
+constexpr int32_t kMaxDilation = 64;
+constexpr int32_t kMaxNfft     = 1 << 14;
+constexpr int32_t kMaxMels     = 1024;
+constexpr int32_t kMaxLabels   = 1 << 16;
+
+constexpr const char * kTag = "ecapa_tdnn";
+
+// GGUF format revision this loader implements. Bumped only when the tensor /
+// KV contract changes incompatibly.
+constexpr int32_t kFormatVersion = 1;
+
+// Required int32 array of exactly `n` entries.
+transcribe_status read_required_i32_array_kv(const gguf_context *   gguf,
+                                             const char *           key,
+                                             size_t                 n,
+                                             std::vector<int32_t> & out) {
+    if (read_int32_array_kv(gguf, key, out) != KvResult::Ok) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: required KV \"%s\" missing or not an int32 array", kTag, key);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (out.size() != n) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: KV \"%s\" has %zu entries, expected %zu", kTag, key, out.size(), n);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    return TRANSCRIBE_OK;
+}
+
+transcribe_status require_string_value(const std::string & got, const char * want, const char * key) {
+    if (got != want) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: unsupported %s \"%s\" (only \"%s\" is implemented)", kTag, key,
+                got.c_str(), want);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    return TRANSCRIBE_OK;
+}
+
+}  // namespace
+
+transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
+    if (gguf == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+
+#define REQ(expr)                             \
+    do {                                      \
+        const transcribe_status _st = (expr); \
+        if (_st != TRANSCRIBE_OK) {           \
+            return _st;                       \
+        }                                     \
+    } while (0)
+
+    // ---- format gate ---------------------------------------------------
+    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.format_version", kTag, hp.format_version));
+    if (hp.format_version != kFormatVersion) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.ecapa_tdnn.format_version is %d, this build implements %d", kTag,
+                hp.format_version, kFormatVersion);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    // ---- front end -----------------------------------------------------
+    REQ(read_required_u32_kv(gguf, "stt.frontend.sample_rate", kTag, hp.sample_rate));
+    REQ(read_required_u32_kv(gguf, "stt.frontend.n_fft", kTag, hp.mel_n_fft));
+    REQ(read_required_u32_kv(gguf, "stt.frontend.hop_length", kTag, hp.mel_hop));
+    REQ(read_required_u32_kv(gguf, "stt.frontend.win_length", kTag, hp.mel_win));
+    REQ(read_required_u32_kv(gguf, "stt.frontend.num_mels", kTag, hp.mel_n_mels));
+    REQ(read_required_string_kv(gguf, "stt.frontend.window", kTag, hp.mel_window));
+    REQ(read_required_string_kv(gguf, "stt.frontend.pad_mode", kTag, hp.mel_pad_mode));
+    REQ(read_required_f32_kv(gguf, "stt.frontend.log_clamp_min", kTag, hp.mel_log_floor));
+    REQ(read_required_f32_kv(gguf, "stt.frontend.top_db", kTag, hp.mel_top_db));
+    REQ(read_required_string_kv(gguf, "stt.frontend.normalize", kTag, hp.mel_normalize));
+
+    REQ(require_string_value(hp.mel_window, "hamming_periodic", "stt.frontend.window"));
+    REQ(require_string_value(hp.mel_pad_mode, "constant", "stt.frontend.pad_mode"));
+    REQ(require_string_value(hp.mel_normalize, "sentence_mean", "stt.frontend.normalize"));
+
+    // ---- embedding network ---------------------------------------------
+    REQ(read_required_i32_array_kv(gguf, "stt.ecapa_tdnn.channels", kNumStages, hp.channels));
+    REQ(read_required_i32_array_kv(gguf, "stt.ecapa_tdnn.kernel_sizes", kNumStages, hp.kernel_sizes));
+    REQ(read_required_i32_array_kv(gguf, "stt.ecapa_tdnn.dilations", kNumStages, hp.dilations));
+    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.res2net_scale", kTag, hp.res2net_scale));
+    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.se_channels", kTag, hp.se_channels));
+    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.attention_channels", kTag, hp.attention_channels));
+    REQ(read_required_f32_kv(gguf, "stt.ecapa_tdnn.asp_eps", kTag, hp.asp_eps));
+    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.embedding_dim", kTag, hp.embedding_dim));
+
+    // ---- classifier ------------------------------------------------------
+    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.classifier_hidden", kTag, hp.classifier_hidden));
+    REQ(read_required_f32_kv(gguf, "stt.ecapa_tdnn.classifier_leaky_slope", kTag, hp.leaky_slope));
+
+    // ---- label count -----------------------------------------------------
+    // The label table itself is built (and validated) by load(); only the
+    // length matters here, for the classifier's output width.
+    {
+        std::vector<std::string> codes;
+        if (read_string_array_kv(gguf, "stt.langid.labels.codes", codes) != KvResult::Ok || codes.empty() ||
+            codes.size() > static_cast<size_t>(kMaxLabels)) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.langid.labels.codes missing, malformed, empty, or too long",
+                    kTag);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+        hp.n_labels = static_cast<int32_t>(codes.size());
+    }
+
+#undef REQ
+
+    // ---- value-domain invariants ----------------------------------------
+    if (hp.sample_rate != 16000) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.frontend.sample_rate is %d; only 16000 is supported", kTag,
+                hp.sample_rate);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (hp.mel_n_fft <= 1 || hp.mel_n_fft > kMaxNfft || hp.mel_hop <= 0 || hp.mel_hop > hp.mel_n_fft ||
+        hp.mel_win <= 0 || hp.mel_n_mels <= 0 || hp.mel_n_mels > kMaxMels) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: front-end dimensions out of range", kTag);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (hp.mel_win != hp.mel_n_fft) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.frontend.win_length (%d) must equal stt.frontend.n_fft (%d)", kTag,
+                hp.mel_win, hp.mel_n_fft);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (!(hp.mel_log_floor > 0.0f) || !(hp.mel_top_db >= 0.0f) || !std::isfinite(hp.mel_top_db)) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.frontend.log_clamp_min must be > 0 and top_db >= 0", kTag);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    for (int i = 0; i < kNumStages; ++i) {
+        const int32_t c = hp.channels[static_cast<size_t>(i)];
+        const int32_t k = hp.kernel_sizes[static_cast<size_t>(i)];
+        const int32_t d = hp.dilations[static_cast<size_t>(i)];
+        if (c <= 0 || c > kMaxChannels || k <= 0 || k > kMaxKernel || d <= 0 || d > kMaxDilation) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stage %d channels/kernel/dilation out of range (%d/%d/%d)", kTag,
+                    i, c, k, d);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+        if (k % 2 == 0) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
+                    "%s: stage %d kernel size %d is even; \"same\" reflect padding needs odd", kTag, i, k);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+    }
+
+    // The MFA stage is a 1x1 convolution over the concatenated block outputs.
+    if (hp.kernel_sizes[kNumStages - 1] != 1 || hp.dilations[kNumStages - 1] != 1) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: MFA stage must be k=1 d=1, got k=%d d=%d", kTag,
+                hp.kernel_sizes[kNumStages - 1], hp.dilations[kNumStages - 1]);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    // The three SERes2Net blocks have no shortcut projection, so every
+    // embedding-block width must be identical.
+    for (int i = 1; i < kNumStages - 1; ++i) {
+        if (hp.channels[static_cast<size_t>(i)] != hp.channels[0]) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
+                    "%s: channels[%d] = %d differs from channels[0] = %d; the SERes2Net "
+                    "residual requires equal widths",
+                    kTag, i, hp.channels[static_cast<size_t>(i)], hp.channels[0]);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+    }
+
+    if (hp.channels[kNumStages - 1] != (kNumSeBlocks * hp.channels[0])) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: MFA channels %d must equal %d x block channels %d", kTag,
+                hp.channels[kNumStages - 1], kNumSeBlocks, hp.channels[0]);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    if (hp.res2net_scale != kRes2NetScale) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: res2net_scale %d is not supported (only %d is implemented)", kTag,
+                hp.res2net_scale, kRes2NetScale);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (hp.channels[0] % hp.res2net_scale != 0) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: channels[0] = %d is not divisible by res2net_scale = %d", kTag,
+                hp.channels[0], hp.res2net_scale);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    // Stage 0 reuses the reflect-index tensor built for the first SERes2Net
+    // block (the graph carries one index input per SERes2Net block), so the
+    // two padding widths have to agree. They do for every published
+    // configuration: k=5 d=1 and k=3 d=2 both give p = 2.
+    if (hp.pad(0) != hp.pad(1)) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
+                "%s: stage 0 reflect padding (%d) differs from stage 1 (%d); this build "
+                "shares one reflect-index input between them",
+                kTag, hp.pad(0), hp.pad(1));
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    for (const int32_t v : { hp.se_channels, hp.attention_channels, hp.embedding_dim, hp.classifier_hidden }) {
+        if (v <= 0 || v > kMaxChannels) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: se/attention/embedding/hidden dimensions out of range", kTag);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+    }
+    if (!(hp.asp_eps > 0.0f) || !std::isfinite(hp.leaky_slope)) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.ecapa_tdnn.asp_eps must be positive (got %g), leaky slope finite",
+                kTag, static_cast<double>(hp.asp_eps));
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    return TRANSCRIBE_OK;
+}
+
+namespace {
+
+using transcribe::weights::find_tensor;
+
+#define GET_F32(slot, name, ...)                                                                    \
+    do {                                                                                            \
+        ggml_tensor * _t = find_tensor(ctx_meta, (name), { GGML_TYPE_F32 }, { __VA_ARGS__ }, kTag); \
+        if (_t == nullptr) {                                                                        \
+            return TRANSCRIBE_ERR_GGUF;                                                             \
+        }                                                                                           \
+        (slot) = _t;                                                                                \
+    } while (0)
+
+#define GET_CONV(slot, name, ...)                                                                                 \
+    do {                                                                                                          \
+        ggml_tensor * _t = find_tensor(ctx_meta, (name), { TRANSCRIBE_QUANT_CONV_TYPES }, { __VA_ARGS__ }, kTag); \
+        if (_t == nullptr) {                                                                                      \
+            return TRANSCRIBE_ERR_GGUF;                                                                           \
+        }                                                                                                         \
+        (slot) = _t;                                                                                              \
+    } while (0)
+
+#define GET_LIN(slot, name, ...)                                                                                    \
+    do {                                                                                                            \
+        ggml_tensor * _t = find_tensor(ctx_meta, (name), { TRANSCRIBE_QUANT_LINEAR_TYPES }, { __VA_ARGS__ }, kTag); \
+        if (_t == nullptr) {                                                                                        \
+            return TRANSCRIBE_ERR_GGUF;                                                                             \
+        }                                                                                                           \
+        (slot) = _t;                                                                                                \
+    } while (0)
+
+std::string cat(const std::string & prefix, const char * suffix) {
+    return prefix + suffix;
+}
+
+// A k=1 TDNNBlock: [in, out] weight, [out] bias, [out] BN scale/shift.
+transcribe_status load_tdnn(ggml_context *      ctx_meta,
+                            const std::string & prefix,
+                            int64_t             in,
+                            int64_t             out,
+                            TdnnLayer &         t) {
+    const std::string n_w     = cat(prefix, ".weight");
+    const std::string n_b     = cat(prefix, ".bias");
+    const std::string n_scale = cat(prefix, ".bn.scale");
+    const std::string n_shift = cat(prefix, ".bn.shift");
+
+    GET_LIN(t.w, n_w.c_str(), in, out);
+    GET_F32(t.b, n_b.c_str(), out);
+    GET_F32(t.bn.scale, n_scale.c_str(), out);
+    GET_F32(t.bn.shift, n_shift.c_str(), out);
+    return TRANSCRIBE_OK;
+}
+
+// One dilated k-tap Res2Net sub-convolution, tap-major ne=[c, c, k].
+transcribe_status load_res2_sub(ggml_context *      ctx_meta,
+                                const std::string & prefix,
+                                int64_t             c,
+                                int64_t             k,
+                                Res2Sub &           s) {
+    const std::string n_w     = cat(prefix, ".conv.weight");
+    const std::string n_b     = cat(prefix, ".conv.bias");
+    const std::string n_scale = cat(prefix, ".bn.scale");
+    const std::string n_shift = cat(prefix, ".bn.shift");
+
+    GET_CONV(s.w, n_w.c_str(), c, c, k);
+    GET_F32(s.b, n_b.c_str(), c);
+    GET_F32(s.bn.scale, n_scale.c_str(), c);
+    GET_F32(s.bn.shift, n_shift.c_str(), c);
+    return TRANSCRIBE_OK;
+}
+
+transcribe_status load_se(ggml_context * ctx_meta, const std::string & prefix, int64_t c, int64_t se, SeBlock & s) {
+    const std::string n_c1_w = cat(prefix, ".c1.weight");
+    const std::string n_c1_b = cat(prefix, ".c1.bias");
+    const std::string n_c2_w = cat(prefix, ".c2.weight");
+    const std::string n_c2_b = cat(prefix, ".c2.bias");
+
+    GET_LIN(s.c1_w, n_c1_w.c_str(), c, se);
+    GET_F32(s.c1_b, n_c1_b.c_str(), se);
+    GET_LIN(s.c2_w, n_c2_w.c_str(), se, c);
+    GET_F32(s.c2_b, n_c2_b.c_str(), c);
+    return TRANSCRIBE_OK;
+}
+
+}  // namespace
+
+transcribe_status build_weights(ggml_context * ctx_meta, const HParams & hp, Weights & w) {
+    if (ctx_meta == nullptr) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+
+    const int64_t n_mels = hp.mel_n_mels;
+    const int64_t n_freq = hp.n_freq();
+    const int64_t C      = hp.c_block();
+    const int64_t Cm     = hp.c_mfa();
+    const int64_t chunk  = hp.c_chunk();
+    const int64_t se     = hp.se_channels;
+    const int64_t att    = hp.attention_channels;
+    const int64_t emb    = hp.embedding_dim;
+    const int64_t hid    = hp.classifier_hidden;
+    const int64_t n_lab  = hp.n_labels;
+    const int64_t k_blk0 = hp.kernel_sizes[0];
+
+    // ---- front end ------------------------------------------------------
+    // Mel-major: each filter's n_freq weights contiguous, so ne[0] = n_freq.
+    GET_F32(w.mel_filters, "frontend.mel_filterbank", n_freq, n_mels);
+
+    // ---- stage 0 --------------------------------------------------------
+    GET_CONV(w.blk0_w, "blk.0.conv.weight", n_mels, C, k_blk0);
+    GET_F32(w.blk0_b, "blk.0.conv.bias", C);
+    GET_F32(w.blk0_bn.scale, "blk.0.bn.scale", C);
+    GET_F32(w.blk0_bn.shift, "blk.0.bn.shift", C);
+
+    // ---- stages 1..3 (SERes2Net) ----------------------------------------
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        SeRes2NetBlock &  blk    = w.blocks[i];
+        const std::string base   = "blk." + std::to_string(i + 1);
+        const int64_t     k_res2 = hp.kernel_sizes[static_cast<size_t>(i + 1)];
+
+        if (auto st = load_tdnn(ctx_meta, base + ".tdnn1", C, C, blk.tdnn1); st != TRANSCRIBE_OK) {
+            return st;
+        }
+        for (int j = 0; j < kRes2NetSubs; ++j) {
+            const std::string p = base + ".res2." + std::to_string(j);
+            if (auto st = load_res2_sub(ctx_meta, p, chunk, k_res2, blk.res2[j]); st != TRANSCRIBE_OK) {
+                return st;
+            }
+        }
+        if (auto st = load_tdnn(ctx_meta, base + ".tdnn2", C, C, blk.tdnn2); st != TRANSCRIBE_OK) {
+            return st;
+        }
+        if (auto st = load_se(ctx_meta, base + ".se", C, se, blk.se); st != TRANSCRIBE_OK) {
+            return st;
+        }
+    }
+
+    // ---- MFA -------------------------------------------------------------
+    GET_LIN(w.mfa_w[0], "mfa.w1.weight", C, Cm);
+    GET_LIN(w.mfa_w[1], "mfa.w2.weight", C, Cm);
+    GET_LIN(w.mfa_w[2], "mfa.w3.weight", C, Cm);
+    GET_F32(w.mfa_b, "mfa.bias", Cm);
+    GET_F32(w.mfa_bn.scale, "mfa.bn.scale", Cm);
+    GET_F32(w.mfa_bn.shift, "mfa.bn.shift", Cm);
+
+    // ---- attentive statistics pooling -------------------------------------
+    GET_LIN(w.asp_wx, "asp.tdnn.x.weight", Cm, att);
+    GET_LIN(w.asp_wm, "asp.tdnn.mean.weight", Cm, att);
+    GET_LIN(w.asp_ws, "asp.tdnn.std.weight", Cm, att);
+    GET_F32(w.asp_b, "asp.tdnn.bias", att);
+    GET_F32(w.asp_bn.scale, "asp.tdnn.bn.scale", att);
+    GET_F32(w.asp_bn.shift, "asp.tdnn.bn.shift", att);
+    GET_LIN(w.asp_attn_w, "asp.attn.weight", att, Cm);
+    GET_F32(w.asp_attn_b, "asp.attn.bias", Cm);
+
+    // ---- embedding head ---------------------------------------------------
+    GET_LIN(w.fc_w, "fc.weight", 2 * Cm, emb);
+    GET_F32(w.fc_b, "fc.bias", emb);
+
+    // ---- classifier -------------------------------------------------------
+    GET_LIN(w.cls_l1_w, "cls.l1.weight", emb, hid);
+    GET_F32(w.cls_l1_b, "cls.l1.bias", hid);
+    GET_LIN(w.cls_out_w, "cls.out.weight", hid, n_lab);
+    GET_F32(w.cls_out_b, "cls.out.bias", n_lab);
+
+    return TRANSCRIBE_OK;
+}
+
+#undef GET_F32
+#undef GET_CONV
+#undef GET_LIN
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/weights.h b/src/arch/ecapa_tdnn/weights.h
new file mode 100644
index 000000000..ffb309263
--- /dev/null
+++ b/src/arch/ecapa_tdnn/weights.h
@@ -0,0 +1,217 @@
+// arch/ecapa_tdnn/weights.h - ECAPA-TDNN hyperparameters, tensor catalogue,
+// and the per-instance weight slots.
+//
+// INTERNAL to src/arch/ecapa_tdnn/.
+//
+// The names and shapes below are the loader contract with
+// scripts/convert-ecapa_tdnn.py. ggml `ne` is fast-to-slow, so a PyTorch
+// Linear [OC, IC] lands as ne = [IC, OC] and a PyTorch Conv1d [OC, IC, K] is
+// stored TAP-MAJOR as ne = [IC, OC, K] (numpy [K, OC, IC]) so every tap is a
+// contiguous [IC, OC] matrix. Names follow the tools/transcribe-quantize
+// rules: `.bias` and `.bn.` stay F32, `.conv.weight` (k>1 kernels) stays
+// F32 / F16, every other `.weight` is a quantizable matmul operand.
+//
+//   frontend.mel_filterbank            F32      ne=[n_freq, n_mels]   mel-major
+//
+//   blk.0.conv.weight                  F32/F16  ne=[n_mels, C, K0]
+//   blk.0.conv.bias                    F32      [C]
+//   blk.0.bn.scale / .shift            F32      [C]
+//
+//   blk.{1,2,3}.tdnn1.weight           quant    ne=[C, C]
+//   blk.{1,2,3}.tdnn1.bias             F32      [C]
+//   blk.{1,2,3}.tdnn1.bn.scale / .shift         [C]
+//   blk.{1,2,3}.res2.{0..S-2}.conv.weight  F32/F16  ne=[C/S, C/S, Ki]
+//   blk.{1,2,3}.res2.{0..S-2}.conv.bias    F32      [C/S]
+//   blk.{1,2,3}.res2.{0..S-2}.bn.scale / .shift     [C/S]
+//   blk.{1,2,3}.tdnn2.weight           quant    ne=[C, C]
+//   blk.{1,2,3}.tdnn2.bias             F32      [C]
+//   blk.{1,2,3}.tdnn2.bn.scale / .shift         [C]
+//   blk.{1,2,3}.se.c1.weight           quant    ne=[C, se]     .bias [se]
+//   blk.{1,2,3}.se.c2.weight           quant    ne=[se, C]     .bias [C]
+//
+//   mfa.w{1,2,3}.weight                quant    ne=[C, Cm]  (Cm = 3C)
+//   mfa.bias                           F32      [Cm]
+//   mfa.bn.scale / .shift              F32      [Cm]
+//
+//   asp.tdnn.{x,mean,std}.weight       quant    ne=[Cm, att]
+//   asp.tdnn.bias                      F32      [att]
+//   asp.tdnn.bn.scale / .shift         F32      [att]
+//   asp.attn.weight                    quant    ne=[att, Cm]   .bias [Cm]
+//
+//   fc.weight                          quant    ne=[2*Cm, emb]  .bias [emb]   (asp_bn folded in)
+//   cls.l1.weight                      quant    ne=[emb, hid]   .bias [hid]   (cls.bn0 folded in)
+//   cls.out.weight                     quant    ne=[hid, n_lab] .bias [n_lab] (cls.bn1 folded in)
+//
+// "quant" is TRANSCRIBE_QUANT_LINEAR_TYPES; k>1 kernels are
+// TRANSCRIBE_QUANT_CONV_TYPES. Every dimension above is derived from HParams
+// so the tiny test fixture and the real checkpoint share one path.
+
+#pragma once
+
+#include "transcribe.h"
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+struct gguf_context;
+struct ggml_context;
+struct ggml_tensor;
+
+namespace transcribe::ecapa_tdnn {
+
+// Number of TDNN stages described by stt.ecapa_tdnn.channels: four embedding
+// blocks (one plain TDNNBlock + three SERes2Net) plus the MFA stage.
+constexpr int kNumStages = 5;
+
+// SERes2Net blocks, i.e. stages 1..3.
+constexpr int kNumSeBlocks = 3;
+
+// Res2Net scale this implementation supports. The per-chunk channel count is
+// derived from HParams (channels[0] / res2net_scale); only the split count
+// itself is pinned, because it fixes the weight-slot array size below.
+constexpr int kRes2NetScale = 8;
+
+// Sub-convolutions inside one Res2Net block: the first chunk is passed
+// through unchanged, so there are scale - 1 of them.
+constexpr int kRes2NetSubs = kRes2NetScale - 1;
+
+// Every stt.* KV the family reads, plus the label count (the length of
+// stt.langid.labels.codes, which sizes the classifier output).
+struct HParams {
+    int32_t format_version = 0;
+    int32_t sample_rate    = 0;
+
+    // Front end (stt.frontend.*).
+    int32_t     mel_n_fft  = 0;
+    int32_t     mel_hop    = 0;
+    int32_t     mel_win    = 0;
+    int32_t     mel_n_mels = 0;
+    std::string mel_window;    // "hamming_periodic"
+    std::string mel_pad_mode;  // "constant"
+    float       mel_log_floor = 0.0f;
+    float       mel_top_db    = 0.0f;
+    std::string mel_normalize;  // "sentence_mean"
+
+    // Embedding network (stt.ecapa_tdnn.*).
+    std::vector<int32_t> channels;      // kNumStages entries
+    std::vector<int32_t> kernel_sizes;  // kNumStages entries
+    std::vector<int32_t> dilations;     // kNumStages entries
+    int32_t              res2net_scale      = 0;
+    int32_t              se_channels        = 0;
+    int32_t              attention_channels = 0;
+    float                asp_eps            = 0.0f;
+    int32_t              embedding_dim      = 0;
+
+    // Classifier (stt.ecapa_tdnn.classifier_*).
+    int32_t classifier_hidden = 0;
+    float   leaky_slope       = 0.0f;
+
+    // Length of stt.langid.labels.codes.
+    int32_t n_labels = 0;
+
+    // ---- derived ------------------------------------------------------
+    int32_t c_block() const { return channels.empty() ? 0 : channels[0]; }
+
+    int32_t c_mfa() const { return channels.size() < kNumStages ? 0 : channels[kNumStages - 1]; }
+
+    int32_t c_chunk() const { return res2net_scale > 0 ? c_block() / res2net_scale : 0; }
+
+    int32_t n_freq() const { return mel_n_fft / 2 + 1; }
+
+    // Reflect padding on each side of stage `i`: d * (k - 1) / 2, the
+    // "same"-padding width SpeechBrain's Conv1d uses.
+    int32_t pad(int i) const {
+        return dilations[static_cast<size_t>(i)] * (kernel_sizes[static_cast<size_t>(i)] - 1) / 2;
+    }
+};
+
+// One TDNNBlock's BatchNorm, stored as an affine map (the converter
+// cannot fold it into the conv: TDNNBlock is conv -> ReLU -> BN).
+struct BnAffine {
+    ggml_tensor * scale = nullptr;
+    ggml_tensor * shift = nullptr;
+};
+
+// A k=1 TDNNBlock: 1x1 conv -> ReLU -> BN.
+struct TdnnLayer {
+    ggml_tensor * w = nullptr;
+    ggml_tensor * b = nullptr;
+    BnAffine      bn;
+};
+
+// One of the scale-1 dilated 3-tap convolutions inside a Res2Net block.
+struct Res2Sub {
+    ggml_tensor * w = nullptr;  // ne=[c_chunk, c_chunk, K]
+    ggml_tensor * b = nullptr;  // [c_chunk]
+    BnAffine      bn;
+};
+
+// Squeeze-and-excitation: mean over T -> 1x1 -> ReLU -> 1x1 -> sigmoid -> scale.
+struct SeBlock {
+    ggml_tensor * c1_w = nullptr;  // ne=[C, se]
+    ggml_tensor * c1_b = nullptr;  // [se]
+    ggml_tensor * c2_w = nullptr;  // ne=[se, C]
+    ggml_tensor * c2_b = nullptr;  // [C]
+};
+
+struct SeRes2NetBlock {
+    TdnnLayer tdnn1;
+    Res2Sub   res2[kRes2NetSubs];
+    TdnnLayer tdnn2;
+    SeBlock   se;
+};
+
+struct Weights {
+    // Front end. Read back to host memory at load time and handed to the
+    // MelFrontend; never touched by the graph.
+    ggml_tensor * mel_filters = nullptr;  // ne=[n_freq, n_mels]
+
+    // Stage 0: plain TDNNBlock, n_mels -> C, k = kernel_sizes[0].
+    ggml_tensor * blk0_w = nullptr;  // ne=[n_mels, C, K0]
+    ggml_tensor * blk0_b = nullptr;  // [C]
+    BnAffine      blk0_bn;
+
+    // Stages 1..3.
+    SeRes2NetBlock blocks[kNumSeBlocks];
+
+    // Multi-layer feature aggregation. The 3C -> 3C 1x1 conv is split into
+    // three C -> 3C blocks applied to the three SERes2Net outputs and summed,
+    // which avoids materialising the concat.
+    ggml_tensor * mfa_w[kNumSeBlocks] = { nullptr, nullptr, nullptr };
+    ggml_tensor * mfa_b               = nullptr;  // [Cm]
+    BnAffine      mfa_bn;
+
+    // Attentive statistics pooling. asp.tdnn's 3*Cm -> att weight is split
+    // into the x / mean / std blocks.
+    ggml_tensor * asp_wx = nullptr;  // ne=[Cm, att]
+    ggml_tensor * asp_wm = nullptr;
+    ggml_tensor * asp_ws = nullptr;
+    ggml_tensor * asp_b  = nullptr;      // [att]
+    BnAffine      asp_bn;
+    ggml_tensor * asp_attn_w = nullptr;  // ne=[att, Cm]
+    ggml_tensor * asp_attn_b = nullptr;  // [Cm]
+
+    // Embedding head (asp_bn folded in).
+    ggml_tensor * fc_w = nullptr;  // ne=[2*Cm, emb]
+    ggml_tensor * fc_b = nullptr;  // [emb]
+
+    // Classifier (cls.bn0 / cls.bn1 folded forward; the LeakyReLUs still run).
+    ggml_tensor * cls_l1_w  = nullptr;  // ne=[emb, hid]
+    ggml_tensor * cls_l1_b  = nullptr;  // [hid]
+    ggml_tensor * cls_out_w = nullptr;  // ne=[hid, n_labels]
+    ggml_tensor * cls_out_b = nullptr;  // [n_labels]
+};
+
+// Read every stt.* KV the family needs and validate the value domain.
+// Anything outside the shapes this implementation can build is rejected with
+// TRANSCRIBE_ERR_GGUF and a log line naming the offending key. Sizes derived
+// from these values are bounded here, so later arithmetic cannot overflow.
+transcribe_status read_hparams(const gguf_context * gguf, HParams & hp);
+
+// Bind every tensor in the catalogue above to a borrowed pointer in
+// `ctx_meta`, validating type and shape against `hp`. On failure the partly
+// built `w` is indeterminate and the caller must discard the model.
+transcribe_status build_weights(ggml_context * ctx_meta, const HParams & hp, Weights & w);
+
+}  // namespace transcribe::ecapa_tdnn
diff --git a/src/transcribe-arch.cpp b/src/transcribe-arch.cpp
index b05e0f228..4487cd98a 100644
--- a/src/transcribe-arch.cpp
+++ b/src/transcribe-arch.cpp
@@ -90,6 +90,10 @@ namespace sortformer {
 extern const Arch arch;
 }
 
+namespace ecapa_tdnn {
+extern const Arch arch;
+}
+
 const Arch * find_arch(const char * name) {
     if (name == nullptr) {
         return nullptr;
@@ -99,7 +103,7 @@ const Arch * find_arch(const char * name) {
         &parakeet::arch,         &cohere::arch,      &canary::arch,     &qwen3_asr::arch,    &voxtral::arch,
         &voxtral_realtime::arch, &canary_qwen::arch, &whisper::arch,    &moonshine::arch,    &moonshine_streaming::arch,
         &sensevoice::arch,       &funasr_nano::arch, &gigaam::arch,     &granite::arch,      &granite_nar::arch,
-        &medasr::arch,           &moss::arch,        &sortformer::arch, &granite5_ctc::arch,
+        &medasr::arch,           &moss::arch,        &sortformer::arch, &granite5_ctc::arch, &ecapa_tdnn::arch,
     };
     constexpr size_t k_n = sizeof(k_archs) / sizeof(k_archs[0]);
 
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 980bf8b23..0f86fbeab 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -83,6 +83,72 @@ add_test(
         -DTRANSCRIBE_SOURCE_DIR=${PROJECT_SOURCE_DIR}
         -P ${CMAKE_CURRENT_SOURCE_DIR}/check_extension_umbrella.cmake)
 
+# -----------------------------------------------------------------------------
+# Synthetic GGUF fixtures (for the loader smoke test)
+# -----------------------------------------------------------------------------
+
+set(TRANSCRIBE_FIXTURE_GENERATOR
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/make_gguf_fixtures.py)
+
+set(TRANSCRIBE_FIXTURE_FILES
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_parakeet.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_sensevoice.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_funasr_nano.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_unknown.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/corrupt_magic.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_v3.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_cache_aware.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_buffered.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_cohere_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_granite5_ctc_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_qwen3_asr_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_labels.gguf)
+
+find_program(TRANSCRIBE_UV uv)
+
+if(TRANSCRIBE_UV)
+    # add_custom_command(OUTPUT ...) makes the .gguf files first-class
+    # build outputs. The build graph treats the generator script as a
+    # source dependency, so the fixtures are regenerated whenever the
+    # script changes — and a failing uv invocation produces a hard
+    # build error rather than a configure-time warning.
+    add_custom_command(
+        OUTPUT  ${TRANSCRIBE_FIXTURE_FILES}
+        COMMAND ${TRANSCRIBE_UV} run --quiet ${TRANSCRIBE_FIXTURE_GENERATOR}
+        DEPENDS ${TRANSCRIBE_FIXTURE_GENERATOR}
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/fixtures
+        COMMENT "Generating synthetic GGUF fixtures"
+        VERBATIM)
+
+    # Aggregate target. The loader test target depends on this; building
+    # `fixtures` directly is the supported way to hand-regenerate.
+    add_custom_target(fixtures
+        DEPENDS ${TRANSCRIBE_FIXTURE_FILES})
+else()
+    message(STATUS
+        "uv not found on PATH; transcribe_loader_smoke will not be "
+        "registered. Install uv (https://docs.astral.sh/uv/) and "
+        "reconfigure to enable.")
+endif()
+
+# -----------------------------------------------------------------------------
+# ecapa_tdnn (LANGID role) fixture smoke: public API only, so it is registered
+# above the shared-library cut and runs against static and shared builds
+# -----------------------------------------------------------------------------
+
+if(TRANSCRIBE_UV)
+    add_executable(transcribe_ecapa_tdnn_smoke ecapa_tdnn_smoke.cpp)
+    target_link_libraries(transcribe_ecapa_tdnn_smoke PRIVATE transcribe)
+    target_compile_definitions(transcribe_ecapa_tdnn_smoke PRIVATE
+        "TRANSCRIBE_TEST_FIXTURES_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/fixtures\"")
+    transcribe_apply_warnings(transcribe_ecapa_tdnn_smoke)
+    add_dependencies(transcribe_ecapa_tdnn_smoke fixtures)
+    add_test(NAME transcribe_ecapa_tdnn_smoke COMMAND transcribe_ecapa_tdnn_smoke)
+endif()
+
 # -----------------------------------------------------------------------------
 # Shared-library build: only the public-ABI smoke is meaningful
 # -----------------------------------------------------------------------------
@@ -343,10 +409,11 @@ add_test(NAME transcribe_run_dispatch_unit
 
 # -----------------------------------------------------------------------------
 # Dispatcher units against fake arches: roles, host thread pool, non-finite
-# input, throwing stream hooks, DIARIZE and LANGID roles
+# input, throwing stream hooks, DIARIZE and LANGID roles, ecapa_tdnn front end
 # -----------------------------------------------------------------------------
 
-foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize_dispatch langid_dispatch)
+foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize_dispatch langid_dispatch
+        ecapa_tdnn_mel)
     add_executable(transcribe_${_t}_unit ${_t}_unit.cpp)
     target_link_libraries(transcribe_${_t}_unit PRIVATE transcribe)
     target_include_directories(transcribe_${_t}_unit PRIVATE ${CMAKE_SOURCE_DIR}/src)
@@ -494,54 +561,6 @@ transcribe_apply_warnings(transcribe_threadpool_oversubscription)
 add_test(NAME transcribe_threadpool_oversubscription
     COMMAND transcribe_threadpool_oversubscription)
 
-# -----------------------------------------------------------------------------
-# Synthetic GGUF fixtures (for the loader smoke test)
-# -----------------------------------------------------------------------------
-
-set(TRANSCRIBE_FIXTURE_GENERATOR
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/make_gguf_fixtures.py)
-
-set(TRANSCRIBE_FIXTURE_FILES
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_parakeet.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_sensevoice.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_funasr_nano.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_unknown.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/corrupt_magic.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_v3.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_cache_aware.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_buffered.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_cohere_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_granite5_ctc_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_qwen3_asr_minimal.gguf)
-
-find_program(TRANSCRIBE_UV uv)
-
-if(TRANSCRIBE_UV)
-    # add_custom_command(OUTPUT ...) makes the .gguf files first-class
-    # build outputs. The build graph treats the generator script as a
-    # source dependency, so the fixtures are regenerated whenever the
-    # script changes — and a failing uv invocation produces a hard
-    # build error rather than a configure-time warning.
-    add_custom_command(
-        OUTPUT  ${TRANSCRIBE_FIXTURE_FILES}
-        COMMAND ${TRANSCRIBE_UV} run --quiet ${TRANSCRIBE_FIXTURE_GENERATOR}
-        DEPENDS ${TRANSCRIBE_FIXTURE_GENERATOR}
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/fixtures
-        COMMENT "Generating synthetic GGUF fixtures"
-        VERBATIM)
-
-    # Aggregate target. The loader test target depends on this; building
-    # `fixtures` directly is the supported way to hand-regenerate.
-    add_custom_target(fixtures
-        DEPENDS ${TRANSCRIBE_FIXTURE_FILES})
-else()
-    message(STATUS
-        "uv not found on PATH; transcribe_loader_smoke will not be "
-        "registered. Install uv (https://docs.astral.sh/uv/) and "
-        "reconfigure to enable.")
-endif()
 
 # -----------------------------------------------------------------------------
 # GGUF loader / arch dispatch smoke
@@ -1360,6 +1379,21 @@ if(TRANSCRIBE_BUILD_REAL_MODEL_TESTS)
         COMMAND transcribe_sortformer_diarize_unit)
     set_tests_properties(transcribe_sortformer_diarize_unit PROPERTIES
         SKIP_RETURN_CODE 77)
+
+    # ecapa_tdnn on the LANGID role: label table, aliases, top-1 on the
+    # committed FLEURS clips, the 30 s crop. Gated by
+    # TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip).
+    add_executable(transcribe_ecapa_tdnn_real_smoke
+        ecapa_tdnn_real_smoke.cpp)
+    target_link_libraries(transcribe_ecapa_tdnn_real_smoke
+        PRIVATE transcribe transcribe-common-example)
+    target_compile_definitions(transcribe_ecapa_tdnn_real_smoke PRIVATE
+        "TRANSCRIBE_TEST_SAMPLES_DIR=\"${CMAKE_SOURCE_DIR}/samples\"")
+    transcribe_apply_warnings(transcribe_ecapa_tdnn_real_smoke)
+    add_test(NAME transcribe_ecapa_tdnn_real_smoke
+        COMMAND transcribe_ecapa_tdnn_real_smoke)
+    set_tests_properties(transcribe_ecapa_tdnn_real_smoke PROPERTIES
+        SKIP_RETURN_CODE 77)
 endif()
 
 # -----------------------------------------------------------------------------
@@ -1548,4 +1582,14 @@ if(TRANSCRIBE_BUILD_EXAMPLES)
             -P ${CMAKE_CURRENT_SOURCE_DIR}/cli_diarize_smoke.cmake)
     set_tests_properties(transcribe_cli_diarize_smoke PROPERTIES
         SKIP_REGULAR_EXPRESSION "SKIP:")
+
+    if(TRANSCRIBE_UV)
+        add_test(NAME transcribe_cli_langid_smoke
+            COMMAND ${CMAKE_COMMAND}
+                -DCLI=$<TARGET_FILE:transcribe-cli>
+                -DMODEL=${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
+                -DWAV=${CMAKE_SOURCE_DIR}/samples/fleurs-en.wav
+                -DTEST_DIR=${CMAKE_CURRENT_BINARY_DIR}/cli-langid-smoke
+                -P ${CMAKE_CURRENT_SOURCE_DIR}/cli_langid_smoke.cmake)
+    endif()
 endif()
diff --git a/tests/cli_langid_smoke.cmake b/tests/cli_langid_smoke.cmake
new file mode 100644
index 000000000..ce2e19a8b
--- /dev/null
+++ b/tests/cli_langid_smoke.cmake
@@ -0,0 +1,67 @@
+# transcribe-cli routes a LANGID model to the language ID path: prints the
+# `language:` / `candidate:` lines, writes the candidates with -o, honours
+# --allow / --top / --max-audio-ms / --repeat, and refuses --batch and
+# --stream-chunk-ms. Structural checks run on the toy fixture (MODEL); with
+# TRANSCRIBE_ECAPA_TDNN_GGUF set, the real model must also call fleurs-en.
+
+file(REMOVE_RECURSE "${TEST_DIR}")
+file(MAKE_DIRECTORY "${TEST_DIR}")
+set(_output "${TEST_DIR}/candidates.txt")
+
+function(run_cli out_var rc_var err_var)
+    execute_process(
+        COMMAND "${CLI}" -q --backend cpu ${ARGN}
+        RESULT_VARIABLE rc
+        OUTPUT_VARIABLE out
+        ERROR_VARIABLE err)
+    set(${out_var} "${out}" PARENT_SCOPE)
+    set(${rc_var} "${rc}" PARENT_SCOPE)
+    set(${err_var} "${err}" PARENT_SCOPE)
+endfunction()
+
+run_cli(out rc err -m "${MODEL}" -o "${_output}" "${WAV}")
+if(NOT rc EQUAL 0 OR NOT out MATCHES "language: (aa|bb|cc|dd|ee) index=[0-4] p=")
+    message(FATAL_ERROR "default run: rc=${rc}\n${out}\n${err}")
+endif()
+file(READ "${_output}" written)
+if(NOT written MATCHES "^candidate: 1 [a-e][a-e] index=[0-4] p=[0-9.]+ p_unrestricted=.*candidate: 5 ")
+    message(FATAL_ERROR "-o wrote unexpected candidates:\n${written}")
+endif()
+
+run_cli(out rc err -m "${MODEL}" --allow bb,dd --repeat 2 "${WAV}")
+if(NOT rc EQUAL 0 OR NOT out MATCHES "language: (bb|dd) " OR out MATCHES "candidate: 3 " OR NOT out MATCHES "2 allowed")
+    message(FATAL_ERROR "--allow bb,dd: rc=${rc}\n${out}\n${err}")
+endif()
+
+run_cli(out rc err -m "${MODEL}" --top 1 "${WAV}")
+if(NOT rc EQUAL 0 OR out MATCHES "candidate: 2 ")
+    message(FATAL_ERROR "--top 1: rc=${rc}\n${out}\n${err}")
+endif()
+
+run_cli(out rc err -m "${MODEL}" --allow zz "${WAV}")
+if(rc EQUAL 0 OR NOT out MATCHES "run: unsupported language")
+    message(FATAL_ERROR "--allow zz: rc=${rc}\n${out}\n${err}")
+endif()
+
+run_cli(out rc err -m "${MODEL}" --max-audio-ms 400 "${WAV}")
+if(rc EQUAL 0 OR NOT err MATCHES "langid session init: invalid argument")
+    message(FATAL_ERROR "--max-audio-ms 400: rc=${rc}\n${out}\n${err}")
+endif()
+
+run_cli(out rc err -m "${MODEL}" --stream-chunk-ms 100 "${WAV}")
+if(rc EQUAL 0 OR NOT err MATCHES "no streaming entry point")
+    message(FATAL_ERROR "--stream-chunk-ms on a langid model: rc=${rc}\n${err}")
+endif()
+
+file(WRITE "${TEST_DIR}/list.txt" "${WAV}\n")
+run_cli(out rc err -m "${MODEL}" --batch "${TEST_DIR}/list.txt")
+if(rc EQUAL 0 OR NOT err MATCHES "--batch is ASR-only")
+    message(FATAL_ERROR "--batch on a langid model: rc=${rc}\n${err}")
+endif()
+
+if(NOT "$ENV{TRANSCRIBE_ECAPA_TDNN_GGUF}" STREQUAL "")
+    run_cli(out rc err -m "$ENV{TRANSCRIBE_ECAPA_TDNN_GGUF}" --top 3 "${WAV}")
+    if(NOT rc EQUAL 0 OR NOT out MATCHES "language: en index=")
+        message(FATAL_ERROR "real model on ${WAV}: rc=${rc}\n${out}\n${err}")
+    endif()
+endif()
diff --git a/tests/ecapa_tdnn_mel_unit.cpp b/tests/ecapa_tdnn_mel_unit.cpp
new file mode 100644
index 000000000..390d81df7
--- /dev/null
+++ b/tests/ecapa_tdnn_mel_unit.cpp
@@ -0,0 +1,449 @@
+// ecapa_tdnn_mel_unit.cpp - hermetic unit tests for the ecapa_tdnn
+// (SpeechBrain) log-mel front end, transcribe::ecapa_tdnn::MelFrontend.
+//
+// Every expected value in this file is either a closed-form constant of
+// the periodic-Hamming / STFT math or is computed in the test itself at
+// fp64. Nothing is read from disk and no model is required, so this test
+// runs everywhere ctest does. The end-to-end comparison against
+// SpeechBrain lives in tests/mel_parity_tool.cpp + scripts/validate.py.
+//
+// What each test pins down (i.e. the bug it would catch):
+//
+//   test_window            periodic vs symmetric Hamming — the classic
+//                          off-by-one that biases every frame slightly.
+//   test_n_frames_for      torch center=True framing: floor(n/hop) + 1,
+//                          no trailing-frame drop.
+//   test_fft               the lifted mixed-radix FFT against a naive
+//                          O(N^2) fp64 DFT: wrong twiddle stride, a bad
+//                          odd-leaf fallback, or a scratch-buffer alias.
+//   test_pipeline_dc       window -> FFT -> power -> filterbank -> log
+//                          wired together with the right scale factor.
+//   test_top_db            the dynamic-range floor, and that it is taken
+//                          over time AND frequency.
+//   test_sentence_mean     per-bin mean subtraction over all frames.
+//   test_thread_invariance bit-identical output for any thread count.
+//   test_errors            config and argument rejection.
+
+#include "arch/ecapa_tdnn/mel.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <random>
+#include <vector>
+
+#ifndef M_PI
+#    define M_PI 3.14159265358979323846
+#endif
+
+namespace {
+
+int g_failures = 0;
+
+#define CHECK(cond)                                                              \
+    do {                                                                         \
+        if (!(cond)) {                                                           \
+            std::fprintf(stderr, "FAIL %s:%d: %s\n", __FILE__, __LINE__, #cond); \
+            ++g_failures;                                                        \
+        }                                                                        \
+    } while (0)
+
+#define CHECK_NEAR(actual, expected, tol)                                                                          \
+    do {                                                                                                           \
+        const double _a = static_cast<double>(actual);                                                             \
+        const double _e = static_cast<double>(expected);                                                           \
+        const double _d = std::fabs(_a - _e);                                                                      \
+        if (!(_d <= (tol))) {                                                                                      \
+            std::fprintf(stderr, "FAIL %s:%d: %s = %.17g, expected %.17g, diff %.6g > %.6g\n", __FILE__, __LINE__, \
+                         #actual, _a, _e, _d, (tol));                                                              \
+            ++g_failures;                                                                                          \
+        }                                                                                                          \
+    } while (0)
+
+constexpr int kNFft  = 400;
+constexpr int kNFreq = 201;
+constexpr int kNMels = 60;
+
+// A synthetic "one-hot" filterbank: mel bin m reads FFT bin 3*m with
+// weight 1 and nothing else (3*59 = 177 < 201). This turns the mel matmul
+// into an identity on a subset of the power spectrum, so the pipeline
+// tests can assert exact closed-form dB values instead of a filterbank
+// convolution. The real SpeechBrain filterbank is never rebuilt in C++;
+// it comes from the GGUF and is validated by the parity tool.
+std::vector<float> one_hot_filterbank() {
+    std::vector<float> fb(static_cast<size_t>(kNMels) * kNFreq, 0.0f);
+    for (int m = 0; m < kNMels; ++m) {
+        fb[static_cast<size_t>(m) * kNFreq + static_cast<size_t>(m) * 3] = 1.0f;
+    }
+    return fb;
+}
+
+// The real VoxLingua107 front-end config, with a caller-supplied
+// filterbank (the tests use the one-hot bank above).
+transcribe::ecapa_tdnn::MelConfig base_config() {
+    transcribe::ecapa_tdnn::MelConfig cfg;
+    cfg.sample_rate = 16000;
+    cfg.n_mels      = kNMels;
+    cfg.n_fft       = kNFft;
+    cfg.win_length  = kNFft;
+    cfg.hop_length  = 160;
+    cfg.filterbank  = one_hot_filterbank();
+    return cfg;
+}
+
+void test_window() {
+    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
+    CHECK(mf.status() == TRANSCRIBE_OK);
+
+    const auto & w = mf.window();
+    CHECK(w.size() == static_cast<size_t>(kNFft));
+    if (w.size() != static_cast<size_t>(kNFft)) {
+        return;
+    }
+
+    // Periodic Hamming, w[n] = 0.54 - 0.46*cos(2*pi*n/400):
+    //   n = 0   -> 0.54 - 0.46          = 0.08
+    //   n = 100 -> 0.54 - 0.46*cos(pi/2)= 0.54
+    //   n = 200 -> 0.54 - 0.46*cos(pi)  = 1.00   (the peak is ON a sample;
+    //                                             a SYMMETRIC Hamming of
+    //                                             length 400 never reaches
+    //                                             1.0 — that is the bug
+    //                                             this line catches)
+    //   n = 300 -> 0.54
+    CHECK_NEAR(w[0], 0.08, 1e-6);
+    CHECK_NEAR(w[100], 0.54, 1e-6);
+    CHECK_NEAR(w[200], 1.0, 1e-6);
+    CHECK_NEAR(w[300], 0.54, 1e-6);
+
+    // w[1] has no closed form; compute the literal at fp64 here so the
+    // test pins the exact formula (2*pi*n/N, not 2*pi*n/(N-1)).
+    const double w1 = 0.54 - 0.46 * std::cos(2.0 * M_PI * 1.0 / 400.0);
+    CHECK_NEAR(w[1], w1, 1e-6);
+    // Cross-check the fp64 expression against its own decimal expansion,
+    // so a bad M_PI or a mistyped denominator cannot make both sides
+    // wrong in the same way.
+    CHECK_NEAR(w1, 0.0800567490584361, 1e-15);
+
+    // Sum over a full period: the cosine term cancels exactly, leaving
+    // 0.54 * N = 216. The measured value is 216.00000069 (400 fp32
+    // roundings of the fp64 window), comfortably inside 1e-6.
+    double sum = 0.0;
+    for (float v : w) {
+        sum += static_cast<double>(v);
+    }
+    CHECK_NEAR(sum, 216.0, 1e-6);
+}
+
+void test_n_frames_for() {
+    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
+
+    // torch center=True: floor(n / hop) + 1, trailing frames kept.
+    CHECK(mf.n_frames_for(16000) == 101);  // 1 s
+    CHECK(mf.n_frames_for(8000) == 51);    // 0.5 s
+    CHECK(mf.n_frames_for(16159) == 101);  // one sample short of the next hop
+    CHECK(mf.n_frames_for(16160) == 102);  // exactly on the next hop
+    CHECK(mf.n_frames_for(1) == 1);        // a single sample still frames
+}
+
+// Straightforward O(N^2) DFT at fp64. Independent of the production LUT
+// and recursion: this is the oracle the mixed-radix FFT is checked
+// against.
+void naive_dft(const float * in, int N, std::vector<double> & re, std::vector<double> & im) {
+    re.assign(static_cast<size_t>(N / 2 + 1), 0.0);
+    im.assign(static_cast<size_t>(N / 2 + 1), 0.0);
+    for (int k = 0; k <= N / 2; ++k) {
+        double sr = 0.0;
+        double si = 0.0;
+        for (int n = 0; n < N; ++n) {
+            const double theta = 2.0 * M_PI * static_cast<double>(k) * static_cast<double>(n) / static_cast<double>(N);
+            sr += static_cast<double>(in[n]) * std::cos(theta);
+            si -= static_cast<double>(in[n]) * std::sin(theta);
+        }
+        re[static_cast<size_t>(k)] = sr;
+        im[static_cast<size_t>(k)] = si;
+    }
+}
+
+void test_fft() {
+    std::mt19937                          rng(20240902u);
+    std::uniform_real_distribution<float> dist(-1.0f, 1.0f);
+
+    for (int trial = 0; trial < 3; ++trial) {
+        std::vector<float> in(static_cast<size_t>(kNFft));
+        for (int i = 0; i < kNFft; ++i) {
+            in[static_cast<size_t>(i)] = dist(rng);
+        }
+
+        std::vector<float> got_re(static_cast<size_t>(kNFreq), 0.0f);
+        std::vector<float> got_im(static_cast<size_t>(kNFreq), 0.0f);
+        transcribe::ecapa_tdnn::mel_test_fft(in.data(), got_re.data(), got_im.data());
+
+        std::vector<double> ref_re;
+        std::vector<double> ref_im;
+        naive_dft(in.data(), kNFft, ref_re, ref_im);
+
+        double max_mag = 0.0;
+        double max_err = 0.0;
+        for (int k = 0; k < kNFreq; ++k) {
+            const double mag = std::hypot(ref_re[static_cast<size_t>(k)], ref_im[static_cast<size_t>(k)]);
+            max_mag          = std::max(max_mag, mag);
+            max_err          = std::max(max_err, std::fabs(static_cast<double>(got_re[static_cast<size_t>(k)]) -
+                                                           ref_re[static_cast<size_t>(k)]));
+            max_err          = std::max(max_err, std::fabs(static_cast<double>(got_im[static_cast<size_t>(k)]) -
+                                                           ref_im[static_cast<size_t>(k)]));
+        }
+        // fp32 FFT vs fp64 DFT: relative to the peak magnitude the
+        // measured error is ~1e-7. 1e-6 keeps 10x headroom for a
+        // different libm while still catching a transform that has gone
+        // meaningfully wrong (a mistyped twiddle stride, a bad odd-leaf
+        // fallback); a looser bound here would guard nothing.
+        CHECK(max_mag > 1.0);
+        CHECK_NEAR(max_err / max_mag, 0.0, 1e-6);
+    }
+}
+
+void test_pipeline_dc() {
+    // Constant 1.0 for 1 s. For any interior frame the windowed signal is
+    // just the window, so the DC bin of the DFT is sum(w) = 216 and the
+    // DC power is 216^2 = 46656 -> 10*log10(46656) = 46.68907502 dB.
+    //
+    // A periodic Hamming has an exactly 3-tap DFT (bins 0, 1 and N-1), so
+    // every other bin is exactly zero in exact arithmetic. The one-hot
+    // filterbank reads bins 3, 6, ..., 177 for mel bins 1..59, all of
+    // which are true zeros: they must land on the log floor,
+    // 10*log10(1e-10) = -100. (Measured fp32 leakage tops out at
+    // -105.4 dB, i.e. 4.6 dB of margin under the floor.)
+    transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+    cfg.normalize                         = "none";
+    cfg.top_db                            = 0.0f;  // disabled
+
+    transcribe::ecapa_tdnn::MelFrontend mf(cfg);
+    CHECK(mf.status() == TRANSCRIBE_OK);
+
+    std::vector<float> pcm(16000, 1.0f);
+    std::vector<float> out;
+    int                n_frames = 0;
+    CHECK(mf.compute(pcm.data(), 16000, out, n_frames, 1) == TRANSCRIBE_OK);
+    CHECK(n_frames == 101);
+    CHECK(out.size() == static_cast<size_t>(101) * kNMels);
+    if (out.size() != static_cast<size_t>(101) * kNMels) {
+        return;
+    }
+
+    const float * frame50 = out.data() + static_cast<size_t>(50) * kNMels;
+    CHECK_NEAR(frame50[0], 46.68907502, 1e-3);
+    for (int m = 1; m < kNMels; ++m) {
+        CHECK_NEAR(frame50[m], -100.0, 1e-3);
+    }
+}
+
+void test_top_db() {
+    // Same input, top_db enabled. The global maximum over time and
+    // frequency is the interior-frame DC value 46.68907502, so the floor
+    // is 46.68907502 - 80 = -33.31092498 and every -100 dB bin rises to
+    // it.
+    transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+    cfg.normalize                         = "none";
+    cfg.top_db                            = 80.0f;
+
+    transcribe::ecapa_tdnn::MelFrontend mf(cfg);
+    CHECK(mf.status() == TRANSCRIBE_OK);
+
+    std::vector<float> pcm(16000, 1.0f);
+    std::vector<float> out;
+    int                n_frames = 0;
+    CHECK(mf.compute(pcm.data(), 16000, out, n_frames, 1) == TRANSCRIBE_OK);
+    CHECK(n_frames == 101);
+    if (out.size() != static_cast<size_t>(101) * kNMels) {
+        return;
+    }
+
+    const float * frame50 = out.data() + static_cast<size_t>(50) * kNMels;
+    CHECK_NEAR(frame50[0], 46.68907502, 1e-3);
+    for (int m = 1; m < kNMels; ++m) {
+        CHECK_NEAR(frame50[m], -33.31092498, 1e-3);
+    }
+
+    // Nothing anywhere may sit below the floor.
+    for (float v : out) {
+        CHECK(static_cast<double>(v) >= -33.31092498 - 1e-3);
+    }
+}
+
+void test_sentence_mean() {
+    // 1 kHz sine, 1 s. After sentence-mean normalisation every mel bin's
+    // mean over frames is zero by construction — this checks the pass runs
+    // over the right axis (per bin over frames, not per frame over bins).
+    transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+    cfg.normalize                         = "sentence_mean";
+
+    transcribe::ecapa_tdnn::MelFrontend mf(cfg);
+    CHECK(mf.status() == TRANSCRIBE_OK);
+
+    std::vector<float> pcm(16000);
+    for (int i = 0; i < 16000; ++i) {
+        pcm[static_cast<size_t>(i)] = static_cast<float>(std::sin(2.0 * M_PI * 1000.0 * i / 16000.0));
+    }
+
+    std::vector<float> out;
+    int                n_frames = 0;
+    CHECK(mf.compute(pcm.data(), 16000, out, n_frames, 1) == TRANSCRIBE_OK);
+    CHECK(n_frames == 101);
+    if (out.size() != static_cast<size_t>(n_frames) * kNMels) {
+        return;
+    }
+
+    for (int m = 0; m < kNMels; ++m) {
+        double sum = 0.0;
+        for (int t = 0; t < n_frames; ++t) {
+            sum += static_cast<double>(out[static_cast<size_t>(t) * kNMels + static_cast<size_t>(m)]);
+        }
+        CHECK_NEAR(sum / n_frames, 0.0, 1e-4);
+    }
+
+    // A 1 kHz tone must not be flat: if the mean pass had subtracted the
+    // wrong axis the per-frame spread would collapse.
+    double spread = 0.0;
+    for (float v : out) {
+        spread = std::max(spread, std::fabs(static_cast<double>(v)));
+    }
+    CHECK(spread > 1.0);
+
+    // Degenerate T=1 (fewer samples than one hop): the single frame is
+    // its own mean, so the output is all zeros. No divide-by-zero, unlike
+    // a variance-normalising front end.
+    std::vector<float> tiny_out;
+    int                tiny_frames = 0;
+    CHECK(mf.compute(pcm.data(), 100, tiny_out, tiny_frames, 1) == TRANSCRIBE_OK);
+    CHECK(tiny_frames == 1);
+    CHECK(tiny_out.size() == static_cast<size_t>(kNMels));
+    for (float v : tiny_out) {
+        CHECK(v == 0.0f);
+    }
+}
+
+void test_thread_invariance() {
+    // 3 s of fixed-seed noise, computed single-threaded and with 4
+    // threads. Every frame is written by exactly one thread into its own
+    // output slot and the log / top-dB / mean passes are single-threaded,
+    // so the results must be bit-identical, not merely close.
+    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
+    CHECK(mf.status() == TRANSCRIBE_OK);
+
+    std::mt19937                          rng(987654321u);
+    std::uniform_real_distribution<float> dist(-0.5f, 0.5f);
+    std::vector<float>                    pcm(48000);
+    for (auto & v : pcm) {
+        v = dist(rng);
+    }
+
+    std::vector<float> out1;
+    std::vector<float> out4;
+    int                frames1 = 0;
+    int                frames4 = 0;
+    CHECK(mf.compute(pcm.data(), 48000, out1, frames1, 1) == TRANSCRIBE_OK);
+    CHECK(mf.compute(pcm.data(), 48000, out4, frames4, 4) == TRANSCRIBE_OK);
+    CHECK(frames1 == 301);
+    CHECK(frames1 == frames4);
+    CHECK(out1.size() == out4.size());
+    if (out1.size() == out4.size() && !out1.empty()) {
+        CHECK(std::memcmp(out1.data(), out4.data(), out1.size() * sizeof(float)) == 0);
+    }
+}
+
+void test_errors() {
+    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
+    std::vector<float>                  out;
+    int                                 n_frames = 0;
+    std::vector<float>                  pcm(16000, 0.1f);
+
+    CHECK(mf.compute(nullptr, 16000, out, n_frames, 1) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(mf.compute(pcm.data(), 0, out, n_frames, 1) == TRANSCRIBE_ERR_INVALID_ARG);
+    CHECK(mf.compute(pcm.data(), -5, out, n_frames, 1) == TRANSCRIBE_ERR_INVALID_ARG);
+    // One sample is legal: the 500 ms minimum is an API-layer rule, not a
+    // front-end one.
+    CHECK(mf.compute(pcm.data(), 1, out, n_frames, 1) == TRANSCRIBE_OK);
+    CHECK(n_frames == 1);
+
+    // Rejected configs. Each must set status() AND make compute() fail
+    // without touching the audio.
+    auto expect_rejected = [&](transcribe::ecapa_tdnn::MelConfig cfg, const char * what) {
+        transcribe::ecapa_tdnn::MelFrontend bad(cfg);
+        if (bad.status() != TRANSCRIBE_ERR_GGUF) {
+            std::fprintf(stderr, "FAIL %s:%d: config '%s' was accepted\n", __FILE__, __LINE__, what);
+            ++g_failures;
+            return;
+        }
+        std::vector<float> bad_out;
+        int                bad_frames = 0;
+        CHECK(bad.compute(pcm.data(), 16000, bad_out, bad_frames, 1) == TRANSCRIBE_ERR_GGUF);
+    };
+
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.window_type                       = "hann_periodic";
+        expect_rejected(cfg, "window_type");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.pad_mode                          = "reflect";
+        expect_rejected(cfg, "pad_mode");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.normalize                         = "per_feature";
+        expect_rejected(cfg, "normalize");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.win_length                        = 320;
+        expect_rejected(cfg, "win_length != n_fft");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.filterbank.clear();
+        expect_rejected(cfg, "empty filterbank");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.filterbank.resize(cfg.filterbank.size() - 1);
+        expect_rejected(cfg, "short filterbank");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.n_fft                             = 401;
+        cfg.win_length                        = 401;
+        expect_rejected(cfg, "odd n_fft");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.hop_length                        = 0;
+        expect_rejected(cfg, "hop_length");
+    }
+    {
+        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
+        cfg.n_mels                            = 0;
+        expect_rejected(cfg, "n_mels");
+    }
+}
+
+}  // namespace
+
+int main() {
+    test_window();
+    test_n_frames_for();
+    test_fft();
+    test_pipeline_dc();
+    test_top_db();
+    test_sentence_mean();
+    test_thread_invariance();
+    test_errors();
+
+    if (g_failures > 0) {
+        std::fprintf(stderr, "mel_unit: %d failures\n", g_failures);
+        return EXIT_FAILURE;
+    }
+    std::fprintf(stdout, "mel_unit: ok\n");
+    return EXIT_SUCCESS;
+}
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
new file mode 100644
index 000000000..339704cb0
--- /dev/null
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -0,0 +1,138 @@
+// ecapa_tdnn_real_smoke.cpp - the real VoxLingua107 ECAPA-TDNN GGUF through
+// the public LANGID API: label table, aliases, top-1 on committed FLEURS
+// clips, the crop on a long clip, and a short clip just over the minimum.
+// Gated by TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip). CPU backend.
+
+#include "transcribe.h"
+#include "transcribe/langid.h"
+#include "wav.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <string>
+#include <vector>
+
+namespace {
+
+int g_failures = 0;
+
+#define CHECK(cond)                                                              \
+    do {                                                                         \
+        if (!(cond)) {                                                           \
+            std::fprintf(stderr, "FAIL %s:%d: %s\n", __FILE__, __LINE__, #cond); \
+            ++g_failures;                                                        \
+        }                                                                        \
+    } while (0)
+
+bool load_sample(const char * name, std::vector<float> & pcm) {
+    std::string err;
+    if (!transcribe_cli::load_wav_mono_16k(std::string(TRANSCRIBE_TEST_SAMPLES_DIR) + "/" + name, pcm, err)) {
+        std::fprintf(stderr, "FAIL: %s: %s\n", name, err.c_str());
+        ++g_failures;
+        return false;
+    }
+    return true;
+}
+
+// Top-1 code and p for one clip, or "" when the run fails.
+std::string top1(transcribe_langid_session * s, const char * wav, float * p_out, int64_t * audio_ms = nullptr) {
+    std::vector<float> pcm;
+    if (!load_sample(wav, pcm)) {
+        return "";
+    }
+    const transcribe_status st = transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr);
+    if (st != TRANSCRIBE_OK) {
+        std::fprintf(stderr, "FAIL: %s: run: %s\n", wav, transcribe_status_string(st));
+        ++g_failures;
+        return "";
+    }
+    transcribe_langid_result r;
+    transcribe_langid_result_init(&r);
+    transcribe_langid_get_result(s, &r);
+    if (audio_ms != nullptr) {
+        *audio_ms = r.audio_ms;
+    }
+    transcribe_langid_candidate c;
+    transcribe_langid_candidate_init(&c);
+    transcribe_langid_get_candidate(s, 0, &c);
+    *p_out = c.p;
+    return c.code != nullptr ? c.code : "";
+}
+
+}  // namespace
+
+int main() {
+    const char * path = std::getenv("TRANSCRIBE_ECAPA_TDNN_GGUF");
+    if (path == nullptr || path[0] == '\0') {
+        std::fprintf(stderr, "ecapa_tdnn_real_smoke: TRANSCRIBE_ECAPA_TDNN_GGUF not set; skipping.\n");
+        return 77;
+    }
+    transcribe_log_set(nullptr, nullptr);
+
+    transcribe_model_load_params mp;
+    transcribe_model_load_params_init(&mp);
+    mp.backend                  = TRANSCRIBE_BACKEND_CPU;
+    transcribe_model *      m   = nullptr;
+    const transcribe_status lst = transcribe_model_load_file(path, &mp, &m);
+    if (lst != TRANSCRIBE_OK) {
+        std::fprintf(stderr, "FAIL: load %s: %s\n", path, transcribe_status_string(lst));
+        return EXIT_FAILURE;
+    }
+
+    CHECK(transcribe_model_roles(m) == TRANSCRIBE_ROLE_LANGID);
+    transcribe_langid_info info;
+    transcribe_langid_info_init(&info);
+    CHECK(transcribe_langid_get_info(m, &info) == TRANSCRIBE_OK);
+    CHECK(info.n_labels == 107 && info.sample_rate == 16000 && info.min_audio_ms == 500);
+    // Modern ISO codes alias the VoxLingua107 legacy labels.
+    CHECK(transcribe_langid_label_index(m, "he") == transcribe_langid_label_index(m, "iw"));
+    CHECK(transcribe_langid_label_index(m, "nb") == transcribe_langid_label_index(m, "no"));
+    CHECK(transcribe_langid_label_index(m, "he") >= 0);
+    const int en = transcribe_langid_label_index(m, "en");
+    CHECK(en >= 0 && std::strcmp(transcribe_langid_label_code(m, en), "en") == 0);
+
+    transcribe_langid_session_params sp;
+    transcribe_langid_session_params_init(&sp);
+    transcribe_langid_session * s = nullptr;
+    CHECK(transcribe_langid_session_init(m, &sp, &s) == TRANSCRIBE_OK);
+
+    const char * cases[][2] = {
+        { "fleurs-en.wav", "en" },
+        { "fleurs-de.wav", "de" },
+        { "fleurs-fr.wav", "fr" },
+        { "fleurs-es.wav", "es" },
+        { "fleurs-ja.wav", "ja" },
+        { "fleurs-zh.wav", "zh" },
+        { "fleurs-ru.wav", "ru" },
+        { "fleurs-id.wav", "id" },
+    };
+    for (const auto & c : cases) {
+        float             p    = 0.0f;
+        const std::string code = top1(s, c[0], &p);
+        if (code != c[1] || !(p >= 0.5f)) {
+            std::fprintf(stderr, "FAIL: %s: top-1 %s (p=%.3f), want %s\n", c[0], code.c_str(), p, c[1]);
+            ++g_failures;
+        }
+    }
+
+    // Long input is scored on its last 30 s; a clip just over the minimum
+    // still runs.
+    float   p        = 0.0f;
+    int64_t audio_ms = 0;
+    top1(s, "long-45s.wav", &p, &audio_ms);
+    CHECK(audio_ms == 30000);
+    top1(s, "short-800ms.wav", &p, &audio_ms);
+    CHECK(audio_ms == 800 && std::isfinite(p));
+
+    transcribe_langid_session_free(s);
+    transcribe_model_free(m);
+
+    if (g_failures != 0) {
+        std::fprintf(stderr, "%d failure(s)\n", g_failures);
+        return EXIT_FAILURE;
+    }
+    std::printf("ecapa_tdnn_real_smoke: ok\n");
+    return EXIT_SUCCESS;
+}
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
new file mode 100644
index 000000000..fe60f4970
--- /dev/null
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -0,0 +1,259 @@
+// ecapa_tdnn_smoke.cpp - end-to-end smoke of the ecapa_tdnn family (LANGID
+// role) against tiny synthetic GGUFs, through the PUBLIC C API only, so it
+// runs the same against static, shared and installed builds.
+//
+// arch_ecapa_tdnn_minimal.gguf (tests/fixtures/make_gguf_fixtures.py) is a
+// 1/32-width model with the exact metadata and tensor contract of the real
+// VoxLingua107 file, so this drives the production loader, front end, ggml
+// graph and role dispatcher over the same code path the real checkpoint
+// uses. It asserts structure and invariants (statuses, orderings,
+// determinism), never specific values, because the weights are random.
+//
+// Everything runs on the CPU backend: the thread-count comparison below is
+// only meaningful there, and it keeps the test hermetic on GPU-equipped CI.
+
+#include "transcribe.h"
+#include "transcribe/langid.h"
+
+#include <cmath>
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <string>
+#include <vector>
+
+#ifndef TRANSCRIBE_TEST_FIXTURES_DIR
+#    error "TRANSCRIBE_TEST_FIXTURES_DIR must be defined by the build"
+#endif
+
+namespace {
+
+int g_failures = 0;
+
+#define CHECK(cond)                                                              \
+    do {                                                                         \
+        if (!(cond)) {                                                           \
+            std::fprintf(stderr, "FAIL %s:%d: %s\n", __FILE__, __LINE__, #cond); \
+            ++g_failures;                                                        \
+        }                                                                        \
+    } while (0)
+
+// Deterministic pseudo-noise in [-0.5, 0.5); a hand-rolled LCG so the
+// samples are identical on every platform and standard library.
+std::vector<float> noise(size_t n, uint32_t seed) {
+    std::vector<float> out(n);
+    uint32_t           s = seed | 1u;
+    for (size_t i = 0; i < n; ++i) {
+        s      = s * 1664525u + 1013904223u;
+        out[i] = static_cast<float>((s >> 8) & 0xFFFFFF) / 16777216.0f - 0.5f;
+    }
+    return out;
+}
+
+std::string fixture(const char * name) {
+    return std::string(TRANSCRIBE_TEST_FIXTURES_DIR) + "/" + name;
+}
+
+transcribe_model * load_cpu(const char * name, transcribe_status * st_out = nullptr) {
+    transcribe_model_load_params mp;
+    transcribe_model_load_params_init(&mp);
+    mp.backend                 = TRANSCRIBE_BACKEND_CPU;
+    transcribe_model *      m  = nullptr;
+    const transcribe_status st = transcribe_model_load_file(fixture(name).c_str(), &mp, &m);
+    if (st_out != nullptr) {
+        *st_out = st;
+    }
+    return m;
+}
+
+transcribe_langid_session * open_session(transcribe_model * m, int n_threads, int max_audio_ms = 0) {
+    transcribe_langid_session_params sp;
+    transcribe_langid_session_params_init(&sp);
+    sp.n_threads                  = n_threads;
+    sp.max_audio_ms               = max_audio_ms;
+    transcribe_langid_session * s = nullptr;
+    CHECK(transcribe_langid_session_init(m, &sp, &s) == TRANSCRIBE_OK);
+    return s;
+}
+
+transcribe_langid_result result_of(const transcribe_langid_session * s) {
+    transcribe_langid_result r;
+    transcribe_langid_result_init(&r);
+    CHECK(transcribe_langid_get_result(s, &r) == TRANSCRIBE_OK);
+    return r;
+}
+
+std::vector<transcribe_langid_candidate> candidates_of(const transcribe_langid_session * s) {
+    const transcribe_langid_result           r = result_of(s);
+    std::vector<transcribe_langid_candidate> out(static_cast<size_t>(r.n_candidates));
+    for (int i = 0; i < r.n_candidates; ++i) {
+        transcribe_langid_candidate_init(&out[static_cast<size_t>(i)]);
+        CHECK(transcribe_langid_get_candidate(s, i, &out[static_cast<size_t>(i)]) == TRANSCRIBE_OK);
+    }
+    return out;
+}
+
+void test_model_surface(transcribe_model * m) {
+    CHECK(transcribe_model_roles(m) == TRANSCRIBE_ROLE_LANGID);
+    CHECK(std::strcmp(transcribe_model_arch_string(m), "ecapa_tdnn") == 0);
+    CHECK(std::strcmp(transcribe_model_variant_string(m), "ecapa-tdnn-toy") == 0);
+    CHECK(transcribe_model_supports(m, TRANSCRIBE_FEATURE_CANCELLATION));
+
+    transcribe_langid_info info;
+    transcribe_langid_info_init(&info);
+    CHECK(transcribe_langid_get_info(m, &info) == TRANSCRIBE_OK);
+    CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500);
+    CHECK(std::strcmp(transcribe_langid_label_code(m, 2), "cc") == 0);
+    CHECK(std::strcmp(transcribe_langid_label_name(m, 2), "Charlie") == 0);
+    CHECK(transcribe_langid_label_code(m, 5) == nullptr);
+    CHECK(transcribe_langid_label_index(m, "xx") == 0);
+    CHECK(transcribe_langid_label_index(m, "zz") == -1);
+
+    // Not an ASR model.
+    transcribe_capabilities caps;
+    transcribe_capabilities_init(&caps);
+    CHECK(transcribe_model_get_capabilities(m, &caps) == TRANSCRIBE_ERR_UNSUPPORTED_ROLE);
+    transcribe_session * asr = nullptr;
+    CHECK(transcribe_session_init(m, nullptr, &asr) == TRANSCRIBE_ERR_UNSUPPORTED_ROLE);
+    CHECK(asr == nullptr);
+}
+
+void test_run(transcribe_model * m) {
+    transcribe_langid_session * s   = open_session(m, 1);
+    const std::vector<float>    pcm = noise(16000, 7);  // 1 s
+
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    const transcribe_langid_result r = result_of(s);
+    CHECK(r.n_candidates == 5 && r.n_allowed == 5 && r.allowed_mass == 1.0f && r.audio_ms == 1000);
+    const auto first = candidates_of(s);
+    double     sum   = 0.0;
+    for (size_t i = 0; i < first.size(); ++i) {
+        CHECK(std::isfinite(first[i].p) && std::isfinite(first[i].logit));
+        CHECK(first[i].p == first[i].p_unrestricted);
+        CHECK(first[i].code != nullptr && first[i].name != nullptr);
+        if (i > 0) {
+            CHECK(first[i - 1].p >= first[i].p);
+        }
+        sum += first[i].p;
+    }
+    CHECK(std::fabs(sum - 1.0) < 1e-5);
+
+    transcribe_timings tm;
+    transcribe_timings_init(&tm);
+    CHECK(transcribe_langid_get_timings(s, &tm) == TRANSCRIBE_OK);
+    CHECK(tm.mel_ms >= 0.0f && tm.encode_ms > 0.0f);
+
+    // Determinism: a second run is bit-identical.
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    const auto again = candidates_of(s);
+    CHECK(again.size() == first.size());
+    for (size_t i = 0; i < first.size() && i < again.size(); ++i) {
+        CHECK(again[i].index == first[i].index && again[i].logit == first[i].logit);
+    }
+
+    // Restricted set: renormalized over {bb, dd}, mass below 1.
+    const char *             bd[] = { "bb", "dd" };
+    transcribe_langid_params lp;
+    transcribe_langid_params_init(&lp);
+    lp.allowed   = bd;
+    lp.n_allowed = 2;
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) == TRANSCRIBE_OK);
+    const transcribe_langid_result rr = result_of(s);
+    CHECK(rr.n_candidates == 2 && rr.n_allowed == 2 && rr.allowed_mass > 0.0f && rr.allowed_mass < 1.0f);
+    const auto restricted = candidates_of(s);
+    CHECK(restricted.size() == 2 && std::fabs(restricted[0].p + restricted[1].p - 1.0f) < 1e-5f);
+
+    // An alias on its own takes all the mass.
+    const char * xx[] = { "xx" };
+    lp.allowed        = xx;
+    lp.n_allowed      = 1;
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) == TRANSCRIBE_OK);
+    const auto alias = candidates_of(s);
+    CHECK(alias.size() == 1 && alias[0].index == 0 && alias[0].p == 1.0f);
+
+    // Unknown code; top_k.
+    const char * unknown[] = { "zz" };
+    lp.allowed             = unknown;
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) ==
+          TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE);
+    transcribe_langid_params_init(&lp);
+    lp.top_k = 2;
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) == TRANSCRIBE_OK);
+    CHECK(result_of(s).n_candidates == 2 && result_of(s).n_allowed == 5);
+
+    // Minimum length, and the crop on long input.
+    CHECK(transcribe_langid_run(s, pcm.data(), 6400, nullptr) == TRANSCRIBE_ERR_INPUT_TOO_SHORT);  // 400 ms
+    CHECK(transcribe_langid_run(s, pcm.data(), 8000, nullptr) == TRANSCRIBE_OK);                   // 500 ms
+    const std::vector<float> longer = noise(16000 * 45, 11);
+    CHECK(transcribe_langid_run(s, longer.data(), static_cast<int>(longer.size()), nullptr) == TRANSCRIBE_OK);
+    CHECK(result_of(s).audio_ms == 30000);
+
+    // Silence is valid input; NaN is not.
+    const std::vector<float> zeros(16000, 0.0f);
+    CHECK(transcribe_langid_run(s, zeros.data(), 16000, nullptr) == TRANSCRIBE_OK);
+    std::vector<float> nan_pcm = pcm;
+    nan_pcm[100]               = NAN;
+    CHECK(transcribe_langid_run(s, nan_pcm.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
+
+    // Abort before compute.
+    transcribe_langid_set_abort_callback(s, [](void *) { return true; }, nullptr);
+    CHECK(transcribe_langid_run(s, pcm.data(), 16000, nullptr) == TRANSCRIBE_ERR_ABORTED);
+    CHECK(result_of(s).n_candidates == 0);
+    transcribe_langid_set_abort_callback(s, nullptr, nullptr);
+
+    transcribe_langid_session_free(s);
+}
+
+// The front end and graph give the same logits for any thread count (the
+// mel frames and the ggml CPU rows are split without changing any sum).
+void test_thread_invariance(transcribe_model * m) {
+    const std::vector<float>    pcm = noise(16000 * 3, 3);
+    transcribe_langid_session * s1  = open_session(m, 1);
+    transcribe_langid_session * s4  = open_session(m, 4);
+    CHECK(transcribe_langid_run(s1, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    CHECK(transcribe_langid_run(s4, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    const auto a = candidates_of(s1);
+    const auto b = candidates_of(s4);
+    CHECK(a.size() == b.size());
+    for (size_t i = 0; i < a.size() && i < b.size(); ++i) {
+        CHECK(a[i].index == b[i].index && std::fabs(a[i].logit - b[i].logit) < 1e-5f);
+    }
+    transcribe_langid_session_free(s1);
+    transcribe_langid_session_free(s4);
+}
+
+// H4 / H6: a duplicate label code fails the load cleanly. Run under ASan /
+// LSan (TRANSCRIBE_SANITIZE) this also proves the half-built model is freed.
+void test_bad_labels() {
+    transcribe_status  st = TRANSCRIBE_OK;
+    transcribe_model * m  = load_cpu("arch_ecapa_tdnn_bad_labels.gguf", &st);
+    CHECK(st == TRANSCRIBE_ERR_GGUF);
+    CHECK(m == nullptr);
+}
+
+}  // namespace
+
+int main() {
+    transcribe_log_set(nullptr, nullptr);
+
+    transcribe_status  st = TRANSCRIBE_OK;
+    transcribe_model * m  = load_cpu("arch_ecapa_tdnn_minimal.gguf", &st);
+    if (st != TRANSCRIBE_OK || m == nullptr) {
+        std::fprintf(stderr, "FAIL: load arch_ecapa_tdnn_minimal.gguf: %s\n", transcribe_status_string(st));
+        return EXIT_FAILURE;
+    }
+    test_model_surface(m);
+    test_run(m);
+    test_thread_invariance(m);
+    transcribe_model_free(m);
+
+    test_bad_labels();
+
+    if (g_failures != 0) {
+        std::fprintf(stderr, "%d failure(s)\n", g_failures);
+        return EXIT_FAILURE;
+    }
+    std::printf("ecapa_tdnn_smoke: ok\n");
+    return EXIT_SUCCESS;
+}
diff --git a/tests/fixtures/make_gguf_fixtures.py b/tests/fixtures/make_gguf_fixtures.py
index 2bdd939c8..8d92dfdb1 100644
--- a/tests/fixtures/make_gguf_fixtures.py
+++ b/tests/fixtures/make_gguf_fixtures.py
@@ -13,7 +13,7 @@
 configure time). Tensor data emission uses Python struct only — no
 numpy dep — because the toy tensors are tiny (~3000 fp32 elements).
 
-Six fixtures are emitted:
+Fixtures emitted (see emit_fixtures for the full list):
 
   arch_parakeet.gguf       -- valid header, KV pairs:
                                 general.architecture = "parakeet"
@@ -1359,6 +1359,141 @@ def _qwen3_asr_tensors() -> list[Tensor]:
 )
 
 
+# ---------------------------------------------------------------------------
+# Toy ecapa_tdnn (LANGID) hparams + tensor catalog
+# ---------------------------------------------------------------------------
+#
+# The same metadata and tensor contract scripts/convert-ecapa_tdnn.py writes
+# for speechbrain/lang-id-voxlingua107-ecapa, at 1/32 width: channels
+# [32]*4 + [96], the real kernel sizes / dilations / res2net scale, a real
+# 60 x 201 front end, five labels aa..ee plus the alias xx=aa. Weights are
+# small seeded pseudo-random values so a forward pass stays well inside float
+# range; tests assert structure and invariants, never specific values.
+
+import random as _random
+
+ECAPA_CHANNELS     = [32, 32, 32, 32, 96]
+ECAPA_KERNELS      = [5, 3, 3, 3, 1]
+ECAPA_DILATIONS    = [1, 2, 3, 4, 1]
+ECAPA_SCALE        = 8
+ECAPA_SE           = 8
+ECAPA_ATT          = 8
+ECAPA_EMB          = 16
+ECAPA_HID          = 16
+ECAPA_N_MELS       = 60
+ECAPA_N_FREQ       = 201
+ECAPA_LABEL_CODES  = ["aa", "bb", "cc", "dd", "ee"]
+ECAPA_LABEL_NAMES  = ["Alpha", "Bravo", "Charlie", "Delta", "Echo"]
+
+
+def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str]) -> list[bytes]:
+    return [
+        _pack_kv_string("stt.frontend.type", "speechbrain_fbank"),
+        _pack_kv_uint32("stt.frontend.sample_rate", 16000),
+        _pack_kv_uint32("stt.frontend.n_fft", 400),
+        _pack_kv_uint32("stt.frontend.hop_length", 160),
+        _pack_kv_uint32("stt.frontend.win_length", 400),
+        _pack_kv_uint32("stt.frontend.num_mels", ECAPA_N_MELS),
+        _pack_kv_string("stt.frontend.window", "hamming_periodic"),
+        _pack_kv_string("stt.frontend.pad_mode", "constant"),
+        _pack_kv_float32("stt.frontend.log_clamp_min", 1e-10),
+        _pack_kv_float32("stt.frontend.top_db", 80.0),
+        _pack_kv_string("stt.frontend.normalize", "sentence_mean"),
+        _pack_kv_uint32("stt.ecapa_tdnn.format_version", 1),
+        _pack_kv_array_int32("stt.ecapa_tdnn.channels", ECAPA_CHANNELS),
+        _pack_kv_array_int32("stt.ecapa_tdnn.kernel_sizes", ECAPA_KERNELS),
+        _pack_kv_array_int32("stt.ecapa_tdnn.dilations", ECAPA_DILATIONS),
+        _pack_kv_uint32("stt.ecapa_tdnn.res2net_scale", ECAPA_SCALE),
+        _pack_kv_uint32("stt.ecapa_tdnn.se_channels", ECAPA_SE),
+        _pack_kv_uint32("stt.ecapa_tdnn.attention_channels", ECAPA_ATT),
+        _pack_kv_float32("stt.ecapa_tdnn.asp_eps", 1e-12),
+        _pack_kv_uint32("stt.ecapa_tdnn.embedding_dim", ECAPA_EMB),
+        _pack_kv_uint32("stt.ecapa_tdnn.classifier_hidden", ECAPA_HID),
+        _pack_kv_float32("stt.ecapa_tdnn.classifier_leaky_slope", 0.01),
+        _pack_kv_array_string("stt.langid.labels.codes", codes),
+        _pack_kv_array_string("stt.langid.labels.names", names),
+        _pack_kv_array_string("stt.langid.labels.aliases", aliases),
+    ]
+
+
+def _ecapa_tdnn_tensors(n_labels: int) -> list[Tensor]:
+    rng = _random.Random(0)
+    out: list[Tensor] = []
+
+    def add(name: str, ne: list[int], values: list[float]) -> None:
+        out.append(Tensor(name, ne, GGML_TYPE_F32, _f32_bytes(values)))
+
+    def weight(name: str, ne: list[int]) -> None:
+        n = 1
+        for d in ne:
+            n *= d
+        fan_in = ne[0] * (ne[2] if len(ne) == 3 else 1)
+        bound = 1.0 / fan_in ** 0.5
+        add(name, ne, [rng.uniform(-bound, bound) for _ in range(n)])
+
+    def vec(name: str, n: int, center: float, spread: float) -> None:
+        add(name, [n], [center + rng.uniform(-spread, spread) for _ in range(n)])
+
+    def tdnn(prefix: str, ic: int, oc: int, k: int, conv: bool) -> None:
+        stem = f"{prefix}.conv" if conv else prefix
+        weight(f"{stem}.weight", [ic, oc, k] if conv else [ic, oc])
+        vec(f"{stem}.bias", oc, 0.0, 0.05)
+        vec(f"{prefix}.bn.scale", oc, 1.0, 0.1)
+        vec(f"{prefix}.bn.shift", oc, 0.0, 0.05)
+
+    # Triangular mel-major filterbank, ne = [n_freq, n_mels].
+    fb = [0.0] * (ECAPA_N_MELS * ECAPA_N_FREQ)
+    for m in range(ECAPA_N_MELS):
+        c = 2 + 3 * m
+        for k in range(c - 3, c + 4):
+            if 0 <= k < ECAPA_N_FREQ:
+                fb[m * ECAPA_N_FREQ + k] = 1.0 - abs(k - c) / 4.0
+    add("frontend.mel_filterbank", [ECAPA_N_FREQ, ECAPA_N_MELS], fb)
+
+    c, cm, chunk = ECAPA_CHANNELS[0], ECAPA_CHANNELS[-1], ECAPA_CHANNELS[0] // ECAPA_SCALE
+    tdnn("blk.0", ECAPA_N_MELS, c, ECAPA_KERNELS[0], conv=True)
+    for i in (1, 2, 3):
+        tdnn(f"blk.{i}.tdnn1", c, c, 1, conv=False)
+        for j in range(ECAPA_SCALE - 1):
+            tdnn(f"blk.{i}.res2.{j}", chunk, chunk, ECAPA_KERNELS[i], conv=True)
+        tdnn(f"blk.{i}.tdnn2", c, c, 1, conv=False)
+        weight(f"blk.{i}.se.c1.weight", [c, ECAPA_SE])
+        vec(f"blk.{i}.se.c1.bias", ECAPA_SE, 0.0, 0.05)
+        weight(f"blk.{i}.se.c2.weight", [ECAPA_SE, c])
+        vec(f"blk.{i}.se.c2.bias", c, 0.0, 0.05)
+    for n in (1, 2, 3):
+        weight(f"mfa.w{n}.weight", [c, cm])
+    vec("mfa.bias", cm, 0.0, 0.05)
+    vec("mfa.bn.scale", cm, 1.0, 0.1)
+    vec("mfa.bn.shift", cm, 0.0, 0.05)
+    for part in ("x", "mean", "std"):
+        weight(f"asp.tdnn.{part}.weight", [cm, ECAPA_ATT])
+    vec("asp.tdnn.bias", ECAPA_ATT, 0.0, 0.05)
+    vec("asp.tdnn.bn.scale", ECAPA_ATT, 1.0, 0.1)
+    vec("asp.tdnn.bn.shift", ECAPA_ATT, 0.0, 0.05)
+    weight("asp.attn.weight", [ECAPA_ATT, cm])
+    vec("asp.attn.bias", cm, 0.0, 0.05)
+    weight("fc.weight", [2 * cm, ECAPA_EMB])
+    vec("fc.bias", ECAPA_EMB, 0.0, 0.05)
+    weight("cls.l1.weight", [ECAPA_EMB, ECAPA_HID])
+    vec("cls.l1.bias", ECAPA_HID, 0.0, 0.05)
+    weight("cls.out.weight", [ECAPA_HID, n_labels])
+    vec("cls.out.bias", n_labels, 0.0, 0.05)
+    return out
+
+
+def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str]) -> bytes:
+    return _build_full_gguf(
+        GGUF_MAGIC,
+        [
+            _pack_kv_string("general.architecture", "ecapa_tdnn"),
+            _pack_kv_string("stt.variant", "ecapa-tdnn-toy"),
+            *_ecapa_tdnn_hparams_kv(codes, names, aliases),
+        ],
+        _ecapa_tdnn_tensors(len(codes)),
+    )
+
+
 def _write(path: Path, data: bytes) -> None:
     path.parent.mkdir(parents=True, exist_ok=True)
     path.write_bytes(data)
@@ -1773,6 +1908,15 @@ def emit_fixtures(out_dir: Path) -> None:
     )
 
 
+    # ecapa_tdnn (LANGID role): a structurally complete toy model, and the
+    # same model with a duplicate label code, which the loader must reject
+    # with TRANSCRIBE_ERR_GGUF without leaking the half-built model (H4/H6).
+    _write(out_dir / "arch_ecapa_tdnn_minimal.gguf",
+           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"]))
+    _write(out_dir / "arch_ecapa_tdnn_bad_labels.gguf",
+           _ecapa_tdnn_gguf(["aa", "bb", "cc", "bb", "ee"], ECAPA_LABEL_NAMES, ["xx=aa"]))
+
+
 def main(argv: list[str]) -> int:
     if len(argv) > 2:
         print(f"usage: {argv[0]} [output_dir]", file=sys.stderr)
diff --git a/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json b/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json
new file mode 100644
index 000000000..ffa7762f1
--- /dev/null
+++ b/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json
@@ -0,0 +1,52 @@
+{
+  "schema": "transcribe-golden-manifest-v1",
+  "family": "ecapa_tdnn",
+  "variant": "lang-id-voxlingua107-ecapa",
+  "role": "langid",
+  "source_model": {
+    "hf_repo": "speechbrain/lang-id-voxlingua107-ecapa",
+    "hf_revision": "0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9"
+  },
+  "reference": {
+    "kind": "speechbrain",
+    "source": "https://github.com/speechbrain/speechbrain",
+    "revision": "v1.1.1",
+    "entrypoint": "scripts/dump_reference_ecapa_tdnn_speechbrain.py",
+    "dump_args": []
+  },
+  "expected_dtype": "float32",
+  "dtype_source": "manual",
+  "frontend": {
+    "sample_rate": 16000,
+    "n_mels": 60,
+    "hop_length": 160,
+    "fft_size": 400,
+    "win_length": 400,
+    "window": "hamming_periodic",
+    "pad_mode": "constant",
+    "normalization": "sentence_mean",
+    "top_db": 80
+  },
+  "tokenizer_summary": {
+    "type": "other",
+    "vocab_size": 0,
+    "special_tokens": {}
+  },
+  "langid": {
+    "n_labels": 107,
+    "label_aliases": ["he=iw", "jv=jw", "fil=tl", "nb=no"],
+    "min_audio_ms": 500,
+    "default_max_audio_ms": 30000
+  },
+  "tolerance_file": "tests/tolerances/ecapa_tdnn.json",
+  "cases": [
+    { "audio": "fleurs-en", "language": null, "expected_language": "en" },
+    { "audio": "fleurs-de", "language": null, "expected_language": "de" },
+    { "audio": "fleurs-fr", "language": null, "expected_language": "fr" },
+    { "audio": "fleurs-es", "language": null, "expected_language": "es" },
+    { "audio": "fleurs-ja", "language": null, "expected_language": "ja" },
+    { "audio": "fleurs-zh", "language": null, "expected_language": "zh" },
+    { "audio": "fleurs-ru", "language": null, "expected_language": "ru" },
+    { "audio": "fleurs-id", "language": null, "expected_language": "id" }
+  ]
+}
diff --git a/tests/tolerances/ecapa_tdnn.json b/tests/tolerances/ecapa_tdnn.json
new file mode 100644
index 000000000..55020da2b
--- /dev/null
+++ b/tests/tolerances/ecapa_tdnn.json
@@ -0,0 +1,131 @@
+{
+  "_comment": [
+    "ECAPA-TDNN / VoxLingua107 per-tensor tolerances for compare_tensors.py.",
+    "",
+    "Carried over unchanged from langid.cpp: on the port, every stage tensor the",
+    "transcribe.cpp ecapa_tdnn family dumps was bit-identical to langid.cpp's",
+    "(CPU, 1 thread, F32), so the measured drift below still holds.",
+    "",
+    "Measured in langid.cpp (handy-computer/langid.cpp @ 96af3a5) against C++ vs",
+    "reference drift. Every Stage-2 provisional flag has been removed.",
+    "",
+    "Regime, identical for every number below:",
+    "  model     models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf",
+    "            (ALL_F32; no quantised tensor takes part in these numbers)",
+    "  runtime   build/bin/transcribe-cli --backend cpu --threads 1",
+    "  front end the production C++ log-mel front end (src/arch/ecapa_tdnn/mel.cpp),",
+    "            not a reference feature dump -- fe.mel is measured, not injected",
+    "  host      macOS / arm64 (Apple Silicon), ggml CPU backend",
+    "  reference speechbrain 1.1.1 on torch 2.13.0, fp32, CPU, 1 thread,",
+    "            checkpoint speechbrain/lang-id-voxlingua107-ecapa @",
+    "            0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9",
+    "  cases     all 8 in tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json",
+    "            (fleurs-{en,de,fr,es,ja,zh,ru,id}, 7.9 s .. 11.2 s)",
+    "",
+    "F16 / Q8_0 / Metal are production variants measured separately and were",
+    "never used to set these numbers.",
+    "",
+    "Recipe, per tensor, over all 8 cases:",
+    "  max_abs  = max(1.5 * max_over_cases(observed max_abs),  provisional, 1e-6)",
+    "  mean_abs = max(1.5 * max_over_cases(observed mean_abs), provisional, 1e-6)",
+    "The Stage-2 provisional budgets were max(1e-4*p99_abs, 1e-6) and",
+    "max(1e-5*rms, 1e-6) from the reference dumps alone.",
+    "",
+    "WIDENED ENTRIES: none. Measured drift came in below the provisional",
+    "budget for every tensor on both metrics, so 1.5*observed never exceeded",
+    "it and every value below is the unchanged Stage-2 number. Nothing was",
+    "loosened to make a case pass.",
+    "",
+    "Dominant drift source: fp32 summation ORDER, not algorithm. Two",
+    "contributors, in order of size:",
+    "  1. fe.mel -- the C++ mixed-radix 400-point FFT (src/arch/ecapa_tdnn/mel.cpp)",
+    "     accumulates the STFT differently from torch.stft's FFT, and the",
+    "     difference is then amplified by 10*log10 on near-floor bins. This",
+    "     is the only stage whose drift is not inherited.",
+    "  2. every matmul -- ggml's blocked/SIMD reduction order over the 1024-",
+    "     and 3072-wide contractions differs from PyTorch's BLAS. The graph",
+    "     itself is exact: an independent fp64 NumPy re-derivation of the",
+    "     forward pass agrees with the ggml graph to 4.5e-07 relative on a",
+    "     synthetic fixture, so no term of the ECAPA algebra is approximated.",
+    "The residual-add topology means block drift accumulates roughly linearly",
+    "(blk.0 5.2e-05 -> blk.3 1.5e-04), then the attention softmax and the",
+    "1/T statistics CONTRACT it again (enc.asp.out 9.5e-06), which is why the",
+    "downstream vectors sit two orders of magnitude inside their budgets.",
+    "",
+    "Tightest entries, i.e. where a slower-drifting host would first complain:",
+    "  enc.blk.0.out  1.5*observed is 99% of the budget",
+    "  enc.mfa.out    1.5*observed is 94% of the budget",
+    "Both are max_abs on wide [T, C] activations; a re-measure on different",
+    "CPU SIMD width may need a fresh Stage-4 pass rather than a hand edit.",
+    "",
+    "cls.logits_raw is the gate; cls.log_probs is informational (it is",
+    "recomputed host-side from the logits, so it carries the logits' drift",
+    "plus one log-sum-exp). SpeechBrain's log_softmax is finite everywhere",
+    "here (no masked positions), so no inf-tolerance escape hatch is needed.",
+    "",
+    "fe.window [400] and fe.filters [201, 60] are dumped by the reference but",
+    "deliberately absent from this file: the C++ runtime recomputes the window",
+    "and reads the filterbank from the GGUF, so it never dumps them.",
+    "compare_tensors reports them MISSING without failing."
+  ],
+  "fe.mel": {
+    "max_abs": 0.004079,
+    "mean_abs": 0.0001782
+  },
+  "enc.blk.0.out": {
+    "max_abs": 7.896e-05,
+    "mean_abs": 1.897e-06
+  },
+  "enc.blk.1.tdnn1.out": {
+    "max_abs": 0.0004054,
+    "mean_abs": 1.008e-05
+  },
+  "enc.blk.1.res2.out": {
+    "max_abs": 0.0003865,
+    "mean_abs": 9.456e-06
+  },
+  "enc.blk.1.se.out": {
+    "max_abs": 0.0001077,
+    "mean_abs": 2.4e-06
+  },
+  "enc.blk.1.out": {
+    "max_abs": 0.0001297,
+    "mean_abs": 2.952e-06
+  },
+  "enc.blk.2.out": {
+    "max_abs": 0.0002627,
+    "mean_abs": 5.964e-06
+  },
+  "enc.blk.3.out": {
+    "max_abs": 0.0006391,
+    "mean_abs": 1.412e-05
+  },
+  "enc.mfa.out": {
+    "max_abs": 0.0001262,
+    "mean_abs": 4.163e-06
+  },
+  "enc.asp.attn_logits": {
+    "max_abs": 0.0003274,
+    "mean_abs": 1.19e-05
+  },
+  "enc.asp.out": {
+    "max_abs": 0.0001753,
+    "mean_abs": 4.176e-06
+  },
+  "enc.emb": {
+    "max_abs": 0.006139,
+    "mean_abs": 0.0002736
+  },
+  "cls.hidden": {
+    "max_abs": 0.002295,
+    "mean_abs": 8.541e-05
+  },
+  "cls.logits_raw": {
+    "max_abs": 0.001809,
+    "mean_abs": 0.0001014
+  },
+  "cls.log_probs": {
+    "max_abs": 0.003533,
+    "mean_abs": 0.0002712
+  }
+}

From 62522202b8289f95f25470836fadc3abd24f54b0 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Mon, 5 Oct 2026 14:41:26 +0800
Subject: [PATCH 03/38] langid: bindings for the LANGID role (Python, Rust,
 Swift, TypeScript)

Regenerate the low-level layers (_generated.py / _generated.ts /
transcribe.abihash via generate.py, transcribe_sys.rs via cargo xtask
bindgen, Swift pinned hash) and add a hand-written LangIdSession to every
binding, on the existing per-model execution bridge (Python
Model._exclusive, Rust ModelInner::with_compute, Swift Model.withCompute,
TypeScript SessionCore.exclusive):

- Model: roles gains LANGID; langid info, label table (codes, names, alias
  lookup) and langid_session(n_threads, max_audio_ms).
- LangIdSession.run(pcm, allowed, top_k) -> result with ranked candidates
  (index, code, name, p, p_unrestricted, logit), n_allowed, allowed_mass,
  audio_ms; cancellation; timings. Results are copied out under the
  compute lock in all four (Rust included).
- allowed: None / nil / null passes NULL ("every label"); an empty list
  is rejected by the binding with InvalidArgument, never marshalled to
  NULL. The array and every encoded string outlive the native call,
  including TypeScript's async worker call.
- TRANSCRIBE_ERR_INPUT_TOO_SHORT maps to InputTooShort /
  Error::InputTooShort / .inputTooShort.

Tests: contract checks on the toy ecapa_tdnn fixture (Python generates it
on the fly; Rust / Swift / TypeScript use the C++-built
tests/fixtures/arch_ecapa_tdnn_minimal.gguf and skip without it), top-1
on the FLEURS clips with TRANSCRIBE_SMOKE_LANGID_MODEL, Busy / lock /
empty-allowed / keep-alive checks model-free. docs/bindings.md gains the
LANGID contract section; each README a language ID section.
---
 bindings/python/README.md                     |  19 ++
 .../python/src/transcribe_cpp/__init__.py     | 168 +++++++++-
 .../python/src/transcribe_cpp/_generated.py   |  66 +++-
 bindings/python/src/transcribe_cpp/errors.py  |   7 +
 bindings/python/tests/conftest.py             |  25 ++
 bindings/python/tests/test_compute_lock.py    |  50 ++-
 bindings/python/tests/test_errors.py          |   1 +
 bindings/python/tests/test_langid.py          | 138 ++++++++
 bindings/rust/sys/src/transcribe_sys.rs       | 223 ++++++++++++-
 bindings/rust/transcribe-cpp/README.md        |  19 ++
 bindings/rust/transcribe-cpp/src/error.rs     |   6 +
 bindings/rust/transcribe-cpp/src/langid.rs    | 298 ++++++++++++++++++
 bindings/rust/transcribe-cpp/src/lib.rs       |   4 +
 bindings/rust/transcribe-cpp/src/model.rs     |  50 +++
 bindings/rust/transcribe-cpp/src/types.rs     |  13 +
 .../rust/transcribe-cpp/tests/common/mod.rs   |  30 +-
 bindings/rust/transcribe-cpp/tests/langid.rs  | 156 +++++++++
 .../rust/transcribe-cpp/tests/no_model.rs     |   6 +
 bindings/swift/README.md                      |  14 +
 .../swift/Sources/TranscribeCpp/ABIHash.swift |   2 +-
 .../swift/Sources/TranscribeCpp/Backend.swift |  10 +
 .../Sources/TranscribeCpp/Cancellation.swift  |  15 +
 .../swift/Sources/TranscribeCpp/LangId.swift  | 187 +++++++++++
 .../swift/Sources/TranscribeCpp/Model.swift   |   3 +-
 .../TranscribeCpp/TranscribeError.swift       |   5 +
 .../TranscribeCppTests/LangIdTests.swift      |  91 ++++++
 .../TranscribeCppTests/NoModelTests.swift     |   8 +-
 .../TranscribeCppTests/TestSupport.swift      |  19 ++
 bindings/typescript/README.md                 |  15 +-
 bindings/typescript/src/_generated.ts         |  40 ++-
 bindings/typescript/src/errors.ts             |   5 +-
 bindings/typescript/src/ffi.ts                |  54 ++++
 bindings/typescript/src/index.ts              | 156 ++++++++-
 bindings/typescript/src/types.ts              |  58 +++-
 bindings/typescript/test/common.mjs           |   8 +
 bindings/typescript/test/errors.test.mjs      |   2 +
 bindings/typescript/test/langid.test.mjs      | 104 ++++++
 docs/bindings.md                              |  34 +-
 include/transcribe.abihash                    |   2 +-
 39 files changed, 2087 insertions(+), 24 deletions(-)
 create mode 100644 bindings/python/tests/test_langid.py
 create mode 100644 bindings/rust/transcribe-cpp/src/langid.rs
 create mode 100644 bindings/rust/transcribe-cpp/tests/langid.rs
 create mode 100644 bindings/swift/Sources/TranscribeCpp/LangId.swift
 create mode 100644 bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
 create mode 100644 bindings/typescript/test/langid.test.mjs

diff --git a/bindings/python/README.md b/bindings/python/README.md
index 9422adf99..c332dbe84 100644
--- a/bindings/python/README.md
+++ b/bindings/python/README.md
@@ -88,6 +88,25 @@ with model.diarize_session() as diarizer:
         print(turn.speaker_id, turn.t0_ms, turn.t1_ms)
 ```
 
+### Language ID
+
+Models whose `model.roles` include `Role.LANGID` (VoxLingua107 ECAPA-TDNN)
+identify the spoken language through a langid session. `run()` returns a
+`LangIdResult` with candidates ranked by `p`. Codes are the model's own labels
+(`"iw"`, `"jw"`; `model.langid_label_index("he")` resolves aliases), so match
+`result.code` against the ASR model's `capabilities.languages` yourself.
+`allowed=None` scores every label; pass the languages you expect, and a low
+`allowed_mass` means the speech is probably none of them. An empty `allowed`
+list raises `InvalidArgument` rather than meaning "all"; clips under
+`model.langid_info.min_audio_ms` raise `InputTooShort`. Locking, `Busy`,
+`cancel()` and `close()` work as on `Session`.
+
+```python
+with model.langid_session() as lid:
+    result = lid.run(pcm, allowed=["en", "de", "fr"], top_k=3)
+    print(result.code, result.candidates[0].p, result.allowed_mass)
+```
+
 ## Backends
 
 `Model(backend=...)` applies a backend policy (`"auto"` uses the best
diff --git a/bindings/python/src/transcribe_cpp/__init__.py b/bindings/python/src/transcribe_cpp/__init__.py
index a77f20225..afe422d9f 100644
--- a/bindings/python/src/transcribe_cpp/__init__.py
+++ b/bindings/python/src/transcribe_cpp/__init__.py
@@ -39,6 +39,7 @@
     BackendError,
     Busy,
     InputTooLong,
+    InputTooShort,
     InvalidArgument,
     ModelFileNotFound,
     ModelLoadError,
@@ -78,6 +79,10 @@
     "Session",
     "DiarizeSession",
     "DiarizeInfo",
+    "LangIdSession",
+    "LangIdInfo",
+    "LangIdResult",
+    "LangIdCandidate",
     "Role",
     "Result",
     "Segment",
@@ -120,6 +125,7 @@
     "Aborted",
     "Busy",
     "InputTooLong",
+    "InputTooShort",
     "OutputTruncated",
     "OutputRepetition",
     "native_version",
@@ -190,6 +196,8 @@
 _Timings = _generated.transcribe_timings
 _Segment = _generated.transcribe_segment
 _SpeakerSegment = _generated.transcribe_speaker_segment
+_LangIdCandidate = _generated.transcribe_langid_candidate
+_LangIdResult = _generated.transcribe_langid_result
 _Word = _generated.transcribe_word
 _Token = _generated.transcribe_token
 _StreamParams = _generated.transcribe_stream_params
@@ -558,10 +566,12 @@ class Capabilities:
 
 class Role(enum.Enum):
     """What a model serves (``Model.roles``): ASR is ``Model.session()``,
-    DIARIZE is ``Model.diarize_session()``."""
+    DIARIZE is ``Model.diarize_session()``, LANGID is
+    ``Model.langid_session()``."""
 
     ASR = _generated.TRANSCRIBE_ROLE_ASR
     DIARIZE = _generated.TRANSCRIBE_ROLE_DIARIZE
+    LANGID = _generated.TRANSCRIBE_ROLE_LANGID
 
 
 @dataclass(frozen=True)
@@ -570,6 +580,45 @@ class DiarizeInfo:
     max_speakers: int  # speaker_id is in [1, max_speakers]
 
 
+@dataclass(frozen=True)
+class LangIdInfo:
+    sample_rate: int
+    n_labels: int      # label indices are [0, n_labels)
+    min_audio_ms: int  # shorter scored audio raises InputTooShort
+
+
+@dataclass(frozen=True)
+class LangIdCandidate:
+    """One ranked label. ``code`` is the model's own label ("en", "iw");
+    ``p`` is renormalized over the allowed set, ``p_unrestricted`` is over
+    every label."""
+
+    index: int
+    code: str
+    name: str
+    p: float
+    p_unrestricted: float
+    logit: float
+
+
+@dataclass(frozen=True)
+class LangIdResult:
+    """``candidates`` are ranked by ``p`` (ties keep label order).
+    ``allowed_mass`` is the unrestricted probability inside the allowed set
+    (1.0 when unrestricted); a low value means the speech is probably outside
+    it. ``audio_ms`` is what was scored, after the crop."""
+
+    candidates: tuple[LangIdCandidate, ...]
+    n_allowed: int
+    allowed_mass: float
+    audio_ms: int
+
+    @property
+    def code(self) -> str | None:
+        """The top candidate's code, or None when there are no candidates."""
+        return self.candidates[0].code if self.candidates else None
+
+
 @dataclass(frozen=True)
 class SessionLimits:
     """Effective per-session limits (model bound narrowed by session params).
@@ -1227,6 +1276,38 @@ def diarize_session(self, *, n_threads: int = 0) -> "DiarizeSession":
         """Raises :class:`UnsupportedRole` without the DIARIZE role."""
         return DiarizeSession(self, n_threads=n_threads)
 
+    @property
+    def langid_info(self) -> LangIdInfo:
+        """Raises :class:`UnsupportedRole` without the LANGID role."""
+        info = _generated.transcribe_langid_info()
+        _lib.transcribe_langid_info_init(_byref(info))
+        _check(_lib.transcribe_langid_get_info(self._h, _byref(info)),
+               "reading langid info")
+        return LangIdInfo(sample_rate=info.sample_rate, n_labels=info.n_labels,
+                          min_audio_ms=info.min_audio_ms)
+
+    @property
+    def langid_labels(self) -> tuple[tuple[str, str], ...]:
+        """``(code, name)`` per label index. Raises :class:`UnsupportedRole`
+        without the LANGID role."""
+        n = self.langid_info.n_labels
+        return tuple((_lib.transcribe_langid_label_code(self._h, i).decode("utf-8"),
+                      _lib.transcribe_langid_label_name(self._h, i).decode("utf-8"))
+                     for i in range(n))
+
+    def langid_label_index(self, code: str) -> int | None:
+        """Label index of a code or alias ("he" and "iw" name the same
+        label), or None. None as well on a model without the LANGID role."""
+        i = _lib.transcribe_langid_label_index(self._h, code.encode("utf-8"))
+        return i if i >= 0 else None
+
+    def langid_session(self, *, n_threads: int = 0,
+                       max_audio_ms: int = 0) -> "LangIdSession":
+        """``max_audio_ms``: longer input is scored on its last
+        ``max_audio_ms`` (0 = 30000). Raises :class:`UnsupportedRole`
+        without the LANGID role."""
+        return LangIdSession(self, n_threads=n_threads, max_audio_ms=max_audio_ms)
+
     def close(self) -> None:
         """Free the model. Any session still open on it is closed first —
         the C contract forbids freeing a model before its sessions, so this
@@ -1888,6 +1969,91 @@ def timings(self) -> Timings:
         return _timings_from(tm)
 
 
+class LangIdSession(_SessionBase):
+    """A language ID context on a model with the LANGID role. Compute
+    locking and ``Busy`` rules: see ``Model``."""
+
+    _free_fn = "transcribe_langid_session_free"
+
+    def __init__(self, model: Model, *, n_threads: int = 0, max_audio_ms: int = 0):
+        self._model = model  # keep the model alive for the session's lifetime
+        params = _generated.transcribe_langid_session_params()
+        _lib.transcribe_langid_session_params_init(_byref(params))
+        params.n_threads = n_threads
+        params.max_audio_ms = max_audio_ms
+
+        handle = ctypes.c_void_p()
+        _check(_lib.transcribe_langid_session_init(model._h, _byref(params), _byref(handle)),
+               "opening langid session")
+        if not handle.value:
+            raise TranscribeError("langid session init returned a null handle")
+        self._handle = handle
+        self._arm_abort(_lib.transcribe_langid_set_abort_callback)
+
+    def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
+            top_k: int = 0) -> LangIdResult:
+        """Identify the language of one clip (16 kHz mono float32 PCM).
+        Longer input than the session's ``max_audio_ms`` is scored on its
+        tail. ``allowed`` restricts the decision to those codes or aliases;
+        None means every label, and an empty list is rejected. ``top_k``
+        keeps the best candidates (0 = every allowed label).
+
+        Raises :class:`InputTooShort` below ``LangIdInfo.min_audio_ms``,
+        :class:`UnsupportedRequest` for an unknown code, :class:`Aborted`
+        after :meth:`cancel`, and :class:`Busy` if a stream is active on this
+        model."""
+        self._cancel.clear()  # before the lock wait, as in Session.run()
+        array, n_samples = _pcm_to_carray(pcm)
+        params = _generated.transcribe_langid_params()
+        _lib.transcribe_langid_params_init(_byref(params))
+        params.top_k = top_k
+        if allowed is not None:
+            if isinstance(allowed, (str, bytes)):
+                raise InvalidArgument("allowed must be a sequence of codes, not a string")
+            codes = list(allowed)
+            # An empty list must not reach native as NULL, which means "all".
+            if not codes:
+                raise InvalidArgument("allowed is empty; pass None for every label")
+            if not all(isinstance(c, str) for c in codes):
+                raise InvalidArgument("allowed entries must be str")
+            encoded = [c.encode("utf-8") for c in codes]
+            arr = (ctypes.c_char_p * len(encoded))(*encoded)
+            params.allowed = ctypes.cast(arr, type(params.allowed))
+            params.n_allowed = len(encoded)
+            params._allowed_keepalive = (encoded, arr)
+        with self._model._exclusive(
+                "langid_run", busy="a stream is active on this model; "
+                                   "finish or drop it before langid run()"):
+            h = self._h  # captured under the lock; close() defers its free
+            _check(_lib.transcribe_langid_run(h, array, n_samples, _byref(params)),
+                   "transcribe_langid_run")
+            res = _LangIdResult()
+            _lib.transcribe_langid_result_init(_byref(res))
+            _check(_lib.transcribe_langid_get_result(h, _byref(res)),
+                   "transcribe_langid_get_result")
+            rows = []
+            for i in range(res.n_candidates):
+                c = _LangIdCandidate()
+                _lib.transcribe_langid_candidate_init(_byref(c))
+                _check(_lib.transcribe_langid_get_candidate(h, i, _byref(c)),
+                       "transcribe_langid_get_candidate")
+                rows.append(LangIdCandidate(
+                    index=c.index, code=c.code.decode("utf-8"), name=c.name.decode("utf-8"),
+                    p=c.p, p_unrestricted=c.p_unrestricted, logit=c.logit))
+            return LangIdResult(candidates=tuple(rows), n_allowed=res.n_allowed,
+                                allowed_mass=res.allowed_mass, audio_ms=res.audio_ms)
+
+    @property
+    def timings(self) -> Timings:
+        """Load time plus the last run's mel / encode time. Not locked, like
+        ``Session.limits``."""
+        tm = _Timings()
+        _lib.transcribe_timings_init(_byref(tm))
+        _check(_lib.transcribe_langid_get_timings(self._h, _byref(tm)),
+               "transcribe_langid_get_timings")
+        return _timings_from(tm)
+
+
 def transcribe(
     model: Model | str | os.PathLike,
     pcm: PCMLike,
diff --git a/bindings/python/src/transcribe_cpp/_generated.py b/bindings/python/src/transcribe_cpp/_generated.py
index f908877ae..616f1315c 100644
--- a/bindings/python/src/transcribe_cpp/_generated.py
+++ b/bindings/python/src/transcribe_cpp/_generated.py
@@ -13,7 +13,7 @@
 # Stable digest of the ABI surface below (structs, enums, macros, layout,
 # prototypes). A native provider package echoes this back so the API
 # package can reject an ABI-mismatched provider before dlopen.
-PUBLIC_HEADER_HASH = "bd3273dabb25a1fe"
+PUBLIC_HEADER_HASH = "318eba8c4b2e4387"
 
 # === enum constants ===
 TRANSCRIBE_OK = 0
@@ -37,6 +37,7 @@
 TRANSCRIBE_ERR_OUTPUT_TRUNCATED = 18
 TRANSCRIBE_ERR_OUTPUT_REPETITION = 19
 TRANSCRIBE_ERR_UNSUPPORTED_ROLE = 20
+TRANSCRIBE_ERR_INPUT_TOO_SHORT = 21
 TRANSCRIBE_ABI_MODEL_LOAD_PARAMS = 0
 TRANSCRIBE_ABI_SESSION_PARAMS = 1
 TRANSCRIBE_ABI_RUN_PARAMS = 2
@@ -56,6 +57,11 @@
 TRANSCRIBE_ABI_DIARIZE_INFO = 16
 TRANSCRIBE_ABI_DIARIZE_SESSION_PARAMS = 17
 TRANSCRIBE_ABI_DIARIZE_PARAMS = 18
+TRANSCRIBE_ABI_LANGID_INFO = 19
+TRANSCRIBE_ABI_LANGID_SESSION_PARAMS = 20
+TRANSCRIBE_ABI_LANGID_PARAMS = 21
+TRANSCRIBE_ABI_LANGID_RESULT = 22
+TRANSCRIBE_ABI_LANGID_CANDIDATE = 23
 TRANSCRIBE_LOG_LEVEL_NONE = 0
 TRANSCRIBE_LOG_LEVEL_INFO = 1
 TRANSCRIBE_LOG_LEVEL_WARN = 2
@@ -98,6 +104,7 @@
 TRANSCRIBE_DEVICE_TYPE_ACCEL = 3
 TRANSCRIBE_ROLE_ASR = 1
 TRANSCRIBE_ROLE_DIARIZE = 2
+TRANSCRIBE_ROLE_LANGID = 4
 TRANSCRIBE_FEATURE_INITIAL_PROMPT = 0
 TRANSCRIBE_FEATURE_TEMPERATURE_FALLBACK = 1
 TRANSCRIBE_FEATURE_LONG_FORM = 2
@@ -177,6 +184,16 @@ class transcribe_diarize_session_params(_c.Structure):
     pass
 class transcribe_diarize_params(_c.Structure):
     pass
+class transcribe_langid_info(_c.Structure):
+    pass
+class transcribe_langid_session_params(_c.Structure):
+    pass
+class transcribe_langid_params(_c.Structure):
+    pass
+class transcribe_langid_result(_c.Structure):
+    pass
+class transcribe_langid_candidate(_c.Structure):
+    pass
 class transcribe_moonshine_streaming_stream_ext(_c.Structure):
     pass
 class transcribe_parakeet_stream_ext(_c.Structure):
@@ -211,6 +228,11 @@ class transcribe_whisper_chunk_trace(_c.Structure):
 transcribe_diarize_info._fields_ = [("struct_size", _c.c_uint64), ("sample_rate", _c.c_int32), ("max_speakers", _c.c_int32)]
 transcribe_diarize_session_params._fields_ = [("struct_size", _c.c_uint64), ("n_threads", _c.c_int32)]
 transcribe_diarize_params._fields_ = [("struct_size", _c.c_uint64), ("family", _c.POINTER(transcribe_ext))]
+transcribe_langid_info._fields_ = [("struct_size", _c.c_uint64), ("sample_rate", _c.c_int32), ("n_labels", _c.c_int32), ("min_audio_ms", _c.c_int32)]
+transcribe_langid_session_params._fields_ = [("struct_size", _c.c_uint64), ("n_threads", _c.c_int32), ("max_audio_ms", _c.c_int32)]
+transcribe_langid_params._fields_ = [("struct_size", _c.c_uint64), ("allowed", _c.POINTER(_c.c_char_p)), ("n_allowed", _c.c_int32), ("top_k", _c.c_int32)]
+transcribe_langid_result._fields_ = [("struct_size", _c.c_uint64), ("n_candidates", _c.c_int32), ("n_allowed", _c.c_int32), ("allowed_mass", _c.c_float), ("audio_ms", _c.c_int64)]
+transcribe_langid_candidate._fields_ = [("struct_size", _c.c_uint64), ("index", _c.c_int32), ("code", _c.c_char_p), ("name", _c.c_char_p), ("p", _c.c_float), ("p_unrestricted", _c.c_float), ("logit", _c.c_float)]
 transcribe_moonshine_streaming_stream_ext._fields_ = [("ext", transcribe_ext), ("min_decode_interval_ms", _c.c_int32)]
 transcribe_parakeet_stream_ext._fields_ = [("ext", transcribe_ext), ("att_context_right", _c.c_int32)]
 transcribe_parakeet_buffered_stream_ext._fields_ = [("ext", transcribe_ext), ("left_ms", _c.c_int32), ("chunk_ms", _c.c_int32), ("right_ms", _c.c_int32)]
@@ -241,6 +263,11 @@ class transcribe_whisper_chunk_trace(_c.Structure):
     'transcribe_diarize_info': 16,
     'transcribe_diarize_session_params': 17,
     'transcribe_diarize_params': 18,
+    'transcribe_langid_info': 19,
+    'transcribe_langid_session_params': 20,
+    'transcribe_langid_params': 21,
+    'transcribe_langid_result': 22,
+    'transcribe_langid_candidate': 23,
 }
 
 # C-compiler layout captured at generation (for offset self-check).
@@ -264,6 +291,11 @@ class transcribe_whisper_chunk_trace(_c.Structure):
     'transcribe_diarize_info': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'sample_rate': 8, 'max_speakers': 12}},
     'transcribe_diarize_session_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_threads': 8}},
     'transcribe_diarize_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'family': 8}},
+    'transcribe_langid_info': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'sample_rate': 8, 'n_labels': 12, 'min_audio_ms': 16}},
+    'transcribe_langid_session_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_threads': 8, 'max_audio_ms': 12}},
+    'transcribe_langid_params': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'allowed': 8, 'n_allowed': 16, 'top_k': 20}},
+    'transcribe_langid_result': {'size': 32, 'align': 8, 'offsets': {'struct_size': 0, 'n_candidates': 8, 'n_allowed': 12, 'allowed_mass': 16, 'audio_ms': 24}},
+    'transcribe_langid_candidate': {'size': 48, 'align': 8, 'offsets': {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'p_unrestricted': 36, 'logit': 40}},
     'transcribe_moonshine_streaming_stream_ext': {'size': 24, 'align': 8, 'offsets': {'ext': 0, 'min_decode_interval_ms': 16}},
     'transcribe_parakeet_stream_ext': {'size': 24, 'align': 8, 'offsets': {'ext': 0, 'att_context_right': 16}},
     'transcribe_parakeet_buffered_stream_ext': {'size': 32, 'align': 8, 'offsets': {'ext': 0, 'left_ms': 16, 'chunk_ms': 20, 'right_ms': 24}},
@@ -378,6 +410,38 @@ def configure(lib):
     lib.transcribe_init_backends_default.argtypes = []
     lib.transcribe_init_backends_ex.restype = _c.c_int
     lib.transcribe_init_backends_ex.argtypes = [_c.POINTER(transcribe_backend_init_params)]
+    lib.transcribe_langid_candidate_init.restype = None
+    lib.transcribe_langid_candidate_init.argtypes = [_c.POINTER(transcribe_langid_candidate)]
+    lib.transcribe_langid_get_candidate.restype = _c.c_int
+    lib.transcribe_langid_get_candidate.argtypes = [_c.c_void_p, _c.c_int, _c.POINTER(transcribe_langid_candidate)]
+    lib.transcribe_langid_get_info.restype = _c.c_int
+    lib.transcribe_langid_get_info.argtypes = [_c.c_void_p, _c.POINTER(transcribe_langid_info)]
+    lib.transcribe_langid_get_result.restype = _c.c_int
+    lib.transcribe_langid_get_result.argtypes = [_c.c_void_p, _c.POINTER(transcribe_langid_result)]
+    lib.transcribe_langid_get_timings.restype = _c.c_int
+    lib.transcribe_langid_get_timings.argtypes = [_c.c_void_p, _c.POINTER(transcribe_timings)]
+    lib.transcribe_langid_info_init.restype = None
+    lib.transcribe_langid_info_init.argtypes = [_c.POINTER(transcribe_langid_info)]
+    lib.transcribe_langid_label_code.restype = _c.c_char_p
+    lib.transcribe_langid_label_code.argtypes = [_c.c_void_p, _c.c_int32]
+    lib.transcribe_langid_label_index.restype = _c.c_int32
+    lib.transcribe_langid_label_index.argtypes = [_c.c_void_p, _c.c_char_p]
+    lib.transcribe_langid_label_name.restype = _c.c_char_p
+    lib.transcribe_langid_label_name.argtypes = [_c.c_void_p, _c.c_int32]
+    lib.transcribe_langid_params_init.restype = None
+    lib.transcribe_langid_params_init.argtypes = [_c.POINTER(transcribe_langid_params)]
+    lib.transcribe_langid_result_init.restype = None
+    lib.transcribe_langid_result_init.argtypes = [_c.POINTER(transcribe_langid_result)]
+    lib.transcribe_langid_run.restype = _c.c_int
+    lib.transcribe_langid_run.argtypes = [_c.c_void_p, _c.POINTER(_c.c_float), _c.c_int, _c.POINTER(transcribe_langid_params)]
+    lib.transcribe_langid_session_free.restype = None
+    lib.transcribe_langid_session_free.argtypes = [_c.c_void_p]
+    lib.transcribe_langid_session_init.restype = _c.c_int
+    lib.transcribe_langid_session_init.argtypes = [_c.c_void_p, _c.POINTER(transcribe_langid_session_params), _c.POINTER(_c.c_void_p)]
+    lib.transcribe_langid_session_params_init.restype = None
+    lib.transcribe_langid_session_params_init.argtypes = [_c.POINTER(transcribe_langid_session_params)]
+    lib.transcribe_langid_set_abort_callback.restype = None
+    lib.transcribe_langid_set_abort_callback.argtypes = [_c.c_void_p, _c.CFUNCTYPE(_c.c_bool, _c.c_void_p), _c.c_void_p]
     lib.transcribe_log_set.restype = None
     lib.transcribe_log_set.argtypes = [_c.CFUNCTYPE(None, _c.c_int, _c.c_char_p, _c.c_void_p), _c.c_void_p]
     lib.transcribe_model_accepts_ext_kind.restype = _c.c_bool
diff --git a/bindings/python/src/transcribe_cpp/errors.py b/bindings/python/src/transcribe_cpp/errors.py
index 236e94c67..b82fea02d 100644
--- a/bindings/python/src/transcribe_cpp/errors.py
+++ b/bindings/python/src/transcribe_cpp/errors.py
@@ -19,6 +19,7 @@
     TRANSCRIBE_ERR_FILE_NOT_FOUND as ERR_FILE_NOT_FOUND,
     TRANSCRIBE_ERR_GGUF as ERR_GGUF,
     TRANSCRIBE_ERR_INPUT_TOO_LONG as ERR_INPUT_TOO_LONG,
+    TRANSCRIBE_ERR_INPUT_TOO_SHORT as ERR_INPUT_TOO_SHORT,
     TRANSCRIBE_ERR_INVALID_ARG as ERR_INVALID_ARG,
     TRANSCRIBE_ERR_NOT_IMPLEMENTED as ERR_NOT_IMPLEMENTED,
     TRANSCRIBE_ERR_OOM as ERR_OOM,
@@ -99,6 +100,11 @@ class InputTooLong(TranscribeError):
     pass
 
 
+class InputTooShort(TranscribeError):
+    """The audio is shorter than the role's minimum (e.g. language ID scores
+    at least ``LangIdInfo.min_audio_ms``). Nothing was computed."""
+
+
 class Busy(TranscribeError):
     """A stream is active on this model, so the call was refused instead of
     started (see ``Model``). Finalize or reset the stream first, or use one
@@ -160,6 +166,7 @@ class OutputRepetition(OutputTruncated):
     ERR_OUTPUT_TRUNCATED: OutputTruncated,
     ERR_OUTPUT_REPETITION: OutputRepetition,
     ERR_UNSUPPORTED_ROLE: UnsupportedRole,
+    ERR_INPUT_TOO_SHORT: InputTooShort,
 }
 
 
diff --git a/bindings/python/tests/conftest.py b/bindings/python/tests/conftest.py
index 392ef2935..a1eb6a5b6 100644
--- a/bindings/python/tests/conftest.py
+++ b/bindings/python/tests/conftest.py
@@ -64,6 +64,9 @@
     / "models/diar_streaming_sortformer_4spk-v2.1"
     / "diar_streaming_sortformer_4spk-v2.1-F32.gguf"
 )
+LANGID_MODEL = (
+    REPO / "models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf"
+)
 PNC_MODEL = REPO / "models/canary-180m-flash/canary-180m-flash-Q8_0.gguf"
 ITN_MODEL = REPO / "models/SenseVoiceSmall/SenseVoiceSmall-Q8_0.gguf"
 
@@ -172,6 +175,28 @@ def sortformer_model_path() -> Path:
     return _family_model("TRANSCRIBE_SMOKE_SORTFORMER_MODEL", SORTFORMER_MODEL)
 
 
+@pytest.fixture(scope="session")
+def langid_model_path() -> Path:
+    """VoxLingua107 ECAPA-TDNN (serves only the LANGID role)."""
+    return _family_model("TRANSCRIBE_SMOKE_LANGID_MODEL", LANGID_MODEL)
+
+
+@pytest.fixture(scope="session")
+def langid_toy_model_path(tmp_path_factory) -> Path:
+    """The toy ecapa_tdnn GGUF from tests/fixtures/make_gguf_fixtures.py
+    (5 labels aa..ee, alias xx=aa, random weights). The generator is
+    dependency-free, so this always runs; results are structural only."""
+    import importlib.util
+
+    gen = REPO / "tests/fixtures/make_gguf_fixtures.py"
+    spec = importlib.util.spec_from_file_location("make_gguf_fixtures", gen)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    path = tmp_path_factory.mktemp("langid") / "ecapa_tdnn_toy.gguf"
+    path.write_bytes(mod._ecapa_tdnn_gguf(mod.ECAPA_LABEL_CODES, mod.ECAPA_LABEL_NAMES, ["xx=aa"]))
+    return path
+
+
 @pytest.fixture(scope="session")
 def pnc_model_path() -> Path:
     """Canary model whose generic PNC run parameter changes the prompt."""
diff --git a/bindings/python/tests/test_compute_lock.py b/bindings/python/tests/test_compute_lock.py
index ef65ddf8a..dc910afcb 100644
--- a/bindings/python/tests/test_compute_lock.py
+++ b/bindings/python/tests/test_compute_lock.py
@@ -55,6 +55,8 @@ def fake(monkeypatch):
                         lambda h: frees.append(("model", h.value)))
     monkeypatch.setattr(t._lib, "transcribe_diarize_session_free",
                         lambda h: frees.append(("diarize", h.value)))
+    monkeypatch.setattr(t._lib, "transcribe_langid_session_free",
+                        lambda h: frees.append(("langid", h.value)))
     resets: list = []  # session handles transcribe_stream_reset was given
     monkeypatch.setattr(t._lib, "transcribe_stream_reset",
                         lambda h: resets.append(h.value))
@@ -183,6 +185,7 @@ def test_every_compute_site_holds_the_lock(fake, monkeypatch):
     m = fake.model()
     s = fake.session(m)
     d = fake.session(m, cls=t.DiarizeSession)
+    lid = fake.session(m, cls=t.LangIdSession)
     seen: list = []
 
     def holds(name, ret=0):
@@ -196,7 +199,8 @@ def probe(*args):
                  "transcribe_stream_begin", "transcribe_stream_feed",
                  "transcribe_stream_finalize", "transcribe_stream_reset",
                  "transcribe_batch_status", "transcribe_diarize_run",
-                 "transcribe_diarize_n_segments"):
+                 "transcribe_diarize_n_segments", "transcribe_langid_run",
+                 "transcribe_langid_get_result"):
         monkeypatch.setattr(t._lib, name, holds(name))
     monkeypatch.setattr(t._lib, "transcribe_batch_n_results",
                         holds("transcribe_batch_n_results", ret=1))
@@ -215,6 +219,7 @@ def materialize(self, h, utt=None):
     stream.finalize()
     stream.reset()
     assert d.run(PCM) == []
+    assert lid.run(PCM).candidates == ()
 
     names = [n for n, _ in seen]
     for site in ("transcribe_run", "transcribe_run_batch",
@@ -222,6 +227,7 @@ def materialize(self, h, utt=None):
                  "transcribe_stream_begin", "transcribe_stream_feed",
                  "transcribe_stream_finalize", "transcribe_stream_reset",
                  "transcribe_diarize_run", "transcribe_diarize_n_segments",
+                 "transcribe_langid_run", "transcribe_langid_get_result",
                  "copy-out"):
         assert site in names, f"{site} never reached"
     assert all(ok for _, ok in seen), [n for n, ok in seen if not ok]
@@ -571,3 +577,45 @@ def test_diarize_run_busy_while_stream_active(fake, native, monkeypatch):
     assert calls == [] and not m._compute_lock.locked()
     stream.finalize()
     assert d.run(PCM) == [] and calls == ["run"]
+
+
+# --- LangIdSession --------------------------------------------------------------
+
+LANGID_BUSY = ("a stream is active on this model; "
+               "finish or drop it before langid run()")
+
+
+def test_langid_run_busy_while_stream_active(fake, native, monkeypatch):
+    m = fake.model()
+    s, lid = fake.session(m), fake.session(m, cls=t.LangIdSession)
+    calls: list = []
+    monkeypatch.setattr(t._lib, "transcribe_langid_run",
+                        lambda *a: calls.append("run") or 0)
+    monkeypatch.setattr(t._lib, "transcribe_langid_get_result", lambda h, out: 0)
+    stream = s.stream()
+    with pytest.raises(t.Busy) as ei:
+        lid.run(PCM)
+    assert str(ei.value) == LANGID_BUSY
+    assert calls == [] and not m._compute_lock.locked()
+    stream.finalize()
+    assert lid.run(PCM).candidates == () and calls == ["run"]
+
+
+def test_langid_allowed_kept_alive_and_empty_rejected(fake, monkeypatch):
+    m = fake.model()
+    lid = fake.session(m, cls=t.LangIdSession)
+    seen: list = []
+
+    def run(h, pcm, n, params):
+        p = params._obj
+        seen.append([p.allowed[i].decode() for i in range(p.n_allowed)])
+        return 0
+
+    monkeypatch.setattr(t._lib, "transcribe_langid_run", run)
+    monkeypatch.setattr(t._lib, "transcribe_langid_get_result", lambda h, out: 0)
+    lid.run(PCM, allowed=["en", "de"])
+    lid.run(PCM, allowed=None)
+    assert seen == [["en", "de"], []]
+    with pytest.raises(t.InvalidArgument):
+        lid.run(PCM, allowed=[])
+    assert len(seen) == 2  # rejected before the native call
diff --git a/bindings/python/tests/test_errors.py b/bindings/python/tests/test_errors.py
index 1c7b64d71..92954b9ad 100644
--- a/bindings/python/tests/test_errors.py
+++ b/bindings/python/tests/test_errors.py
@@ -43,6 +43,7 @@ def test_every_status_maps_to_documented_subclass():
         errors.ERR_OUTPUT_TRUNCATED: t.OutputTruncated,
         errors.ERR_OUTPUT_REPETITION: t.OutputRepetition,
         errors.ERR_UNSUPPORTED_ROLE: t.UnsupportedRole,
+        errors.ERR_INPUT_TOO_SHORT: t.InputTooShort,
     }
     # The mapping table covers every non-OK status the header defines, and
     # nothing else (a new C status must be mapped deliberately, not by
diff --git a/bindings/python/tests/test_langid.py b/bindings/python/tests/test_langid.py
new file mode 100644
index 000000000..cf1f1983a
--- /dev/null
+++ b/bindings/python/tests/test_langid.py
@@ -0,0 +1,138 @@
+"""LANGID role: roles, info, label table, LangIdSession.run and its allowed /
+top_k contract. Contract tests run on the toy ecapa_tdnn fixture (always
+available); accuracy tests need the real VoxLingua107 GGUF. The locking /
+Busy / close / cancel rules are pinned model-free in test_compute_lock.py."""
+
+from __future__ import annotations
+
+import array
+import math
+
+import pytest
+
+import transcribe_cpp as t
+from conftest import SAMPLES, load_wav
+
+
+def _noise(n: int, seed: int = 7) -> array.array:
+    out = array.array("f")
+    s = seed | 1
+    for _ in range(n):
+        s = (s * 1664525 + 1013904223) & 0xFFFFFFFF
+        out.append(((s >> 8) & 0xFFFFFF) / 16777216.0 - 0.5)
+    return out
+
+
+@pytest.fixture(scope="module")
+def noise_1s():
+    return _noise(16000)
+
+
+def test_toy_roles_info_labels(langid_toy_model_path):
+    with t.Model(langid_toy_model_path, backend="cpu") as model:
+        assert model.roles == {t.Role.LANGID}
+        assert model.langid_info == t.LangIdInfo(sample_rate=16000, n_labels=5, min_audio_ms=500)
+        assert model.langid_labels[2] == ("cc", "Charlie")
+        assert model.langid_label_index("xx") == 0
+        assert model.langid_label_index("zz") is None
+        with pytest.raises(t.UnsupportedRole):
+            model.capabilities
+        with pytest.raises(t.UnsupportedRole):
+            model.session()
+        with pytest.raises(t.UnsupportedRole):
+            model.diarize_session()
+
+
+def test_asr_only_model_rejects_langid(model_path):
+    with t.Model(model_path) as model:
+        with pytest.raises(t.UnsupportedRole):
+            model.langid_info
+        with pytest.raises(t.UnsupportedRole):
+            model.langid_session()
+        assert model.langid_label_index("en") is None
+
+
+def test_toy_run_contract(langid_toy_model_path, noise_1s):
+    with t.Model(langid_toy_model_path, backend="cpu") as model:
+        with model.langid_session(n_threads=1) as lid:
+            r = lid.run(noise_1s)
+            assert len(r.candidates) == 5 and r.n_allowed == 5
+            assert r.allowed_mass == 1.0 and r.audio_ms == 1000
+            assert r.code == r.candidates[0].code
+            assert math.isclose(sum(c.p for c in r.candidates), 1.0, rel_tol=1e-5)
+            ps = [c.p for c in r.candidates]
+            assert ps == sorted(ps, reverse=True)
+            assert all(c.p == c.p_unrestricted for c in r.candidates)
+
+            restricted = lid.run(noise_1s, allowed=["bb", "dd"])
+            assert {c.code for c in restricted.candidates} == {"bb", "dd"}
+            assert restricted.n_allowed == 2 and 0.0 < restricted.allowed_mass < 1.0
+
+            alias = lid.run(noise_1s, allowed=("xx",))
+            assert [c.code for c in alias.candidates] == ["aa"] and alias.candidates[0].p == 1.0
+
+            top = lid.run(noise_1s, top_k=2)
+            assert len(top.candidates) == 2 and top.n_allowed == 5
+
+            assert lid.timings.encode_ms > 0.0
+
+
+def test_toy_allowed_rejections(langid_toy_model_path, noise_1s):
+    with t.Model(langid_toy_model_path, backend="cpu") as model:
+        with model.langid_session() as lid:
+            # An empty list must not silently mean "all labels".
+            with pytest.raises(t.InvalidArgument):
+                lid.run(noise_1s, allowed=[])
+            with pytest.raises(t.InvalidArgument):
+                lid.run(noise_1s, allowed="en")
+            with pytest.raises(t.InvalidArgument):
+                lid.run(noise_1s, allowed=["aa", None])
+            with pytest.raises(t.UnsupportedRequest):
+                lid.run(noise_1s, allowed=["zz"])
+            with pytest.raises(t.InvalidArgument):
+                lid.run(noise_1s, top_k=-1)
+            lid.run(noise_1s, allowed=None)  # None is every label
+
+
+def test_toy_input_rules(langid_toy_model_path, noise_1s):
+    with t.Model(langid_toy_model_path, backend="cpu") as model:
+        with model.langid_session() as lid:
+            with pytest.raises(t.InputTooShort):
+                lid.run(noise_1s[:6400])  # 400 ms
+            assert lid.run(noise_1s[:8000]).audio_ms == 500
+            assert lid.run(_noise(16000 * 31)).audio_ms == 30000
+            bad = array.array("f", noise_1s)
+            bad[10] = float("nan")
+            with pytest.raises(t.InvalidArgument):
+                lid.run(bad)
+        with pytest.raises(t.InvalidArgument):
+            model.langid_session(max_audio_ms=400)
+        with model.langid_session(max_audio_ms=500) as short:
+            assert short.run(noise_1s).audio_ms == 500
+
+
+def test_toy_cancel(langid_toy_model_path, noise_1s):
+    with t.Model(langid_toy_model_path, backend="cpu") as model:
+        with model.langid_session() as lid:
+            lid.cancel()
+            # run() clears a stale cancel before it starts, so it completes.
+            assert lid.run(noise_1s).candidates
+
+
+@pytest.mark.parametrize("code", ["en", "de", "fr", "es", "ja", "zh", "ru", "id"])
+def test_real_fleurs_top1(langid_model_path, code):
+    pcm = load_wav(SAMPLES / f"fleurs-{code}.wav")
+    with t.Model(langid_model_path, backend="cpu") as model:
+        assert model.langid_info.n_labels == 107
+        assert model.langid_label_index("he") == model.langid_label_index("iw")
+        with model.langid_session() as lid:
+            r = lid.run(pcm, top_k=3)
+    assert r.code == code and r.candidates[0].p >= 0.5
+
+
+def test_real_allowed_mass_signals_out_of_set(langid_model_path):
+    pcm = load_wav(SAMPLES / "fleurs-ja.wav")
+    with t.Model(langid_model_path, backend="cpu") as model:
+        with model.langid_session() as lid:
+            r = lid.run(pcm, allowed=["en", "de"])
+    assert r.code in ("en", "de") and r.allowed_mass < 0.01
diff --git a/bindings/rust/sys/src/transcribe_sys.rs b/bindings/rust/sys/src/transcribe_sys.rs
index 2c08ac549..d1e592f60 100644
--- a/bindings/rust/sys/src/transcribe_sys.rs
+++ b/bindings/rust/sys/src/transcribe_sys.rs
@@ -1,11 +1,11 @@
 // @generated by `cargo xtask bindgen` from include/transcribe/extensions.h
 // DO NOT EDIT BY HAND. Regenerate: `cargo xtask bindgen`.
-// Pinned to include/transcribe.abihash = bd3273dabb25a1fe
+// Pinned to include/transcribe.abihash = 318eba8c4b2e4387
 
 /// The public-ABI digest these bindings were generated against
 /// (sha256/16 over the normalized FFI surface). The load-time version
 /// gate and the CI drift check both anchor on this value.
-pub const PUBLIC_HEADER_HASH: &str = "bd3273dabb25a1fe";
+pub const PUBLIC_HEADER_HASH: &str = "318eba8c4b2e4387";
 
 /* automatically generated by rust-bindgen 0.72.1 */
 
@@ -44,6 +44,7 @@ impl transcribe_status {
     pub const TRANSCRIBE_ERR_OUTPUT_TRUNCATED: transcribe_status = transcribe_status(18);
     pub const TRANSCRIBE_ERR_OUTPUT_REPETITION: transcribe_status = transcribe_status(19);
     pub const TRANSCRIBE_ERR_UNSUPPORTED_ROLE: transcribe_status = transcribe_status(20);
+    pub const TRANSCRIBE_ERR_INPUT_TOO_SHORT: transcribe_status = transcribe_status(21);
 }
 #[repr(transparent)]
 #[derive(Debug, Copy, Clone, Hash, PartialEq, Eq)]
@@ -79,6 +80,12 @@ impl transcribe_abi_struct {
     pub const TRANSCRIBE_ABI_DIARIZE_SESSION_PARAMS: transcribe_abi_struct =
         transcribe_abi_struct(17);
     pub const TRANSCRIBE_ABI_DIARIZE_PARAMS: transcribe_abi_struct = transcribe_abi_struct(18);
+    pub const TRANSCRIBE_ABI_LANGID_INFO: transcribe_abi_struct = transcribe_abi_struct(19);
+    pub const TRANSCRIBE_ABI_LANGID_SESSION_PARAMS: transcribe_abi_struct =
+        transcribe_abi_struct(20);
+    pub const TRANSCRIBE_ABI_LANGID_PARAMS: transcribe_abi_struct = transcribe_abi_struct(21);
+    pub const TRANSCRIBE_ABI_LANGID_RESULT: transcribe_abi_struct = transcribe_abi_struct(22);
+    pub const TRANSCRIBE_ABI_LANGID_CANDIDATE: transcribe_abi_struct = transcribe_abi_struct(23);
 }
 #[repr(transparent)]
 #[derive(Debug, Copy, Clone, Hash, PartialEq, Eq)]
@@ -489,6 +496,7 @@ unsafe extern "C" {
 impl transcribe_role {
     pub const TRANSCRIBE_ROLE_ASR: transcribe_role = transcribe_role(1);
     pub const TRANSCRIBE_ROLE_DIARIZE: transcribe_role = transcribe_role(2);
+    pub const TRANSCRIBE_ROLE_LANGID: transcribe_role = transcribe_role(4);
 }
 #[repr(transparent)]
 #[derive(Debug, Copy, Clone, Hash, PartialEq, Eq)]
@@ -1281,6 +1289,217 @@ unsafe extern "C" {
 }
 #[repr(C)]
 #[derive(Debug, Copy, Clone)]
+pub struct transcribe_langid_session {
+    _unused: [u8; 0],
+}
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct transcribe_langid_info {
+    pub struct_size: u64,
+    pub sample_rate: i32,
+    pub n_labels: i32,
+    pub min_audio_ms: i32,
+}
+#[allow(clippy::unnecessary_operation, clippy::identity_op)]
+const _: () = {
+    ["Size of transcribe_langid_info"][::std::mem::size_of::<transcribe_langid_info>() - 24usize];
+    ["Alignment of transcribe_langid_info"]
+        [::std::mem::align_of::<transcribe_langid_info>() - 8usize];
+    ["Offset of field: transcribe_langid_info::struct_size"]
+        [::std::mem::offset_of!(transcribe_langid_info, struct_size) - 0usize];
+    ["Offset of field: transcribe_langid_info::sample_rate"]
+        [::std::mem::offset_of!(transcribe_langid_info, sample_rate) - 8usize];
+    ["Offset of field: transcribe_langid_info::n_labels"]
+        [::std::mem::offset_of!(transcribe_langid_info, n_labels) - 12usize];
+    ["Offset of field: transcribe_langid_info::min_audio_ms"]
+        [::std::mem::offset_of!(transcribe_langid_info, min_audio_ms) - 16usize];
+};
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct transcribe_langid_session_params {
+    pub struct_size: u64,
+    pub n_threads: i32,
+    pub max_audio_ms: i32,
+}
+#[allow(clippy::unnecessary_operation, clippy::identity_op)]
+const _: () = {
+    ["Size of transcribe_langid_session_params"]
+        [::std::mem::size_of::<transcribe_langid_session_params>() - 16usize];
+    ["Alignment of transcribe_langid_session_params"]
+        [::std::mem::align_of::<transcribe_langid_session_params>() - 8usize];
+    ["Offset of field: transcribe_langid_session_params::struct_size"]
+        [::std::mem::offset_of!(transcribe_langid_session_params, struct_size) - 0usize];
+    ["Offset of field: transcribe_langid_session_params::n_threads"]
+        [::std::mem::offset_of!(transcribe_langid_session_params, n_threads) - 8usize];
+    ["Offset of field: transcribe_langid_session_params::max_audio_ms"]
+        [::std::mem::offset_of!(transcribe_langid_session_params, max_audio_ms) - 12usize];
+};
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct transcribe_langid_params {
+    pub struct_size: u64,
+    pub allowed: *const *const ::std::os::raw::c_char,
+    pub n_allowed: i32,
+    pub top_k: i32,
+}
+#[allow(clippy::unnecessary_operation, clippy::identity_op)]
+const _: () = {
+    ["Size of transcribe_langid_params"]
+        [::std::mem::size_of::<transcribe_langid_params>() - 24usize];
+    ["Alignment of transcribe_langid_params"]
+        [::std::mem::align_of::<transcribe_langid_params>() - 8usize];
+    ["Offset of field: transcribe_langid_params::struct_size"]
+        [::std::mem::offset_of!(transcribe_langid_params, struct_size) - 0usize];
+    ["Offset of field: transcribe_langid_params::allowed"]
+        [::std::mem::offset_of!(transcribe_langid_params, allowed) - 8usize];
+    ["Offset of field: transcribe_langid_params::n_allowed"]
+        [::std::mem::offset_of!(transcribe_langid_params, n_allowed) - 16usize];
+    ["Offset of field: transcribe_langid_params::top_k"]
+        [::std::mem::offset_of!(transcribe_langid_params, top_k) - 20usize];
+};
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct transcribe_langid_result {
+    pub struct_size: u64,
+    pub n_candidates: i32,
+    pub n_allowed: i32,
+    pub allowed_mass: f32,
+    pub audio_ms: i64,
+}
+#[allow(clippy::unnecessary_operation, clippy::identity_op)]
+const _: () = {
+    ["Size of transcribe_langid_result"]
+        [::std::mem::size_of::<transcribe_langid_result>() - 32usize];
+    ["Alignment of transcribe_langid_result"]
+        [::std::mem::align_of::<transcribe_langid_result>() - 8usize];
+    ["Offset of field: transcribe_langid_result::struct_size"]
+        [::std::mem::offset_of!(transcribe_langid_result, struct_size) - 0usize];
+    ["Offset of field: transcribe_langid_result::n_candidates"]
+        [::std::mem::offset_of!(transcribe_langid_result, n_candidates) - 8usize];
+    ["Offset of field: transcribe_langid_result::n_allowed"]
+        [::std::mem::offset_of!(transcribe_langid_result, n_allowed) - 12usize];
+    ["Offset of field: transcribe_langid_result::allowed_mass"]
+        [::std::mem::offset_of!(transcribe_langid_result, allowed_mass) - 16usize];
+    ["Offset of field: transcribe_langid_result::audio_ms"]
+        [::std::mem::offset_of!(transcribe_langid_result, audio_ms) - 24usize];
+};
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct transcribe_langid_candidate {
+    pub struct_size: u64,
+    pub index: i32,
+    pub code: *const ::std::os::raw::c_char,
+    pub name: *const ::std::os::raw::c_char,
+    pub p: f32,
+    pub p_unrestricted: f32,
+    pub logit: f32,
+}
+#[allow(clippy::unnecessary_operation, clippy::identity_op)]
+const _: () = {
+    ["Size of transcribe_langid_candidate"]
+        [::std::mem::size_of::<transcribe_langid_candidate>() - 48usize];
+    ["Alignment of transcribe_langid_candidate"]
+        [::std::mem::align_of::<transcribe_langid_candidate>() - 8usize];
+    ["Offset of field: transcribe_langid_candidate::struct_size"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, struct_size) - 0usize];
+    ["Offset of field: transcribe_langid_candidate::index"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, index) - 8usize];
+    ["Offset of field: transcribe_langid_candidate::code"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, code) - 16usize];
+    ["Offset of field: transcribe_langid_candidate::name"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, name) - 24usize];
+    ["Offset of field: transcribe_langid_candidate::p"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, p) - 32usize];
+    ["Offset of field: transcribe_langid_candidate::p_unrestricted"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, p_unrestricted) - 36usize];
+    ["Offset of field: transcribe_langid_candidate::logit"]
+        [::std::mem::offset_of!(transcribe_langid_candidate, logit) - 40usize];
+};
+unsafe extern "C" {
+    pub fn transcribe_langid_info_init(out: *mut transcribe_langid_info);
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_session_params_init(params: *mut transcribe_langid_session_params);
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_params_init(params: *mut transcribe_langid_params);
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_result_init(out: *mut transcribe_langid_result);
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_candidate_init(out: *mut transcribe_langid_candidate);
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_get_info(
+        model: *const transcribe_model,
+        out: *mut transcribe_langid_info,
+    ) -> transcribe_status;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_label_code(
+        model: *const transcribe_model,
+        i: i32,
+    ) -> *const ::std::os::raw::c_char;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_label_name(
+        model: *const transcribe_model,
+        i: i32,
+    ) -> *const ::std::os::raw::c_char;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_label_index(
+        model: *const transcribe_model,
+        code_or_alias: *const ::std::os::raw::c_char,
+    ) -> i32;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_session_init(
+        model: *mut transcribe_model,
+        params: *const transcribe_langid_session_params,
+        out_session: *mut *mut transcribe_langid_session,
+    ) -> transcribe_status;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_session_free(session: *mut transcribe_langid_session);
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_set_abort_callback(
+        session: *mut transcribe_langid_session,
+        cb: transcribe_abort_callback,
+        user_data: *mut ::std::os::raw::c_void,
+    );
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_run(
+        session: *mut transcribe_langid_session,
+        pcm: *const f32,
+        n_samples: ::std::os::raw::c_int,
+        params: *const transcribe_langid_params,
+    ) -> transcribe_status;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_get_result(
+        session: *const transcribe_langid_session,
+        out: *mut transcribe_langid_result,
+    ) -> transcribe_status;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_get_candidate(
+        session: *const transcribe_langid_session,
+        i: ::std::os::raw::c_int,
+        out: *mut transcribe_langid_candidate,
+    ) -> transcribe_status;
+}
+unsafe extern "C" {
+    pub fn transcribe_langid_get_timings(
+        session: *const transcribe_langid_session,
+        out: *mut transcribe_timings,
+    ) -> transcribe_status;
+}
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
 pub struct transcribe_moonshine_streaming_stream_ext {
     pub ext: transcribe_ext,
     pub min_decode_interval_ms: i32,
diff --git a/bindings/rust/transcribe-cpp/README.md b/bindings/rust/transcribe-cpp/README.md
index dd12b78c7..bfb8fff18 100644
--- a/bindings/rust/transcribe-cpp/README.md
+++ b/bindings/rust/transcribe-cpp/README.md
@@ -93,6 +93,25 @@ for turn in diarize.run(&pcm, &DiarizeOptions::default())? {
 # Ok::<(), transcribe_cpp::Error>(())
 ```
 
+### Language ID
+
+A model whose `roles()` contain `Role::LangId` (VoxLingua107 ECAPA-TDNN)
+opens a `LangIdSession`. Codes are the model's own labels; match them against
+an ASR model's `capabilities().languages` yourself. `allowed: None` scores
+every label, `Some(vec![])` is `Error::InvalidArgument`, and clips under
+`langid_info()?.min_audio_ms` are `Error::InputTooShort`. A low
+`allowed_mass` means the speech is probably outside the allowed set.
+
+```rust
+use transcribe_cpp::{LangIdOptions, Model};
+let model = Model::load("lang-id-voxlingua107-ecapa-Q8_0.gguf")?;
+let mut lid = model.langid_session()?;
+let opts = LangIdOptions { allowed: Some(vec!["en".into(), "de".into()]), top_k: 3 };
+let result = lid.run(&pcm, &opts)?;
+println!("{:?} (mass {})", result.code(), result.allowed_mass);
+# Ok::<(), transcribe_cpp::Error>(())
+```
+
 Runnable examples:
 
 ```sh
diff --git a/bindings/rust/transcribe-cpp/src/error.rs b/bindings/rust/transcribe-cpp/src/error.rs
index eaa274add..7ac4af05d 100644
--- a/bindings/rust/transcribe-cpp/src/error.rs
+++ b/bindings/rust/transcribe-cpp/src/error.rs
@@ -78,6 +78,10 @@ pub enum Error {
     /// `TRANSCRIBE_ERR_UNSUPPORTED_ROLE` — see [`Model::roles`](crate::Model::roles).
     #[error("unsupported role: {0}")]
     UnsupportedRole(String),
+    /// `TRANSCRIBE_ERR_INPUT_TOO_SHORT` — the audio is shorter than the role's
+    /// minimum (e.g. [`LangIdInfo::min_audio_ms`](crate::LangIdInfo)).
+    #[error("input too short: {0}")]
+    InputTooShort(String),
     /// The loaded library's base version disagrees with the headers this crate
     /// was generated against (the pre-1.0 version lock). Raised on first use.
     #[error("native library version mismatch: {0}")]
@@ -117,6 +121,7 @@ impl Error {
             Error::OutputTruncated { .. } => S::TRANSCRIBE_ERR_OUTPUT_TRUNCATED,
             Error::OutputRepetition { .. } => S::TRANSCRIBE_ERR_OUTPUT_REPETITION,
             Error::UnsupportedRole(_) => S::TRANSCRIBE_ERR_UNSUPPORTED_ROLE,
+            Error::InputTooShort(_) => S::TRANSCRIBE_ERR_INPUT_TOO_SHORT,
             _ => S::TRANSCRIBE_OK,
         };
         s.0 as i32
@@ -191,6 +196,7 @@ pub(crate) fn error_for_status(status: sys::transcribe_status, context: &str) ->
             partial: None,
         },
         S::TRANSCRIBE_ERR_UNSUPPORTED_ROLE => Error::UnsupportedRole(msg),
+        S::TRANSCRIBE_ERR_INPUT_TOO_SHORT => Error::InputTooShort(msg),
         _ => Error::Other(msg),
     }
 }
diff --git a/bindings/rust/transcribe-cpp/src/langid.rs b/bindings/rust/transcribe-cpp/src/langid.rs
new file mode 100644
index 000000000..882413cde
--- /dev/null
+++ b/bindings/rust/transcribe-cpp/src/langid.rs
@@ -0,0 +1,298 @@
+//! [`LangIdSession`] — the LANGID role (which language is spoken), from
+//! [`Model::langid_session`]. Threading, lifetime, and compute-lock rules are
+//! those of [`Session`](crate::Session).
+
+use std::ffi::CString;
+use std::os::raw::{c_char, c_void};
+use std::sync::atomic::AtomicBool;
+use std::sync::Arc;
+
+use transcribe_cpp_sys as sys;
+
+use crate::cancel::{abort_trampoline, CancelToken};
+use crate::error::{check, Error, Result};
+use crate::model::{Model, ModelInner};
+use crate::result::{owned_str, Timings};
+use crate::session::clamp_len;
+
+/// Static facts about a language ID model ([`Model::langid_info`]).
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+#[non_exhaustive]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct LangIdInfo {
+    /// Input PCM rate (16000).
+    pub sample_rate: i32,
+    /// Label indices are `[0, n_labels)`.
+    pub n_labels: i32,
+    /// Shorter scored audio is [`Error::InputTooShort`].
+    pub min_audio_ms: i32,
+}
+
+/// Options for creating a language ID session.
+#[derive(Debug, Clone, Default, PartialEq, Eq)]
+#[cfg_attr(
+    feature = "serde",
+    derive(serde::Serialize, serde::Deserialize),
+    serde(default)
+)]
+pub struct LangIdSessionOptions {
+    /// CPU threads for ops that run on CPU; 0 = library default.
+    pub n_threads: i32,
+    /// Longer input is scored on its last `max_audio_ms`; 0 = 30000. Below
+    /// [`LangIdInfo::min_audio_ms`] is [`Error::InvalidArgument`].
+    pub max_audio_ms: i32,
+}
+
+/// Per-run language ID parameters.
+#[derive(Debug, Clone, Default, PartialEq, Eq)]
+#[cfg_attr(
+    feature = "serde",
+    derive(serde::Serialize, serde::Deserialize),
+    serde(default)
+)]
+pub struct LangIdOptions {
+    /// Restrict the decision to these codes or aliases. `None` means every
+    /// label; `Some` of an empty list is [`Error::InvalidArgument`]. An
+    /// unknown code is [`Error::Unsupported`].
+    pub allowed: Option<Vec<String>>,
+    /// Keep the best `top_k` candidates; 0 = every allowed label.
+    pub top_k: i32,
+}
+
+/// One ranked label. `code` is the model's own label (`"en"`, `"iw"`).
+#[derive(Debug, Clone, PartialEq)]
+#[non_exhaustive]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct LangIdCandidate {
+    /// Label index.
+    pub index: i32,
+    pub code: String,
+    pub name: String,
+    /// Softmax renormalized over the allowed set.
+    pub p: f32,
+    /// Softmax over every label.
+    pub p_unrestricted: f32,
+    pub logit: f32,
+}
+
+/// The result of one [`LangIdSession::run`].
+#[derive(Debug, Clone, PartialEq)]
+#[non_exhaustive]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct LangIdResult {
+    /// Ranked by `p`, descending; ties keep label order.
+    pub candidates: Vec<LangIdCandidate>,
+    /// Labels in the allowed set (before `top_k`).
+    pub n_allowed: i32,
+    /// Unrestricted probability inside the allowed set (1.0 when
+    /// unrestricted); a low value means the speech is probably outside it.
+    pub allowed_mass: f32,
+    /// Audio actually scored, after the crop.
+    pub audio_ms: i64,
+}
+
+impl LangIdResult {
+    /// The top candidate's code, if any.
+    pub fn code(&self) -> Option<&str> {
+        self.candidates.first().map(|c| c.code.as_str())
+    }
+}
+
+/// A language ID session.
+pub struct LangIdSession {
+    ptr: *mut sys::transcribe_langid_session,
+    // Keeps the native model alive and carries the per-model compute lock.
+    model: Arc<ModelInner>,
+    // Keeps the abort callback's userdata alive while installed.
+    cancel: Option<Arc<AtomicBool>>,
+}
+
+impl std::fmt::Debug for LangIdSession {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("LangIdSession").finish_non_exhaustive()
+    }
+}
+
+// SAFETY: as for `Session` — `&mut self` on the mutating calls keeps use to
+// one thread at a time; deliberately NOT Sync.
+unsafe impl Send for LangIdSession {}
+
+impl Drop for LangIdSession {
+    fn drop(&mut self) {
+        unsafe { sys::transcribe_langid_session_free(self.ptr) };
+    }
+}
+
+impl LangIdSession {
+    pub(crate) fn new(model: &Model, options: &LangIdSessionOptions) -> Result<LangIdSession> {
+        let mut params: sys::transcribe_langid_session_params = unsafe { std::mem::zeroed() };
+        unsafe { sys::transcribe_langid_session_params_init(&mut params) };
+        params.n_threads = options.n_threads;
+        params.max_audio_ms = options.max_audio_ms;
+
+        let mut out: *mut sys::transcribe_langid_session = std::ptr::null_mut();
+        let status =
+            unsafe { sys::transcribe_langid_session_init(model.inner.ptr, &params, &mut out) };
+        check(status, "langid session init")?;
+        debug_assert!(!out.is_null());
+
+        Ok(LangIdSession {
+            ptr: out,
+            model: Arc::clone(&model.inner),
+            cancel: None,
+        })
+    }
+
+    /// Install a [`CancelToken`] so an in-flight run can be aborted from
+    /// another thread (the run then returns [`Error::Aborted`]).
+    /// Replaces any previously installed token.
+    pub fn set_cancel_token(&mut self, token: &CancelToken) {
+        let flag = Arc::clone(&token.flag);
+        let userdata = Arc::as_ptr(&flag) as *mut c_void;
+        unsafe {
+            sys::transcribe_langid_set_abort_callback(self.ptr, Some(abort_trampoline), userdata)
+        };
+        self.cancel = Some(flag);
+    }
+
+    /// Remove any installed cancel token.
+    pub fn clear_cancel_token(&mut self) {
+        unsafe { sys::transcribe_langid_set_abort_callback(self.ptr, None, std::ptr::null_mut()) };
+        self.cancel = None;
+    }
+
+    /// Identify the language of one clip of 16 kHz mono float32 PCM. Input
+    /// longer than the session's `max_audio_ms` is scored on its tail.
+    pub fn run(&mut self, pcm: &[f32], options: &LangIdOptions) -> Result<LangIdResult> {
+        let n = clamp_len(pcm.len())?;
+        // The CStrings and the pointer array stay alive until the end of this
+        // call, past the native run.
+        let codes: Option<Vec<CString>> = match &options.allowed {
+            None => None,
+            Some(list) if list.is_empty() => {
+                // NULL would mean "every label", the opposite of an empty list.
+                return Err(Error::InvalidArgument(
+                    "allowed is empty; pass None for every label".into(),
+                ));
+            }
+            Some(list) => Some(
+                list.iter()
+                    .map(|c| CString::new(c.as_str()))
+                    .collect::<std::result::Result<_, _>>()?,
+            ),
+        };
+        let ptrs: Option<Vec<*const c_char>> = codes
+            .as_ref()
+            .map(|cs| cs.iter().map(|c| c.as_ptr()).collect());
+        let mut params: sys::transcribe_langid_params = unsafe { std::mem::zeroed() };
+        unsafe { sys::transcribe_langid_params_init(&mut params) };
+        if let Some(p) = &ptrs {
+            params.allowed = p.as_ptr();
+            params.n_allowed = i32::try_from(p.len())
+                .map_err(|_| Error::InvalidArgument("allowed list too long".into()))?;
+        }
+        params.top_k = options.top_k;
+
+        // Results are copied out under the compute lock, like every other
+        // binding, so a concurrent run on the same model cannot interleave.
+        let ptr = self.ptr;
+        self.model.with_compute(
+            Some("a stream is active on this model; finish or drop it before langid run()"),
+            |_| -> Result<LangIdResult> {
+                check(
+                    unsafe { sys::transcribe_langid_run(ptr, pcm.as_ptr(), n, &params) },
+                    "langid run",
+                )?;
+                let mut res: sys::transcribe_langid_result = unsafe { std::mem::zeroed() };
+                unsafe { sys::transcribe_langid_result_init(&mut res) };
+                check(
+                    unsafe { sys::transcribe_langid_get_result(ptr, &mut res) },
+                    "langid result",
+                )?;
+                let candidates = (0..res.n_candidates)
+                    .map(|i| {
+                        let mut raw: sys::transcribe_langid_candidate =
+                            unsafe { std::mem::zeroed() };
+                        unsafe { sys::transcribe_langid_candidate_init(&mut raw) };
+                        let _ = unsafe { sys::transcribe_langid_get_candidate(ptr, i, &mut raw) };
+                        LangIdCandidate {
+                            index: raw.index,
+                            code: owned_str(raw.code),
+                            name: owned_str(raw.name),
+                            p: raw.p,
+                            p_unrestricted: raw.p_unrestricted,
+                            logit: raw.logit,
+                        }
+                    })
+                    .collect();
+                Ok(LangIdResult {
+                    candidates,
+                    n_allowed: res.n_allowed,
+                    allowed_mass: res.allowed_mass,
+                    audio_ms: res.audio_ms,
+                })
+            },
+        )?
+    }
+
+    /// Model load time plus the last run's stage timings (`decode_ms` is 0).
+    pub fn timings(&self) -> Timings {
+        let mut raw: sys::transcribe_timings = unsafe { std::mem::zeroed() };
+        unsafe { sys::transcribe_timings_init(&mut raw) };
+        let _ = unsafe { sys::transcribe_langid_get_timings(self.ptr, &mut raw) };
+        Timings::from_raw(&raw)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use std::sync::Mutex;
+
+    fn null_session(streaming: bool) -> LangIdSession {
+        // Null native handles (both frees are NULL no-ops): every check below
+        // fires before any native call.
+        LangIdSession {
+            ptr: std::ptr::null_mut(),
+            model: Arc::new(ModelInner {
+                ptr: std::ptr::null_mut(),
+                compute_lock: Mutex::new(streaming),
+            }),
+            cancel: None,
+        }
+    }
+
+    #[test]
+    fn run_is_busy_while_a_stream_holds_the_lease() {
+        let err = null_session(true)
+            .run(&[0.0; 160], &LangIdOptions::default())
+            .unwrap_err();
+        let Error::Busy(msg) = err else {
+            panic!("expected Busy, got {err:?}");
+        };
+        assert_eq!(
+            msg,
+            "a stream is active on this model; finish or drop it before langid run()"
+        );
+    }
+
+    #[test]
+    fn empty_allowed_is_rejected_not_all() {
+        let opts = LangIdOptions {
+            allowed: Some(vec![]),
+            top_k: 0,
+        };
+        let err = null_session(false).run(&[0.0; 160], &opts).unwrap_err();
+        assert!(matches!(err, Error::InvalidArgument(_)), "{err:?}");
+    }
+
+    #[test]
+    fn interior_nul_in_allowed_is_rejected() {
+        let opts = LangIdOptions {
+            allowed: Some(vec!["e\0n".into()]),
+            top_k: 0,
+        };
+        let err = null_session(false).run(&[0.0; 160], &opts).unwrap_err();
+        assert!(matches!(err, Error::Nul(_)), "{err:?}");
+    }
+}
diff --git a/bindings/rust/transcribe-cpp/src/lib.rs b/bindings/rust/transcribe-cpp/src/lib.rs
index 4c4c5d707..afec3b874 100644
--- a/bindings/rust/transcribe-cpp/src/lib.rs
+++ b/bindings/rust/transcribe-cpp/src/lib.rs
@@ -51,6 +51,7 @@ mod cancel;
 mod diarize;
 mod error;
 mod family;
+mod langid;
 mod logging;
 mod model;
 mod result;
@@ -71,6 +72,9 @@ pub use family::{
     ParakeetStreamOptions, RunExtension, SortformerDiarizeOptions, SortformerPreset,
     StreamExtension, VoxtralRealtimeStreamOptions, WhisperRunOptions,
 };
+pub use langid::{
+    LangIdCandidate, LangIdInfo, LangIdOptions, LangIdResult, LangIdSession, LangIdSessionOptions,
+};
 pub use logging::{disable_logging, init_logging};
 pub use model::{Capabilities, Model, ModelOptions, SessionLimits, SessionOptions};
 pub use result::{Segment, SpeakerSegment, Timings, Token, Transcript, Word};
diff --git a/bindings/rust/transcribe-cpp/src/model.rs b/bindings/rust/transcribe-cpp/src/model.rs
index 615f28506..fd902900b 100644
--- a/bindings/rust/transcribe-cpp/src/model.rs
+++ b/bindings/rust/transcribe-cpp/src/model.rs
@@ -25,6 +25,7 @@ use transcribe_cpp_sys as sys;
 use crate::backend::Device;
 use crate::diarize::{DiarizeInfo, DiarizeSession, DiarizeSessionOptions};
 use crate::error::{check, Error, Result};
+use crate::langid::{LangIdInfo, LangIdSession, LangIdSessionOptions};
 use crate::result::owned_str;
 use crate::session::Session;
 use crate::types::{Backend, ExtSlot, Feature, Roles, TimestampKind};
@@ -200,6 +201,55 @@ impl Model {
         })
     }
 
+    /// Open a language ID session with default options. Errors with
+    /// [`Error::UnsupportedRole`] unless the model serves [`Role::LangId`](crate::Role).
+    pub fn langid_session(&self) -> Result<LangIdSession> {
+        self.langid_session_with(&LangIdSessionOptions::default())
+    }
+
+    /// Open a language ID session with explicit options.
+    pub fn langid_session_with(&self, options: &LangIdSessionOptions) -> Result<LangIdSession> {
+        LangIdSession::new(self, options)
+    }
+
+    /// Static facts about a language ID model. Errors with
+    /// [`Error::UnsupportedRole`] unless the model serves [`Role::LangId`](crate::Role).
+    pub fn langid_info(&self) -> Result<LangIdInfo> {
+        let mut raw: sys::transcribe_langid_info = unsafe { std::mem::zeroed() };
+        unsafe { sys::transcribe_langid_info_init(&mut raw) };
+        check(
+            unsafe { sys::transcribe_langid_get_info(self.inner.ptr, &mut raw) },
+            "langid info",
+        )?;
+        Ok(LangIdInfo {
+            sample_rate: raw.sample_rate,
+            n_labels: raw.n_labels,
+            min_audio_ms: raw.min_audio_ms,
+        })
+    }
+
+    /// `(code, name)` per label index. Errors with [`Error::UnsupportedRole`]
+    /// unless the model serves [`Role::LangId`](crate::Role).
+    pub fn langid_labels(&self) -> Result<Vec<(String, String)>> {
+        let n = self.langid_info()?.n_labels;
+        Ok((0..n)
+            .map(|i| unsafe {
+                (
+                    owned_str(sys::transcribe_langid_label_code(self.inner.ptr, i)),
+                    owned_str(sys::transcribe_langid_label_name(self.inner.ptr, i)),
+                )
+            })
+            .collect())
+    }
+
+    /// Label index of a code or alias (`"he"` and `"iw"` name the same label);
+    /// `None` when unknown or the model does not serve the LANGID role.
+    pub fn langid_label_index(&self, code: &str) -> Option<i32> {
+        let c = CString::new(code).ok()?;
+        let i = unsafe { sys::transcribe_langid_label_index(self.inner.ptr, c.as_ptr()) };
+        (i >= 0).then_some(i)
+    }
+
     /// The roles (kinds of work) this model serves.
     pub fn roles(&self) -> Roles {
         Roles(unsafe { sys::transcribe_model_roles(self.inner.ptr) })
diff --git a/bindings/rust/transcribe-cpp/src/types.rs b/bindings/rust/transcribe-cpp/src/types.rs
index eb1554eda..6210863dd 100644
--- a/bindings/rust/transcribe-cpp/src/types.rs
+++ b/bindings/rust/transcribe-cpp/src/types.rs
@@ -324,6 +324,11 @@ pub enum AbiStruct {
     DiarizeInfo,
     DiarizeSessionParams,
     DiarizeParams,
+    LangIdInfo,
+    LangIdSessionParams,
+    LangIdParams,
+    LangIdResult,
+    LangIdCandidate,
 }
 
 impl AbiStruct {
@@ -349,6 +354,11 @@ impl AbiStruct {
             AbiStruct::DiarizeInfo => A::TRANSCRIBE_ABI_DIARIZE_INFO,
             AbiStruct::DiarizeSessionParams => A::TRANSCRIBE_ABI_DIARIZE_SESSION_PARAMS,
             AbiStruct::DiarizeParams => A::TRANSCRIBE_ABI_DIARIZE_PARAMS,
+            AbiStruct::LangIdInfo => A::TRANSCRIBE_ABI_LANGID_INFO,
+            AbiStruct::LangIdSessionParams => A::TRANSCRIBE_ABI_LANGID_SESSION_PARAMS,
+            AbiStruct::LangIdParams => A::TRANSCRIBE_ABI_LANGID_PARAMS,
+            AbiStruct::LangIdResult => A::TRANSCRIBE_ABI_LANGID_RESULT,
+            AbiStruct::LangIdCandidate => A::TRANSCRIBE_ABI_LANGID_CANDIDATE,
         }
     }
 }
@@ -386,6 +396,8 @@ pub enum Role {
     Asr,
     /// Speaker diarization: [`DiarizeSession`](crate::DiarizeSession).
     Diarize,
+    /// Language identification: [`LangIdSession`](crate::LangIdSession).
+    LangId,
 }
 
 /// The set of [`Role`]s a model serves ([`Model::roles`](crate::Model::roles)).
@@ -399,6 +411,7 @@ impl Roles {
         let bit = match role {
             Role::Asr => sys::transcribe_role::TRANSCRIBE_ROLE_ASR,
             Role::Diarize => sys::transcribe_role::TRANSCRIBE_ROLE_DIARIZE,
+            Role::LangId => sys::transcribe_role::TRANSCRIBE_ROLE_LANGID,
         };
         self.0 & bit.0 != 0
     }
diff --git a/bindings/rust/transcribe-cpp/tests/common/mod.rs b/bindings/rust/transcribe-cpp/tests/common/mod.rs
index 6e66a8a12..4ea467d4c 100644
--- a/bindings/rust/transcribe-cpp/tests/common/mod.rs
+++ b/bindings/rust/transcribe-cpp/tests/common/mod.rs
@@ -128,6 +128,34 @@ pub fn smoke_sortformer_fixtures(test: &str) -> Option<(PathBuf, Vec<f32>)> {
     }
 }
 
+/// VoxLingua107 ECAPA-TDNN (LANGID role only), or `None` (with a skip note).
+pub fn smoke_langid_model(test: &str) -> Option<PathBuf> {
+    let model = family_model(
+        "TRANSCRIBE_SMOKE_LANGID_MODEL",
+        "models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf",
+    );
+    if model.is_none() {
+        eprintln!("skip {test}: langid model absent (set TRANSCRIBE_SMOKE_LANGID_MODEL)");
+    }
+    model
+}
+
+/// The toy ecapa_tdnn GGUF the C++ build generates under tests/fixtures/
+/// (5 labels aa..ee, alias xx=aa, random weights), or `None` (with a skip
+/// note) before the C++ test fixtures have been built.
+pub fn langid_toy_model(test: &str) -> Option<PathBuf> {
+    ensure_backends();
+    let path = repo_root().join("tests/fixtures/arch_ecapa_tdnn_minimal.gguf");
+    if !path.is_file() {
+        eprintln!(
+            "skip {test}: {} absent (build the C++ `fixtures` target)",
+            path.display()
+        );
+        return None;
+    }
+    Some(path)
+}
+
 /// Both fixtures together; prints a skip note and returns `None` if either is
 /// missing (so the caller can `return` early — the Rust equivalent of skip).
 pub fn smoke_fixtures(test: &str) -> Option<(PathBuf, Vec<f32>)> {
@@ -142,7 +170,7 @@ pub fn smoke_fixtures(test: &str) -> Option<(PathBuf, Vec<f32>)> {
     }
 }
 
-fn load_wav(path: &std::path::Path) -> Vec<f32> {
+pub fn load_wav(path: &std::path::Path) -> Vec<f32> {
     let mut reader = hound::WavReader::open(path).expect("open wav");
     let spec = reader.spec();
     assert_eq!(spec.channels, 1, "{path:?} must be mono");
diff --git a/bindings/rust/transcribe-cpp/tests/langid.rs b/bindings/rust/transcribe-cpp/tests/langid.rs
new file mode 100644
index 000000000..e474a8835
--- /dev/null
+++ b/bindings/rust/transcribe-cpp/tests/langid.rs
@@ -0,0 +1,156 @@
+//! LANGID role: contract checks on the toy ecapa_tdnn fixture
+//! (tests/fixtures/arch_ecapa_tdnn_minimal.gguf, built by the C++ test
+//! fixtures) and top-1 on the real VoxLingua107 model
+//! (TRANSCRIBE_SMOKE_LANGID_MODEL). Busy / empty-allowed are covered in
+//! `src/langid.rs`.
+
+mod common;
+
+use transcribe_cpp::{
+    Backend, CancelToken, Error, LangIdOptions, LangIdSessionOptions, Model, ModelOptions, Role,
+};
+
+fn noise(n: usize, seed: u32) -> Vec<f32> {
+    let mut s = seed | 1;
+    (0..n)
+        .map(|_| {
+            s = s.wrapping_mul(1664525).wrapping_add(1013904223);
+            ((s >> 8) & 0xFF_FFFF) as f32 / 16_777_216.0 - 0.5
+        })
+        .collect()
+}
+
+fn load_cpu(path: &std::path::Path) -> Model {
+    let opts = ModelOptions {
+        backend: Backend::Cpu,
+        ..Default::default()
+    };
+    Model::load_with(path, &opts).unwrap()
+}
+
+#[test]
+fn toy_roles_info_labels() {
+    let Some(path) = common::langid_toy_model("toy_roles_info_labels") else {
+        return;
+    };
+    let model = load_cpu(&path);
+    let roles = model.roles();
+    assert!(roles.contains(Role::LangId) && !roles.contains(Role::Asr));
+    let info = model.langid_info().unwrap();
+    assert_eq!(
+        (info.sample_rate, info.n_labels, info.min_audio_ms),
+        (16000, 5, 500)
+    );
+    assert_eq!(
+        model.langid_labels().unwrap()[2],
+        ("cc".into(), "Charlie".into())
+    );
+    assert_eq!(model.langid_label_index("xx"), Some(0));
+    assert_eq!(model.langid_label_index("zz"), None);
+    assert!(matches!(
+        model.capabilities(),
+        Err(Error::UnsupportedRole(_))
+    ));
+    assert!(matches!(model.session(), Err(Error::UnsupportedRole(_))));
+}
+
+#[test]
+fn toy_run_contract() {
+    let Some(path) = common::langid_toy_model("toy_run_contract") else {
+        return;
+    };
+    let model = load_cpu(&path);
+    let mut lid = model.langid_session().unwrap();
+    let pcm = noise(16000, 7);
+
+    let r = lid.run(&pcm, &LangIdOptions::default()).unwrap();
+    assert_eq!((r.candidates.len(), r.n_allowed, r.audio_ms), (5, 5, 1000));
+    assert_eq!(r.allowed_mass, 1.0);
+    assert_eq!(r.code(), Some(r.candidates[0].code.as_str()));
+    let sum: f32 = r.candidates.iter().map(|c| c.p).sum();
+    assert!((sum - 1.0).abs() < 1e-5);
+    assert!(r.candidates.windows(2).all(|w| w[0].p >= w[1].p));
+
+    let opts = LangIdOptions {
+        allowed: Some(vec!["bb".into(), "dd".into()]),
+        top_k: 0,
+    };
+    let r = lid.run(&pcm, &opts).unwrap();
+    assert_eq!(r.n_allowed, 2);
+    assert!(r
+        .candidates
+        .iter()
+        .all(|c| c.code == "bb" || c.code == "dd"));
+    assert!(r.allowed_mass > 0.0 && r.allowed_mass < 1.0);
+
+    let opts = LangIdOptions {
+        allowed: Some(vec!["zz".into()]),
+        top_k: 0,
+    };
+    assert!(matches!(lid.run(&pcm, &opts), Err(Error::Unsupported(_))));
+    assert!(matches!(
+        lid.run(&pcm[..6400], &LangIdOptions::default()),
+        Err(Error::InputTooShort(_))
+    ));
+    let top = LangIdOptions {
+        allowed: None,
+        top_k: 2,
+    };
+    assert_eq!(lid.run(&pcm, &top).unwrap().candidates.len(), 2);
+    assert!(lid.timings().encode_ms > 0.0);
+
+    let bad = LangIdSessionOptions {
+        n_threads: 0,
+        max_audio_ms: 400,
+    };
+    assert!(matches!(
+        model.langid_session_with(&bad),
+        Err(Error::InvalidArgument(_))
+    ));
+}
+
+#[test]
+fn toy_cancel_aborts() {
+    let Some(path) = common::langid_toy_model("toy_cancel_aborts") else {
+        return;
+    };
+    let model = load_cpu(&path);
+    let mut lid = model.langid_session().unwrap();
+    let token = CancelToken::new();
+    lid.set_cancel_token(&token);
+    token.cancel();
+    assert!(matches!(
+        lid.run(&noise(16000, 3), &LangIdOptions::default()),
+        Err(Error::Aborted { .. })
+    ));
+    lid.clear_cancel_token();
+    assert!(lid.run(&noise(16000, 3), &LangIdOptions::default()).is_ok());
+}
+
+#[test]
+fn real_fleurs_top1() {
+    let Some(path) = common::smoke_langid_model("real_fleurs_top1") else {
+        return;
+    };
+    let model = load_cpu(&path);
+    assert_eq!(model.langid_info().unwrap().n_labels, 107);
+    assert_eq!(
+        model.langid_label_index("he"),
+        model.langid_label_index("iw")
+    );
+    let mut lid = model.langid_session().unwrap();
+    for code in ["en", "de", "fr", "es", "ja", "zh", "ru", "id"] {
+        let pcm = common::load_wav(&common::repo_root().join(format!("samples/fleurs-{code}.wav")));
+        let r = lid
+            .run(
+                &pcm,
+                &LangIdOptions {
+                    allowed: None,
+                    top_k: 3,
+                },
+            )
+            .unwrap();
+        assert_eq!(r.code(), Some(code), "{code}: {:?}", r.candidates);
+        assert!(r.candidates[0].p >= 0.5);
+    }
+}
diff --git a/bindings/rust/transcribe-cpp/tests/no_model.rs b/bindings/rust/transcribe-cpp/tests/no_model.rs
index ed7df746d..5d3985554 100644
--- a/bindings/rust/transcribe-cpp/tests/no_model.rs
+++ b/bindings/rust/transcribe-cpp/tests/no_model.rs
@@ -38,6 +38,11 @@ fn abi_struct_sizes_are_live() {
         AbiStruct::DiarizeInfo,
         AbiStruct::DiarizeSessionParams,
         AbiStruct::DiarizeParams,
+        AbiStruct::LangIdInfo,
+        AbiStruct::LangIdSessionParams,
+        AbiStruct::LangIdParams,
+        AbiStruct::LangIdResult,
+        AbiStruct::LangIdCandidate,
     ] {
         assert!(abi_struct_size(which) > 0, "{which:?} reported size 0");
     }
@@ -142,5 +147,6 @@ fn handles_are_send_sync() {
     assert_send_sync::<Model>();
     assert_send::<transcribe_cpp::Session>();
     assert_send::<transcribe_cpp::DiarizeSession>();
+    assert_send::<transcribe_cpp::LangIdSession>();
     // Sessions are intentionally NOT Sync (single-threaded use).
 }
diff --git a/bindings/swift/README.md b/bindings/swift/README.md
index 8a5bed710..328228a56 100644
--- a/bindings/swift/README.md
+++ b/bindings/swift/README.md
@@ -115,6 +115,20 @@ for t in turns { print(t.speakerId, t.t0Ms, t.t1Ms) }
 
 `capabilities` and `session()` throw `.unsupportedRole` on a model without `.asr`.
 
+## Language ID
+
+Language ID models (`.langId`, e.g. VoxLingua107 ECAPA-TDNN) rank the model's
+own label codes from a `LangIdSession`; match `result.code` against an ASR
+model's `capabilities.languages` yourself. `allowed: nil` scores every label,
+an empty array throws `.invalidArgument`, and clips under
+`langIdInfo.minAudioMs` throw `.inputTooShort`.
+
+```swift
+let model = try Model(path: "lang-id-voxlingua107-ecapa-Q8_0.gguf")
+let result = try model.langIdSession().run(pcm, options: LangIdOptions(allowed: ["en", "de"], topK: 3))
+print(result.code ?? "-", result.allowedMass)
+```
+
 ## Backends
 
 Backends are compiled into the xcframework per Apple slice:
diff --git a/bindings/swift/Sources/TranscribeCpp/ABIHash.swift b/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
index 5cae2c4dd..1f92e5a25 100644
--- a/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
+++ b/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
@@ -13,7 +13,7 @@ import CTranscribe
 extension Transcribe {
     /// sha256/16 of the normalized public FFI surface, pinned to the value in
     /// include/transcribe.abihash at the time this binding was last reviewed.
-    public static let pinnedHeaderHash = "bd3273dabb25a1fe"
+    public static let pinnedHeaderHash = "318eba8c4b2e4387"
 
     /// The public-ABI digest this binding was reviewed against (16 hex chars).
     public static func headerHash() -> String { pinnedHeaderHash }
diff --git a/bindings/swift/Sources/TranscribeCpp/Backend.swift b/bindings/swift/Sources/TranscribeCpp/Backend.swift
index 9eab54f63..837d037ff 100644
--- a/bindings/swift/Sources/TranscribeCpp/Backend.swift
+++ b/bindings/swift/Sources/TranscribeCpp/Backend.swift
@@ -113,6 +113,11 @@ public enum AbiStruct: Sendable {
     case diarizeInfo
     case diarizeSessionParams
     case diarizeParams
+    case langIdInfo
+    case langIdSessionParams
+    case langIdParams
+    case langIdResult
+    case langIdCandidate
 
     var cValue: transcribe_abi_struct {
         switch self {
@@ -133,6 +138,11 @@ public enum AbiStruct: Sendable {
         case .diarizeInfo: return TRANSCRIBE_ABI_DIARIZE_INFO
         case .diarizeSessionParams: return TRANSCRIBE_ABI_DIARIZE_SESSION_PARAMS
         case .diarizeParams: return TRANSCRIBE_ABI_DIARIZE_PARAMS
+        case .langIdInfo: return TRANSCRIBE_ABI_LANGID_INFO
+        case .langIdSessionParams: return TRANSCRIBE_ABI_LANGID_SESSION_PARAMS
+        case .langIdParams: return TRANSCRIBE_ABI_LANGID_PARAMS
+        case .langIdResult: return TRANSCRIBE_ABI_LANGID_RESULT
+        case .langIdCandidate: return TRANSCRIBE_ABI_LANGID_CANDIDATE
         }
     }
 }
diff --git a/bindings/swift/Sources/TranscribeCpp/Cancellation.swift b/bindings/swift/Sources/TranscribeCpp/Cancellation.swift
index fe5221932..7142c3685 100644
--- a/bindings/swift/Sources/TranscribeCpp/Cancellation.swift
+++ b/bindings/swift/Sources/TranscribeCpp/Cancellation.swift
@@ -39,6 +39,21 @@ extension Session {
     }
 }
 
+extension LangIdSession {
+    /// Install a cancellation token; a cancelled run throws `.aborted`.
+    public func setCancellationToken(_ token: CancellationToken) {
+        cancelToken = token
+        let context = Unmanaged.passUnretained(token).toOpaque()
+        transcribe_langid_set_abort_callback(ptr, abortTrampoline, context)
+    }
+
+    /// Remove any installed cancellation token.
+    public func clearCancellationToken() {
+        transcribe_langid_set_abort_callback(ptr, nil, nil)
+        cancelToken = nil
+    }
+}
+
 extension DiarizeSession {
     /// Install a cancellation token; a cancelled run throws `.aborted`.
     public func setCancellationToken(_ token: CancellationToken) {
diff --git a/bindings/swift/Sources/TranscribeCpp/LangId.swift b/bindings/swift/Sources/TranscribeCpp/LangId.swift
new file mode 100644
index 000000000..e35af1262
--- /dev/null
+++ b/bindings/swift/Sources/TranscribeCpp/LangId.swift
@@ -0,0 +1,187 @@
+import CTranscribe
+import Foundation
+
+/// Static facts about a language ID model.
+public struct LangIdInfo: Sendable, Equatable {
+    /// Input PCM rate (16000).
+    public let sampleRate: Int32
+    /// Label indices are `0..<nLabels`.
+    public let nLabels: Int32
+    /// Shorter scored audio throws `.inputTooShort`.
+    public let minAudioMs: Int32
+}
+
+public struct LangIdOptions: Sendable {
+    /// Restrict the decision to these codes or aliases. `nil` means every
+    /// label; an empty array throws `.invalidArgument`, and an unknown code
+    /// throws `.unsupported`.
+    public var allowed: [String]?
+    /// Keep the best `topK` candidates; 0 = every allowed label.
+    public var topK: Int32
+    public init(allowed: [String]? = nil, topK: Int32 = 0) {
+        self.allowed = allowed
+        self.topK = topK
+    }
+}
+
+/// One ranked label. `code` is the model's own label ("en", "iw").
+public struct LangIdCandidate: Sendable, Equatable {
+    public let index: Int32
+    public let code: String
+    public let name: String
+    /// Softmax renormalized over the allowed set.
+    public let p: Float
+    /// Softmax over every label.
+    public let pUnrestricted: Float
+    public let logit: Float
+}
+
+/// The result of one `LangIdSession.run`.
+public struct LangIdResult: Sendable, Equatable {
+    /// Ranked by `p`, descending; ties keep label order.
+    public let candidates: [LangIdCandidate]
+    /// Labels in the allowed set (before `topK`).
+    public let nAllowed: Int32
+    /// Unrestricted probability inside the allowed set (1.0 when
+    /// unrestricted); a low value means the speech is probably outside it.
+    public let allowedMass: Float
+    /// Audio actually scored, after the crop.
+    public let audioMs: Int64
+    /// The top candidate's code, if any.
+    public var code: String? { candidates.first?.code }
+}
+
+extension Model {
+    /// Throws `.unsupportedRole` when `roles` lacks `.langId`.
+    public var langIdInfo: LangIdInfo {
+        get throws {
+            var info = transcribe_langid_info()
+            transcribe_langid_info_init(&info)
+            try TranscribeError.check(transcribe_langid_get_info(ptr, &info), context: "langid_get_info")
+            return LangIdInfo(sampleRate: info.sample_rate, nLabels: info.n_labels, minAudioMs: info.min_audio_ms)
+        }
+    }
+
+    /// `(code, name)` per label index. Throws `.unsupportedRole` when `roles`
+    /// lacks `.langId`.
+    public var langIdLabels: [(code: String, name: String)] {
+        get throws {
+            let n = try langIdInfo.nLabels
+            return (0..<n).map { i in
+                (String(cString: transcribe_langid_label_code(ptr, i)),
+                 String(cString: transcribe_langid_label_name(ptr, i)))
+            }
+        }
+    }
+
+    /// Label index of a code or alias ("he" and "iw" name the same label), or
+    /// `nil` (also on a model without the LANGID role).
+    public func langIdLabelIndex(_ code: String) -> Int32? {
+        let i = transcribe_langid_label_index(ptr, code)
+        return i >= 0 ? i : nil
+    }
+
+    /// Create a LANGID session (`threads` 0 = library default). Longer input
+    /// than `maxAudioMs` is scored on its tail (0 = 30000). Throws
+    /// `.unsupportedRole` when `roles` lacks `.langId`.
+    public func langIdSession(threads: Int32 = 0, maxAudioMs: Int32 = 0) throws -> LangIdSession {
+        var params = transcribe_langid_session_params()
+        transcribe_langid_session_params_init(&params)
+        params.n_threads = threads
+        params.max_audio_ms = maxAudioMs
+        var out: OpaquePointer?
+        try TranscribeError.check(
+            transcribe_langid_session_init(ptr, &params, &out), context: "creating langid session")
+        guard let out else {
+            throw TranscribeError.other(status: 0, message: "null langid session handle")
+        }
+        return LangIdSession(model: self, ptr: out)
+    }
+}
+
+/// A LANGID-role session: which language is spoken. Same threading and
+/// lifetime contract as `Session` (single-threaded; holds its `Model` alive),
+/// and its runs share the model's compute lock and stream lease with every
+/// `Session`.
+public final class LangIdSession {
+    let model: Model
+    let ptr: OpaquePointer
+    /// Strong ref to the installed cancellation token (see `Session`).
+    var cancelToken: CancellationToken?
+
+    init(model: Model, ptr: OpaquePointer) {
+        self.model = model
+        self.ptr = ptr
+    }
+
+    deinit { transcribe_langid_session_free(ptr) }
+
+    /// Identify the language of one clip (16 kHz mono float32).
+    public func run(_ pcm: [Float], options: LangIdOptions = .init()) throws -> LangIdResult {
+        if let allowed = options.allowed, allowed.isEmpty {
+            // NULL would mean "every label", the opposite of an empty list.
+            throw TranscribeError.invalidArgument("allowed is empty; pass nil for every label")
+        }
+        // strdup'd copies stay alive until the native call returns.
+        let owned: [UnsafeMutablePointer<CChar>?] = options.allowed?.map { strdup($0) } ?? []
+        defer { owned.forEach { free($0) } }
+        let codes: [UnsafePointer<CChar>?] = owned.map { UnsafePointer($0) }
+
+        return try model.withCompute(
+            busyIfStreaming: "a stream is active on this model; finish or drop it before langid run()"
+        ) {
+            let status = codes.withUnsafeBufferPointer { codeBuf -> transcribe_status in
+                var params = transcribe_langid_params()
+                transcribe_langid_params_init(&params)
+                if options.allowed != nil {
+                    params.allowed = codeBuf.baseAddress
+                    params.n_allowed = Int32(codeBuf.count)
+                }
+                params.top_k = options.topK
+                return pcm.withUnsafeBufferPointer {
+                    transcribe_langid_run(ptr, $0.baseAddress, Int32($0.count), &params)
+                }
+            }
+            try TranscribeError.check(status, context: "langid_run")
+            var res = transcribe_langid_result(); transcribe_langid_result_init(&res)
+            try TranscribeError.check(transcribe_langid_get_result(ptr, &res), context: "langid_get_result")
+            let candidates = (0..<res.n_candidates).map { i -> LangIdCandidate in
+                var c = transcribe_langid_candidate(); transcribe_langid_candidate_init(&c)
+                _ = transcribe_langid_get_candidate(ptr, i, &c)
+                return LangIdCandidate(
+                    index: c.index, code: c.code.map { String(cString: $0) } ?? "",
+                    name: c.name.map { String(cString: $0) } ?? "",
+                    p: c.p, pUnrestricted: c.p_unrestricted, logit: c.logit)
+            }
+            return LangIdResult(candidates: candidates, nAllowed: res.n_allowed,
+                                allowedMass: res.allowed_mass, audioMs: res.audio_ms)
+        }
+    }
+
+    /// `run` hopped off the caller's thread/actor onto a background queue, with
+    /// Swift task cancellation bridged to the native abort. Same contract as
+    /// `Session.run(_:options:) async`.
+    public func run(_ pcm: [Float], options: LangIdOptions = .init()) async throws -> LangIdResult {
+        nonisolated(unsafe) let this = self
+        let bridged = (cancelToken == nil) ? CancellationToken() : nil
+        if let bridged { setCancellationToken(bridged) }
+        defer { if bridged != nil { clearCancellationToken() } }
+        return try await withTaskCancellationHandler {
+            try await withCheckedThrowingContinuation {
+                (cont: CheckedContinuation<LangIdResult, Error>) in
+                DispatchQueue.global().async {
+                    cont.resume(with: Result { try this.run(pcm, options: options) })
+                }
+            }
+        } onCancel: {
+            bridged?.cancel()
+        }
+    }
+
+    /// Timings from the most recent run (`decodeMs` is 0).
+    public var timings: Timings {
+        var t = transcribe_timings(); transcribe_timings_init(&t)
+        _ = transcribe_langid_get_timings(ptr, &t)
+        return Timings(t)
+    }
+}
diff --git a/bindings/swift/Sources/TranscribeCpp/Model.swift b/bindings/swift/Sources/TranscribeCpp/Model.swift
index 34ef8dc7e..c3a32d870 100644
--- a/bindings/swift/Sources/TranscribeCpp/Model.swift
+++ b/bindings/swift/Sources/TranscribeCpp/Model.swift
@@ -2,12 +2,13 @@ import CTranscribe
 import Foundation
 
 /// The kinds of work a model serves (`transcribe_model_roles`). ASR is
-/// `Session`; DIARIZE is `DiarizeSession`.
+/// `Session`; DIARIZE is `DiarizeSession`; LANGID is `LangIdSession`.
 public struct Roles: OptionSet, Sendable {
     public let rawValue: UInt32
     public init(rawValue: UInt32) { self.rawValue = rawValue }
     public static let asr = Roles(rawValue: TRANSCRIBE_ROLE_ASR.rawValue)
     public static let diarize = Roles(rawValue: TRANSCRIBE_ROLE_DIARIZE.rawValue)
+    public static let langId = Roles(rawValue: TRANSCRIBE_ROLE_LANGID.rawValue)
 }
 
 /// A loaded model. Safe to share across threads (`@unchecked Sendable`): the C
diff --git a/bindings/swift/Sources/TranscribeCpp/TranscribeError.swift b/bindings/swift/Sources/TranscribeCpp/TranscribeError.swift
index 4e0db09c9..ea6798609 100644
--- a/bindings/swift/Sources/TranscribeCpp/TranscribeError.swift
+++ b/bindings/swift/Sources/TranscribeCpp/TranscribeError.swift
@@ -30,6 +30,9 @@ public enum TranscribeError: Error {
     case outputRepetition(message: String, partial: Transcript?)
     /// The model's `roles` lack the one the call needs (`TRANSCRIBE_ERR_UNSUPPORTED_ROLE`).
     case unsupportedRole(String)
+    /// The audio is shorter than the role's minimum (`TRANSCRIBE_ERR_INPUT_TOO_SHORT`,
+    /// e.g. `LangIdInfo.minAudioMs`).
+    case inputTooShort(String)
     case versionMismatch(String)
     case busy(String)
     case other(status: Int32, message: String)
@@ -73,6 +76,8 @@ public enum TranscribeError: Error {
             return .outputRepetition(message: message, partial: nil)
         case TRANSCRIBE_ERR_UNSUPPORTED_ROLE:
             return .unsupportedRole(message)
+        case TRANSCRIBE_ERR_INPUT_TOO_SHORT:
+            return .inputTooShort(message)
         default:
             return .other(status: raw, message: message)
         }
diff --git a/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift b/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
new file mode 100644
index 000000000..8602bf869
--- /dev/null
+++ b/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
@@ -0,0 +1,91 @@
+import XCTest
+
+@testable import TranscribeCpp
+
+/// LANGID role (`LangIdSession`): contract checks on the toy ecapa_tdnn
+/// fixture, top-1 on the real VoxLingua107 model.
+final class LangIdTests: XCTestCase {
+    private func noise(_ n: Int, seed: UInt32 = 7) -> [Float] {
+        var s = seed | 1
+        return (0..<n).map { _ in
+            s = s &* 1664525 &+ 1013904223
+            return Float((s >> 8) & 0xFFFFFF) / 16777216.0 - 0.5
+        }
+    }
+
+    private func cpuModel(_ path: String) throws -> Model {
+        try Model(path: path, options: ModelOptions(backend: .cpu))
+    }
+
+    func testToyRolesInfoLabels() throws {
+        let model = try cpuModel(try Fixtures.langIdToyModelPath())
+        XCTAssertEqual(model.roles, .langId)
+        XCTAssertEqual(try model.langIdInfo, LangIdInfo(sampleRate: 16000, nLabels: 5, minAudioMs: 500))
+        let labels = try model.langIdLabels
+        XCTAssertEqual(labels[2].code, "cc")
+        XCTAssertEqual(labels[2].name, "Charlie")
+        XCTAssertEqual(model.langIdLabelIndex("xx"), 0)
+        XCTAssertNil(model.langIdLabelIndex("zz"))
+        XCTAssertThrowsError(try model.session()) { error in
+            guard case TranscribeError.unsupportedRole = error else { return XCTFail("\(error)") }
+        }
+    }
+
+    func testToyRunContract() throws {
+        let model = try cpuModel(try Fixtures.langIdToyModelPath())
+        let lid = try model.langIdSession(threads: 1)
+        let pcm = noise(16000)
+
+        let r = try lid.run(pcm)
+        XCTAssertEqual(r.candidates.count, 5)
+        XCTAssertEqual(r.nAllowed, 5)
+        XCTAssertEqual(r.allowedMass, 1.0)
+        XCTAssertEqual(r.audioMs, 1000)
+        XCTAssertEqual(r.code, r.candidates[0].code)
+        XCTAssertEqual(r.candidates.map(\.p).reduce(0, +), 1.0, accuracy: 1e-5)
+
+        let restricted = try lid.run(pcm, options: LangIdOptions(allowed: ["bb", "dd"]))
+        XCTAssertEqual(Set(restricted.candidates.map(\.code)), ["bb", "dd"])
+        XCTAssertLessThan(restricted.allowedMass, 1.0)
+
+        XCTAssertEqual(try lid.run(pcm, options: LangIdOptions(topK: 2)).candidates.count, 2)
+
+        XCTAssertThrowsError(try lid.run(pcm, options: LangIdOptions(allowed: []))) { error in
+            guard case TranscribeError.invalidArgument = error else { return XCTFail("\(error)") }
+        }
+        XCTAssertThrowsError(try lid.run(pcm, options: LangIdOptions(allowed: ["zz"]))) { error in
+            guard case TranscribeError.unsupported = error else { return XCTFail("\(error)") }
+        }
+        XCTAssertThrowsError(try lid.run(Array(pcm[..<6400]))) { error in
+            guard case TranscribeError.inputTooShort = error else { return XCTFail("\(error)") }
+        }
+        XCTAssertGreaterThan(lid.timings.encodeMs, 0)
+    }
+
+    func testToyCancelAborts() throws {
+        let model = try cpuModel(try Fixtures.langIdToyModelPath())
+        let lid = try model.langIdSession()
+        let token = CancellationToken()
+        lid.setCancellationToken(token)
+        token.cancel()
+        XCTAssertThrowsError(try lid.run(noise(16000))) { error in
+            guard case TranscribeError.aborted = error else { return XCTFail("\(error)") }
+        }
+        lid.clearCancellationToken()
+        XCTAssertNoThrow(try lid.run(noise(16000)))
+    }
+
+    func testRealFleursTop1() throws {
+        let model = try cpuModel(try Fixtures.langIdModelPath())
+        XCTAssertEqual(try model.langIdInfo.nLabels, 107)
+        XCTAssertEqual(model.langIdLabelIndex("he"), model.langIdLabelIndex("iw"))
+        let lid = try model.langIdSession()
+        for code in ["en", "de", "fr", "es", "ja", "zh", "ru", "id"] {
+            let pcm = try Fixtures.loadWav(
+                Fixtures.repoRoot().appendingPathComponent("samples/fleurs-\(code).wav").path)
+            let r = try lid.run(pcm, options: LangIdOptions(topK: 3))
+            XCTAssertEqual(r.code, code)
+            XCTAssertGreaterThanOrEqual(r.candidates[0].p, 0.5)
+        }
+    }
+}
diff --git a/bindings/swift/Tests/TranscribeCppTests/NoModelTests.swift b/bindings/swift/Tests/TranscribeCppTests/NoModelTests.swift
index 77d5d0f5a..dc4fc5973 100644
--- a/bindings/swift/Tests/TranscribeCppTests/NoModelTests.swift
+++ b/bindings/swift/Tests/TranscribeCppTests/NoModelTests.swift
@@ -21,7 +21,7 @@ final class NoModelTests: XCTestCase {
 
     func testAbiStructSizesAreLive() {
         // A real layout is non-zero; a garbage/empty one would be 0.
-        for s in [AbiStruct.runParams, .capabilities, .segment, .sessionLimits] {
+        for s in [AbiStruct.runParams, .capabilities, .segment, .sessionLimits, .langIdResult, .langIdCandidate] {
             XCTAssertGreaterThan(Transcribe.abiStructSize(s), 0, "\(s)")
         }
     }
@@ -60,15 +60,15 @@ final class NoModelTests: XCTestCase {
             10: "unsupported", 11: "unsupported", 12: "unsupported",
             13: "aborted", 14: "badStructSize", 15: "unsupported",
             16: "unsupported", 17: "inputTooLong", 18: "outputTruncated",
-            19: "outputRepetition", 20: "unsupportedRole",
+            19: "outputRepetition", 20: "unsupportedRole", 21: "inputTooShort",
         ]
-        for raw in 1...20 {
+        for raw in 1...21 {
             let status = transcribe_status(rawValue: UInt32(raw))
             XCTAssertNotEqual(Transcribe.statusString(Int32(raw)), "unknown status", "status \(raw)")
             let name = String(describing: TranscribeError.make(status)).prefix { $0 != "(" }
             XCTAssertEqual(String(name), expected[Int32(raw)], "status \(raw)")
         }
-        XCTAssertEqual(Transcribe.statusString(21), "unknown status",
+        XCTAssertEqual(Transcribe.statusString(22), "unknown status",
                        "a new status was appended; map it in TranscribeError.make")
     }
 
diff --git a/bindings/swift/Tests/TranscribeCppTests/TestSupport.swift b/bindings/swift/Tests/TranscribeCppTests/TestSupport.swift
index 5d495ecaa..9875f77e4 100644
--- a/bindings/swift/Tests/TranscribeCppTests/TestSupport.swift
+++ b/bindings/swift/Tests/TranscribeCppTests/TestSupport.swift
@@ -96,6 +96,25 @@ enum Fixtures {
         return (model, try loadWav(audio))
     }
 
+    /// VoxLingua107 ECAPA-TDNN (LANGID only), or `XCTSkip` when absent.
+    static func langIdModelPath() throws -> String {
+        guard let model = familyModel(
+            "TRANSCRIBE_SMOKE_LANGID_MODEL",
+            "models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf")
+        else { throw XCTSkip("no language ID model (set TRANSCRIBE_SMOKE_LANGID_MODEL)") }
+        return model
+    }
+
+    /// The toy ecapa_tdnn GGUF the C++ build generates under tests/fixtures/,
+    /// or `XCTSkip` before the C++ test fixtures have been built.
+    static func langIdToyModelPath() throws -> String {
+        let path = repoRoot().appendingPathComponent("tests/fixtures/arch_ecapa_tdnn_minimal.gguf").path
+        guard FileManager.default.fileExists(atPath: path) else {
+            throw XCTSkip("no toy ecapa_tdnn fixture (build the C++ `fixtures` target)")
+        }
+        return path
+    }
+
     /// The model path + decoded PCM, or `XCTSkip` when either is absent.
     static func modelAndAudio() throws -> (model: String, pcm: [Float]) {
         guard let model = modelPath() else {
diff --git a/bindings/typescript/README.md b/bindings/typescript/README.md
index 0a33edcf8..0f3d25155 100644
--- a/bindings/typescript/README.md
+++ b/bindings/typescript/README.md
@@ -127,9 +127,22 @@ for (const t of turns) console.log(t.speakerId, t.t0Ms, t.t1Ms);
 `diarizer.timings` reports the last run. Diarize runs wait on the same
 model-wide lock as other compute calls (see below).
 
+### Language ID (LANGID role)
+
+A `"langid"` model (VoxLingua107 ECAPA-TDNN) ranks its own label codes; match
+`result.code` against an ASR model's `capabilities.languages` yourself.
+Omitting `allowed` scores every label, `[]` throws `InvalidArgument`, and
+clips under `model.langidInfo.minAudioMs` throw `InputTooShort`.
+
+```ts
+using lid = model.createLangIdSession();
+const result = await lid.run(pcm, { allowed: ["en", "de", "fr"], topK: 3 });
+console.log(result.code, result.candidates[0].p, result.allowedMass);
+```
+
 ### Resource management
 
-`TranscribeModel`, `Session`, `DiarizeSession`, and `Stream` all implement
+`TranscribeModel`, `Session`, `DiarizeSession`, `LangIdSession`, and `Stream` all implement
 `Symbol.dispose`, so `using` works (TypeScript 5.2+ / Node 22+):
 
 ```ts
diff --git a/bindings/typescript/src/_generated.ts b/bindings/typescript/src/_generated.ts
index 5e7ea6f31..56777d031 100644
--- a/bindings/typescript/src/_generated.ts
+++ b/bindings/typescript/src/_generated.ts
@@ -11,7 +11,7 @@
 // Stable digest of the ABI surface (structs, enums, macros, layout,
 // prototypes), computed by the Python oracle and pinned here so a header
 // ABI change turns this binding's drift check red for conscious review.
-export const PUBLIC_HEADER_HASH = "bd3273dabb25a1fe";
+export const PUBLIC_HEADER_HASH = "318eba8c4b2e4387";
 
 // === enum constants ===
 export const TRANSCRIBE_OK = 0;
@@ -35,6 +35,7 @@ export const TRANSCRIBE_ERR_INPUT_TOO_LONG = 17;
 export const TRANSCRIBE_ERR_OUTPUT_TRUNCATED = 18;
 export const TRANSCRIBE_ERR_OUTPUT_REPETITION = 19;
 export const TRANSCRIBE_ERR_UNSUPPORTED_ROLE = 20;
+export const TRANSCRIBE_ERR_INPUT_TOO_SHORT = 21;
 export const TRANSCRIBE_ABI_MODEL_LOAD_PARAMS = 0;
 export const TRANSCRIBE_ABI_SESSION_PARAMS = 1;
 export const TRANSCRIBE_ABI_RUN_PARAMS = 2;
@@ -54,6 +55,11 @@ export const TRANSCRIBE_ABI_BACKEND_INIT_PARAMS = 15;
 export const TRANSCRIBE_ABI_DIARIZE_INFO = 16;
 export const TRANSCRIBE_ABI_DIARIZE_SESSION_PARAMS = 17;
 export const TRANSCRIBE_ABI_DIARIZE_PARAMS = 18;
+export const TRANSCRIBE_ABI_LANGID_INFO = 19;
+export const TRANSCRIBE_ABI_LANGID_SESSION_PARAMS = 20;
+export const TRANSCRIBE_ABI_LANGID_PARAMS = 21;
+export const TRANSCRIBE_ABI_LANGID_RESULT = 22;
+export const TRANSCRIBE_ABI_LANGID_CANDIDATE = 23;
 export const TRANSCRIBE_LOG_LEVEL_NONE = 0;
 export const TRANSCRIBE_LOG_LEVEL_INFO = 1;
 export const TRANSCRIBE_LOG_LEVEL_WARN = 2;
@@ -96,6 +102,7 @@ export const TRANSCRIBE_DEVICE_TYPE_IGPU = 2;
 export const TRANSCRIBE_DEVICE_TYPE_ACCEL = 3;
 export const TRANSCRIBE_ROLE_ASR = 1;
 export const TRANSCRIBE_ROLE_DIARIZE = 2;
+export const TRANSCRIBE_ROLE_LANGID = 4;
 export const TRANSCRIBE_FEATURE_INITIAL_PROMPT = 0;
 export const TRANSCRIBE_FEATURE_TEMPERATURE_FALLBACK = 1;
 export const TRANSCRIBE_FEATURE_LONG_FORM = 2;
@@ -157,6 +164,11 @@ export const STRUCT_LAYOUT: Record<string, StructLayout> = {
   'transcribe_diarize_info': { size: 16, align: 8, offsets: {'struct_size': 0, 'sample_rate': 8, 'max_speakers': 12} },
   'transcribe_diarize_session_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_threads': 8} },
   'transcribe_diarize_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'family': 8} },
+  'transcribe_langid_info': { size: 24, align: 8, offsets: {'struct_size': 0, 'sample_rate': 8, 'n_labels': 12, 'min_audio_ms': 16} },
+  'transcribe_langid_session_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_threads': 8, 'max_audio_ms': 12} },
+  'transcribe_langid_params': { size: 24, align: 8, offsets: {'struct_size': 0, 'allowed': 8, 'n_allowed': 16, 'top_k': 20} },
+  'transcribe_langid_result': { size: 32, align: 8, offsets: {'struct_size': 0, 'n_candidates': 8, 'n_allowed': 12, 'allowed_mass': 16, 'audio_ms': 24} },
+  'transcribe_langid_candidate': { size: 48, align: 8, offsets: {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'p_unrestricted': 36, 'logit': 40} },
   'transcribe_moonshine_streaming_stream_ext': { size: 24, align: 8, offsets: {'ext': 0, 'min_decode_interval_ms': 16} },
   'transcribe_parakeet_stream_ext': { size: 24, align: 8, offsets: {'ext': 0, 'att_context_right': 16} },
   'transcribe_parakeet_buffered_stream_ext': { size: 32, align: 8, offsets: {'ext': 0, 'left_ms': 16, 'chunk_ms': 20, 'right_ms': 24} },
@@ -186,6 +198,11 @@ export const ABI_STRUCT_IDS: Record<string, number> = {
   'transcribe_diarize_info': 16,
   'transcribe_diarize_session_params': 17,
   'transcribe_diarize_params': 18,
+  'transcribe_langid_info': 19,
+  'transcribe_langid_session_params': 20,
+  'transcribe_langid_params': 21,
+  'transcribe_langid_result': 22,
+  'transcribe_langid_candidate': 23,
 };
 
 // Build koffi struct types; returns a name -> koffi.IKoffiCType map.
@@ -210,6 +227,11 @@ export function defineTypes(koffi: any): Record<string, any> {
   T['transcribe_diarize_info'] = koffi.struct({ struct_size: 'uint64_t', sample_rate: 'int32_t', max_speakers: 'int32_t' });
   T['transcribe_diarize_session_params'] = koffi.struct({ struct_size: 'uint64_t', n_threads: 'int32_t' });
   T['transcribe_diarize_params'] = koffi.struct({ struct_size: 'uint64_t', family: 'void *' });
+  T['transcribe_langid_info'] = koffi.struct({ struct_size: 'uint64_t', sample_rate: 'int32_t', n_labels: 'int32_t', min_audio_ms: 'int32_t' });
+  T['transcribe_langid_session_params'] = koffi.struct({ struct_size: 'uint64_t', n_threads: 'int32_t', max_audio_ms: 'int32_t' });
+  T['transcribe_langid_params'] = koffi.struct({ struct_size: 'uint64_t', allowed: 'void *', n_allowed: 'int32_t', top_k: 'int32_t' });
+  T['transcribe_langid_result'] = koffi.struct({ struct_size: 'uint64_t', n_candidates: 'int32_t', n_allowed: 'int32_t', allowed_mass: 'float', audio_ms: 'int64_t' });
+  T['transcribe_langid_candidate'] = koffi.struct({ struct_size: 'uint64_t', index: 'int32_t', code: 'char *', name: 'char *', p: 'float', p_unrestricted: 'float', logit: 'float' });
   T['transcribe_moonshine_streaming_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], min_decode_interval_ms: 'int32_t' });
   T['transcribe_parakeet_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], att_context_right: 'int32_t' });
   T['transcribe_parakeet_buffered_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], left_ms: 'int32_t', chunk_ms: 'int32_t', right_ms: 'int32_t' });
@@ -273,6 +295,22 @@ export const FUNCTION_SIGNATURES: Record<string, FnSig> = {
   'transcribe_init_backends': { ret: 'transcribe_status', args: ['const char *'] },
   'transcribe_init_backends_default': { ret: 'transcribe_status', args: [] },
   'transcribe_init_backends_ex': { ret: 'transcribe_status', args: ['const struct transcribe_backend_init_params *'] },
+  'transcribe_langid_candidate_init': { ret: 'void', args: ['struct transcribe_langid_candidate *'] },
+  'transcribe_langid_get_candidate': { ret: 'transcribe_status', args: ['const struct transcribe_langid_session *', 'int', 'struct transcribe_langid_candidate *'] },
+  'transcribe_langid_get_info': { ret: 'transcribe_status', args: ['const struct transcribe_model *', 'struct transcribe_langid_info *'] },
+  'transcribe_langid_get_result': { ret: 'transcribe_status', args: ['const struct transcribe_langid_session *', 'struct transcribe_langid_result *'] },
+  'transcribe_langid_get_timings': { ret: 'transcribe_status', args: ['const struct transcribe_langid_session *', 'struct transcribe_timings *'] },
+  'transcribe_langid_info_init': { ret: 'void', args: ['struct transcribe_langid_info *'] },
+  'transcribe_langid_label_code': { ret: 'const char *', args: ['const struct transcribe_model *', 'int32_t'] },
+  'transcribe_langid_label_index': { ret: 'int32_t', args: ['const struct transcribe_model *', 'const char *'] },
+  'transcribe_langid_label_name': { ret: 'const char *', args: ['const struct transcribe_model *', 'int32_t'] },
+  'transcribe_langid_params_init': { ret: 'void', args: ['struct transcribe_langid_params *'] },
+  'transcribe_langid_result_init': { ret: 'void', args: ['struct transcribe_langid_result *'] },
+  'transcribe_langid_run': { ret: 'transcribe_status', args: ['struct transcribe_langid_session *', 'const float *', 'int', 'const struct transcribe_langid_params *'] },
+  'transcribe_langid_session_free': { ret: 'void', args: ['struct transcribe_langid_session *'] },
+  'transcribe_langid_session_init': { ret: 'transcribe_status', args: ['struct transcribe_model *', 'const struct transcribe_langid_session_params *', 'struct transcribe_langid_session **'] },
+  'transcribe_langid_session_params_init': { ret: 'void', args: ['struct transcribe_langid_session_params *'] },
+  'transcribe_langid_set_abort_callback': { ret: 'void', args: ['struct transcribe_langid_session *', 'transcribe_abort_callback', 'void *'] },
   'transcribe_log_set': { ret: 'void', args: ['transcribe_log_callback', 'void *'] },
   'transcribe_model_accepts_ext_kind': { ret: '_Bool', args: ['const struct transcribe_model *', 'transcribe_ext_slot', 'uint32_t'] },
   'transcribe_model_arch_string': { ret: 'const char *', args: ['const struct transcribe_model *'] },
diff --git a/bindings/typescript/src/errors.ts b/bindings/typescript/src/errors.ts
index f0495ea83..de4b753ac 100644
--- a/bindings/typescript/src/errors.ts
+++ b/bindings/typescript/src/errors.ts
@@ -35,9 +35,11 @@ export class BackendError extends TranscribeError {}
 export class UnsupportedRequest extends TranscribeError {}
 export class AbiError extends TranscribeError {}
 export class InputTooLong extends TranscribeError {}
+/** Raised when the audio is shorter than the role's minimum (e.g. language ID's minAudioMs). */
+export class InputTooShort extends TranscribeError {}
 export class VersionMismatch extends TranscribeError {}
 
-/** Raised when the model does not serve the role (ASR, diarize) the call needs. */
+/** Raised when the model does not serve the role (ASR, diarize, langid) the call needs. */
 export class UnsupportedRole extends TranscribeError {}
 
 /** Raised when a run is cancelled; carries any partial transcript in `partialResult`. */
@@ -74,6 +76,7 @@ const STATUS_TO_EXC: Record<number, new (m: string, s?: number) => TranscribeErr
   [g.TRANSCRIBE_ERR_OUTPUT_TRUNCATED]: OutputTruncated,
   [g.TRANSCRIBE_ERR_OUTPUT_REPETITION]: OutputRepetition,
   [g.TRANSCRIBE_ERR_UNSUPPORTED_ROLE]: UnsupportedRole,
+  [g.TRANSCRIBE_ERR_INPUT_TOO_SHORT]: InputTooShort,
 };
 
 /** Build (do not throw) the mapped exception for a status. */
diff --git a/bindings/typescript/src/ffi.ts b/bindings/typescript/src/ffi.ts
index c6d3c4bf6..7ae9bc9dd 100644
--- a/bindings/typescript/src/ffi.ts
+++ b/bindings/typescript/src/ffi.ts
@@ -220,6 +220,60 @@ export function bindLibrary(libraryPath: string): Bound {
       iop(T.transcribe_timings),
     ]),
 
+    // langid role
+    langidInfoInit: lib.func("transcribe_langid_info_init", "void", [
+      outp(T.transcribe_langid_info),
+    ]),
+    langidGetInfo: lib.func("transcribe_langid_get_info", "int", [
+      "void *",
+      iop(T.transcribe_langid_info),
+    ]),
+    langidLabelCode: lib.func("transcribe_langid_label_code", "const char *", ["void *", "int32_t"]),
+    langidLabelName: lib.func("transcribe_langid_label_name", "const char *", ["void *", "int32_t"]),
+    langidLabelIndex: lib.func("transcribe_langid_label_index", "int32_t", ["void *", "const char *"]),
+    langidSessionParamsInit: lib.func("transcribe_langid_session_params_init", "void", [
+      outp(T.transcribe_langid_session_params),
+    ]),
+    langidSessionInit: lib.func("transcribe_langid_session_init", "int", [
+      "void *",
+      inp(T.transcribe_langid_session_params),
+      handleOut,
+    ]),
+    langidSessionFree: lib.func("transcribe_langid_session_free", "void", ["void *"]),
+    langidSetAbortCallback: lib.func("transcribe_langid_set_abort_callback", "void", [
+      "void *",
+      "void *",
+      "void *",
+    ]),
+    langidParamsInit: lib.func("transcribe_langid_params_init", "void", [
+      outp(T.transcribe_langid_params),
+    ]),
+    langidRun: lib.func("transcribe_langid_run", "int", [
+      "void *",
+      inp("float"),
+      "int",
+      inp(T.transcribe_langid_params),
+    ]),
+    langidResultInit: lib.func("transcribe_langid_result_init", "void", [
+      outp(T.transcribe_langid_result),
+    ]),
+    langidGetResult: lib.func("transcribe_langid_get_result", "int", [
+      "void *",
+      iop(T.transcribe_langid_result),
+    ]),
+    langidCandidateInit: lib.func("transcribe_langid_candidate_init", "void", [
+      outp(T.transcribe_langid_candidate),
+    ]),
+    langidGetCandidate: lib.func("transcribe_langid_get_candidate", "int", [
+      "void *",
+      "int",
+      iop(T.transcribe_langid_candidate),
+    ]),
+    langidGetTimings: lib.func("transcribe_langid_get_timings", "int", [
+      "void *",
+      iop(T.transcribe_timings),
+    ]),
+
     // batch (offline)
     runBatch: lib.func("transcribe_run_batch", "int", [
       "void *",
diff --git a/bindings/typescript/src/index.ts b/bindings/typescript/src/index.ts
index 32adc1eaf..6a0e03976 100644
--- a/bindings/typescript/src/index.ts
+++ b/bindings/typescript/src/index.ts
@@ -42,6 +42,11 @@ import type {
   Feature,
   Itn,
   KvType,
+  LangIdCandidate,
+  LangIdInfo,
+  LangIdOptions,
+  LangIdResult,
+  LangIdSessionOptions,
   ModelOptions,
   PcmLike,
   Pnc,
@@ -129,6 +134,7 @@ const FEATURES: Record<Feature, number> = {
 const ROLES: Record<Role, number> = {
   asr: g.TRANSCRIBE_ROLE_ASR,
   diarize: g.TRANSCRIBE_ROLE_DIARIZE,
+  langid: g.TRANSCRIBE_ROLE_LANGID,
 };
 
 // ---- helpers ---------------------------------------------------------------
@@ -1434,13 +1440,118 @@ export class DiarizeSession {
   }
 }
 
+// ---- LangIdSession ---------------------------------------------------------
+
+/** A LANGID-role session: which language is spoken. Same compute rules as Session. */
+export class LangIdSession {
+  #n: Native;
+  #core: SessionCore;
+  #model: TranscribeModel; // keep the model alive while this session lives
+  #untrack: (self: LangIdSession) => void;
+
+  /** @internal */
+  constructor(
+    n: Native,
+    model: TranscribeModel,
+    handle: any,
+    lock: Mutex,
+    untrack: (self: LangIdSession) => void,
+  ) {
+    this.#n = n;
+    this.#model = model;
+    this.#core = new SessionCore(n, handle, lock, n.F.langidSetAbortCallback);
+    this.#untrack = untrack;
+  }
+
+  /**
+   * Identify the language of one clip; input longer than the session's
+   * maxAudioMs is scored on its tail. The input PCM is borrowed, not copied
+   * (see Session.run).
+   */
+  async run(pcm: PcmLike, opts: LangIdOptions = {}): Promise<LangIdResult> {
+    const n = this.#n;
+    const F = n.F;
+    const h = this.#core.handle;
+    const samples = toFloat32(pcm);
+    const p: any = {};
+    F.langidParamsInit(p);
+    if (opts.topK !== undefined) p.top_k = opts.topK;
+    if (opts.allowed !== undefined && opts.allowed !== null) {
+      const codes = opts.allowed;
+      if (!Array.isArray(codes) || !codes.every((c) => typeof c === "string"))
+        throw new InvalidArgument("allowed must be an array of strings");
+      // NULL would mean "every label", the opposite of an empty list.
+      if (codes.length === 0)
+        throw new InvalidArgument("allowed is empty; omit it for every label");
+      codes.forEach((c) => cstr(c, "allowed"));
+      // Freed after the call returns (including async worker calls).
+      const type = n.koffi.array("char *", codes.length);
+      const arr = n.koffi.alloc(type, 1);
+      n.koffi.encode(arr, type, codes);
+      p.allowed = arr;
+      p.n_allowed = codes.length;
+    }
+
+    return this.#core.exclusive("langid", async (call) => {
+      const status = await call("run()", opts.signal, F.langidRun, h, samples, samples.length, p);
+      check(n, status, "transcribe_langid_run");
+      const res: any = {};
+      F.langidResultInit(res);
+      check(n, F.langidGetResult(h, res), "transcribe_langid_get_result");
+      const candidates: LangIdCandidate[] = [];
+      for (let i = 0; i < res.n_candidates; i++) {
+        const c: any = {};
+        F.langidCandidateInit(c);
+        check(n, F.langidGetCandidate(h, i, c), "transcribe_langid_get_candidate");
+        candidates.push({
+          index: c.index,
+          code: c.code ?? "",
+          name: c.name ?? "",
+          p: c.p,
+          pUnrestricted: c.p_unrestricted,
+          logit: c.logit,
+        });
+      }
+      return {
+        candidates,
+        code: candidates.length > 0 ? candidates[0].code : null,
+        nAllowed: res.n_allowed,
+        allowedMass: res.allowed_mass,
+        audioMs: Number(res.audio_ms),
+      };
+    }).finally(() => {
+      if (p.allowed) {
+        n.koffi.free(p.allowed);
+        p.allowed = null;
+      }
+    });
+  }
+
+  /** load_ms plus the last run's mel / encode time. */
+  get timings(): Timings {
+    this.#core.assertNotComputing("session timings");
+    const h = this.#core.handle;
+    return readTimings(this.#n, (o) => this.#n.F.langidGetTimings(h, o));
+  }
+
+  dispose(): void {
+    if (this.#core.disposed) return;
+    this.#untrack(this);
+    this.#core.dispose(this.#n.F.langidSessionFree);
+  }
+
+  [Symbol.dispose](): void {
+    this.dispose();
+  }
+}
+
 // ---- Model -----------------------------------------------------------------
 
 export class TranscribeModel {
   #n: Native;
   #h: any;
   #disposed = false;
-  #sessions = new Set<Session | DiarizeSession>();
+  #sessions = new Set<Session | DiarizeSession | LangIdSession>();
   #lock = new Mutex(); // serializes compute across all sessions of this model
 
   private constructor(n: Native, handle: any) {
@@ -1554,6 +1665,49 @@ export class TranscribeModel {
     return session;
   }
 
+  /** Static facts of a "langid" model; UnsupportedRole otherwise. */
+  get langidInfo(): LangIdInfo {
+    const n = this.#n;
+    const info: any = {};
+    n.F.langidInfoInit(info);
+    check(n, n.F.langidGetInfo(this.handle, info), "reading langid info");
+    return { sampleRate: info.sample_rate, nLabels: info.n_labels, minAudioMs: info.min_audio_ms };
+  }
+
+  /** [code, name] per label index of a "langid" model; UnsupportedRole otherwise. */
+  get langidLabels(): Array<[string, string]> {
+    const n = this.langidInfo.nLabels;
+    const F = this.#n.F;
+    const out: Array<[string, string]> = [];
+    for (let i = 0; i < n; i++)
+      out.push([F.langidLabelCode(this.handle, i) ?? "", F.langidLabelName(this.handle, i) ?? ""]);
+    return out;
+  }
+
+  /** Label index of a code or alias ("he" and "iw" name the same label), or null. */
+  langidLabelIndex(code: string): number | null {
+    const i = this.#n.F.langidLabelIndex(this.handle, cstr(code, "code"));
+    return i >= 0 ? i : null;
+  }
+
+  /** Open a LANGID-role session; UnsupportedRole on a model without "langid". */
+  createLangIdSession(opts: LangIdSessionOptions = {}): LangIdSession {
+    const n = this.#n;
+    const p: any = {};
+    n.F.langidSessionParamsInit(p);
+    if (opts.nThreads !== undefined) p.n_threads = opts.nThreads;
+    if (opts.maxAudioMs !== undefined) p.max_audio_ms = opts.maxAudioMs;
+    const out: any[] = [null];
+    check(n, n.F.langidSessionInit(this.handle, p, out), "opening langid session");
+    if (!out[0])
+      throw new TranscribeError("langid session init returned a null handle");
+    const session = new LangIdSession(n, this, out[0], this.#lock, (s) =>
+      this.#sessions.delete(s),
+    );
+    this.#sessions.add(session);
+    return session;
+  }
+
   get capabilities(): Capabilities {
     const n = this.#n;
     const c: any = {};
diff --git a/bindings/typescript/src/types.ts b/bindings/typescript/src/types.ts
index 1ddd6bfb1..0a84d1f7e 100644
--- a/bindings/typescript/src/types.ts
+++ b/bindings/typescript/src/types.ts
@@ -291,8 +291,8 @@ export type FamilyExtension =
 
 // ---- roles -----------------------------------------------------------------
 
-/** What a model serves: "asr" (transcription) and/or "diarize" (speaker turns). */
-export type Role = "asr" | "diarize";
+/** What a model serves: "asr" (transcription), "diarize" (speaker turns), "langid" (language). */
+export type Role = "asr" | "diarize" | "langid";
 
 export interface DiarizeInfo {
   /** Input PCM rate. */
@@ -312,3 +312,57 @@ export interface DiarizeOptions {
   /** A diarize_run-slot family extension (e.g. sortformer_diarize). */
   family?: FamilyExtension;
 }
+
+export interface LangIdInfo {
+  /** Input PCM rate. */
+  sampleRate: number;
+  /** Label indices are [0, nLabels). */
+  nLabels: number;
+  /** Shorter scored audio throws InputTooShort. */
+  minAudioMs: number;
+}
+
+export interface LangIdSessionOptions {
+  /** CPU threads for CPU-side ops; 0 = library default. */
+  nThreads?: number;
+  /** Longer input is scored on its last maxAudioMs; 0 = 30000. */
+  maxAudioMs?: number;
+}
+
+export interface LangIdOptions {
+  /** Cancel the run cooperatively. */
+  signal?: AbortSignal;
+  /**
+   * Restrict the decision to these codes or aliases. Omitted (or undefined /
+   * null) means every label; an empty array throws InvalidArgument; an
+   * unknown code throws UnsupportedRequest.
+   */
+  allowed?: readonly string[] | null;
+  /** Keep the best topK candidates; 0 = every allowed label. */
+  topK?: number;
+}
+
+/** One ranked label; `code` is the model's own label ("en", "iw"). */
+export interface LangIdCandidate {
+  index: number;
+  code: string;
+  name: string;
+  /** Softmax renormalized over the allowed set. */
+  p: number;
+  /** Softmax over every label. */
+  pUnrestricted: number;
+  logit: number;
+}
+
+export interface LangIdResult {
+  /** Ranked by p, descending; ties keep label order. */
+  candidates: LangIdCandidate[];
+  /** The top candidate's code, or null with no candidates. */
+  code: string | null;
+  /** Labels in the allowed set (before topK). */
+  nAllowed: number;
+  /** Unrestricted probability inside the allowed set (1 when unrestricted). */
+  allowedMass: number;
+  /** Audio actually scored, after the crop. */
+  audioMs: number;
+}
diff --git a/bindings/typescript/test/common.mjs b/bindings/typescript/test/common.mjs
index de5e68e0e..c14942fa5 100644
--- a/bindings/typescript/test/common.mjs
+++ b/bindings/typescript/test/common.mjs
@@ -26,6 +26,14 @@ export const VOXTRAL_MODEL = process.env.TRANSCRIBE_SMOKE_VOXTRAL_MODEL || "";
 export const SORTFORMER_MODEL = process.env.TRANSCRIBE_SMOKE_SORTFORMER_MODEL || "";
 export const SORTFORMER_AUDIO = path.resolve(HERE, "../../../samples/sortformer-2spk-mix.wav");
 
+// VoxLingua107 ECAPA-TDNN (LANGID role only); the toy fixture is generated by
+// the C++ test build (tests/fixtures), so it skips until that has been built.
+export const LANGID_MODEL =
+  process.env.TRANSCRIBE_SMOKE_LANGID_MODEL ||
+  path.resolve(HERE, "../../../models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf");
+export const LANGID_TOY_MODEL = path.resolve(HERE, "../../../tests/fixtures/arch_ecapa_tdnn_minimal.gguf");
+export const SAMPLES = path.resolve(HERE, "../../../samples");
+
 // jfk.wav ships in-repo; fetch-canary exports only the model paths.
 export const AUDIO =
   process.env.TRANSCRIBE_SMOKE_AUDIO || path.resolve(HERE, "../../../samples/jfk.wav");
diff --git a/bindings/typescript/test/errors.test.mjs b/bindings/typescript/test/errors.test.mjs
index 0c0d13a2c..3dafd907e 100644
--- a/bindings/typescript/test/errors.test.mjs
+++ b/bindings/typescript/test/errors.test.mjs
@@ -20,6 +20,7 @@ import {
   InputTooLong,
   OutputTruncated,
   OutputRepetition,
+  InputTooShort,
 } from "../dist/index.js";
 
 const EXPECTED = {
@@ -43,6 +44,7 @@ const EXPECTED = {
   TRANSCRIBE_ERR_OUTPUT_TRUNCATED: OutputTruncated,
   TRANSCRIBE_ERR_OUTPUT_REPETITION: OutputRepetition,
   TRANSCRIBE_ERR_UNSUPPORTED_ROLE: UnsupportedRole,
+  TRANSCRIBE_ERR_INPUT_TOO_SHORT: InputTooShort,
 };
 
 test("every transcribe_status maps to its documented error class", () => {
diff --git a/bindings/typescript/test/langid.test.mjs b/bindings/typescript/test/langid.test.mjs
new file mode 100644
index 000000000..69e0bbdff
--- /dev/null
+++ b/bindings/typescript/test/langid.test.mjs
@@ -0,0 +1,104 @@
+// LANGID role: model.roles, langidInfo / labels and LangIdSession wiring.
+// Contract checks run on the toy ecapa_tdnn fixture; top-1 on the real
+// VoxLingua107 model.
+
+import assert from "node:assert/strict";
+import * as path from "node:path";
+import { modelTest, LANGID_MODEL, LANGID_TOY_MODEL, SAMPLES, readWav } from "./common.mjs";
+import {
+  TranscribeModel,
+  InvalidArgument,
+  InputTooShort,
+  UnsupportedRequest,
+  UnsupportedRole,
+  Aborted,
+} from "../dist/index.js";
+
+function noise(n, seed = 7) {
+  const out = new Float32Array(n);
+  let s = (seed | 1) >>> 0;
+  for (let i = 0; i < n; i++) {
+    s = (Math.imul(s, 1664525) + 1013904223) >>> 0;
+    out[i] = ((s >>> 8) & 0xffffff) / 16777216 - 0.5;
+  }
+  return out;
+}
+
+modelTest("toy: roles, info, labels; ASR calls are refused", LANGID_TOY_MODEL, async () => {
+  const m = await TranscribeModel.load(LANGID_TOY_MODEL, { backend: "cpu" });
+  try {
+    assert.deepEqual(m.roles, ["langid"]);
+    assert.deepEqual(m.langidInfo, { sampleRate: 16000, nLabels: 5, minAudioMs: 500 });
+    assert.deepEqual(m.langidLabels[2], ["cc", "Charlie"]);
+    assert.equal(m.langidLabelIndex("xx"), 0);
+    assert.equal(m.langidLabelIndex("zz"), null);
+    assert.throws(() => m.capabilities, UnsupportedRole);
+    assert.throws(() => m.createSession(), UnsupportedRole);
+  } finally {
+    m.dispose();
+  }
+});
+
+modelTest("toy: run contract (allowed, topK, crop, minimum)", LANGID_TOY_MODEL, async () => {
+  const m = await TranscribeModel.load(LANGID_TOY_MODEL, { backend: "cpu" });
+  const lid = m.createLangIdSession({ nThreads: 1 });
+  try {
+    const pcm = noise(16000);
+    const r = await lid.run(pcm);
+    assert.equal(r.candidates.length, 5);
+    assert.equal(r.nAllowed, 5);
+    assert.equal(r.allowedMass, 1);
+    assert.equal(r.audioMs, 1000);
+    assert.equal(r.code, r.candidates[0].code);
+    const sum = r.candidates.reduce((a, c) => a + c.p, 0);
+    assert.ok(Math.abs(sum - 1) < 1e-5);
+
+    const restricted = await lid.run(pcm, { allowed: ["bb", "dd"] });
+    assert.deepEqual(new Set(restricted.candidates.map((c) => c.code)), new Set(["bb", "dd"]));
+    assert.ok(restricted.allowedMass < 1);
+
+    assert.equal((await lid.run(pcm, { topK: 2 })).candidates.length, 2);
+    assert.equal((await lid.run(pcm, { allowed: null })).nAllowed, 5);
+    // An empty list must not silently mean "every label".
+    await assert.rejects(lid.run(pcm, { allowed: [] }), InvalidArgument);
+    await assert.rejects(lid.run(pcm, { allowed: ["zz"] }), UnsupportedRequest);
+    await assert.rejects(lid.run(pcm.subarray(0, 6400)), InputTooShort);
+    assert.equal((await lid.run(noise(16000 * 31))).audioMs, 30000);
+    assert.ok(lid.timings.encodeMs > 0);
+    assert.throws(() => m.createLangIdSession({ maxAudioMs: 400 }), InvalidArgument);
+  } finally {
+    lid.dispose();
+    m.dispose();
+  }
+});
+
+modelTest("toy: an aborted signal cancels the run", LANGID_TOY_MODEL, async () => {
+  const m = await TranscribeModel.load(LANGID_TOY_MODEL, { backend: "cpu" });
+  const lid = m.createLangIdSession();
+  try {
+    const ac = new AbortController();
+    ac.abort();
+    await assert.rejects(lid.run(noise(16000), { signal: ac.signal }), Aborted);
+    assert.ok((await lid.run(noise(16000))).candidates.length > 0);
+  } finally {
+    lid.dispose();
+    m.dispose();
+  }
+});
+
+modelTest("real model: top-1 on the FLEURS clips", LANGID_MODEL, async () => {
+  const m = await TranscribeModel.load(LANGID_MODEL, { backend: "cpu" });
+  const lid = m.createLangIdSession();
+  try {
+    assert.equal(m.langidInfo.nLabels, 107);
+    assert.equal(m.langidLabelIndex("he"), m.langidLabelIndex("iw"));
+    for (const code of ["en", "de", "fr", "es", "ja", "zh", "ru", "id"]) {
+      const r = await lid.run(readWav(path.join(SAMPLES, `fleurs-${code}.wav`)), { topK: 3 });
+      assert.equal(r.code, code);
+      assert.ok(r.candidates[0].p >= 0.5);
+    }
+  } finally {
+    lid.dispose();
+    m.dispose();
+  }
+});
diff --git a/docs/bindings.md b/docs/bindings.md
index 9440f3621..831d3ce39 100644
--- a/docs/bindings.md
+++ b/docs/bindings.md
@@ -110,16 +110,17 @@ several sessions of one model each hold an active stream and interleave
 their feeds; a binding allows one active stream per model. Starting a stream
 takes the model's stream lease, and ending it (finalize, reset, a feed that
 leaves the stream FAILED, or dropping / closing the stream) releases it.
-While the lease is held, `run`, `run_batch`, a new stream and a diarize run
-on any session of that model raise `Busy` instead of waiting. A feed
+While the lease is held, `run`, `run_batch`, a new stream, a diarize run and
+a langid run on any session of that model raise `Busy` instead of waiting. A feed
 rejected before the native call (e.g. NaN input) keeps the lease.
 
 ## Roles and the DIARIZE session
 
 Each binding exposes the role mask as `Model.roles` (Python `frozenset[Role]`,
-TypeScript `readonly ('asr' | 'diarize')[]`, Rust `Roles`, Swift `Roles`
-option set). Status 20 surfaces as `UnsupportedRole` (`.unsupportedRole` in
-Swift), including from capabilities on a model without ASR.
+TypeScript `readonly ('asr' | 'diarize' | 'langid')[]`, Rust `Roles`, Swift
+`Roles` option set). Status 20 surfaces as `UnsupportedRole`
+(`.unsupportedRole` in Swift), including from capabilities on a model without
+ASR. Status 21 surfaces as `InputTooShort` (`.inputTooShort` in Swift).
 
 A DIARIZE model (`docs/roles.md`) gets its own session type: `DiarizeSession`
 from `model.diarize_session()` (Python, Rust), `model.diarizeSession()`
@@ -134,6 +135,29 @@ model-wide compute lock, waits behind other compute on the model, raises
 keeps its model alive, honours cancellation, and defers native frees that
 race an in-flight call.
 
+## The LANGID session
+
+A LANGID model gets `LangIdSession` from `model.langid_session()` (Python,
+Rust), `model.langIdSession()` (Swift) or `model.createLangIdSession()`
+(TypeScript), with `max_audio_ms` / `maxAudioMs` as a session option, plus
+`langid_info` / `langIdInfo` / `langidInfo` (sample rate, label count,
+minimum audio) and the label table (codes, names, and alias lookup).
+`run(pcm, allowed=…, top_k=…)` returns a copied-out result: candidates ranked
+by `p` (index, code, name, `p`, `p_unrestricted`, `logit`), `n_allowed`,
+`allowed_mass` and the scored `audio_ms`.
+
+`allowed` is the first caller-owned `const char * const *` input. Every
+binding keeps the array and each encoded string alive until the native call
+returns (TypeScript frees them only after its async worker call settles).
+Omitted / `None` / `nil` / `null` passes NULL, meaning every label. An empty
+list is rejected by the binding itself with `InvalidArgument`: many
+marshallers turn an empty array into NULL, which would silently flip the
+meaning to "all labels".
+
+A langid run follows the same execution rules as an ASR or diarize run:
+model-wide compute lock, `Busy` under a stream lease, results copied out
+under the lock, cancellation, and deferred frees.
+
 ## Raw text
 
 Every first-class binding exposes `raw_text` / `rawText` on the materialized
diff --git a/include/transcribe.abihash b/include/transcribe.abihash
index 8fce33b22..daf7d6d22 100644
--- a/include/transcribe.abihash
+++ b/include/transcribe.abihash
@@ -1 +1 @@
-bd3273dabb25a1fe
+318eba8c4b2e4387

From 796545af2a0f35de166471696cc5667c40b4c623 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Mon, 5 Oct 2026 16:25:59 +0800
Subject: [PATCH 04/38] wip langid port

---
 .claude/skills/porting-1-intake/SKILL.md      |  11 +-
 .claude/skills/porting-7-wer/SKILL.md         |   2 +-
 .gitignore                                    |   2 +
 README.md                                     |   8 +
 catalog/_schema.json                          |  22 +-
 catalog/lang-id-voxlingua107-ecapa.json       |  71 ++
 docs/environment-variables.md                 |   1 +
 docs/langid.md                                | 184 +++++
 docs/migrating-to-0.4.md                      |  27 +-
 docs/models/lang-id-voxlingua107-ecapa.md     | 221 ++++++
 docs/porting/families/ecapa_tdnn.md           | 135 ++++
 docs/roles.md                                 |   1 +
 scripts/catalog/common.py                     |  37 +-
 scripts/catalog/db.py                         |  10 +-
 scripts/catalog/render.py                     |   4 +-
 scripts/hf_cards/generate.py                  |   4 +-
 .../hf_cards/lang-id-voxlingua107-ecapa.yaml  |  69 ++
 scripts/langid/bench.py                       | 102 +++
 scripts/langid/compare.py                     | 260 +++++++
 scripts/langid/ingest.py                      | 283 +++++++
 scripts/langid/run.py                         | 498 +++++++++++++
 scripts/langid/score.py                       | 696 ++++++++++++++++++
 scripts/langid/selections.json                |  48 ++
 23 files changed, 2670 insertions(+), 26 deletions(-)
 create mode 100644 catalog/lang-id-voxlingua107-ecapa.json
 create mode 100644 docs/langid.md
 create mode 100644 docs/models/lang-id-voxlingua107-ecapa.md
 create mode 100644 docs/porting/families/ecapa_tdnn.md
 create mode 100644 scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
 create mode 100644 scripts/langid/bench.py
 create mode 100755 scripts/langid/compare.py
 create mode 100755 scripts/langid/ingest.py
 create mode 100755 scripts/langid/run.py
 create mode 100755 scripts/langid/score.py
 create mode 100644 scripts/langid/selections.json

diff --git a/.claude/skills/porting-1-intake/SKILL.md b/.claude/skills/porting-1-intake/SKILL.md
index 0da7b7a89..ebfa1198d 100644
--- a/.claude/skills/porting-1-intake/SKILL.md
+++ b/.claude/skills/porting-1-intake/SKILL.md
@@ -53,13 +53,20 @@ field semantics.
 
 1. **Reference framework choice.**: The reference implementation should always be the source of truth. If there is not one, this is a red flag and must immediately be told to the human.
 
-2. **Architecture pattern.** One of `encoder-transducer`, `encoder-decoder`, `audio-llm`, `encoder-ctc`. The script's `config.architecture_candidates` is a heuristic starting point. If it doesn't fit, propose a new pattern and have the user accept it based on your research of the architecture.
+2. **Architecture pattern.** One of `encoder-transducer`, `encoder-decoder`, `audio-llm`, `encoder-ctc` (`encoder-diarizer` / `encoder-classifier` for the DIARIZE / LANGID roles). The script's `config.architecture_candidates` is a heuristic starting point. If it doesn't fit, propose a new pattern and have the user accept it based on your research of the architecture.
 
    **Role.** ASR (the product is a transcript) or DIARIZE (the product is
    who spoke when; see `docs/roles.md`). A DIARIZE port implements
    `DiarizeOps` (`src/transcribe-diarize.h`) instead of the ASR hooks, sets
    `"role": "diarize"` in its catalog record, and is accepted on DER
-   (`scripts/diar/`), not WER.
+   (`scripts/diar/`), not WER. A LANGID port (the product is a language
+   decision; `docs/langid.md`) implements `LangidOps`
+   (`src/transcribe-langid.h`) and only returns logits over its label table,
+   sets `"role": "langid"` with `metric: "accuracy"` / `acc_pct` rows, and
+   is accepted on top-1 decision parity with the reference over FLEURS
+   (`scripts/langid/`), not WER. Its golden cases set `"language": null`
+   and carry the expected label as `expected_language`, so validate.py never
+   hands the answer to the model.
 
 3. **Acceptance dataset.** Default: LibriSpeech test-clean. Capture any
    publisher-reported score in `upstream_benchmarks` when available for
diff --git a/.claude/skills/porting-7-wer/SKILL.md b/.claude/skills/porting-7-wer/SKILL.md
index 4005e5e55..a58afac33 100644
--- a/.claude/skills/porting-7-wer/SKILL.md
+++ b/.claude/skills/porting-7-wer/SKILL.md
@@ -42,7 +42,7 @@ WER progress:
 
 ### Step 1: Acceptance manifest (execute or ask-point)
 
-Read the acceptance dataset and metric from `upstream_benchmarks[0].{dataset, metric}`. Slugify: lowercase, spaces → hyphens. `"LibriSpeech test-clean"` → `librispeech-test-clean`. Resolve to `$MANIFEST`; every later step uses `$MANIFEST`, never a reconstructed path. Confirm `metric` is `wer` or `cer` (`der` for a DIARIZE-role model: score with `scripts/diar/` instead; anything else is out of scope). Do not use any publisher-reported score for pass/fail; the measured Oracle reference score is the gate target.
+Read the acceptance dataset and metric from `upstream_benchmarks[0].{dataset, metric}`. Slugify: lowercase, spaces → hyphens. `"LibriSpeech test-clean"` → `librispeech-test-clean`. Resolve to `$MANIFEST`; every later step uses `$MANIFEST`, never a reconstructed path. Confirm `metric` is `wer` or `cer` (`der` for a DIARIZE-role model: score with `scripts/diar/` instead; `accuracy` for a LANGID-role model: run `scripts/langid/run.py` for the reference and every shipped quant, gate the ref dtype with `scripts/langid/compare.py` against the reference, and score with `scripts/langid/score.py`; anything else is out of scope). Do not use any publisher-reported score for pass/fail; the measured Oracle reference score is the gate target.
 
 If the intake's dataset is not covered by `scripts/wer/ingest.py`, extend
 that script before running this step.
diff --git a/.gitignore b/.gitignore
index 3c60cc0cd..7a3c3366a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -59,6 +59,8 @@ __pycache__/
 
 # WER evaluation data + generated working reports.
 /samples/wer/
+# FLEURS language ID corpus (scripts/langid/ingest.py).
+/samples/langid/
 # Diarization eval corpora (AMI audio + fetched RTTMs); regenerable via
 # scripts/diar/ingest_ami.py + fetch_ami_forced_alignment.py. The committed
 # oracle case lives at samples/sortformer-2spk-mix.wav (outside samples/diar/).
diff --git a/README.md b/README.md
index 1e9309777..5941d9b9f 100644
--- a/README.md
+++ b/README.md
@@ -39,6 +39,14 @@ C/C++ speech-to-text inference library. Runs diverse STT model families via [GGU
 | Streaming Sortformer Diarizer 4spk v2.1 | `diar_streaming_sortformer_4spk-v2.1` | diarize, streaming | [docs/models/diar_streaming_sortformer_4spk-v2.1.md](docs/models/diar_streaming_sortformer_4spk-v2.1.md) |
 <!-- /catalog -->
 
+**Language ID models** (no transcription; verified by top-1 decision parity on FLEURS; see [`docs/langid.md`](docs/langid.md)):
+
+<!-- catalog:family-index role=langid -->
+| Family | Variants | Available capabilities | Docs |
+| --- | --- | --- | --- |
+| VoxLingua107 ECAPA-TDNN (language ID) | `lang-id-voxlingua107-ecapa` | language ID (107 languages) | [docs/models/lang-id-voxlingua107-ecapa.md](docs/models/lang-id-voxlingua107-ecapa.md) |
+<!-- /catalog -->
+
 Per-variant model cards live under [`docs/models/`](docs/models/).
 
 ## Model catalog
diff --git a/catalog/_schema.json b/catalog/_schema.json
index 219e539b4..0dabdbe26 100644
--- a/catalog/_schema.json
+++ b/catalog/_schema.json
@@ -25,7 +25,7 @@
       "description": "Loader family (the GGUF's general.architecture)"
     },
     "role": {
-      "enum": ["asr", "diarize"],
+      "enum": ["asr", "diarize", "langid"],
       "description": "The role the model is published for (transcribe_model_roles). Absent means asr."
     },
     "display_name": {
@@ -286,7 +286,7 @@
         "type": "object",
         "additionalProperties": false,
         "required": [
-          "dataset", "split", "language", "quant", "metric", "err_pct",
+          "dataset", "split", "language", "quant", "metric",
           "ci95", "n_utts", "batch_size", "timestamps", "engine_sha"
         ],
         "properties": {
@@ -320,7 +320,14 @@
           },
           "err_pct": {
             "type": "number",
-            "minimum": 0
+            "minimum": 0,
+            "description": "Error rate in percent (wer / cer / der / cpwer rows). Accuracy rows carry acc_pct instead."
+          },
+          "acc_pct": {
+            "type": "number",
+            "minimum": 0,
+            "maximum": 100,
+            "description": "Top-1 accuracy in percent; only on metric=accuracy rows (language ID), in place of err_pct."
           },
           "ci95": {
             "type": "array",
@@ -392,7 +399,14 @@
             "type": "string",
             "description": "Decoding mode for models that publish one metric under several modes (multitalker \"kernel\" / \"masked\" cpWER). Rows with a mode are a separate result set."
           }
-        }
+        },
+        "allOf": [
+          {
+            "if": {"properties": {"metric": {"const": "accuracy"}}},
+            "then": {"required": ["acc_pct"], "not": {"required": ["err_pct"]}},
+            "else": {"required": ["err_pct"], "not": {"required": ["acc_pct"]}}
+          }
+        ]
       }
     },
     "headline_benchmark": {
diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
new file mode 100644
index 000000000..77134d183
--- /dev/null
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -0,0 +1,71 @@
+{
+  "schema": "transcribe-catalog-v1",
+  "variant": "lang-id-voxlingua107-ecapa",
+  "family": "ecapa_tdnn",
+  "role": "langid",
+  "display_name": "VoxLingua107 ECAPA-TDNN",
+  "params": 21244679,
+  "license": {
+    "spdx": "apache-2.0",
+    "display": "Apache-2.0"
+  },
+  "upstream_repo": "speechbrain/lang-id-voxlingua107-ecapa",
+  "upstream_commit": "0253049",
+  "published_repo": "handy-computer/lang-id-voxlingua107-ecapa-gguf",
+  "docs_page": "lang-id-voxlingua107-ecapa.md",
+  "languages": [
+    "ab", "af", "am", "ar", "as", "az", "ba", "be", "bg", "bn", "bo", "br",
+    "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et",
+    "eu", "fa", "fi", "fo", "fr", "gl", "gn", "gu", "gv", "ha", "haw", "hi",
+    "hr", "ht", "hu", "hy", "ia", "id", "is", "it", "iw", "ja", "jw", "ka",
+    "kk", "km", "kn", "ko", "la", "lb", "ln", "lo", "lt", "lv", "mg", "mi",
+    "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "nn", "no", "oc",
+    "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sco", "sd", "si", "sk", "sl",
+    "sn", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "tg", "th", "tk",
+    "tl", "tr", "tt", "uk", "ur", "uz", "vi", "war", "yi", "yo", "zh"
+  ],
+  "language_tag_form": "bare-bcp47",
+  "long_form_strategy": "hard-cap",
+  "max_audio_s": 30,
+  "capabilities": {
+    "transcribe": {"supported":false},
+    "translate": {"supported":false},
+    "lang_detect": {"supported":false},
+    "timestamps": {"supported":false},
+    "streaming": {"supported":false},
+    "diarize": {"supported":false},
+    "batching": {"supported":false}
+  },
+  "downloads": [
+    {"quant":"F32","filename":"lang-id-voxlingua107-ecapa-F32.gguf","size_bytes":84993248},
+    {"quant":"F16","filename":"lang-id-voxlingua107-ecapa-F16.gguf","size_bytes":45299936},
+    {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693696}
+  ],
+  "accuracy_benchmarks": [
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.2,"ci95":[84.1,86.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.2,"ci95":[84.0,86.4],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":84.3,"ci95":[83.0,85.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"}
+  ],
+  "headline_benchmark": {
+    "dataset": "fleurs",
+    "split": "test",
+    "language": "mul",
+    "metric": "accuracy",
+    "batch_size": 1,
+    "timestamps": "none"
+  },
+  "speed_benchmarks": [
+    {"machine":"m4-max","backend":"cpu","quant":"F32","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":147.12,"xrt_compute":67.97,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"F32","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":450.9,"xrt_compute":66.53,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F32","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":14.29,"xrt_compute":699.79,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F32","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":37.11,"xrt_compute":808.41,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"F16","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":82.47,"xrt_compute":121.26,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"F16","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":251.53,"xrt_compute":119.27,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F16","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":14.09,"xrt_compute":709.72,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F16","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":35.65,"xrt_compute":841.51,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"Q8_0","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":74.86,"xrt_compute":133.58,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"Q8_0","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":229.53,"xrt_compute":130.7,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":14.22,"xrt_compute":703.23,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":36.23,"xrt_compute":828.04,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null}
+  ]
+}
diff --git a/docs/environment-variables.md b/docs/environment-variables.md
index 053b0f7e6..4e1ae2237 100644
--- a/docs/environment-variables.md
+++ b/docs/environment-variables.md
@@ -87,6 +87,7 @@ its var is unset. Convention: `TRANSCRIBE_<FAMILY>_GGUF`.
 | `TRANSCRIBE_GIGAAM_GGUF` | `gigaam_workspace_release_smoke` |
 | `TRANSCRIBE_MULTITALKER_BUNDLE_GGUF` | `parakeet_multitalker_e2e_smoke` |
 | `TRANSCRIBE_SORTFORMER_GGUF` | `sortformer_diarize_unit`, `cli_diarize_smoke` |
+| `TRANSCRIBE_ECAPA_TDNN_GGUF` | `ecapa_tdnn_real_smoke`, `cli_langid_smoke` (its real-model check) |
 | `TRANSCRIBE_COHERE_GGUF` | `cohere_real_smoke`, `cohere_e2e_smoke` |
 | `TRANSCRIBE_GRANITE5_CTC_GGUF` | `granite5_ctc_real_smoke`, `granite5_ctc_e2e_smoke` |
 | `TRANSCRIBE_WHISPER_GGUF` | `whisper_e2e_smoke`, `whisper_tokenize_parity` |
diff --git a/docs/langid.md b/docs/langid.md
new file mode 100644
index 000000000..b768e35d9
--- /dev/null
+++ b/docs/langid.md
@@ -0,0 +1,184 @@
+# Language ID
+
+A LANGID model (`transcribe_model_roles()` has `TRANSCRIBE_ROLE_LANGID`)
+answers "which language is this?" API: `include/transcribe/langid.h`. The one
+shipped family is ecapa_tdnn, SpeechBrain's VoxLingua107 ECAPA-TDNN
+(`docs/models/lang-id-voxlingua107-ecapa.md`): a classifier over 107 labels.
+
+Almost all of the difference between a good and a bad integration is in what
+you feed it and what you let it choose from. This page is the protocol, with
+the measured numbers behind each rule. They come from FLEURS `test`, 15
+languages x 200 utterances (`scripts/langid/`), scored with the SpeechBrain
+reference; the C++ engine makes the same decisions on the same audio.
+
+## Minimal use
+
+```c
+#include "transcribe/langid.h"
+
+struct transcribe_model * m = NULL;
+transcribe_model_load_file("lang-id-voxlingua107-ecapa-Q8_0.gguf", NULL, &m);
+
+struct transcribe_langid_session * lid = NULL;
+transcribe_langid_session_init(m, NULL, &lid);
+
+const char * allowed[] = { "en", "de", "fr" };
+struct transcribe_langid_params lp;
+transcribe_langid_params_init(&lp);
+lp.allowed   = allowed;
+lp.n_allowed = 3;
+transcribe_langid_run(lid, pcm, n_samples, &lp);   /* 16 kHz mono float32 */
+
+struct transcribe_langid_result r;
+transcribe_langid_result_init(&r);
+transcribe_langid_get_result(lid, &r);
+struct transcribe_langid_candidate c;
+transcribe_langid_candidate_init(&c);
+transcribe_langid_get_candidate(lid, 0, &c);       /* c.code, c.p, r.allowed_mass */
+
+transcribe_langid_session_free(lid);
+transcribe_model_free(m);
+```
+
+From the CLI: `transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en,de,fr --top 3 clip.wav`.
+
+## 1. Feed speech, not the raw capture
+
+Trim leading silence before you crop. Any VAD or energy gate will do; the
+evaluation's "trimmed" recipe drops audio up to the first 20 ms frame whose
+RMS exceeds 5% of the clip's loudest frame.
+
+Open-set top-1 accuracy, mean over the 15 languages:
+
+| | 3 s | 5 s | 10 s | full |
+|---|---|---|---|---|
+| crop from sample 0 | 67.0 | 85.2 | 91.1 | 91.4 |
+| crop from first speech | 83.4 | 89.1 | 91.7 | 91.5 |
+
+At 3 s that is +16.4 points for a change that costs nothing. FLEURS clips open
+with silence, so an untrimmed "3 s" is roughly 2 s of speech. By 10 s the gap
+has closed: the amount of speech is what matters, not the amount of audio.
+Give it at least 3 s of speech and prefer 5 s.
+
+## 2. Always pass `allowed`
+
+A dictation app knows which languages its user has enabled. Passing them in
+`transcribe_langid_params::allowed` masks every other logit before the
+argmax. This is the largest accuracy lever, and it is free.
+
+Accuracy over a user's selection, trimmed crops:
+
+| selection | 3 s | 5 s | 10 s | full |
+|---|---|---|---|---|
+| en+zh | 99.8 | 100.0 | 100.0 | 100.0 |
+| en+ru | 99.5 | 100.0 | 100.0 | 100.0 |
+| es+pt | 100.0 | 99.8 | 100.0 | 100.0 |
+| ja+ko | 96.0 | 99.8 | 100.0 | 100.0 |
+| no+da | 99.8 | 100.0 | 100.0 | 100.0 |
+| en+fr+de+es | 96.9 | 97.9 | 99.8 | 99.5 |
+| de+fr+ja | 94.2 | 96.8 | 99.7 | 99.3 |
+| cs+sk | 92.8 | 93.8 | 96.5 | 96.2 |
+| id+ms | 88.2 | 90.2 | 89.8 | 89.0 |
+
+`en+ru` is 99.5% at 3 s while open-set Russian is 54.0%: open-set errors go
+to neighbouring languages (`ru` -> `be`), and a selection without the
+neighbour removes them. A wider but bounded 40-language list lifts the
+untrimmed open-set mean from 67.0 / 85.2 / 91.1 to 77.4 / 92.3 / 96.6 at
+3 / 5 / 10 s.
+
+If the user has exactly one language enabled, don't call language ID at all.
+
+Only NULL (with `n_allowed == 0`) means "every label". A non-NULL list with
+`n_allowed <= 0`, a NULL list with `n_allowed > 0`, or a NULL element is
+`TRANSCRIBE_ERR_INVALID_ARG`; an unknown code or alias is
+`TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE`; duplicates count once.
+
+## 3. Decide at 5 s, re-decide at 10 s if you are not confident
+
+Identify on the first 5 s of speech; if the top `p` is below 0.9, identify
+again at 10 s and use that. What 0.9 buys you, measured on full clips (mean
+~12 s), so this is the confidence-to-precision relation rather than the 5 s
+operating point:
+
+| decision space | threshold | coverage % | precision % |
+|---|---|---|---|
+| open (107) | 0.5 | 97.7 | 92.6 |
+| open (107) | 0.7 | 88.8 | 95.9 |
+| open (107) | 0.9 | 77.1 | 99.0 |
+| 40-language list | 0.5 | 98.7 | 97.3 |
+| 40-language list | 0.7 | 95.9 | 98.6 |
+| 40-language list | 0.9 | 92.0 | 99.4 |
+
+The median top probability is 0.999 when the model is right and 0.650 when it
+is wrong, so a 0.9 gate is a real signal.
+
+## 4. `allowed_mass` is the "not in your selection" signal
+
+`transcribe_langid_result::allowed_mass` is the unrestricted probability the
+allowed set captured (1.0 when unrestricted). When a user with `en+de` starts
+speaking Japanese, `p` still confidently picks `en` or `de` (the mask cannot
+express "neither"), but `allowed_mass` collapses. Use it to prompt or to hold
+off an automatic switch, not as a hard reject: it also drops on noise and on
+very short audio.
+
+This is a closed-set classifier: there is no "no speech" or "unknown
+language" label, and silence, music or noise still produce a confident-looking
+top candidate. Gate the input with VAD; there is no rejection threshold you
+can rely on from the scores alone.
+
+## 5. Long dictations: re-identify on a rolling window
+
+Re-identify every 20-30 s on recent speech rather than on the whole recording.
+`transcribe_langid_session_params::max_audio_ms` (default 30000) caps the
+scored span, and longer input is scored on its last `max_audio_ms`, so handing
+the growing buffer to `transcribe_langid_run` is a trailing-window classifier.
+`transcribe_langid_result::audio_ms` reports what was scored.
+
+The log-mel is mean-normalised over the whole scored span, so a 5 s window's
+result is not the prefix of the 10 s result: overlapping calls are independent
+decisions. Cost scales with the scored span, not the buffer.
+
+## 6. Labels are the model's own codes
+
+Codes are mostly ISO 639-1 with VoxLingua107's legacy spellings (`iw` Hebrew,
+`jw` Javanese, `tl` Filipino, `no` Norwegian Bokmal). The GGUF stores aliases,
+so `he`, `jv`, `fil` and `nb` are accepted in `allowed` and by
+`transcribe_langid_label_index`, but results always report the model's code.
+
+There is no canonicalization across models. To feed the answer to an ASR
+model, match `code` against that model's `transcribe_capabilities::languages`
+yourself (and map `iw` / `he` etc. where they differ).
+
+## 7. Known weak spots
+
+Top open-set confusions (untrimmed, summed over all crops):
+
+| true -> predicted | errors |
+|---|---|
+| ru -> be (Belarusian) | 271 |
+| no -> nn (Norwegian Nynorsk) | 229 |
+| id -> jw (Javanese) | 200 |
+| ms -> id | 157 |
+| cs -> sk | 59 |
+
+- **`no` / `nn`** are not reliably separable. If you mean "Norwegian", put
+  `no` in the selection and leave `nn` out.
+- **`ru` / `be`**: excluding `be` fixes it.
+- **`id` / `ms`** stay at 88-90% even restricted to the pair, at every crop
+  length; distinguish them downstream if you must.
+- **`cs` / `sk`** improve with audio but stay around 96%.
+- **No Cantonese label**: Cantonese is classified as `zh`.
+- **`de`** is weak open-set (68.5% at 3 s trimmed), mostly to `ro`, `yi`, `nn`.
+
+## 8. Input, threading and placement
+
+- 16 kHz mono float32; NaN / Inf is `TRANSCRIBE_ERR_INVALID_ARG`.
+- Scored audio under 500 ms (`transcribe_langid_info::min_audio_ms`) is
+  `TRANSCRIBE_ERR_INPUT_TOO_SHORT`. Between 500 ms and 3 s the answer is weak;
+  treat it as provisional.
+- `top_k` trims the returned list only; the mask and softmax are unaffected.
+- Sessions, model lifetime and the one-compute-per-model rule are those of
+  every role (`docs/roles.md`). Budget `n_threads` explicitly when language ID
+  and ASR run on the CPU at the same time.
+- Placement is per model (`transcribe_model_load_params`); a language ID model
+  can sit on the CPU while ASR uses the GPU.
diff --git a/docs/migrating-to-0.4.md b/docs/migrating-to-0.4.md
index 42c8ed57f..ccdd66e05 100644
--- a/docs/migrating-to-0.4.md
+++ b/docs/migrating-to-0.4.md
@@ -25,6 +25,26 @@ Segments are byte-identical to 0.3 for the same audio, preset and backend.
 Speaker-attributing ASR models (granite, moss, multitalker parakeet) are
 unchanged; see `docs/roles.md`.
 
+## New: the LANGID role
+
+Language identification is a role of its own (`include/transcribe/langid.h`,
+`docs/langid.md`), served by the ecapa_tdnn family (VoxLingua107 ECAPA-TDNN,
+107 labels). It is new API, so nothing migrates; it replaces the separate
+langid.cpp library and its `langid_*` API:
+
+| langid.cpp | 0.4 |
+| --- | --- |
+| `langid_model_load` / `langid_context_init` | `transcribe_model_load_file` / `transcribe_langid_session_init` |
+| `langid_identify` | `transcribe_langid_run` |
+| `langid_get_result` / `langid_get_candidate` | `transcribe_langid_get_result` / `transcribe_langid_get_candidate` |
+| candidate `prob` / `prob_open` | `p` / `p_unrestricted` |
+| `LANGID_ERR_AUDIO_TOO_SHORT` / `_UNKNOWN_LANGUAGE` | `TRANSCRIBE_ERR_INPUT_TOO_SHORT` (new, 21) / `TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE` |
+| non-NULL `allowed` with `n_allowed <= 0` meant "all" | `TRANSCRIBE_ERR_INVALID_ARG`; only NULL means "all" |
+| `max_audio_ms` below 500 ms accepted | `TRANSCRIBE_ERR_INVALID_ARG` at session init |
+| accessors before a result: `INVALID_ARG` | OK with zeroed output, like every other accessor |
+| `langid_embed` | not available (no EMBED role yet) |
+| langid.cpp GGUFs (`langid.*` keys) | reconvert with `scripts/convert-ecapa_tdnn.py` (`stt.*` keys, renamed tensors) |
+
 ## Capabilities are ASR-only
 
 `transcribe_model_get_capabilities` returns `TRANSCRIBE_ERR_UNSUPPORTED_ROLE`
@@ -46,8 +66,9 @@ from the role's own query (`transcribe_diarize_get_info`).
 
 ## Language bindings
 
-- New `Model.roles`, `DiarizeSession` and an `UnsupportedRole` error for
-  status 20 (`.unsupportedRole` in Swift); see `docs/bindings.md`. The
+- New `Model.roles`, `DiarizeSession`, `LangIdSession`, an `UnsupportedRole`
+  error for status 20 (`.unsupportedRole` in Swift) and an `InputTooShort`
+  error for status 21; see `docs/bindings.md`. The
   Sortformer ASR-path extension (`SortformerStreamOptions` and equivalents)
   is removed. Sortformer runs through a diarize session instead:
 
@@ -68,7 +89,7 @@ from the role's own query (`transcribe_diarize_get_info`).
   (e.g. NaN) keeps the lease and the stream stays usable.
 - **Rust:** `Model::capabilities()` returns `Result<Capabilities>`
   (`Err(Error::UnsupportedRole)` on a model without ASR). `ExtSlot` gains
-  `DiarizeRun` and `AbiStruct` gains the three diarize structs; both are now
+  `DiarizeRun` and `AbiStruct` gains the diarize and langid structs; both are now
   `#[non_exhaustive]`, so later roles add variants without another break.
 - **Swift:** `Model.capabilities` is `get throws`.
 - **Python:** calls on one model now serialize (they used to race), and a
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
new file mode 100644
index 000000000..24ae1944e
--- /dev/null
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -0,0 +1,221 @@
+# VoxLingua107 ECAPA-TDNN (language ID)
+
+<!-- catalog:intro -->
+Upstream: [`speechbrain/lang-id-voxlingua107-ecapa`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa) at [`0253049`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa/commit/0253049).
+
+Spoken language identification over 107 languages: SpeechBrain's
+ECAPA-TDNN trained on VoxLingua107. NOT a transcription model: a run
+returns the model's language labels ranked by probability, optionally
+restricted to a caller-chosen set. Takes 16 kHz mono WAV; scores up to
+the last 30 s of a clip.
+<!-- /catalog -->
+
+## What it's for
+
+Spoken language identification: which of 107 languages is this clip in?
+This is **not a transcription model**. It serves the LANGID role
+(`include/transcribe/langid.h`): a run returns the model's language labels
+ranked by probability, optionally restricted to the languages your user has
+enabled, plus `allowed_mass`, the signal that the speech is outside that set.
+Use it to pick the `language` for an ASR model, or to route audio.
+
+How to get good answers out of it (trim silence, pass the allowed set, decide
+at 5 s, re-decide at 10 s) is in [`docs/langid.md`](../langid.md). See
+SpeechBrain's [model card](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa)
+for training data and the label list.
+
+<!-- catalog:pin -->
+Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa/commit/0253049), pinned 2026-10-05. Validated against the SpeechBrain reference at transcribe.cpp commit [`62522202`](https://github.com/handy-computer/transcribe.cpp/tree/62522202) on 2026-10-05.
+<!-- /catalog -->
+
+## Download
+
+<!-- catalog:downloads -->
+| Quantization | Download |  Size | Top-1 accuracy (FLEURS multilingual) |
+| --- | --- | ---: | ---: |
+| F32          | [lang-id-voxlingua107-ecapa-F32.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F32.gguf) | 85 MB | 85.20% |
+| F16          | [lang-id-voxlingua107-ecapa-F16.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F16.gguf) | 45 MB | 85.20% |
+| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 84.30% |
+<!-- /catalog -->
+
+<!-- catalog:recipe -->
+Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `62522202` on 2026-10-05.
+<!-- /catalog -->
+
+<!-- catalog:prose field=wer.notes -->
+Open-set top-1 accuracy over all 107 labels, mean of 15 FLEURS languages
+(200 test utterances each), on the first 5 s of each clip without silence
+trimming. Accuracy is higher with an allowed set and with trimmed speech;
+see docs/langid.md. The C++ F32 port makes the same decision as the
+SpeechBrain reference on all 12000 FLEURS decisions (3 / 5 / 10 s / full).
+<!-- /catalog -->
+
+Only near-reference tiers ship (F32 / F16 / Q8_0): the decision is an argmax
+over 107 logits, and the k-quant tiers would save a few MB at most.
+
+## Accuracy
+
+FLEURS `test`, 15 languages x 200 utterances, crops from the start of each
+clip (no silence trimming), C++ F32 on CPU. Top-1 accuracy, %:
+
+| decision space | 3 s | 5 s | 10 s | full |
+|---|---|---|---|---|
+| open set (all 107 labels) | 67.0 | 85.2 | 91.1 | 91.4 |
+| a 40-language dictation list | 77.4 | 92.3 | 96.6 | 96.6 |
+| en+ru | 98.8 | 100.0 | 100.0 | 100.0 |
+| en+fr+de+es | 88.6 | 94.1 | 98.9 | 99.4 |
+| cs+sk | 83.0 | 92.2 | 96.8 | 96.5 |
+| id+ms | 87.8 | 89.5 | 89.5 | 88.8 |
+
+Per-language tables, confidence / coverage and confusions:
+`uv run scripts/langid/score.py <run>.jsonl --md <run>.md`.
+
+Agreement with the SpeechBrain reference (top-1, the same 12000 decisions:
+3000 clips x 3 / 5 / 10 s / full):
+
+| GGUF | agreement | max abs logit difference | open set 3 / 5 / 10 s / full |
+|---|---|---|---|
+| F32 | 12000 / 12000 | 6.5e-05 | 67.0 / 85.2 / 91.1 / 91.4 |
+| F16 | 11991 / 12000 | 0.098 | 67.0 / 85.2 / 91.1 / 91.5 |
+| Q8_0 | 11175 / 12000 | 7.5 | 65.4 / 84.3 / 90.9 / 91.4 |
+
+F16's nine flips are all near-ties (top-two margin under 0.025 logit). Q8_0
+also quantizes the activations of every large matmul inside ggml, which moves
+6.9% of decisions, almost all on uncertain 3-5 s crops: it costs 1.6 points
+open-set at 3 s, 0.9 at 5 s and nothing on full clips. langid.cpp measured the
+same on its own Q8_0 (93.19% agreement, 65.3 / 84.1 / 90.8 / 91.4).
+
+## Quick Start
+
+```bash
+cmake -B build
+cmake --build build
+
+build/bin/transcribe-cli \
+  -m models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf \
+  --allow en,de,fr --top 3 audio.wav
+# language: de index=18 p=0.999998
+#   ...
+```
+
+If your audio is not already 16 kHz mono WAV, convert it first:
+
+```bash
+ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
+```
+
+From the C API, use the LANGID role (`include/transcribe/langid.h`):
+`transcribe_langid_session_init`, `transcribe_langid_run`, then
+`transcribe_langid_get_result` / `transcribe_langid_get_candidate`. Labels are
+the model's own codes (`iw`, `jw`, `tl`, `no`; `he`, `jv`, `fil`, `nb` are
+accepted as aliases). Match them against your ASR model's
+`transcribe_capabilities::languages` yourself.
+
+## Performance
+
+### Apple M4 Max
+
+Compute latency (mel + encode), median of 20 warm runs, transcribe.cpp
+`62522202`, `scripts/langid/bench.py`. CPU uses the library default thread
+count (8).
+
+| Backend | Audio | F32 | F16 | Q8_0 |
+|---|---|---:|---:|---:|
+| Metal | 3 s | 6.6 ms | 6.4 ms | 6.1 ms |
+| Metal | 5 s | 9.4 ms | 9.5 ms | 9.3 ms |
+| Metal | 10 s | 14.3 ms | 14.1 ms | 14.2 ms |
+| Metal | 30 s | 37.1 ms | 35.7 ms | 36.2 ms |
+| CPU | 3 s | 40.1 ms | 25.1 ms | 22.9 ms |
+| CPU | 5 s | 70.1 ms | 41.4 ms | 37.7 ms |
+| CPU | 10 s | 147.1 ms | 82.5 ms | 74.9 ms |
+| CPU | 30 s | 450.9 ms | 251.5 ms | 229.5 ms |
+
+`AUTO` resolves like every other family (the GPU when there is one). Metal is
+5-10x faster here; CPU is still well under real time and avoids contending
+with an ASR model on the GPU, so load with `TRANSCRIBE_BACKEND_CPU` when that
+matters. Not yet measured on the AMD Ryzen 7 PRO 4750U publication rig, or on
+Vulkan / CUDA.
+
+```bash
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py \
+  --library build-shared/src/libtranscribe.dylib \
+  --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-{F32,F16,Q8_0}.gguf \
+  --backends cpu,metal --durations 3,5,10,30
+```
+
+## Numerical Validation
+
+transcribe.cpp is validated tensor-by-tensor against SpeechBrain 1.1.1 on
+eight committed FLEURS clips (`samples/fleurs-*.wav`): 17 stage tensors
+(front end, every encoder block, pooling, embedding, logits) within family
+tolerance, and the top-1 label equal to the reference on every clip. The
+dataset gate is the FLEURS decision parity above.
+
+| Field | Value |
+| --- | --- |
+| Reference | SpeechBrain 1.1.1, `speechbrain/lang-id-voxlingua107-ecapa` |
+| Dump script | `scripts/dump_reference_ecapa_tdnn_speechbrain.py` |
+| Manifest | `tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json` |
+| Command | `uv run scripts/validate.py all --family ecapa_tdnn` |
+| Dataset gate | `scripts/langid/compare.py` (FLEURS, 12000 decisions) |
+
+## Known Limitations
+
+- **Closed set.** There is no "unknown" or "no speech" label: silence, music
+  and out-of-set languages still get a confident-looking top candidate. Gate
+  with VAD and watch `allowed_mass`.
+- **Confusable pairs.** `ru`/`be`, `no`/`nn`, `id`/`jw`/`ms`, `cs`/`sk`; `id`
+  vs `ms` stays near 89% even restricted to the pair.
+- **No Cantonese label**: Cantonese is classified as `zh`.
+- **Minimum 500 ms** of scored audio (`TRANSCRIBE_ERR_INPUT_TOO_SHORT`);
+  answers under ~3 s of speech are weak.
+- **Scores at most the last 30 s** by default
+  (`transcribe_langid_session_params::max_audio_ms`).
+- **Q8_0 on an ARM CPU is slower than langid.cpp** (about 1.7x): its CPU
+  weight-repacking buffer is not ported yet. F16 and the other backends are
+  at parity.
+
+## Reproduction
+
+### Convert
+
+Loads the SpeechBrain checkpoint through SpeechBrain (the only implementation)
+from the Hugging Face cache, and writes F32 only.
+
+```bash
+uv run --project scripts/envs/ecapa_tdnn \
+  scripts/convert-ecapa_tdnn.py speechbrain/lang-id-voxlingua107-ecapa
+```
+
+### Quantize
+
+```bash
+build/bin/transcribe-quantize \
+  models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
+  models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf \
+  --quant Q8_0
+# repeat with F16
+```
+
+### Validate
+
+```bash
+uv run scripts/validate.py all --family ecapa_tdnn
+```
+
+### Accuracy acceptance
+
+```bash
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine speechbrain \
+  --model speechbrain/lang-id-voxlingua107-ecapa \
+  --manifest samples/langid/fleurs-*.manifest.jsonl --crops 3,5,10,full \
+  --out reports/langid/ref-speechbrain-untrimmed.jsonl
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp \
+  --model models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
+  --library build-shared/src/libtranscribe.dylib \
+  --manifest samples/langid/fleurs-*.manifest.jsonl --crops 3,5,10,full \
+  --out reports/langid/cpp-f32-untrimmed.jsonl
+uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl \
+  reports/langid/cpp-f32-untrimmed.jsonl
+```
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
new file mode 100644
index 000000000..1ea35e49c
--- /dev/null
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -0,0 +1,135 @@
+# ECAPA-TDNN (language ID)
+
+Status: supported (LANGID role). Ported from `handy-computer/langid.cpp`
+(`96af3a5`), where the C++ was first written and validated against
+SpeechBrain; that repo is superseded by this family.
+
+ECAPA-TDNN is SpeechBrain's speaker / language embedder (architecture pattern
+`encoder-classifier`): SpeechBrain Fbank front end, a TDNN block, three
+SERes2Net blocks, multi-layer feature aggregation, attentive statistics
+pooling, a 256-d embedding and a two-layer classifier over the VoxLingua107
+labels. It is not a transcription model. The family only produces logits; the
+LANGID role dispatcher (`src/transcribe-langid.cpp`) owns the crop to the
+last `max_audio_ms`, the allowed set, softmax, `allowed_mass`, ranking and
+top-k (`docs/langid.md`).
+
+Acceptance: tensor parity on eight FLEURS clips (`validate.py`), and top-1
+decision parity with SpeechBrain on FLEURS (15 languages x 200 utterances x
+3 / 5 / 10 s / full = 12000 decisions): **12000 / 12000**, max abs logit
+difference 6.5e-05 (C++ F32, CPU). Shipped matrix: F32 + F16 + Q8_0;
+F16 agrees on 11991 / 12000 (near-ties only), Q8_0 on 11175 / 12000 (the
+activation quantization inside ggml's Q8_0 matmul; langid.cpp's Q8_0 measured
+the same 93.2%).
+
+## Identity
+
+- Family key: `ecapa_tdnn`
+- Upstream architecture: `speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN` + `speechbrain.lobes.models.Xvector.Classifier`
+- Hugging Face repo: `speechbrain/lang-id-voxlingua107-ecapa`
+- Hugging Face revision: `0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9`
+- License: Apache-2.0
+- Variants: `lang-id-voxlingua107-ecapa` (107 labels; legacy codes `iw`, `jw`, `tl`, `no` with aliases `he`, `jv`, `fil`, `nb`)
+
+## References
+
+- Canonical reference: SpeechBrain 1.1.1 `EncoderClassifier` (the only
+  implementation; `hyperparams.yaml` instantiates SpeechBrain classes by name),
+  torch 2.13.0, CPU, one thread.
+- Instrumented reference: `scripts/dump_reference_ecapa_tdnn_speechbrain.py`
+  (forward hooks on one `classify_batch`; asserts every hook fires once).
+- Cross-check reference: `scripts/lib/ecapa_numpy.py`, a NumPy forward pass
+  driven only by the GGUF, which proves the converter's rewrites.
+
+## GGUF contract
+
+Keys: `stt.variant`, `stt.frontend.*` (`window = hamming_periodic`,
+`pad_mode = constant`, `log_clamp_min = 1e-10`, `top_db = 80`,
+`normalize = sentence_mean`, plus the usual sizes), `stt.ecapa_tdnn.*`
+(format version, channels, kernel sizes, dilations, res2net scale, SE /
+attention / embedding / classifier widths, `asp_eps`, leaky slope) and
+`stt.langid.labels.{codes,names,aliases}`. Integer scalars are uint32.
+
+The converter applies four exact rewrites, each proven by the NumPy
+reference: BatchNorm to a `scale` / `shift` pair (TDNNBlock is conv -> ReLU
+-> BN, so it cannot fold backward); the three activation-free BNs folded
+forward into `fc`, `cls.l1`, `cls.out`; and the ASP and MFA input weights
+split per concatenated operand. Tensor names follow the
+`tools/transcribe-quantize` rules (`.bias` / `.bn.` F32, `.conv.weight` for
+k>1 tap-major kernels F32 / F16, every other `.weight` quantizable,
+`frontend.mel_filterbank` F32), so the quant policy has no ECAPA entries.
+The full catalogue is in `src/arch/ecapa_tdnn/weights.h`.
+
+The front end is family-local (`src/arch/ecapa_tdnn/mel.cpp`):
+`transcribe-mel` has no periodic Hamming window, `10*log10`, top-db floor or
+per-bin mean subtraction. The filterbank is SpeechBrain's own, captured by the
+converter and stored in the GGUF; it is never rebuilt.
+
+## Commands
+
+Reference dumps and tensor parity:
+
+```bash
+uv run scripts/validate.py all --family ecapa_tdnn
+```
+
+Conversion (F32 only; quantize afterwards):
+
+```bash
+uv run --project scripts/envs/ecapa_tdnn scripts/convert-ecapa_tdnn.py speechbrain/lang-id-voxlingua107-ecapa
+build/bin/transcribe-quantize models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
+  models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-Q8_0.gguf --quant Q8_0
+```
+
+Accuracy and decision parity (FLEURS from the HF cache):
+
+```bash
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine speechbrain ... --out reports/langid/ref-speechbrain-untrimmed.jsonl
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp --library build-shared/src/libtranscribe.dylib ... --out reports/langid/cpp-f32-untrimmed.jsonl
+uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl reports/langid/cpp-f32-untrimmed.jsonl
+uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl --md reports/langid/cpp-f32-untrimmed.md
+```
+
+`compare.py` fails on any disagreement that is not on a reviewed near-tie
+list (`--near-ties`), and refuses runs whose labels, recipe, manifest hashes
+or checkpoint revision differ.
+
+Benchmarks: `scripts/langid/bench.py` (`tools/transcribe-bench` is ASR-only).
+
+## Capability Validation
+
+| Capability | Mode | Command / test | Expected observable | Target | Status |
+|------------|------|----------------|---------------------|--------|--------|
+| Language ID | open set | `build/bin/transcribe-cli -m models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf samples/fleurs-ja.wav` | `language: ja` | MUST PASS | PASS |
+| Language ID | allowed set | `... --allow en,de samples/fleurs-de.wav` | `language: de`, 2 candidates | MUST PASS | PASS |
+| Crop | 30 s window | `transcribe_ecapa_tdnn_real_smoke` (`long-45s.wav`) | `audio_ms == 30000` | MUST PASS | PASS |
+| Minimum length | 500 ms | `transcribe_ecapa_tdnn_smoke`, `transcribe_langid_dispatch_unit` | `INPUT_TOO_SHORT` below 500 ms | MUST PASS | PASS |
+| Transcribe / translate / timestamps / streaming | n/a | `transcribe_session_init` | `UNSUPPORTED_ROLE` | OUT OF SCOPE — not an ASR model | SKIP — not exposed by runtime |
+| Batch (offline) | n/a | `transcribe-cli --batch` | refused: ASR-only | OUT OF SCOPE — no batch entry points for new roles in v1 | ACCEPTED GAP — one clip per call |
+
+## Notes
+
+- Regime for every parity number: F32 GGUF, CPU backend, one thread,
+  production C++ front end, macOS arm64. On the port, every stage tensor was
+  bit-identical to langid.cpp's (ggml 0.20.2 there, 0.25.3 here), so
+  `tests/tolerances/ecapa_tdnn.json` carries over unchanged.
+- Hardening from the langid.cpp review, fixed in the role dispatcher and
+  tested in `tests/langid_dispatch_unit.cpp`: H1 minimum checked after the
+  crop and sub-minimum `max_audio_ms` rejected at init; H2 only NULL means
+  "all labels"; H3 non-finite PCM and logits rejected; H6 malformed label
+  tables fail the load. H4 (no leak when label metadata is bad) is covered by
+  `arch_ecapa_tdnn_bad_labels.gguf` in `transcribe_ecapa_tdnn_smoke`
+  (`leaks --atExit`: 0 leaks).
+- Performance follow-up (not ported): langid.cpp's CPU weight-repacking
+  buffer. On the M4 Max CPU (10 s, Q8_0, median of three interleaved rounds)
+  it takes 76-81 ms to about 45-51 ms here and 36-39 ms in langid.cpp;
+  without it the two are at parity, as is F16 and Metal (14 ms). ARM only:
+  ggml has no Q8_0 repack on x86. `cls.out.weight` (107 rows) cannot be
+  repacked, so the candidate filter must check the row count or langid.cpp's
+  abandon path disables repacking for the whole file. The performance-core
+  thread default did not help here (8 threads ties or beats 12).
+- `AUTO` placement is unchanged (GPU first). Metal is 5-10x faster than CPU
+  on the M4 Max; CPU stays well under real time.
+- Q8_0 conv kernels stay F32 (the quantizer's Conv bucket is F32 in every
+  preset), so the Q8_0 file is 26.7 MB against langid.cpp's 24.1 MB, and
+  `cls.out.weight` is Q8_0 where langid.cpp kept it F16.
diff --git a/docs/roles.md b/docs/roles.md
index 7330de12a..e6f3109e3 100644
--- a/docs/roles.md
+++ b/docs/roles.md
@@ -9,6 +9,7 @@ A loaded model serves one or more **roles**, the kinds of work it can do.
 |---|---|---|---|
 | `TRANSCRIBE_ROLE_ASR` | `include/transcribe.h` | `transcribe_session` | text, timestamps, speaker-attributed segments |
 | `TRANSCRIBE_ROLE_DIARIZE` | `include/transcribe/diarize.h` | `transcribe_diarize_session` | who spoke when |
+| `TRANSCRIBE_ROLE_LANGID` | `include/transcribe/langid.h` | `transcribe_langid_session` | which language (`docs/langid.md`) |
 
 Every role follows the same rules:
 
diff --git a/scripts/catalog/common.py b/scripts/catalog/common.py
index f1bba3ad4..dc0d5d057 100644
--- a/scripts/catalog/common.py
+++ b/scripts/catalog/common.py
@@ -108,10 +108,18 @@ def dataset_spec(row: dict) -> str:
 
 def dataset_label(dataset: str, split: str, language: str) -> str:
     if dataset == "fleurs":
-        return f"FLEURS {language}"
+        # "mul": a multilingual pool (language ID scores several FLEURS
+        # languages as one result set).
+        return "FLEURS multilingual" if language == "mul" else f"FLEURS {language}"
     return DATASET_LABELS.get((dataset, split), f"{dataset} {split}")
 
 
+def metric_label(metric: str) -> str:
+    """A metric as a column header prints it: WER / CER / DER / CPWER, or
+    "Top-1 accuracy" for language ID."""
+    return "Top-1 accuracy" if metric == "accuracy" else metric.upper()
+
+
 def headline(record: dict) -> dict | None:
     """The benchmark row-set a variant publishes in its download table.
 
@@ -156,20 +164,24 @@ def headline_recipe(record: dict) -> str:
     sample = measured[0]
     n_utts = max(row["n_utts"] for row in rows)
     unit = "meetings" if target["metric"] in ("der", "cpwer") else "utterances"
-    parts = [f"{target['metric'].upper()} on the full {headline_label(record)} split "
-             f"({n_utts:,} {unit})"]
+    # Language ID scores a pooled, cropped subset (the row's notes say which),
+    # and has no batch size or timestamps.
+    lid = target["metric"] == "accuracy"
+    scope = "" if lid else "the full "
+    parts = [f"{metric_label(target['metric'])} on {scope}{headline_label(record)} "
+             f"{'' if lid else 'split '}({n_utts:,} {unit})"]
     batch_sizes = sorted({row["batch_size"] for row in rows
-                          if row.get("batch_size") is not None})
+                          if row.get("batch_size") is not None and not lid})
     if len(batch_sizes) == 1:
         parts.append(f"batch size {batch_sizes[0]}")
     elif batch_sizes:
         parts.append("batch sizes " + " and ".join(str(size) for size in batch_sizes))
-    if sample.get("timestamps"):
+    if sample.get("timestamps") and not lid:
         parts.append(f"timestamps {sample['timestamps']}")
     if sample.get("language_hint"):
         parts.append(f"language hint `{sample['language_hint']}`")
     if sample.get("backend"):
-        parts.append(f"decoded on {sample['backend']}")
+        parts.append(f"{'scored' if lid else 'decoded'} on {sample['backend']}")
     text = ", ".join(parts) + "."
     shas = sorted({(row["engine_sha"], row.get("measured_on") or "")
                    for row in rows if row.get("engine_sha")})
@@ -181,11 +193,18 @@ def headline_recipe(record: dict) -> str:
     return text
 
 
+def row_pct(row: dict) -> float:
+    """A row's percentage: the error rate, or the accuracy on metric=accuracy
+    (language ID) rows, which carry acc_pct instead of err_pct."""
+    return row["acc_pct"] if row.get("metric") == "accuracy" else row["err_pct"]
+
+
 def fmt_err(row: dict | None, dp: int = 2) -> str:
-    """An error rate as a card prints it. `-` when the cell was not measured."""
+    """An error rate (or, for accuracy rows, the accuracy) as a card prints
+    it. `-` when the cell was not measured."""
     if row is None:
         return "-"
-    return f"{row['err_pct']:.{dp}f}%"
+    return f"{row_pct(row):.{dp}f}%"
 
 
 # --------------------------------------------------------------------------
@@ -243,6 +262,8 @@ def capabilities_summary(record: dict) -> str:
     """The extras beyond plain transcription, as a short comma list."""
     caps = record.get("capabilities", {})
     out = []
+    if record.get("role") == "langid":
+        out.append(f"language ID ({len(record.get('languages', []))} languages)")
     for name, label in (("translate", "translate"), ("streaming", "streaming"),
                         ("diarize", "diarize")):
         if caps.get(name, {}).get("supported"):
diff --git a/scripts/catalog/db.py b/scripts/catalog/db.py
index fbd39628b..6e4d83576 100755
--- a/scripts/catalog/db.py
+++ b/scripts/catalog/db.py
@@ -101,7 +101,8 @@
     metric TEXT NOT NULL,
     language_hint TEXT,
     backend TEXT,
-    err_pct REAL NOT NULL CHECK(err_pct >= 0),
+    err_pct REAL CHECK(err_pct >= 0),
+    acc_pct REAL CHECK(acc_pct BETWEEN 0 AND 100),
     ci_lo REAL,
     ci_hi REAL,
     n_utts INTEGER NOT NULL CHECK(n_utts > 0),
@@ -153,7 +154,7 @@
 -- The per-quant column a model card and its doc print.
 CREATE VIEW headline AS
 SELECT a.variant, d.dataset, d.split, d.language,
-       a.quant, a.metric, a.err_pct, a.ci_lo, a.ci_hi, a.n_utts
+       a.quant, a.metric, a.err_pct, a.acc_pct, a.ci_lo, a.ci_hi, a.n_utts
 FROM accuracy a
 JOIN models m ON m.variant = a.variant
 JOIN datasets d ON d.dataset_id = a.dataset_id
@@ -237,9 +238,10 @@ def build(records: dict[str, dict], out: pathlib.Path) -> dict[str, int]:
                 (variant, item["quant"], item["filename"], item["size_bytes"])
                 for item in record.get("downloads", [])])
             con.executemany(
-                "INSERT INTO accuracy VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", [
+                "INSERT INTO accuracy VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", [
                     (dataset_id(row), variant, row["quant"], row["metric"],
-                     row.get("language_hint"), row.get("backend"), row["err_pct"],
+                     row.get("language_hint"), row.get("backend"), row.get("err_pct"),
+                     row.get("acc_pct"),
                      (row.get("ci95") or [None, None])[0],
                      (row.get("ci95") or [None, None])[1], row["n_utts"],
                      row.get("batch_size"), row.get("timestamps"), row.get("engine_sha"),
diff --git a/scripts/catalog/render.py b/scripts/catalog/render.py
index 3a38d4f47..b1f54eafd 100755
--- a/scripts/catalog/render.py
+++ b/scripts/catalog/render.py
@@ -84,7 +84,7 @@ def block_downloads(record: dict, attrs: dict[str, str]) -> list[str]:
     aligns = ["l", "l", "r"]
     if want_metric:
         label = attrs.get("label") or common.headline_label(record)
-        metric = attrs.get("metric_name") or target["metric"].upper()
+        metric = attrs.get("metric_name") or common.metric_label(target["metric"])
         header.append(f"{metric} ({label})")
         aligns.append("r")
 
@@ -352,7 +352,7 @@ def block_family(records: dict[str, dict], attrs: dict[str, str]) -> list[str]:
         body.append([
             f"`{name}`", common.fmt_params(record["params"]), common.languages_summary(record),
             common.fmt_size(download["size_bytes"]) if download else "-",
-            (f"{common.headline_label(record)} ({headline['metric'].upper()})"
+            (f"{common.headline_label(record)} ({common.metric_label(headline['metric'])})"
              if headline else "-"),
             common.fmt_err(common.headline_rows(record).get(quant)),
             common.capabilities_summary(record), link])
diff --git a/scripts/hf_cards/generate.py b/scripts/hf_cards/generate.py
index 1741ac06e..af35949d0 100755
--- a/scripts/hf_cards/generate.py
+++ b/scripts/hf_cards/generate.py
@@ -134,7 +134,7 @@ def derive_metric_blocks(record: dict) -> dict[str, dict[str, float]]:
             chosen[cell] = row
     blocks: dict[str, dict[str, float]] = {}
     for (key, quant), row in chosen.items():
-        blocks.setdefault(key, {})[quant] = row["err_pct"]
+        blocks.setdefault(key, {})[quant] = common.row_pct(row)
     return blocks
 
 
@@ -233,7 +233,7 @@ def build_context(record: dict, spec: dict) -> dict:
         "transcribe_docs_url": docs_url(record),
     }
     if headline.get("metric"):
-        ctx["metric"] = headline["metric"].upper()
+        ctx["metric"] = common.metric_label(headline["metric"])
     for key in ("name", "link"):
         if record["license"].get(key):
             ctx[f"license_{key}"] = record["license"][key]
diff --git a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
new file mode 100644
index 000000000..3de665aad
--- /dev/null
+++ b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
@@ -0,0 +1,69 @@
+# Spec for the HF README of handy-computer/lang-id-voxlingua107-ecapa-gguf.
+# Prose only; numbers and metadata come from catalog/<variant>.json. See README.md.
+#
+# Language ID family (LANGID role): metric is top-1 accuracy (not WER).
+
+pin_date: 2026-10-05
+
+# Validation pin for the most recent upload. Updated on each release —
+# older HF revisions carry whatever value was current at their upload time.
+validation:
+  reference: SpeechBrain
+  commit: 62522202
+  date: 2026-10-05
+
+pipeline_tag: audio-classification
+tags:
+  - gguf
+  - transcribe.cpp
+  - language-identification
+  - spoken-language-identification
+  - ecapa-tdnn
+  - voxlingua107
+  - speechbrain
+
+summary: |
+  Spoken language identification over 107 languages: SpeechBrain's
+  ECAPA-TDNN trained on VoxLingua107. NOT a transcription model: a run
+  returns the model's language labels ranked by probability, optionally
+  restricted to a caller-chosen set. Takes 16 kHz mono WAV; scores up to
+  the last 30 s of a clip.
+
+usage: |
+  Build transcribe.cpp from source:
+
+  ```bash
+  git clone git@github.com:handy-computer/transcribe.cpp.git
+  cd transcribe.cpp
+  cmake -B build && cmake --build build
+  ```
+
+  Run on a 16 kHz mono WAV. This is a language identifier, not a
+  transcription model: the CLI prints ranked language candidates.
+
+  ```bash
+  build/bin/transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en,de,fr --top 3 input.wav
+  # language: de index=18 p=0.999998
+  #   ...
+  ```
+
+  From the C API, use the LANGID role (`include/transcribe/langid.h`); the
+  usage protocol (trim silence, pass the allowed set, decide at 5 s) is in
+  `docs/langid.md`.
+
+  If your audio isn't already 16 kHz mono WAV, convert it first:
+
+  ```bash
+  ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
+  ```
+
+  See the [transcribe.cpp model page](https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/lang-id-voxlingua107-ecapa.md) for performance
+  numbers, numerical validation, and reproduction steps.
+
+wer:
+  notes: |
+    Open-set top-1 accuracy over all 107 labels, mean of 15 FLEURS languages
+    (200 test utterances each), on the first 5 s of each clip without silence
+    trimming. Accuracy is higher with an allowed set and with trimmed speech;
+    see docs/langid.md. The C++ F32 port makes the same decision as the
+    SpeechBrain reference on all 12000 FLEURS decisions (3 / 5 / 10 s / full).
diff --git a/scripts/langid/bench.py b/scripts/langid/bench.py
new file mode 100644
index 000000000..72a95b0f9
--- /dev/null
+++ b/scripts/langid/bench.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""
+bench.py - language ID latency through the Python binding: backend x GGUF x
+scored duration, median / p90 of the native mel + encode time over repeated
+runs on one warm session (tools/transcribe-bench is ASR-only).
+
+Usage:
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py \\
+      --library build-shared/src/libtranscribe.dylib \\
+      --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-{F32,F16,Q8_0}.gguf \\
+      --backends cpu,metal --durations 3,5,10,30 --out reports/langid/bench-m4-max.json
+
+Audio: the first N seconds of samples/long-45s.wav (52 s of FLEURS English),
+so every duration scores real speech. The session's max_audio_ms is 60000,
+so nothing is cropped away.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import platform
+import statistics
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--gguf", nargs="+", type=Path, required=True)
+    p.add_argument("--backends", default="cpu,metal")
+    p.add_argument("--durations", default="3,5,10,30")
+    p.add_argument("--threads", type=int, default=0, help="0 = library default")
+    p.add_argument("--warmup", type=int, default=3)
+    p.add_argument("--repeat", type=int, default=20)
+    p.add_argument("--library", type=Path, default=None)
+    p.add_argument("--out", type=Path, default=None)
+    args = p.parse_args(argv)
+
+    if args.library is not None:
+        os.environ["TRANSCRIBE_LIBRARY"] = str(args.library.resolve())
+    sys.path.insert(0, str(REPO_ROOT / "bindings" / "python" / "src"))
+    import soundfile as sf
+    import transcribe_cpp as t
+
+    pcm, sr = sf.read(str(REPO_ROOT / "samples" / "long-45s.wav"), dtype="float32")
+    assert sr == 16000
+    durations = [float(d) for d in args.durations.split(",")]
+    rows = []
+    for gguf in args.gguf:
+        for backend in args.backends.split(","):
+            t0 = time.perf_counter()
+            model = t.Model(str(gguf), backend=backend)
+            load_ms = (time.perf_counter() - t0) * 1000
+            lid = model.langid_session(n_threads=args.threads, max_audio_ms=60000)
+            for d in durations:
+                clip = np.ascontiguousarray(pcm[: int(d * 16000)])
+                for _ in range(args.warmup):
+                    lid.run(clip)
+                compute, wall = [], []
+                for _ in range(args.repeat):
+                    w0 = time.perf_counter()
+                    lid.run(clip)
+                    wall.append((time.perf_counter() - w0) * 1000)
+                    tm = lid.timings
+                    compute.append(tm.mel_ms + tm.encode_ms)
+                compute.sort()
+                row = {
+                    "gguf": gguf.name, "backend": backend, "bound": model.backend,
+                    "duration_s": d, "threads": args.threads,
+                    "median_ms": round(statistics.median(compute), 2),
+                    "p90_ms": round(compute[int(0.9 * (len(compute) - 1))], 2),
+                    "wall_median_ms": round(statistics.median(wall), 2),
+                    "load_ms": round(load_ms, 1),
+                }
+                rows.append(row)
+                print(f"{gguf.name:42} {backend:6} {d:5.0f}s  median {row['median_ms']:8.2f} ms  "
+                      f"p90 {row['p90_ms']:8.2f}  wall {row['wall_median_ms']:8.2f}", flush=True)
+            lid.close()
+            model.close()
+
+    if args.out:
+        commit = subprocess.run(["git", "rev-parse", "--short", "HEAD"], cwd=REPO_ROOT,
+                                capture_output=True, text=True).stdout.strip()
+        args.out.parent.mkdir(parents=True, exist_ok=True)
+        args.out.write_text(json.dumps({
+            "machine": platform.processor() or platform.machine(),
+            "engine_sha": commit, "warmup": args.warmup, "repeat": args.repeat,
+            "audio": "samples/long-45s.wav (first N s)", "rows": rows}, indent=2) + "\n")
+        print(f"wrote {args.out}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/langid/compare.py b/scripts/langid/compare.py
new file mode 100755
index 000000000..6f4026ac7
--- /dev/null
+++ b/scripts/langid/compare.py
@@ -0,0 +1,260 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.11"
+# dependencies = []
+# ///
+"""
+compare.py - the dataset gate: does the C++ engine make the same decisions
+as the SpeechBrain reference on the same audio?
+
+The gate: the C++ F32 result makes the same top-1 decision as the SpeechBrain
+reference on the same audio. Every disagreement must be an explicitly listed,
+reviewed near-tie (--near-ties); anything else fails, whatever the aggregate
+agreement rate. The aggregate is still reported against --gate (99.9%).
+
+Provenance is enforced, not just printed: both runs must share the label set,
+the recipe (trim, crops, utterance count, crop dir, logit kind, sample rate)
+and the manifest hashes, and a C++ run must score a GGUF converted from the
+same checkpoint revision the reference run loaded.
+
+Usage:
+    uv run scripts/langid/compare.py \\
+        reports/langid/ref-speechbrain-untrimmed.jsonl \\
+        reports/langid/cpp-f32-untrimmed.jsonl
+
+Rows are joined on (id, crop_s, trim). Both runs must cover the same keys;
+a key present in only one side is reported and counted as a failure, because
+a silently dropped utterance would otherwise raise the agreement rate.
+
+Exit status:
+    0  every disagreement is a listed near-tie, agreement >= the gate, same keys
+    1  an unlisted disagreement, agreement below the gate, or coverage differs
+    2  usage / input / provenance error
+
+Near-tie list (JSON): [{"id", "crop_s", "trim", "a_top1", "b_top1",
+"max_gap", "note"}]. An entry only excuses that exact disagreement while both
+engines' margins stay within max_gap; review every entry by listening.
+
+Every disagreement is printed with both engines' top-1 and, for each engine,
+that engine's own margin between the two codes in question. A near-tie shows
+up as a margin near zero on BOTH sides: the two engines put the same two
+labels within noise of each other and rounded opposite ways. A disagreement
+with a large margin on either side is a real numerical divergence and must be
+chased, not waived.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+GATE = 0.999
+MAX_LISTED_DISAGREEMENTS = 50
+
+
+def load(path: Path) -> tuple[dict, dict[tuple, dict]]:
+    if not path.exists():
+        print(f"error: {path} does not exist", file=sys.stderr)
+        raise SystemExit(2)
+    lines = [l for l in path.read_text().splitlines() if l.strip()]
+    if not lines:
+        print(f"error: {path} is empty", file=sys.stderr)
+        raise SystemExit(2)
+    header = json.loads(lines[0])
+    if header.get("type") != "header":
+        print(f"error: {path} does not start with a header line", file=sys.stderr)
+        raise SystemExit(2)
+    rows = {}
+    for line in lines[1:]:
+        r = json.loads(line)
+        key = (r["id"], str(r["crop_s"]), r["trim"])
+        if key in rows:
+            print(f"error: {path} has duplicate row {key}", file=sys.stderr)
+            raise SystemExit(2)
+        rows[key] = r
+    return header, rows
+
+
+def percentile(sorted_vals: list[float], q: float) -> float:
+    """Nearest-rank percentile; no numpy so this stays a stdlib PEP-723 tool."""
+    if not sorted_vals:
+        return float("nan")
+    k = max(0, min(len(sorted_vals) - 1, math.ceil(q * len(sorted_vals)) - 1))
+    return sorted_vals[k]
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("a", type=Path, help="reference run JSONL (baseline)")
+    p.add_argument("b", type=Path, help="run JSONL under test")
+    p.add_argument("--gate", type=float, default=GATE,
+                   help=f"minimum top-1 agreement rate (default {GATE})")
+    p.add_argument("--max-listed", type=int, default=MAX_LISTED_DISAGREEMENTS)
+    p.add_argument("--near-ties", type=Path, default=None,
+                   help="JSON list of reviewed near-tie disagreements")
+    args = p.parse_args(argv)
+    near_ties: dict[tuple, dict] = {}
+    if args.near_ties is not None:
+        for e in json.loads(args.near_ties.read_text()):
+            near_ties[(e["id"], str(e["crop_s"]), e["trim"])] = e
+
+    ha, ra = load(args.a)
+    hb, rb = load(args.b)
+
+    labels_a = ha["labels"]
+    labels_b = hb["labels"]
+    if labels_a != labels_b:
+        print("error: the two runs disagree on the label set; a shared label "
+              "order is what makes the logit vectors comparable", file=sys.stderr)
+        return 2
+
+    print(f"A  {args.a}  engine={ha['engine']}  model={ha['model']}")
+    print(f"B  {args.b}  engine={hb['engine']}  model={hb['model']}")
+    provenance_ok = True
+    for field in ("trim", "crops", "n_utterances", "crop_dir", "logit_kind",
+                  "sample_rate", "manifest_sha256"):
+        va, vb = ha["recipe"].get(field), hb["recipe"].get(field)
+        provenance_ok &= va == vb
+        if field == "manifest_sha256" and va == vb:
+            print(f"   recipe.{field}: equal ({len(va or {})} manifests)")
+            continue
+        flag = "" if va == vb else "   <-- DIFFERS"
+        print(f"   recipe.{field}: A={va!r} B={vb!r}{flag}")
+    # A C++ run must score a GGUF converted from the reference's checkpoint.
+    revisions = {h["engine"]: h["recipe"].get("revision") or h["recipe"].get("gguf_source_commit")
+                 for h in (ha, hb)}
+    print(f"   checkpoint revision: {revisions}")
+    if len({r for r in revisions.values()}) != 1 or None in revisions.values():
+        provenance_ok = False
+        print("   <-- the runs do not trace back to one checkpoint revision")
+    print()
+    if not provenance_ok:
+        print("error: provenance mismatch; refusing to compare", file=sys.stderr)
+        return 2
+
+    only_a = sorted(set(ra) - set(rb))
+    only_b = sorted(set(rb) - set(ra))
+    keys = sorted(set(ra) & set(rb))
+    if only_a or only_b:
+        print(f"coverage mismatch: {len(only_a)} rows only in A, "
+              f"{len(only_b)} rows only in B")
+        for k in (only_a + only_b)[:10]:
+            print(f"  {k}")
+        print()
+    if not keys:
+        print("error: no shared (id, crop_s, trim) keys", file=sys.stderr)
+        return 2
+
+    n_agree = 0
+    per_group: dict[tuple[str, str], list[int]] = defaultdict(lambda: [0, 0])
+    abs_deltas: list[float] = []
+    disagreements = []
+
+    for key in keys:
+        x, y = ra[key], rb[key]
+        la, lb = x["logits"], y["logits"]
+        if len(la) != len(lb):
+            print(f"error: {key} has {len(la)} vs {len(lb)} logits", file=sys.stderr)
+            return 2
+        for u, v in zip(la, lb):
+            abs_deltas.append(abs(u - v))
+
+        ia = max(range(len(la)), key=lambda i: la[i])
+        ib = max(range(len(lb)), key=lambda i: lb[i])
+        group = (x["language"], str(x["crop_s"]))
+        per_group[group][1] += 1
+        if ia == ib:
+            n_agree += 1
+            per_group[group][0] += 1
+        else:
+            # Each engine's own margin between the two contested labels. Both
+            # near zero == a near-tie rounded opposite ways.
+            disagreements.append({
+                "key": key,
+                "language": x["language"],
+                "a_top1": labels_a[ia],
+                "b_top1": labels_a[ib],
+                "a_gap": la[ia] - la[ib],
+                "b_gap": lb[ib] - lb[ia],
+                "max_abs_delta": max(abs(u - v) for u, v in zip(la, lb)),
+            })
+
+    n = len(keys)
+    rate = n_agree / n
+    abs_deltas.sort()
+
+    print(f"joined rows: {n}")
+    print(f"top-1 agreement: {n_agree}/{n} = {100 * rate:.4f}%  "
+          f"(gate {100 * args.gate:.2f}%)")
+    print(f"|delta logit| over {len(abs_deltas)} values: "
+          f"max={abs_deltas[-1]:.6g}  p99={percentile(abs_deltas, 0.99):.6g}  "
+          f"median={percentile(abs_deltas, 0.50):.6g}")
+    print()
+
+    langs = sorted({g[0] for g in per_group})
+    crops = sorted({g[1] for g in per_group},
+                   key=lambda c: (c == "full", float(c) if c != "full" else 0))
+    print("agreement % by language x crop")
+    print("lang   " + "".join(f"{c:>9}" for c in crops) + f"{'all':>9}")
+    for lg in langs:
+        cells = ""
+        tot_ok = tot_n = 0
+        for c in crops:
+            ok, tot = per_group.get((lg, c), [0, 0])
+            tot_ok += ok
+            tot_n += tot
+            cells += f"{(100 * ok / tot if tot else float('nan')):9.3f}"
+        cells += f"{(100 * tot_ok / tot_n if tot_n else float('nan')):9.3f}"
+        print(f"{lg:<7}{cells}")
+    print()
+
+    if disagreements:
+        disagreements.sort(key=lambda d: -max(abs(d["a_gap"]), abs(d["b_gap"])))
+        print(f"{len(disagreements)} disagreements "
+              f"(listed worst-margin first, up to {args.max_listed}):")
+        print(f"{'id':<20} {'crop':>5} {'lang':>5} {'A top1':>7} {'B top1':>7} "
+              f"{'A gap':>10} {'B gap':>10} {'max|dlogit|':>12}")
+        for d in disagreements[: args.max_listed]:
+            uid, crop, _trim = d["key"]
+            print(f"{uid:<20} {crop:>5} {d['language']:>5} {d['a_top1']:>7} "
+                  f"{d['b_top1']:>7} {d['a_gap']:10.5f} {d['b_gap']:10.5f} "
+                  f"{d['max_abs_delta']:12.3g}")
+        worst = max(max(abs(d["a_gap"]), abs(d["b_gap"])) for d in disagreements)
+        print(f"\nlargest contested margin on either side: {worst:.5f}")
+        print("A near-tie has both gaps near zero. A large gap on either side "
+              "is a real divergence, not a rounding coincidence.")
+        print()
+
+    unlisted = []
+    for d in disagreements:
+        e = near_ties.get(d["key"])
+        ok = (e is not None and e["a_top1"] == d["a_top1"] and e["b_top1"] == d["b_top1"]
+              and abs(d["a_gap"]) <= e["max_gap"] and abs(d["b_gap"]) <= e["max_gap"])
+        if not ok:
+            unlisted.append(d)
+    stale = sorted(set(near_ties) - {d["key"] for d in disagreements})
+    if stale:
+        print(f"note: {len(stale)} listed near-ties did not disagree this run: {stale[:5]}")
+
+    if only_a or only_b:
+        print("FAIL: the two runs do not cover the same rows")
+        return 1
+    if unlisted:
+        print(f"FAIL: {len(unlisted)} disagreements are not reviewed near-ties "
+              f"(add them to --near-ties only after listening): "
+              f"{[d['key'] for d in unlisted[:10]]}")
+        return 1
+    if rate < args.gate:
+        print(f"FAIL: agreement {100 * rate:.4f}% < {100 * args.gate:.2f}%")
+        return 1
+    print(f"PASS: agreement {100 * rate:.4f}% >= {100 * args.gate:.2f}%")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/langid/ingest.py b/scripts/langid/ingest.py
new file mode 100755
index 000000000..78f8b0b6c
--- /dev/null
+++ b/scripts/langid/ingest.py
@@ -0,0 +1,283 @@
+#!/usr/bin/env python3
+"""
+ingest.py - build an evaluation corpus from a named dataset source.
+
+Sources:
+  fleurs   Reads the FLEURS parquet already present in the local Hugging
+           Face hub cache (`datasets--google--fleurs`). Nothing is
+           downloaded; the 15 configs the evaluation uses are cached.
+
+Usage:
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang en
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
+
+Output (gitignored, see .gitignore `/samples/langid/`):
+  samples/langid/fleurs-<code>/<id>.wav        16-bit PCM mono 16 kHz
+  samples/langid/fleurs-<code>.manifest.jsonl  {"id","audio","language","duration_s",...}
+
+Selection rule: the first N utterances in parquet order whose decoded
+duration is >= 1 s. Parquet order is the only ordering; no shuffling, no
+hashing, so the same cache always produces the same manifest.
+
+Utterance ids are `fleurs-<code>-<NNNN>` where NNNN is the index among the
+selected rows, NOT the FLEURS sentence id: FLEURS records the same sentence
+with several speakers, so sentence ids repeat within a split and would
+collide as filenames. The sentence id is kept in the manifest as
+`fleurs_id` for provenance.
+
+Idempotent: an existing manifest is left alone unless --force.
+"""
+
+from __future__ import annotations
+
+import argparse
+import glob
+import io
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+
+SAMPLE_RATE = 16000
+MIN_DURATION_S = 1.0
+DEFAULT_N = 200
+SPLIT = "test"
+DATASET = "google/fleurs"
+LICENCE = "CC-BY-4.0"
+
+# FLEURS config -> VoxLingua107 label code, in the dataset gate's order. `es_419` is Latin-American Spanish,
+# `cmn_hans_cn` is Mandarin in simplified Han, `nb_no` is Bokmal (the label
+# set spells Norwegian `no`); VoxLingua107 has one label for each.
+FLEURS_LANGUAGES: list[tuple[str, str]] = [
+    ("en_us", "en"),
+    ("cmn_hans_cn", "zh"),
+    ("de_de", "de"),
+    ("fr_fr", "fr"),
+    ("es_419", "es"),
+    ("pt_br", "pt"),
+    ("ja_jp", "ja"),
+    ("ko_kr", "ko"),
+    ("ru_ru", "ru"),
+    ("cs_cz", "cs"),
+    ("sk_sk", "sk"),
+    ("nb_no", "no"),
+    ("da_dk", "da"),
+    ("id_id", "id"),
+    ("ms_my", "ms"),
+]
+
+CODE_TO_CONFIG = {code: config for config, code in FLEURS_LANGUAGES}
+
+
+def fleurs_parquet(config: str) -> Path:
+    pattern = (
+        Path.home()
+        / ".cache/huggingface/hub/datasets--google--fleurs/snapshots/*/parquet-data"
+        / config
+        / f"{SPLIT}-00000-of-00001.parquet"
+    )
+    matches = sorted(glob.glob(str(pattern)))
+    if not matches:
+        raise SystemExit(
+            f"error: no cached FLEURS parquet for config {config!r} "
+            f"(looked at {pattern}). This script never downloads; populate "
+            f"the Hugging Face cache first."
+        )
+    return Path(matches[-1])
+
+
+def iter_rows(path: Path, split: str):
+    """Yield parquet rows in file order without materialising the table.
+
+    FLEURS test splits carry ~400-900 utterances of ~12 s each; a whole-table
+    `to_pylist()` is a few hundred MB per language for the 200 rows we keep.
+    """
+    import pyarrow.parquet as pq
+
+    pf = pq.ParquetFile(str(path))
+    cols = set(pf.schema_arrow.names)
+    for needed in ("audio", "id"):
+        if needed not in cols:
+            raise SystemExit(
+                f"error: {path} has no {needed!r} column "
+                f"(columns: {sorted(cols)})"
+            )
+    for batch in pf.iter_batches(batch_size=16):
+        for row in batch.to_pylist():
+            yield row
+
+
+def decode_audio(row) -> tuple[np.ndarray, int]:
+    import soundfile as sf
+
+    audio = row["audio"]
+    data = audio["bytes"] if isinstance(audio, dict) else audio
+    pcm, sr = sf.read(io.BytesIO(data), dtype="float32", always_2d=False)
+    if pcm.ndim > 1:
+        pcm = pcm.mean(axis=1)
+    return np.ascontiguousarray(pcm, dtype=np.float32), int(sr)
+
+
+def resample_to_16k(pcm: np.ndarray, sr: int) -> np.ndarray:
+    """Resample to 16 kHz. Only fires if FLEURS ever ships a non-16k config.
+
+    Resampling rather than skipping the row keeps "the first N in parquet
+    order" true; skipping would silently change which utterances the
+    evaluation covers.
+    """
+    if sr == SAMPLE_RATE:
+        return pcm
+    import torch
+    import torchaudio
+
+    out = torchaudio.functional.resample(
+        torch.from_numpy(pcm).unsqueeze(0), sr, SAMPLE_RATE
+    )
+    return np.ascontiguousarray(out.squeeze(0).numpy(), dtype=np.float32)
+
+
+def write_wav(path: Path, pcm: np.ndarray) -> None:
+    import soundfile as sf
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    sf.write(str(path), pcm, SAMPLE_RATE, subtype="PCM_16")
+
+
+def ingest_language(config: str, code: str, args) -> dict:
+    out_dir = REPO_ROOT / "samples" / "langid" / f"fleurs-{code}"
+    manifest_path = REPO_ROOT / "samples" / "langid" / f"fleurs-{code}.manifest.jsonl"
+
+    if manifest_path.exists() and not args.force:
+        rows = [json.loads(l) for l in manifest_path.read_text().splitlines() if l.strip()]
+        print(f"{code}: {manifest_path.name} exists with {len(rows)} rows; "
+              f"skipping (--force to rebuild)", file=sys.stderr)
+        return summarise(code, config, rows, manifest_path)
+
+    parquet = fleurs_parquet(config)
+    entries: list[dict] = []
+    n_short = 0
+    n_resampled = 0
+    for row_index, row in enumerate(iter_rows(parquet, args.split)):
+        if len(entries) >= args.n:
+            break
+        pcm, sr = decode_audio(row)
+        if sr != SAMPLE_RATE:
+            pcm = resample_to_16k(pcm, sr)
+            n_resampled += 1
+        if pcm.size < MIN_DURATION_S * SAMPLE_RATE:
+            n_short += 1
+            continue
+        utt_id = f"fleurs-{code}-{len(entries):04d}"
+        wav_path = out_dir / f"{utt_id}.wav"
+        write_wav(wav_path, pcm)
+        entries.append({
+            "id": utt_id,
+            "audio": str(wav_path.relative_to(REPO_ROOT)),
+            "language": code,
+            "duration_s": round(pcm.size / SAMPLE_RATE, 3),
+            "fleurs_id": int(row["id"]),
+            "config": config,
+            "split": args.split,
+            "row_index": row_index,
+            "dataset": DATASET,
+            "licence": LICENCE,
+        })
+
+    if len(entries) < args.n:
+        print(f"warning: {code}: only {len(entries)} utterances of the "
+              f"requested {args.n} (split exhausted)", file=sys.stderr)
+
+    manifest_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(manifest_path, "w") as f:
+        for e in entries:
+            f.write(json.dumps(e) + "\n")
+
+    info = summarise(code, config, entries, manifest_path)
+    print(f"{code}: {len(entries)} utts  dur min/mean/max = "
+          f"{info['duration_min_s']:.2f}/{info['duration_mean_s']:.2f}/"
+          f"{info['duration_max_s']:.2f} s  (skipped {n_short} under "
+          f"{MIN_DURATION_S} s, resampled {n_resampled})  -> {manifest_path.name}",
+          file=sys.stderr)
+    return info
+
+
+def summarise(code: str, config: str, rows: list[dict], manifest_path: Path) -> dict:
+    durs = [r["duration_s"] for r in rows] or [0.0]
+    return {
+        "language": code,
+        "config": config,
+        "n": len(rows),
+        "duration_min_s": min(durs),
+        "duration_mean_s": sum(durs) / len(durs),
+        "duration_max_s": max(durs),
+        "total_s": sum(durs),
+        "manifest": str(manifest_path.relative_to(REPO_ROOT)),
+    }
+
+
+def cmd_fleurs(args) -> int:
+    if args.lang == "all":
+        wanted = list(FLEURS_LANGUAGES)
+    else:
+        codes = [c.strip() for c in args.lang.split(",") if c.strip()]
+        wanted = []
+        for c in codes:
+            if c in CODE_TO_CONFIG:
+                wanted.append((CODE_TO_CONFIG[c], c))
+            elif c in dict(FLEURS_LANGUAGES):
+                wanted.append((c, dict(FLEURS_LANGUAGES)[c]))
+            else:
+                raise SystemExit(
+                    f"error: unknown language {c!r}; known: "
+                    f"{', '.join(sorted(CODE_TO_CONFIG))} or 'all'"
+                )
+
+    infos = [ingest_language(config, code, args) for config, code in wanted]
+
+    print("\nlang  n     min_s  mean_s  max_s   total_s  manifest")
+    for i in infos:
+        print(f"{i['language']:<5} {i['n']:<5} {i['duration_min_s']:6.2f} "
+              f"{i['duration_mean_s']:7.2f} {i['duration_max_s']:6.2f} "
+              f"{i['total_s']:8.0f}  {i['manifest']}")
+    total = sum(i["n"] for i in infos)
+    longest = max((i["duration_max_s"] for i in infos), default=0.0)
+    print(f"\ntotal utterances: {total}  longest clip: {longest:.2f} s")
+    # The C++ context scores at most `max_audio_ms` (default 30 s) and uses
+    # the tail of longer input, while SpeechBrain scores the whole clip. If
+    # any clip is longer than 30 s the two engines would score different
+    # audio on the `full` crop unless run.py raises the limit.
+    if longest > 30.0:
+        print("note: clips longer than 30 s exist; the cpp engine needs an "
+              "explicit --max-audio-ms above the longest clip for the `full` "
+              "crop to score the same audio as SpeechBrain.")
+    else:
+        print("note: every clip is under 30 s, so the C++ default "
+              "max_audio_ms=30000 never truncates; both engines score the "
+              "same audio on every crop.")
+    return 0
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    sub = p.add_subparsers(dest="source", required=True)
+
+    fp = sub.add_parser("fleurs", help="FLEURS from the local HF parquet cache")
+    fp.add_argument("--lang", required=True,
+                    help="VoxLingua107 code, comma-separated list, or 'all'")
+    fp.add_argument("--split", default=SPLIT, help=f"dataset split (default: {SPLIT})")
+    fp.add_argument("--n", type=int, default=DEFAULT_N,
+                    help=f"utterances per language (default: {DEFAULT_N})")
+    fp.add_argument("--force", action="store_true",
+                    help="rebuild even when the manifest already exists")
+    fp.set_defaults(func=cmd_fleurs)
+
+    args = p.parse_args(argv)
+    return args.func(args)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/langid/run.py b/scripts/langid/run.py
new file mode 100755
index 000000000..371b037d1
--- /dev/null
+++ b/scripts/langid/run.py
@@ -0,0 +1,498 @@
+#!/usr/bin/env python3
+"""
+run.py - score a language ID evaluation corpus with the SpeechBrain
+reference or transcribe.cpp (through the Python binding, one model load per
+run), and write one self-describing JSONL report.
+
+Usage:
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \
+      --engine speechbrain --model speechbrain/lang-id-voxlingua107-ecapa \
+      --manifest samples/langid/fleurs-*.manifest.jsonl \
+      --crops 3,5,10,full --trim none \
+      --out reports/langid/ref-speechbrain-untrimmed.jsonl
+
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \
+      --engine cpp --model models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
+      --manifest samples/langid/fleurs-*.manifest.jsonl \
+      --crops 3,5,10,full --trim none --backend cpu --threads 4 \
+      --library build-shared/src/libtranscribe.dylib \
+      --out reports/langid/cpp-f32-untrimmed.jsonl
+
+Output JSONL:
+  line 1   {"type":"header","engine","model","recipe":{...},"labels":[107],"created"}
+  line n   {"id","language","crop_s","trim","audio_s","logits":[107],"top1","top1_prob"}
+
+All 107 raw logits are stored per (utterance, crop) so every restricted
+selection can be scored offline by score.py from a single sweep
+. `top1`/`top1_prob` are the open-set decision, kept as a
+convenience; score.py recomputes everything from `logits`.
+
+Crop semantics
+--------------
+`--trim none`  : the first N seconds of the raw clip. This is what the
+                 langid.cpp original evaluation measured, so "3 s" is roughly
+                 2 s of speech on FLEURS (the clips open with silence).
+`--trim energy`: drop everything before the first 20 ms frame whose RMS
+                 exceeds 5% of the clip's maximum frame RMS, then take the
+                 first N seconds of what is left.
+`full`         : the whole clip (after trimming, for --trim energy).
+A clip shorter than the crop is used whole; the row's `audio_s` records
+what was actually scored.
+
+Both engines read the SAME 16-bit wav files
+-------------------------------------------
+Crops are materialised once to `build/langid/crops/<trim>/<crop>/<id>.wav`
+and both engines read those files back. Scoring in-memory floats on one
+side and 16-bit wavs on the other would make compare.py's 99.9% agreement
+gate measure PCM rounding instead of the port. The crop directory is
+keyed by the recipe, not by the run name, so a cpp run and the reference
+run with the same recipe share bytes on disk.
+"""
+
+from __future__ import annotations
+
+import argparse
+import datetime as _dt
+import json
+import math
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+SCRIPTS_DIR = REPO_ROOT / "scripts"
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+SAMPLE_RATE = 16000
+N_LABELS = 107
+TRIM_FRAME_MS = 20
+TRIM_REL_THRESHOLD = 0.05
+MIN_KEEP_S = 0.5  # the LANGID role's TRANSCRIBE_ERR_INPUT_TOO_SHORT floor
+
+
+# ---------------------------------------------------------------------------
+# Manifests, crops
+# ---------------------------------------------------------------------------
+
+
+def read_manifest(path: Path) -> list[dict]:
+    rows = []
+    for line in path.read_text().splitlines():
+        line = line.strip()
+        if line:
+            rows.append(json.loads(line))
+    return rows
+
+
+def read_wav(path: Path) -> np.ndarray:
+    import soundfile as sf
+
+    pcm, sr = sf.read(str(path), dtype="float32", always_2d=False)
+    if pcm.ndim > 1:
+        pcm = pcm.mean(axis=1)
+    if int(sr) != SAMPLE_RATE:
+        raise SystemExit(f"error: {path} is {sr} Hz, expected {SAMPLE_RATE}")
+    return np.ascontiguousarray(pcm, dtype=np.float32)
+
+
+def write_wav(path: Path, pcm: np.ndarray) -> None:
+    import soundfile as sf
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    sf.write(str(path), pcm, SAMPLE_RATE, subtype="PCM_16")
+
+
+def energy_trim_start(pcm: np.ndarray) -> int:
+    """First sample of speech: the start of the first 20 ms frame whose RMS
+    exceeds 5% of the loudest frame's RMS.
+
+    Relative to the clip's own maximum, so it is level-independent. The
+    result is clamped so at least MIN_KEEP_S remains: a clip whose only
+    loud frame is at the very end would otherwise be trimmed below the
+    library's 500 ms floor.
+    """
+    frame = int(TRIM_FRAME_MS * SAMPLE_RATE / 1000)  # 320
+    n_frames = pcm.size // frame
+    if n_frames == 0:
+        return 0
+    frames = pcm[: n_frames * frame].reshape(n_frames, frame)
+    rms = np.sqrt(np.mean(frames.astype(np.float64) ** 2, axis=1))
+    peak = float(rms.max())
+    if peak <= 0.0:
+        return 0
+    above = np.flatnonzero(rms > TRIM_REL_THRESHOLD * peak)
+    if above.size == 0:
+        return 0
+    start = int(above[0]) * frame
+    return min(start, max(0, pcm.size - int(MIN_KEEP_S * SAMPLE_RATE)))
+
+
+def make_crop(pcm: np.ndarray, crop: str | int, trim: str) -> np.ndarray:
+    start = energy_trim_start(pcm) if trim == "energy" else 0
+    cut = pcm[start:]
+    if crop == "full":
+        return cut
+    n = int(float(crop) * SAMPLE_RATE)
+    return cut[:n] if cut.size > n else cut
+
+
+def crop_dir(trim: str, crop: str | int) -> Path:
+    return REPO_ROOT / "build" / "langid" / "crops" / trim / str(crop)
+
+
+def materialise_crops(rows: list[dict], crop: str | int, trim: str) -> list[tuple[dict, Path]]:
+    """Write every (utterance, crop) wav and return (row, path) pairs.
+
+    Always rewrites: the write is cheap next to a forward pass, and an
+    unconditional write means a stale crop from an earlier recipe can never
+    be scored by accident.
+    """
+    out = crop_dir(trim, crop)
+    out.mkdir(parents=True, exist_ok=True)
+    pairs: list[tuple[dict, Path]] = []
+    for row in rows:
+        src = REPO_ROOT / row["audio"]
+        dst = out / f"{row['id']}.wav"
+        write_wav(dst, make_crop(read_wav(src), crop, trim))
+        pairs.append((row, dst))
+    return pairs
+
+
+# ---------------------------------------------------------------------------
+# SpeechBrain engine
+# ---------------------------------------------------------------------------
+
+
+class _DumperArgs:
+    """Argument shim for the dumper's loader (same pattern as
+    make_samples_fleurs.py)."""
+
+    def __init__(self, model: str, revision: str, threads: int) -> None:
+        self.model = model
+        self.revision = revision
+        self.device = "cpu"
+        self.torch_threads = threads
+
+
+class SpeechBrainEngine:
+    """The pinned SpeechBrain checkpoint, loaded once, scored one clip at a
+    time.
+
+    Batch of one on purpose. Batching pads the shorter clips and drives
+    `wav_lens` off 1.0, which changes InputNormalization's span and the ASP
+    masks - i.e. it changes the numbers this file is supposed to be the
+    reference for.
+
+    The logits are taken from a forward hook on `mods.classifier.out`, the
+    Linear(512->107) whose output is pre-softmax, exactly as the reference
+    dumper's `cls.logits_raw`. `classify_batch` only returns log-softmax.
+    """
+
+    logit_kind = "pre_softmax"
+
+    def __init__(self, model: str, revision: str, threads: int) -> None:
+        import torch
+
+        import dump_reference_ecapa_tdnn_speechbrain as dumper
+
+        self.dumper = dumper
+        self.torch = torch
+        args = _DumperArgs(model, revision, threads)
+        dumper.configure_torch(args)
+        self.clf, self.checkpoint_dir = dumper.load_reference(args)
+
+        ind2lab = self.clf.hparams.label_encoder.ind2lab
+        if len(ind2lab) != N_LABELS:
+            raise SystemExit(f"error: label encoder has {len(ind2lab)} labels, "
+                             f"expected {N_LABELS}")
+        self.labels = [dumper.split_label(str(ind2lab[i]))[0] for i in range(N_LABELS)]
+
+        self._captured: list = []
+
+        def capture(module, inputs, output):
+            self._captured.append(output)
+
+        self._handle = self.clf.mods.classifier.out.register_forward_hook(capture)
+
+        import speechbrain
+
+        self.versions = {
+            "speechbrain": speechbrain.__version__,
+            "torch": torch.__version__,
+        }
+
+    def logits(self, pcm: np.ndarray) -> np.ndarray:
+        torch = self.torch
+        self._captured.clear()
+        wav = torch.from_numpy(pcm).unsqueeze(0)
+        # wav_lens is RELATIVE to the padded batch max; batch of one and
+        # unpadded means 1.0. A sample count here would silently mask the
+        # wrong span.
+        with torch.inference_mode():
+            self.clf.classify_batch(wav, torch.ones(1))
+        if len(self._captured) != 1:
+            raise SystemExit(f"error: classifier.out fired {len(self._captured)} "
+                             f"times for one clip, expected 1")
+        t = self._captured[0]
+        if t.numel() != N_LABELS:
+            raise SystemExit(f"error: classifier.out produced {t.numel()} values, "
+                             f"expected {N_LABELS}")
+        return np.ascontiguousarray(
+            t.detach().to(dtype=torch.float32, device="cpu").reshape(-1).numpy(),
+            dtype=np.float32)
+
+    def close(self) -> None:
+        self._handle.remove()
+
+
+# ---------------------------------------------------------------------------
+# C++ engine
+# ---------------------------------------------------------------------------
+
+
+class CppEngine:
+    """transcribe.cpp through the Python binding (bindings/python): the GGUF
+    loads once and every clip is one ``LangIdSession.run`` with ``top_k`` 0,
+    so all 107 logits come back and are reassembled by label index."""
+
+    logit_kind = "pre_softmax"
+
+    def __init__(self, model: Path, backend: str, threads: int, max_audio_ms: int,
+                 library: Path | None) -> None:
+        import hashlib
+        import os
+
+        if library is not None:
+            os.environ["TRANSCRIBE_LIBRARY"] = str(library.resolve())
+        binding = REPO_ROOT / "bindings" / "python" / "src"
+        if str(binding) not in sys.path:
+            sys.path.insert(0, str(binding))
+        import transcribe_cpp
+
+        if not model.exists():
+            raise SystemExit(f"error: {model} does not exist")
+        self.model = transcribe_cpp.Model(str(model), backend=backend)
+        self.session = self.model.langid_session(n_threads=threads, max_audio_ms=max_audio_ms)
+        self.labels = [code for code, _name in self.model.langid_labels]
+        if len(self.labels) != N_LABELS:
+            raise SystemExit(f"error: {model} has {len(self.labels)} labels, expected {N_LABELS}")
+        h = hashlib.sha256()
+        with open(model, "rb") as f:
+            for chunk in iter(lambda: f.read(1 << 20), b""):
+                h.update(chunk)
+        self.provenance = {
+            "gguf_sha256": h.hexdigest(),
+            "gguf_source_commit": _gguf_string(model, "general.source.commit"),
+            "gguf_source_repo": _gguf_string(model, "general.name"),
+            "native_version": transcribe_cpp.native_version(),
+            "native_commit": transcribe_cpp.native_commit(),
+            "backend_bound": self.model.backend,
+        }
+
+    def logits(self, pcm: np.ndarray) -> np.ndarray:
+        result = self.session.run(pcm, top_k=0)
+        logits = np.zeros(N_LABELS, dtype=np.float32)
+        for c in result.candidates:
+            logits[c.index] = c.logit
+        return logits
+
+    def close(self) -> None:
+        self.session.close()
+        self.model.close()
+
+
+def _gguf_string(path: Path, key: str) -> str | None:
+    """A string KV from the GGUF header, for provenance."""
+    from gguf import GGUFReader
+
+    field = GGUFReader(str(path)).fields.get(key)
+    return None if field is None else str(field.contents())
+
+
+# ---------------------------------------------------------------------------
+# Report
+# ---------------------------------------------------------------------------
+
+
+def softmax_top(logits: np.ndarray) -> tuple[int, float]:
+    z = logits.astype(np.float64)
+    z = z - z.max()
+    e = np.exp(z)
+    p = e / e.sum()
+    i = int(np.argmax(p))
+    return i, float(p[i])
+
+
+def _sha256_file(path: Path) -> str:
+    import hashlib
+
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def parse_crops(spec: str) -> list[str | int]:
+    crops: list[str | int] = []
+    for tok in spec.split(","):
+        tok = tok.strip()
+        if not tok:
+            continue
+        if tok == "full":
+            crops.append("full")
+        else:
+            crops.append(int(tok) if float(tok).is_integer() else float(tok))
+    if not crops:
+        raise SystemExit("error: --crops is empty")
+    return crops
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--engine", required=True, choices=("speechbrain", "cpp"))
+    p.add_argument("--model", required=True,
+                   help="HF repo id / checkpoint dir (speechbrain) or GGUF path (cpp)")
+    p.add_argument("--manifest", required=True, nargs="+", type=Path)
+    p.add_argument("--crops", default="3,5,10,full")
+    p.add_argument("--trim", default="none", choices=("none", "energy"))
+    p.add_argument("--out", required=True, type=Path)
+    p.add_argument("--library", type=Path, default=None,
+                   help="cpp engine only; a shared libtranscribe (TRANSCRIBE_LIBRARY)")
+    p.add_argument("--backend", default="cpu")
+    p.add_argument("--threads", type=int, default=4)
+    p.add_argument("--max-utts", type=int, default=0,
+                   help="cap utterances per manifest (0 = all); for smoke tests")
+    p.add_argument("--max-audio-ms", type=int, default=0,
+                   help="cpp engine only; 0 = derived from the longest clip")
+    p.add_argument("--revision",
+                   default="0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9",
+                   help="speechbrain engine only; pinned HF revision")
+    args = p.parse_args(argv)
+
+    crops = parse_crops(args.crops)
+
+    # Deterministic ordering: manifests in the order given, utterances in
+    # manifest order, crops in the order given.
+    manifests = [Path(m) for m in args.manifest]
+    rows: list[dict] = []
+    for m in manifests:
+        if not m.exists():
+            raise SystemExit(f"error: manifest {m} does not exist")
+        mr = read_manifest(m)
+        if args.max_utts > 0:
+            mr = mr[: args.max_utts]
+        rows.extend(mr)
+    if not rows:
+        raise SystemExit("error: manifests are empty")
+    longest_s = max(r["duration_s"] for r in rows)
+    print(f"{len(rows)} utterances from {len(manifests)} manifests, "
+          f"{len(crops)} crops, trim={args.trim}", file=sys.stderr)
+
+    max_audio_ms = args.max_audio_ms or int(math.ceil(longest_s / 10.0) * 10.0 * 1000)
+
+    engine: SpeechBrainEngine | CppEngine
+    if args.engine == "speechbrain":
+        engine = SpeechBrainEngine(args.model, args.revision, args.threads)
+        labels = engine.labels
+        extra_recipe = {
+            "revision": args.revision,
+            "checkpoint_dir": str(engine.checkpoint_dir),
+            "device": "cpu",
+            "model_dtype": "f32",
+            "batch_size": 1,
+            "torch_threads": args.threads,
+            **engine.versions,
+        }
+    else:
+        engine = CppEngine(Path(args.model), args.backend, args.threads, max_audio_ms,
+                           args.library)
+        labels = engine.labels
+        extra_recipe = {
+            "backend": args.backend,
+            "threads": args.threads,
+            "max_audio_ms": max_audio_ms,
+            **engine.provenance,
+        }
+
+    recipe = {
+        "crops": [str(c) for c in crops],
+        "trim": args.trim,
+        "trim_rule": (f"first {TRIM_FRAME_MS} ms frame with RMS > "
+                      f"{TRIM_REL_THRESHOLD:g} * max frame RMS"
+                      if args.trim == "energy" else
+                      "none (crops start at sample 0 of the raw clip)"),
+        "crop_dir": str(crop_dir(args.trim, "<crop>").relative_to(REPO_ROOT)),
+        "manifests": [str(m) for m in manifests],
+        "manifest_sha256": {str(m): _sha256_file(m) for m in manifests},
+        "n_utterances": len(rows),
+        "sample_rate": SAMPLE_RATE,
+        "crop_dtype": "pcm_s16le wav (both engines read the same files)",
+        "logit_kind": engine.logit_kind,
+        "longest_clip_s": longest_s,
+        **extra_recipe,
+    }
+
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    t_start = time.time()
+    n_written = 0
+    with open(args.out, "w") as f:
+        pending_header = True
+
+        for crop in crops:
+            t_crop = time.time()
+            print(f"[{args.trim}/{crop}] materialising {len(rows)} crops ...",
+                  file=sys.stderr)
+            pairs = materialise_crops(rows, crop, args.trim)
+
+            batch_rows = []
+            for i, (row, wav_path) in enumerate(pairs):
+                pcm = read_wav(wav_path)
+                logits = engine.logits(pcm)
+                top_i, top_p = softmax_top(logits)
+                batch_rows.append({
+                    "id": row["id"],
+                    "language": row["language"],
+                    "crop_s": crop,
+                    "trim": args.trim,
+                    "audio_s": round(pcm.size / SAMPLE_RATE, 4),
+                    "logits": [round(float(x), 6) for x in logits],
+                    "top1": labels[top_i],
+                    "top1_prob": round(top_p, 6),
+                })
+                if (i + 1) % 100 == 0 or i + 1 == len(pairs):
+                    el = time.time() - t_crop
+                    print(f"  [{args.trim}/{crop}] {i + 1}/{len(pairs)}  "
+                          f"{el:.0f}s  ({el / (i + 1) * 1000:.0f} ms/utt)",
+                          file=sys.stderr)
+
+            if pending_header:
+                header = {
+                    "type": "header",
+                    "engine": args.engine,
+                    "model": str(args.model),
+                    "recipe": recipe,
+                    "labels": labels,
+                    "created": _dt.datetime.now(_dt.timezone.utc)
+                                  .replace(microsecond=0).isoformat(),
+                }
+                f.write(json.dumps(header) + "\n")
+                pending_header = False
+            for r in batch_rows:
+                f.write(json.dumps(r) + "\n")
+                n_written += 1
+            f.flush()
+            print(f"[{args.trim}/{crop}] done in {time.time() - t_crop:.0f}s",
+                  file=sys.stderr)
+
+    engine.close()
+    dt = time.time() - t_start
+    print(f"wrote {args.out} ({n_written} rows) in {dt:.0f}s "
+          f"({dt / max(n_written, 1) * 1000:.0f} ms/row)", file=sys.stderr)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/langid/score.py b/scripts/langid/score.py
new file mode 100755
index 000000000..e18c1224a
--- /dev/null
+++ b/scripts/langid/score.py
@@ -0,0 +1,696 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "numpy>=1.26",
+# ]
+# ///
+"""
+score.py - turn a run.py sweep into accuracy tables.
+
+Everything below is computed from the stored 107-logit vectors, so a new
+decision space costs a re-score, not a re-run.
+
+Usage:
+    uv run scripts/langid/score.py reports/langid/ref-speechbrain-untrimmed.jsonl \\
+        --md reports/langid/ref-speechbrain-untrimmed.md
+
+    # docs/accuracy.md is written from both reference recipes at once
+    uv run scripts/langid/score.py \\
+        reports/langid/ref-speechbrain-untrimmed.jsonl \\
+        reports/langid/ref-speechbrain-trimmed.jsonl \\
+        --docs docs/accuracy.md
+
+Tables produced for each run:
+  (a) open-set top-1 accuracy per language and mean, per crop, with a 95%
+      bootstrap CI on the mean;
+  (b) "handy40" restricted accuracy (logits masked to the 40-language set,
+      softmax over the mask, argmax), per crop;
+  (c) each restricted selection's accuracy over that selection's utterances;
+  (d) confidence: median top-prob when right vs wrong, and coverage /
+      precision at thresholds 0.5 / 0.7 / 0.9, on full clips, open-set and
+      handy40;
+  (e) the 10 confusion pairs with the most open-set errors.
+
+Correctness is strict code equality. `nn` is not credited for `no`, `be` is
+not credited for `ru`, `jw` is not credited for `id`: those label artefacts
+are exactly what the restricted spaces are supposed to remove, and crediting
+them would erase the effect the tables are measuring.
+
+The mean is the MACRO mean over languages (each language weighs 1/15,
+matching langid.cpp's original ONNX evaluation). The bootstrap resamples utterances within each
+language and re-averages the per-language accuracies, so the CI describes
+the same statistic the point estimate does.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import Counter, defaultdict
+from pathlib import Path
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+
+CROP_ORDER_HINT = {"3": 0, "5": 1, "10": 2, "full": 99}
+N_BOOT = 1000
+BOOT_SEED = 42
+THRESHOLDS = (0.5, 0.7, 0.9)
+TOP_CONFUSIONS = 10
+
+
+# ---------------------------------------------------------------------------
+# Loading
+# ---------------------------------------------------------------------------
+
+
+class Run:
+    def __init__(self, path: Path) -> None:
+        self.path = path
+        lines = [l for l in path.read_text().splitlines() if l.strip()]
+        if not lines:
+            raise SystemExit(f"error: {path} is empty")
+        self.header = json.loads(lines[0])
+        if self.header.get("type") != "header":
+            raise SystemExit(f"error: {path} does not start with a header line")
+        self.labels: list[str] = self.header["labels"]
+        self.index_of = {c: i for i, c in enumerate(self.labels)}
+
+        rows = [json.loads(l) for l in lines[1:]]
+        if not rows:
+            raise SystemExit(f"error: {path} has a header but no rows")
+        self.crops = list(dict.fromkeys(str(r["crop_s"]) for r in rows))
+        self.crops.sort(key=lambda c: CROP_ORDER_HINT.get(c, float(c) if c.replace(".", "").isdigit() else 50))
+        self.languages = list(dict.fromkeys(r["language"] for r in rows))
+
+        # Per crop: a [N, 107] logit matrix plus aligned id/language vectors.
+        # One array per crop keeps every accuracy a vectorised argmax.
+        self.by_crop: dict[str, dict] = {}
+        for crop in self.crops:
+            sel = [r for r in rows if str(r["crop_s"]) == crop]
+            self.by_crop[crop] = {
+                "ids": [r["id"] for r in sel],
+                "lang": np.array([r["language"] for r in sel]),
+                "logits": np.array([r["logits"] for r in sel], dtype=np.float64),
+                "audio_s": np.array([r["audio_s"] for r in sel], dtype=np.float64),
+            }
+        self.n_rows = len(rows)
+
+    @property
+    def name(self) -> str:
+        return self.path.stem
+
+
+def softmax_masked(logits: np.ndarray, mask_idx: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Argmax and top-probability over a masked decision space.
+
+    `logits` is [N, 107]; `mask_idx` selects the allowed columns. The softmax
+    is taken over the allowed columns only, which is what
+    langid_candidate.prob is.
+    """
+    z = logits[:, mask_idx]
+    z = z - z.max(axis=1, keepdims=True)
+    e = np.exp(z)
+    p = e / e.sum(axis=1, keepdims=True)
+    arg = np.argmax(p, axis=1)
+    return mask_idx[arg], p[np.arange(p.shape[0]), arg]
+
+
+# ---------------------------------------------------------------------------
+# Selections
+# ---------------------------------------------------------------------------
+
+
+def load_selections(path: Path, labels: list[str]) -> dict:
+    spec = json.loads(path.read_text())
+    aliases: dict[str, str] = spec.get("aliases", {})
+    index_of = {c: i for i, c in enumerate(labels)}
+
+    def resolve(codes: list[str], where: str) -> list[str]:
+        out = []
+        for c in codes:
+            real = aliases.get(c, c)
+            if real not in index_of:
+                raise SystemExit(
+                    f"error: {where}: code {c!r} (resolved {real!r}) is not in "
+                    f"this model's label set")
+            out.append(real)
+        if len(set(out)) != len(out):
+            dupes = [c for c, n in Counter(out).items() if n > 1]
+            raise SystemExit(f"error: {where}: duplicate codes after aliasing: {dupes}")
+        return out
+
+    restricted = {}
+    for name, entry in spec.get("restricted", {}).items():
+        codes = resolve(entry["codes"], f"restricted.{name}")
+        restricted[name] = {
+            "label": entry.get("label", name),
+            "placeholder": bool(entry.get("placeholder")),
+            "codes": codes,
+        }
+    selections = []
+    for entry in spec.get("selections", []):
+        selections.append({
+            "name": entry["name"],
+            "codes": resolve(entry["codes"], f"selections.{entry['name']}"),
+        })
+    return {"aliases": aliases, "restricted": restricted, "selections": selections,
+            "path": path}
+
+
+def check_handy40(entry: dict, eval_languages: list[str]) -> list[str]:
+    """Sanity assertions on the 40-language set, returned as warnings.
+
+    An eval language missing from the set would score 0 by construction, and
+    keeping `be` / `nn` / `jw` would defeat the whole point of the table
+    (langid.cpp's original ONNX evaluation: restriction removes exactly those label artefacts).
+    """
+    warnings = []
+    codes = entry["codes"]
+    if len(codes) != 40:
+        warnings.append(f"handy40 has {len(codes)} codes, not 40")
+    missing = [c for c in eval_languages if c not in codes]
+    if missing:
+        warnings.append(f"handy40 omits evaluated languages {missing}; they "
+                        f"score 0 by construction")
+    artefacts = [c for c in ("be", "nn", "jw") if c in codes]
+    if artefacts:
+        warnings.append(f"handy40 contains the label artefacts {artefacts} that "
+                        f"the restricted table exists to remove")
+    return warnings
+
+
+# ---------------------------------------------------------------------------
+# Metrics
+# ---------------------------------------------------------------------------
+
+
+def per_language_accuracy(pred_codes: np.ndarray, langs: np.ndarray,
+                          order: list[str]) -> tuple[dict[str, float], float]:
+    acc = {}
+    for lg in order:
+        m = langs == lg
+        acc[lg] = float((pred_codes[m] == lg).mean()) if m.any() else float("nan")
+    vals = [a for a in acc.values() if a == a]
+    return acc, (sum(vals) / len(vals) if vals else float("nan"))
+
+
+def bootstrap_macro_ci(correct: np.ndarray, langs: np.ndarray, order: list[str],
+                       n_boot: int = N_BOOT, seed: int = BOOT_SEED,
+                       ci: float = 0.95) -> tuple[float, float]:
+    """95% CI on the macro-mean accuracy, stratified by language.
+
+    Utterances are resampled with replacement inside each language and the
+    per-language accuracies re-averaged, so the resampled statistic is the
+    same macro mean the point estimate reports.
+    """
+    rng = np.random.default_rng(seed)
+    blocks = []
+    for lg in order:
+        m = langs == lg
+        if m.any():
+            blocks.append(correct[m].astype(np.float64))
+    if not blocks:
+        return float("nan"), float("nan")
+    means = np.empty(n_boot, dtype=np.float64)
+    for b in range(n_boot):
+        acc = 0.0
+        for blk in blocks:
+            idx = rng.integers(0, blk.size, blk.size)
+            acc += blk[idx].mean()
+        means[b] = acc / len(blocks)
+    means.sort()
+    lo = means[int((1 - ci) / 2 * n_boot)]
+    hi = means[min(n_boot - 1, int((1 + ci) / 2 * n_boot))]
+    return float(lo), float(hi)
+
+
+def rel(p: Path) -> str:
+    """Repo-relative path when possible; reports are read in-tree."""
+    try:
+        return str(Path(p).resolve().relative_to(REPO_ROOT))
+    except ValueError:
+        return str(p)
+
+
+def crop_head(c: str) -> str:
+    return "full" if c == "full" else f"{c} s"
+
+
+def crop_list(crops: list[str]) -> str:
+    return ", ".join(crop_head(c) for c in crops)
+
+
+def pct(x: float, digits: int = 1) -> str:
+    if x != x:
+        return "-"
+    return f"{100.0 * x:.{digits}f}"
+
+
+# ---------------------------------------------------------------------------
+# Report sections
+# ---------------------------------------------------------------------------
+
+
+def md_table(header: list[str], rows: list[list[str]]) -> list[str]:
+    out = ["| " + " | ".join(header) + " |",
+           "|" + "|".join("---" for _ in header) + "|"]
+    for r in rows:
+        out.append("| " + " | ".join(r) + " |")
+    out.append("")
+    return out
+
+
+def section_setup(run: Run, sel: dict, warnings: list[str]) -> list[str]:
+    h = run.header
+    r = h["recipe"]
+    lines = [f"## Setup", ""]
+    lines.append(f"- Engine: `{h['engine']}`, model `{h['model']}`")
+    if "revision" in r:
+        lines.append(f"- Revision: `{r['revision']}` "
+                     f"(speechbrain {r.get('speechbrain')}, torch {r.get('torch')}, "
+                     f"{r.get('device')}, {r.get('model_dtype')}, batch "
+                     f"{r.get('batch_size')})")
+    if "backend" in r:
+        lines.append(f"- Backend `{r['backend']}`, threads {r.get('threads')}, "
+                     f"max_audio_ms {r.get('max_audio_ms')}")
+    lines.append(f"- Dataset: FLEURS `test`, {len(run.languages)} languages "
+                 f"x {r['n_utterances'] // max(len(run.languages), 1)} utterances "
+                 f"= {r['n_utterances']} clips, longest {r['longest_clip_s']:.1f} s")
+    lines.append(f"- Crops: {crop_list(run.crops)}, from the "
+                 f"{'first speech frame' if r['trim'] == 'energy' else 'start of the clip'}"
+                 f" (`--trim {r['trim']}`)")
+    lines.append(f"- Trim rule: {r['trim_rule']}")
+    lines.append(f"- Logits: `{r['logit_kind']}`; both engines read the same "
+                 f"16-bit crops under `{r['crop_dir']}`")
+    lines.append(f"- Decision spaces from `{rel(sel['path'])}`; "
+                 f"bootstrap {N_BOOT} resamples, seed {BOOT_SEED}")
+    lines.append(f"- Correctness is strict label-code equality "
+                 f"(`nn` is not credited for `no`, `be` not for `ru`, `jw` not for `id`)")
+    lines.append("")
+    if warnings:
+        lines.append("> **Warnings**")
+        for w in warnings:
+            lines.append(f"> - {w}")
+        lines.append("")
+    return lines
+
+
+def section_openset(run: Run) -> tuple[list[str], dict]:
+    langs_order = run.languages
+    rows = []
+    means: dict[str, tuple[float, float, float]] = {}
+    per_crop_acc: dict[str, dict[str, float]] = {}
+    for crop in run.crops:
+        d = run.by_crop[crop]
+        pred = np.array(run.labels)[np.argmax(d["logits"], axis=1)]
+        acc, mean = per_language_accuracy(pred, d["lang"], langs_order)
+        lo, hi = bootstrap_macro_ci(pred == d["lang"], d["lang"], langs_order)
+        means[crop] = (mean, lo, hi)
+        per_crop_acc[crop] = acc
+
+    for lg in langs_order:
+        rows.append([lg] + [pct(per_crop_acc[c][lg]) for c in run.crops])
+    rows.append(["**mean**"] + [f"**{pct(means[c][0])}**" for c in run.crops])
+    rows.append(["95% CI"] + [f"{pct(means[c][1])}-{pct(means[c][2])}" for c in run.crops])
+
+    lines = ["## (a) Open-set top-1 accuracy (all 107 labels), %", ""]
+    lines += md_table(["lang"] + [crop_head(c) for c in run.crops], rows)
+    return lines, {"per_language": per_crop_acc, "mean": means}
+
+
+def section_restricted(run: Run, sel: dict) -> tuple[list[str], dict]:
+    lines = []
+    out = {}
+    for name, entry in sel["restricted"].items():
+        idx = np.array([run.index_of[c] for c in entry["codes"]])
+        rows = []
+        per_crop_acc = {}
+        means = {}
+        for crop in run.crops:
+            d = run.by_crop[crop]
+            arg, _p = softmax_masked(d["logits"], idx)
+            pred = np.array(run.labels)[arg]
+            acc, mean = per_language_accuracy(pred, d["lang"], run.languages)
+            lo, hi = bootstrap_macro_ci(pred == d["lang"], d["lang"], run.languages)
+            per_crop_acc[crop] = acc
+            means[crop] = (mean, lo, hi)
+        for lg in run.languages:
+            rows.append([lg] + [pct(per_crop_acc[c][lg]) for c in run.crops])
+        rows.append(["**mean**"] + [f"**{pct(means[c][0])}**" for c in run.crops])
+        rows.append(["95% CI"] + [f"{pct(means[c][1])}-{pct(means[c][2])}" for c in run.crops])
+        tag = " (PLACEHOLDER list)" if entry["placeholder"] else ""
+        lines += [f"## (b) Restricted to {entry['label']}"
+                  f" - {len(entry['codes'])} labels{tag}, %", ""]
+        lines += [f"`{' '.join(entry['codes'])}`", ""]
+        lines += md_table(["lang"] + [crop_head(c) for c in run.crops],
+                          rows)
+        out[name] = {"per_language": per_crop_acc, "mean": means,
+                     "codes": entry["codes"]}
+    return lines, out
+
+
+def section_selections(run: Run, sel: dict) -> tuple[list[str], dict]:
+    rows = []
+    out = {}
+    for entry in sel["selections"]:
+        codes = entry["codes"]
+        idx = np.array([run.index_of[c] for c in codes])
+        cells = []
+        per_crop = {}
+        for crop in run.crops:
+            d = run.by_crop[crop]
+            m = np.isin(d["lang"], codes)
+            if not m.any():
+                cells.append("-")
+                per_crop[crop] = float("nan")
+                continue
+            arg, _p = softmax_masked(d["logits"][m], idx)
+            pred = np.array(run.labels)[arg]
+            acc = float((pred == d["lang"][m]).mean())
+            per_crop[crop] = acc
+            cells.append(pct(acc))
+        n = int(np.isin(run.by_crop[run.crops[0]]["lang"], codes).sum())
+        rows.append([entry["name"], str(n)] + cells)
+        out[entry["name"]] = per_crop
+    lines = ["## (c) Restricted to a user's selection "
+             "(accuracy over that selection's utterances), %", ""]
+    lines += md_table(["selection", "n utts"] +
+                      [crop_head(c) for c in run.crops], rows)
+    return lines, out
+
+
+def section_confidence(run: Run, sel: dict) -> tuple[list[str], dict]:
+    """Median top-prob right vs wrong, and coverage/precision at thresholds.
+
+    Reported on the `full` crop (the realistic case: the caller has the whole
+    utterance) in two decision spaces - open-set and handy40. `coverage` is
+    the share of decisions kept, `precision` the share of kept decisions that
+    are right; a threshold is only useful when it trades a little of the
+    first for a lot of the second.
+    """
+    crop = "full" if "full" in run.crops else run.crops[-1]
+    d = run.by_crop[crop]
+    spaces: list[tuple[str, np.ndarray]] = [
+        ("open (107)", np.arange(len(run.labels)))]
+    for name, entry in sel["restricted"].items():
+        spaces.append((f"{name} ({len(entry['codes'])})",
+                       np.array([run.index_of[c] for c in entry["codes"]])))
+
+    rows = []
+    thr_rows = []
+    out = {}
+    for space_name, idx in spaces:
+        arg, prob = softmax_masked(d["logits"], idx)
+        pred = np.array(run.labels)[arg]
+        right = pred == d["lang"]
+        med_r = float(np.median(prob[right])) if right.any() else float("nan")
+        med_w = float(np.median(prob[~right])) if (~right).any() else float("nan")
+        rows.append([space_name, f"{med_r:.3f}", f"{med_w:.3f}",
+                     f"{pct(float(right.mean()))}"])
+        per_thr = {}
+        for t in THRESHOLDS:
+            keep = prob >= t
+            cov = float(keep.mean())
+            prec = float(right[keep].mean()) if keep.any() else float("nan")
+            thr_rows.append([space_name, f"{t:.1f}", pct(cov), pct(prec)])
+            per_thr[t] = (cov, prec)
+        out[space_name] = {"median_right": med_r, "median_wrong": med_w,
+                           "thresholds": per_thr}
+
+    lines = [f"## (d) Confidence, `{crop}` clips", ""]
+    lines += md_table(["decision space", "median top-prob right",
+                       "median top-prob wrong", "accuracy %"], rows)
+    lines += md_table(["decision space", "threshold", "coverage %",
+                       "precision %"], thr_rows)
+    return lines, out
+
+
+def section_confusions(run: Run) -> tuple[list[str], list]:
+    counts: dict[tuple[str, str], Counter] = defaultdict(Counter)
+    for crop in run.crops:
+        d = run.by_crop[crop]
+        pred = np.array(run.labels)[np.argmax(d["logits"], axis=1)]
+        wrong = pred != d["lang"]
+        for t, p in zip(d["lang"][wrong], pred[wrong]):
+            counts[(str(t), str(p))][crop] += 1
+    ranked = sorted(counts.items(), key=lambda kv: -sum(kv[1].values()))[:TOP_CONFUSIONS]
+    rows = []
+    for (t, p), per_crop in ranked:
+        rows.append([f"{t} -> {p}", str(sum(per_crop.values()))] +
+                    [str(per_crop.get(c, 0)) for c in run.crops])
+    lines = [f"## (e) Top {TOP_CONFUSIONS} open-set confusion pairs "
+             f"(errors, all crops)", ""]
+    lines += md_table(["true -> predicted", "total"] +
+                      [crop_head(c) for c in run.crops], rows)
+    return lines, [(k, dict(v)) for k, v in ranked]
+
+
+# ---------------------------------------------------------------------------
+# langid.cpp original-evaluation comparison
+# ---------------------------------------------------------------------------
+
+# langid.cpp's first evaluation, measured on an ONNX export of the same checkpoint,
+# untrimmed crops from the start of the clip. Reproduced here so score.py can
+# print the delta instead of a human eyeballing two documents.
+PLAN_V1_OPEN = {"3": 0.67, "5": 0.85, "10": 0.91, "full": 0.91}
+PLAN_V1_HANDY40 = {"3": 0.77, "5": 0.92, "10": 0.965, "full": 0.966}
+PLAN_V1_SELECTIONS = {
+    "en+zh":       {"3": 0.90, "5": 0.99,   "10": 1.000},
+    "en+es+pt":    {"3": 0.98, "5": 0.995,  "10": 1.000},
+    "es+pt":       {"3": 0.99, "5": 0.995,  "10": 1.000},
+    "ja+ko":       {"3": 0.88, "5": 0.98,   "10": 1.000},
+    "en+ru":       {"3": 0.99, "5": 1.00,   "10": 1.000},
+    "no+da":       {"3": 0.96, "5": 0.998,  "10": 1.000},
+    "en+de+ja":    {"3": 0.82, "5": 0.95,   "10": 0.998},
+    "en+fr+de+es": {"3": 0.89, "5": 0.94,   "10": 0.990},
+    "de+fr+ja":    {"3": 0.80, "5": 0.91,   "10": 0.985},
+    "cs+sk":       {"3": 0.83, "5": 0.92,   "10": 0.970},
+    "id+ms":       {"3": 0.88, "5": 0.895,  "10": 0.895},
+}
+
+
+def section_planv1(run: Run, openset: dict, restricted: dict,
+                   selections: dict) -> list[str]:
+    lines = ["## Delta vs langid.cpp's original ONNX evaluation (ONNX export of the same "
+             "checkpoint, untrimmed)", ""]
+    if run.header["recipe"]["trim"] != "none":
+        lines.append("langid.cpp's original numbers are untrimmed; this run uses "
+                     f"`--trim {run.header['recipe']['trim']}`, so the deltas "
+                     "below are a recipe difference, not a discrepancy.")
+        lines.append("")
+    rows = []
+    for crop in run.crops:
+        ref = PLAN_V1_OPEN.get(crop)
+        got = openset["mean"][crop][0]
+        rows.append(["all 107", crop, pct(ref) if ref else "-", pct(got),
+                     f"{100 * (got - ref):+.1f}" if ref else "-"])
+    if "handy40" in restricted:
+        for crop in run.crops:
+            ref = PLAN_V1_HANDY40.get(crop)
+            got = restricted["handy40"]["mean"][crop][0]
+            rows.append(["Handy's 40*", crop, pct(ref) if ref else "-", pct(got),
+                         f"{100 * (got - ref):+.1f}" if ref else "-"])
+    lines += md_table(["decision space", "crop", "original %", "this run %", "delta pp"],
+                      rows)
+    if "handy40" in restricted:
+        lines.append("\\* langid.cpp's original 40-language set is not recorded anywhere; "
+                     "this row compares against the placeholder list in "
+                     "`scripts/langid/selections.json`, so a delta of a point or "
+                     "two is expected from the set alone.")
+        lines.append("")
+
+    rows = []
+    crops_cmp = [c for c in run.crops if c in ("3", "5", "10")]
+    for name, ref in PLAN_V1_SELECTIONS.items():
+        got = selections.get(name)
+        if got is None:
+            continue
+        cells = []
+        for c in crops_cmp:
+            if c in ref and got.get(c) == got.get(c):
+                cells += [pct(ref[c]), pct(got[c]), f"{100 * (got[c] - ref[c]):+.1f}"]
+            else:
+                cells += ["-", "-", "-"]
+        rows.append([name] + cells)
+    head = ["selection"]
+    for c in crops_cmp:
+        head += [f"v1 {c}s", f"now {c}s", f"d {c}s"]
+    lines += md_table(head, rows)
+    return lines
+
+
+# ---------------------------------------------------------------------------
+# Driver
+# ---------------------------------------------------------------------------
+
+
+def score_run(run: Run, sel: dict) -> tuple[list[str], dict]:
+    warnings = []
+    if "handy40" in sel["restricted"]:
+        warnings += check_handy40(sel["restricted"]["handy40"], run.languages)
+        if sel["restricted"]["handy40"]["placeholder"]:
+            warnings.append(
+                "`handy40` is a PLACEHOLDER 40-language list, "
+                "not the real Handy selection. Replace `restricted.handy40.codes` "
+                "in scripts/langid/selections.json and re-run score.py; run.py does "
+                "not need to run again.")
+
+    lines = [f"# Language-ID accuracy - `{run.name}`", ""]
+    lines += section_setup(run, sel, warnings)
+    a, openset = section_openset(run)
+    b, restricted = section_restricted(run, sel)
+    c, selections = section_selections(run, sel)
+    d, confidence = section_confidence(run, sel)
+    e, confusions = section_confusions(run)
+    lines += a + b + c + d + e
+    lines += section_planv1(run, openset, restricted, selections)
+    summary = {"open": openset, "restricted": restricted, "selections": selections,
+               "confidence": confidence, "confusions": confusions,
+               "warnings": warnings}
+    return lines, summary
+
+
+def docs_accuracy(runs: list[tuple[Run, dict, list[str]]], sel: dict,
+                  gate_note: str | None = None) -> list[str]:
+    """docs/accuracy.md: the two reference recipes side by side."""
+    first = runs[0][0]
+    r = first.header["recipe"]
+    lines = [
+        "# Accuracy",
+        "",
+        "> **These are the SpeechBrain reference numbers, not a separate "
+        "measurement of langid.cpp.**",
+        "> The C++ F32 result is required to agree with the reference on "
+        ">= 99.9% of decisions over this same corpus.",
+    ]
+    if gate_note:
+        lines.append(f"> {gate_note}")
+    else:
+        lines.append("> That gate has not been measured yet; until it is, treat "
+                     "this page as the reference's numbers only.")
+    lines += [
+        "",
+        "## Setup",
+        "",
+        f"- Model: `{first.header['model']}` @ `{r.get('revision')}` "
+        f"(speechbrain {r.get('speechbrain')}, torch {r.get('torch')}, CPU, fp32, "
+        f"batch {r.get('batch_size')}).",
+        f"- Dataset: FLEURS `test`, {len(first.languages)} languages "
+        f"(`{' '.join(first.languages)}`), the first "
+        f"{r['n_utterances'] // max(len(first.languages), 1)} utterances of at "
+        f"least 1 s in parquet order per language = {r['n_utterances']} clips, "
+        f"mean ~12 s.",
+        f"- Crops: {', '.join(c + ' s' for c in first.crops if c != 'full')} and "
+        f"the full clip. A clip shorter than the crop is scored whole.",
+        "- Two trim recipes:",
+        "  - **untrimmed** - the crop starts at sample 0 of the raw clip. This "
+        "is what langid.cpp's original ONNX evaluation measured; FLEURS clips open with silence, so "
+        "\"3 s\" is roughly 2 s of speech.",
+        "  - **trimmed** - leading audio is dropped up to the first 20 ms frame "
+        "whose RMS exceeds 5% of the clip's loudest frame, then the crop is "
+        "taken. This is the usage protocol langid.cpp asks callers to follow "
+        "(feed speech, not the raw capture), so it is the honest number for a "
+        "dictation app.",
+        "- Correctness is strict label-code equality. `nn` is not credited for "
+        "`no`, `be` not for `ru`, `jw` not for `id`.",
+        "- The mean is the macro mean over languages; the CI is a "
+        f"{N_BOOT}-resample stratified bootstrap, seed {BOOT_SEED}.",
+        "- Generated by `scripts/langid/score.py` from "
+        + ", ".join(f"`{rel(ru.path)}`" for ru, _s, _l in runs)
+        + ".",
+        "",
+    ]
+
+    crops = first.crops
+    lines += ["## Top-1 accuracy, mean over the "
+              f"{len(first.languages)} languages, %", ""]
+    rows = []
+    for ru, summary, label in runs:
+        rows.append([f"all 107 ({label})"] +
+                    [pct(summary["open"]["mean"][c][0]) for c in crops])
+    for ru, summary, label in runs:
+        if "handy40" in summary["restricted"]:
+            rows.append([f"handy40* ({label})"] +
+                        [pct(summary["restricted"]["handy40"]["mean"][c][0])
+                         for c in crops])
+    lines += md_table(["decision space"] +
+                      [crop_head(c) for c in crops], rows)
+    lines.append("\\* `handy40` is a **placeholder** 40-language dictation list "
+                 ", not the real Handy selection; see "
+                 "`scripts/langid/selections.json`.")
+    lines.append("")
+
+    lines += ["## Restricted to a user's selection, %", ""]
+    head = ["selection"]
+    for _ru, _s, label in runs:
+        head += [f"{label} {crop_head(c)}" for c in crops]
+    rows = []
+    for entry in sel["selections"]:
+        row = [entry["name"]]
+        for _ru, summary, _label in runs:
+            row += [pct(summary["selections"][entry["name"]].get(c, float("nan")))
+                    for c in crops]
+        rows.append(row)
+    lines += md_table(head, rows)
+
+    lines += ["## Per-run detail", ""]
+    for ru, _s, label in runs:
+        lines.append(f"- **{label}**: `reports/langid/{ru.name}.md`")
+    lines.append("")
+    return lines
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("runs", nargs="+", type=Path, help="run.py JSONL report(s)")
+    p.add_argument("--selections", type=Path,
+                   default=Path(__file__).resolve().parent / "selections.json")
+    p.add_argument("--md", type=Path,
+                   help="write the first run's markdown report here")
+    p.add_argument("--docs", type=Path,
+                   help="write a combined docs/accuracy.md from every run given")
+    p.add_argument("--gate-note", default=None,
+                   help="one sentence recording the measured dataset-gate "
+                        "result; goes into --docs verbatim. Passing a claim "
+                        "compare.py did not produce is how this page starts "
+                        "lying, so quote its output.")
+    args = p.parse_args(argv)
+
+    loaded = [Run(r) for r in args.runs]
+    sel = load_selections(args.selections, loaded[0].labels)
+
+    scored = []
+    for run in loaded:
+        lines, summary = score_run(run, sel)
+        label = run.header["recipe"]["trim"]
+        label = "untrimmed" if label == "none" else "trimmed"
+        scored.append((run, summary, label, lines))
+
+    text = "\n".join(scored[0][3]) + "\n"
+    sys.stdout.write(text)
+    if args.md:
+        args.md.parent.mkdir(parents=True, exist_ok=True)
+        args.md.write_text(text)
+        print(f"wrote {args.md}", file=sys.stderr)
+    for run, _summary, _label, lines in scored[1:]:
+        if args.md:
+            alt = args.md.with_name(f"{run.name}.md")
+            alt.write_text("\n".join(lines) + "\n")
+            print(f"wrote {alt}", file=sys.stderr)
+
+    if args.docs:
+        doc = docs_accuracy([(r, s, l) for r, s, l, _ln in scored], sel,
+                            args.gate_note)
+        args.docs.parent.mkdir(parents=True, exist_ok=True)
+        args.docs.write_text("\n".join(doc) + "\n")
+        print(f"wrote {args.docs}", file=sys.stderr)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/langid/selections.json b/scripts/langid/selections.json
new file mode 100644
index 000000000..15204d1ef
--- /dev/null
+++ b/scripts/langid/selections.json
@@ -0,0 +1,48 @@
+{
+  "schema": "langid-eval-selections-v1",
+  "_comment": [
+    "Decision spaces scored by scripts/langid/score.py from a single run.py sweep.",
+    "Codes are VoxLingua107 label codes; `aliases` maps the spellings a caller",
+    "is likely to use onto them (the same aliases the GGUF carries).",
+    "",
+    "handy40 IS A PLACEHOLDER. The real Handy language list",
+    "was not available when this was written, so it is a plausible 40-language",
+    "dictation set: the 15 evaluated languages plus 25 widely-dictated ones.",
+    "Replace `handy40.codes` with the real list and re-run score.py; nothing",
+    "else has to change, because run.py stores all 107 logits per utterance.",
+    "",
+    "The 11 pair/triple selections are langid.cpp's original evaluation table,",
+    "with `nb+da` written `no+da`: VoxLingua107 spells Norwegian Bokmal `no`."
+  ],
+  "aliases": {
+    "he": "iw",
+    "jv": "jw",
+    "fil": "tl",
+    "nb": "no"
+  },
+  "restricted": {
+    "handy40": {
+      "label": "Handy's 40 (placeholder)",
+      "placeholder": true,
+      "codes": [
+        "en", "zh", "de", "fr", "es", "pt", "ja", "ko", "ru", "cs",
+        "sk", "no", "da", "id", "ms", "it", "nl", "pl", "tr", "sv",
+        "fi", "el", "hu", "ro", "uk", "bg", "hr", "sr", "vi", "th",
+        "hi", "ar", "he", "fa", "ta", "bn", "ca", "gl", "sl", "ur"
+      ]
+    }
+  },
+  "selections": [
+    { "name": "en+zh",       "codes": ["en", "zh"] },
+    { "name": "en+es+pt",    "codes": ["en", "es", "pt"] },
+    { "name": "es+pt",       "codes": ["es", "pt"] },
+    { "name": "ja+ko",       "codes": ["ja", "ko"] },
+    { "name": "en+ru",       "codes": ["en", "ru"] },
+    { "name": "no+da",       "codes": ["no", "da"], "plan_v1_name": "nb+da" },
+    { "name": "en+de+ja",    "codes": ["en", "de", "ja"] },
+    { "name": "en+fr+de+es", "codes": ["en", "fr", "de", "es"] },
+    { "name": "de+fr+ja",    "codes": ["de", "fr", "ja"] },
+    { "name": "cs+sk",       "codes": ["cs", "sk"] },
+    { "name": "id+ms",       "codes": ["id", "ms"] }
+  ]
+}

From e8a7745d26aca67d4bd4a8f7deff5fc7eb187373 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Mon, 5 Oct 2026 19:14:17 +0800
Subject: [PATCH 05/38] wip

---
 catalog/lang-id-voxlingua107-ecapa.json   |   2 +-
 docs/models/lang-id-voxlingua107-ecapa.md |  20 +++--
 docs/porting/families/ecapa_tdnn.md       |  24 ++---
 scripts/langid/ingest.py                  |   1 +
 src/arch/ecapa_tdnn/model.cpp             | 102 +++++++++++++++++++++-
 tests/CMakeLists.txt                      |   4 +-
 tests/ecapa_tdnn_smoke.cpp                |  29 ++++++
 tests/fixtures/make_gguf_fixtures.py      |  67 ++++++++++++--
 8 files changed, 219 insertions(+), 30 deletions(-)

diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index 77134d183..286cbc3f1 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -44,7 +44,7 @@
   "accuracy_benchmarks": [
     {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.2,"ci95":[84.1,86.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"},
     {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.2,"ci95":[84.0,86.4],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":84.3,"ci95":[83.0,85.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"}
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.2,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"}
   ],
   "headline_benchmark": {
     "dataset": "fleurs",
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 24ae1944e..a82ce4f95 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -35,7 +35,7 @@ Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface
 | --- | --- | ---: | ---: |
 | F32          | [lang-id-voxlingua107-ecapa-F32.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F32.gguf) | 85 MB | 85.20% |
 | F16          | [lang-id-voxlingua107-ecapa-F16.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F16.gguf) | 45 MB | 85.20% |
-| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 84.30% |
+| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 86.30% |
 <!-- /catalog -->
 
 <!-- catalog:recipe -->
@@ -77,13 +77,15 @@ Agreement with the SpeechBrain reference (top-1, the same 12000 decisions:
 |---|---|---|---|
 | F32 | 12000 / 12000 | 6.5e-05 | 67.0 / 85.2 / 91.1 / 91.4 |
 | F16 | 11991 / 12000 | 0.098 | 67.0 / 85.2 / 91.1 / 91.5 |
-| Q8_0 | 11175 / 12000 | 7.5 | 65.4 / 84.3 / 90.9 / 91.4 |
+| Q8_0 | 11703 / 12000 | 2.8 | 67.2 / 86.3 / 92.0 / 92.3 |
 
 F16's nine flips are all near-ties (top-two margin under 0.025 logit). Q8_0
-also quantizes the activations of every large matmul inside ggml, which moves
-6.9% of decisions, almost all on uncertain 3-5 s crops: it costs 1.6 points
-open-set at 3 s, 0.9 at 5 s and nothing on full clips. langid.cpp measured the
-same on its own Q8_0 (93.19% agreement, 65.3 / 84.1 / 90.8 / 91.4).
+is a download format: transcribe.cpp widens its weights to F16 at load, so it
+computes like F16 (45 MB resident) and its accuracy matches F32 within the
+confidence interval. Its flips are the 8-bit weights alone, on decisions F32
+also finds uncertain. Computed in Q8_0, as langid.cpp does, ggml also rounds
+the activations to 8 bits; that moves 6.9% of decisions (93.1% agreement,
+65.4 / 84.3 / 90.9 / 91.4).
 
 ## Quick Start
 
@@ -171,9 +173,9 @@ dataset gate is the FLEURS decision parity above.
   answers under ~3 s of speech are weak.
 - **Scores at most the last 30 s** by default
   (`transcribe_langid_session_params::max_audio_ms`).
-- **Q8_0 on an ARM CPU is slower than langid.cpp** (about 1.7x): its CPU
-  weight-repacking buffer is not ported yet. F16 and the other backends are
-  at parity.
+- **Q8_0 runs at F16 speed.** langid.cpp computes Q8_0 with ggml's ARM
+  weight-repacking kernels, about 1.3x faster than F16 on an Apple CPU, at
+  the accuracy cost above; transcribe.cpp widens Q8_0 to F16 instead.
 
 ## Reproduction
 
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index 1ea35e49c..979b9bdb5 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -17,9 +17,11 @@ Acceptance: tensor parity on eight FLEURS clips (`validate.py`), and top-1
 decision parity with SpeechBrain on FLEURS (15 languages x 200 utterances x
 3 / 5 / 10 s / full = 12000 decisions): **12000 / 12000**, max abs logit
 difference 6.5e-05 (C++ F32, CPU). Shipped matrix: F32 + F16 + Q8_0;
-F16 agrees on 11991 / 12000 (near-ties only), Q8_0 on 11175 / 12000 (the
-activation quantization inside ggml's Q8_0 matmul; langid.cpp's Q8_0 measured
-the same 93.2%).
+F16 agrees on 11991 / 12000 (near-ties only), Q8_0 on 11703 / 12000 at F32
+accuracy. The loader widens Q8_0 weights to F16 (`widen_q8_0_weights` in
+`src/arch/ecapa_tdnn/model.cpp`): computed in Q8_0, ggml also rounds the
+activations to 8 bits and agreement drops to 11175 / 12000 (langid.cpp's
+Q8_0 measured the same 93.2%), 1.6 points open-set at 3 s.
 
 ## Identity
 
@@ -120,14 +122,14 @@ Benchmarks: `scripts/langid/bench.py` (`tools/transcribe-bench` is ASR-only).
   tables fail the load. H4 (no leak when label metadata is bad) is covered by
   `arch_ecapa_tdnn_bad_labels.gguf` in `transcribe_ecapa_tdnn_smoke`
   (`leaks --atExit`: 0 leaks).
-- Performance follow-up (not ported): langid.cpp's CPU weight-repacking
-  buffer. On the M4 Max CPU (10 s, Q8_0, median of three interleaved rounds)
-  it takes 76-81 ms to about 45-51 ms here and 36-39 ms in langid.cpp;
-  without it the two are at parity, as is F16 and Metal (14 ms). ARM only:
-  ggml has no Q8_0 repack on x86. `cls.out.weight` (107 rows) cannot be
-  repacked, so the candidate filter must check the row count or langid.cpp's
-  abandon path disables repacking for the whole file. The performance-core
-  thread default did not help here (8 threads ties or beats 12).
+- Not ported: langid.cpp's CPU weight-repacking buffer. It only speeds up
+  Q8_0 compute (ARM only; ggml has no Q8_0 repack on x86), which the Q8_0
+  widening replaces. Measured on the M4 Max CPU at 10 s, generic build
+  flags: Q8_0 widened 49 ms, Q8_0 computed 56 ms, Q8_0 + repack 36 ms;
+  Metal 14 ms for all. `cls.out.weight` (107 rows) cannot be repacked, so a
+  future repack path must filter on the row count or langid.cpp's abandon
+  path disables repacking for the whole file. The performance-core thread
+  default did not help (8 threads ties or beats 12).
 - `AUTO` placement is unchanged (GPU first). Metal is 5-10x faster than CPU
   on the M4 Max; CPU stays well under real time.
 - Q8_0 conv kernels stay F32 (the quantizer's Conv bucket is F32 in every
diff --git a/scripts/langid/ingest.py b/scripts/langid/ingest.py
index 78f8b0b6c..dd4acdb36 100755
--- a/scripts/langid/ingest.py
+++ b/scripts/langid/ingest.py
@@ -67,6 +67,7 @@
     ("da_dk", "da"),
     ("id_id", "id"),
     ("ms_my", "ms"),
+    ("it_it", "it"),
 ]
 
 CODE_TO_CONFIG = {code: config for config, code in FLEURS_LANGUAGES}
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index 26753a8b0..8d2bf9812 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -22,6 +22,7 @@
 #include "transcribe-loader.h"
 #include "transcribe-log.h"
 #include "transcribe-meta.h"
+#include "transcribe-path.h"
 #include "weights.h"
 
 #include <algorithm>
@@ -29,6 +30,8 @@
 #include <cmath>
 #include <cstddef>
 #include <cstdint>
+#include <fstream>
+#include <ios>
 #include <memory>
 #include <string>
 #include <vector>
@@ -110,6 +113,98 @@ transcribe_status read_labels(const gguf_context * gguf, LangidLabels & out) {
     return build_langid_labels(std::move(codes), std::move(names), aliases, kTag, out);
 }
 
+// Q8_0 is a download format here, not a compute format: every Q8_0 weight is
+// widened to F16 at load. ggml's Q8_0 matmuls also quantize the ACTIVATIONS
+// to 8 bits, and on FLEURS that, not the 8-bit weights, is what moves the
+// decision: computed in Q8_0 the published file agrees with SpeechBrain on
+// 93.1% of 12000 top-1 decisions; the same weights computed in F16 agree on
+// 97.5% and score at F32's accuracy. The cost is F16 memory for those weights
+// (45 MB instead of 27 MB resident) and F16 speed, which is no slower than
+// Q8_0 on any backend measured without the CPU repack path.
+//
+// Retype in place: the tensors come from the no_alloc gguf context and have
+// no data or views yet, so only type and strides change. Returns the count.
+int widen_q8_0_weights(ggml_context * ctx_meta) {
+    int n = 0;
+    for (ggml_tensor * t = ggml_get_first_tensor(ctx_meta); t != nullptr; t = ggml_get_next_tensor(ctx_meta, t)) {
+        if (t->type != GGML_TYPE_Q8_0) {
+            continue;
+        }
+        t->type  = GGML_TYPE_F16;
+        t->nb[0] = ggml_type_size(GGML_TYPE_F16);
+        for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+            t->nb[i] = t->nb[i - 1] * static_cast<size_t>(t->ne[i - 1]);
+        }
+        ++n;
+    }
+    return n;
+}
+
+// load_common::stream_tensor_data, plus the Q8_0 -> F16 widening: a tensor
+// whose file type is Q8_0 and whose bound type is F16 is dequantized with
+// ggml's own Q8_0 reference and rounded to F16 on the way in. Every other
+// tensor must be bound with its file type and is copied as-is.
+transcribe_status stream_weights(const std::string & path, const gguf_context * gguf, ggml_context * ctx_meta) {
+    std::ifstream fin(path_from_utf8(path), std::ios::binary);
+    if (!fin) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: failed to reopen %s for tensor data", kTag, path.c_str());
+        return TRANSCRIBE_ERR_GGUF;
+    }
+
+    const size_t             data_offset = gguf_get_data_offset(gguf);
+    const ggml_to_float_t    q8_to_f32   = ggml_get_type_traits(GGML_TYPE_Q8_0)->to_float;
+    std::vector<uint8_t>     staging;
+    std::vector<float>       f32;
+    std::vector<ggml_fp16_t> f16;
+
+    for (ggml_tensor * t = ggml_get_first_tensor(ctx_meta); t != nullptr; t = ggml_get_next_tensor(ctx_meta, t)) {
+        const int64_t idx = gguf_find_tensor(gguf, t->name);
+        if (idx < 0) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: tensor \"%s\" not in gguf data", kTag, t->name);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+        const ggml_type file_type = gguf_get_tensor_type(gguf, idx);
+        const bool      widen     = file_type == GGML_TYPE_Q8_0 && t->type == GGML_TYPE_F16;
+        if (file_type != t->type && !widen) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: tensor \"%s\" is %s in the file but bound as %s", kTag, t->name,
+                    ggml_type_name(file_type), ggml_type_name(t->type));
+            return TRANSCRIBE_ERR_GGUF;
+        }
+        const size_t nbytes = gguf_get_tensor_size(gguf, idx);
+        if (!widen && nbytes != ggml_nbytes(t)) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: tensor \"%s\" size mismatch", kTag, t->name);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+
+        fin.seekg(static_cast<std::streamoff>(data_offset) +
+                  static_cast<std::streamoff>(gguf_get_tensor_offset(gguf, idx)));
+        if (staging.size() < nbytes) {
+            staging.resize(nbytes);
+        }
+        fin.read(reinterpret_cast<char *>(staging.data()), static_cast<std::streamsize>(nbytes));
+        if (!fin) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: short read for tensor \"%s\" (%zu bytes)", kTag, t->name, nbytes);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+
+        if (!widen) {
+            ggml_backend_tensor_set(t, staging.data(), 0, nbytes);
+            continue;
+        }
+        const int64_t n = ggml_nelements(t);
+        if (nbytes != ggml_row_size(GGML_TYPE_Q8_0, n)) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: Q8_0 tensor \"%s\" size mismatch", kTag, t->name);
+            return TRANSCRIBE_ERR_GGUF;
+        }
+        f32.resize(static_cast<size_t>(n));
+        f16.resize(static_cast<size_t>(n));
+        q8_to_f32(staging.data(), f32.data(), n);
+        ggml_fp32_to_fp16_row(f32.data(), f16.data(), n);
+        ggml_backend_tensor_set(t, f16.data(), 0, ggml_nbytes(t));
+    }
+    return TRANSCRIBE_OK;
+}
+
 // ---------------------------------------------------------------------------
 // load
 // ---------------------------------------------------------------------------
@@ -190,6 +285,11 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
         return st;
     }
 
+    // After build_weights, which validated each weight's file type.
+    if (const int n_widened = widen_q8_0_weights(m->ctx_meta); n_widened > 0) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: %d Q8_0 weights widened to F16 at load", kTag, n_widened);
+    }
+
     const transcribe_backend_request backend_req = params != nullptr ? params->backend : TRANSCRIBE_BACKEND_AUTO;
     if (auto st = load_common::init_backends(backend_req, params != nullptr ? params->device : nullptr, kTag, m->plan);
         st != TRANSCRIBE_OK) {
@@ -205,7 +305,7 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
     }
     ggml_backend_buffer_set_usage(m->backend_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
 
-    if (auto st = load_common::stream_tensor_data(loader.path(), guard.ctx, m->ctx_meta, kTag); st != TRANSCRIBE_OK) {
+    if (auto st = stream_weights(loader.path(), guard.ctx, m->ctx_meta); st != TRANSCRIBE_OK) {
         return st;
     }
 
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 0f86fbeab..807ea3d09 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -105,7 +105,9 @@ set(TRANSCRIBE_FIXTURE_FILES
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_granite5_ctc_minimal.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_qwen3_asr_minimal.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_labels.gguf)
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_labels.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf)
 
 find_program(TRANSCRIBE_UV uv)
 
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index fe60f4970..0a2992b7f 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -232,6 +232,34 @@ void test_bad_labels() {
     CHECK(m == nullptr);
 }
 
+// Q8_0 weights are widened to F16 at load: a Q8_0 model must give exactly
+// the logits of the same model stored as F16 holding the dequantized values.
+void test_q8_0_widened_to_f16() {
+    transcribe_model * q8  = load_cpu("arch_ecapa_tdnn_q8_0.gguf");
+    transcribe_model * ref = load_cpu("arch_ecapa_tdnn_q8_0_as_f16.gguf");
+    CHECK(q8 != nullptr && ref != nullptr);
+    if (q8 == nullptr || ref == nullptr) {
+        transcribe_model_free(q8);
+        transcribe_model_free(ref);
+        return;
+    }
+    const std::vector<float>    pcm = noise(16000 * 2, 5);
+    transcribe_langid_session * sq  = open_session(q8, 2);
+    transcribe_langid_session * sr  = open_session(ref, 2);
+    CHECK(transcribe_langid_run(sq, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    CHECK(transcribe_langid_run(sr, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    const auto a = candidates_of(sq);
+    const auto b = candidates_of(sr);
+    CHECK(a.size() == 5 && a.size() == b.size());
+    for (size_t i = 0; i < a.size() && i < b.size(); ++i) {
+        CHECK(a[i].index == b[i].index && a[i].logit == b[i].logit);
+    }
+    transcribe_langid_session_free(sq);
+    transcribe_langid_session_free(sr);
+    transcribe_model_free(q8);
+    transcribe_model_free(ref);
+}
+
 }  // namespace
 
 int main() {
@@ -249,6 +277,7 @@ int main() {
     transcribe_model_free(m);
 
     test_bad_labels();
+    test_q8_0_widened_to_f16();
 
     if (g_failures != 0) {
         std::fprintf(stderr, "%d failure(s)\n", g_failures);
diff --git a/tests/fixtures/make_gguf_fixtures.py b/tests/fixtures/make_gguf_fixtures.py
index 8d92dfdb1..7fbc9a8f1 100644
--- a/tests/fixtures/make_gguf_fixtures.py
+++ b/tests/fixtures/make_gguf_fixtures.py
@@ -110,6 +110,7 @@
 
 from __future__ import annotations
 
+import math
 import struct
 import sys
 from pathlib import Path
@@ -132,11 +133,16 @@
 # ggml_type enum values used for tensor data. Pinned here so we are not
 # at the mercy of upstream renumbering — a mismatch would surface as a
 # loader test failure (the most useful possible signal).
-GGML_TYPE_F32 = 0
+GGML_TYPE_F32  = 0
+GGML_TYPE_F16  = 1
+GGML_TYPE_Q8_0 = 8
 
-# Bytes per element for each ggml_type we emit.
+# (bytes per block, elements per block) for each ggml_type we emit. Q8_0 is
+# 32 int8 quants behind one fp16 scale, blocked along ne[0].
 GGML_TYPE_SIZE = {
-    GGML_TYPE_F32: 4,
+    GGML_TYPE_F32:  (4, 1),
+    GGML_TYPE_F16:  (2, 1),
+    GGML_TYPE_Q8_0: (34, 32),
 }
 
 
@@ -262,7 +268,7 @@ def _string_kvs(pairs: list[tuple[str, str]]) -> list[bytes]:
 # A "Tensor" here is just a (name, ne, dtype, data_bytes) tuple. ne is
 # fast-to-slow dim order matching ggml_tensor::ne[]. data_bytes is the
 # raw little-endian bytes of the tensor's elements, length must equal
-# product(ne) * GGML_TYPE_SIZE[dtype].
+# product(ne) / block * block_bytes (GGML_TYPE_SIZE[dtype]).
 #
 # _build_full_gguf assembles header + KV section + tensor info section
 # + aligned tensor data blob in one pass. The layout follows
@@ -276,10 +282,13 @@ class Tensor:
     def __init__(
         self, name: str, ne: list[int], dtype: int, data: bytes
     ) -> None:
+        block_bytes, block = GGML_TYPE_SIZE[dtype]
+        if ne[0] % block != 0:
+            raise ValueError(f"tensor {name!r}: ne[0]={ne[0]} is not a multiple of {block}")
         nbytes = 1
         for d in ne:
             nbytes *= d
-        nbytes *= GGML_TYPE_SIZE[dtype]
+        nbytes = nbytes // block * block_bytes
         if len(data) != nbytes:
             raise ValueError(
                 f"tensor {name!r}: ne={ne} dtype={dtype} expects "
@@ -1482,7 +1491,44 @@ def tdnn(prefix: str, ic: int, oc: int, k: int, conv: bool) -> None:
     return out
 
 
-def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str]) -> bytes:
+def _q8_0_blocks(values: list[float]) -> tuple[bytes, list[float]]:
+    """ggml's quantize_row_q8_0_ref, plus the values its dequantize_row_q8_0
+    gives back (fp16 scale times int8, exact in float)."""
+    data = bytearray()
+    deq: list[float] = []
+    for b in range(0, len(values), 32):
+        x = values[b:b + 32]
+        d = max(abs(v) for v in x) / 127.0
+        inv = 1.0 / d if d else 0.0
+        q = [int(math.copysign(math.floor(abs(v * inv) + 0.5), v)) for v in x]
+        d16 = struct.pack("<e", d)
+        data += d16 + struct.pack("<32b", *q)
+        deq += [struct.unpack("<e", d16)[0] * qi for qi in q]
+    return bytes(data), deq
+
+
+def _ecapa_tdnn_q8_0(tensors: list[Tensor], as_f16: bool) -> list[Tensor]:
+    """Every 2-D weight the quantizer would make Q8_0 (ne[0] % 32 == 0), as
+    Q8_0, or (as_f16) as F16 holding exactly the dequantized Q8_0 values:
+    what the ecapa_tdnn loader must produce when it widens Q8_0 to F16."""
+    out = []
+    for t in tensors:
+        if len(t.ne) != 2 or not t.name.endswith(".weight") or t.ne[0] % 32 != 0 or t.name.startswith("frontend."):
+            out.append(t)
+            continue
+        values = list(struct.unpack(f"<{len(t.data) // 4}f", t.data))
+        q8, deq = _q8_0_blocks(values)
+        if as_f16:
+            out.append(Tensor(t.name, t.ne, GGML_TYPE_F16, struct.pack(f"<{len(deq)}e", *deq)))
+        else:
+            out.append(Tensor(t.name, t.ne, GGML_TYPE_Q8_0, q8))
+    return out
+
+
+def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "") -> bytes:
+    tensors = _ecapa_tdnn_tensors(len(codes))
+    if q8_0:
+        tensors = _ecapa_tdnn_q8_0(tensors, as_f16=(q8_0 == "as_f16"))
     return _build_full_gguf(
         GGUF_MAGIC,
         [
@@ -1490,7 +1536,7 @@ def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str]) ->
             _pack_kv_string("stt.variant", "ecapa-tdnn-toy"),
             *_ecapa_tdnn_hparams_kv(codes, names, aliases),
         ],
-        _ecapa_tdnn_tensors(len(codes)),
+        tensors,
     )
 
 
@@ -1915,6 +1961,13 @@ def emit_fixtures(out_dir: Path) -> None:
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"]))
     _write(out_dir / "arch_ecapa_tdnn_bad_labels.gguf",
            _ecapa_tdnn_gguf(["aa", "bb", "cc", "bb", "ee"], ECAPA_LABEL_NAMES, ["xx=aa"]))
+    # The toy model with its Q8_0-eligible weights in Q8_0, and the same
+    # weights as F16 holding the dequantized values. The loader widens Q8_0
+    # to F16, so the two must give bit-identical logits on the CPU.
+    _write(out_dir / "arch_ecapa_tdnn_q8_0.gguf",
+           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="q8_0"))
+    _write(out_dir / "arch_ecapa_tdnn_q8_0_as_f16.gguf",
+           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="as_f16"))
 
 
 def main(argv: list[str]) -> int:

From 5e7b5cd261c8922900a354920536131e673232ca Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Tue, 6 Oct 2026 14:30:26 +0800
Subject: [PATCH 06/38] kernel work

---
 catalog/_benchmark_profiles.json              |  50 ++
 catalog/_schema.json                          |  11 +
 catalog/lang-id-voxlingua107-ecapa.json       |   6 +-
 docs/langid.md                                |  17 +-
 docs/models/lang-id-voxlingua107-ecapa.md     | 146 ++--
 docs/porting/families/ecapa_tdnn.md           |  34 +-
 samples/README.md                             |   4 -
 samples/long-45s.wav                          | Bin 1673004 -> 0 bytes
 scripts/catalog/check.py                      |  76 +-
 scripts/catalog/db.py                         |  12 +-
 scripts/catalog/ingest_accuracy.py            | 129 +++-
 scripts/catalog/ingest_perf.py                |  29 +-
 scripts/catalog/profiles.py                   |  47 +-
 scripts/catalog/render.py                     |  50 +-
 scripts/catalog/test_db_mapping.py            |   3 +-
 .../dump_reference_ecapa_tdnn_speechbrain.py  |  16 +-
 scripts/ecapa_numpy_reference.py              |   6 +-
 scripts/envs/ecapa_tdnn/pyproject.toml        |   6 +-
 .../hf_cards/lang-id-voxlingua107-ecapa.yaml  |  10 +-
 scripts/langid/bench.py                       | 208 ++++--
 scripts/langid/compare.py                     |  53 +-
 scripts/langid/ingest.py                      |  22 +-
 scripts/langid/run.py                         |  12 +-
 scripts/langid/score.py                       | 469 ++++--------
 scripts/langid/selections.json                |  24 +-
 scripts/lib/ecapa_numpy.py                    |  12 +-
 scripts/lib/quant_policy.py                   |   5 +-
 src/CMakeLists.txt                            |  10 +
 src/arch/ecapa_tdnn/cpu_gemm.cpp              | 426 +++++++++++
 src/arch/ecapa_tdnn/cpu_gemm.h                | 113 +++
 src/arch/ecapa_tdnn/cpu_ops.cpp               | 669 ++++++++++++++++++
 src/arch/ecapa_tdnn/cpu_ops.h                 |  98 +++
 src/arch/ecapa_tdnn/ecapa_tdnn.h              |  24 +-
 src/arch/ecapa_tdnn/graph.cpp                 | 381 +++++++---
 src/arch/ecapa_tdnn/graph.h                   |  27 +-
 src/arch/ecapa_tdnn/mel.cpp                   |  32 +-
 src/arch/ecapa_tdnn/mel.h                     |   7 +
 src/arch/ecapa_tdnn/model.cpp                 | 189 ++++-
 src/arch/ecapa_tdnn/weights.cpp               |  12 -
 src/arch/ecapa_tdnn/weights.h                 |  16 +-
 tests/CMakeLists.txt                          |  13 +-
 tests/ecapa_tdnn_gemm_unit.cpp                | 282 ++++++++
 tests/ecapa_tdnn_real_smoke.cpp               |   2 +-
 tests/ecapa_tdnn_smoke.cpp                    |  61 +-
 tests/fixtures/make_gguf_fixtures.py          |  33 +-
 45 files changed, 3063 insertions(+), 789 deletions(-)
 delete mode 100644 samples/long-45s.wav
 create mode 100644 src/arch/ecapa_tdnn/cpu_gemm.cpp
 create mode 100644 src/arch/ecapa_tdnn/cpu_gemm.h
 create mode 100644 src/arch/ecapa_tdnn/cpu_ops.cpp
 create mode 100644 src/arch/ecapa_tdnn/cpu_ops.h
 create mode 100644 tests/ecapa_tdnn_gemm_unit.cpp

diff --git a/catalog/_benchmark_profiles.json b/catalog/_benchmark_profiles.json
index ae2fd44b7..9d84754e4 100644
--- a/catalog/_benchmark_profiles.json
+++ b/catalog/_benchmark_profiles.json
@@ -1,5 +1,8 @@
 {
   "default": "asr-publication-v2",
+  "roles": {
+    "langid": "langid-publication-v1"
+  },
   "profiles": {
     "asr-publication-v2": {
       "description": "The complete benchmark set published for transcription models. v2: accuracy on L40S; batch_size is the recommended setting for new runs, and a cell is satisfied at any batch size (the row records the one used).",
@@ -67,6 +70,53 @@
           "reason": "A variant that supports exactly one non-English language is benched on that language at the same two clip lengths as jfk/dots. English jfk/dots decode out of distribution on a single-language fine-tune and can loop until the position cap, and whether that happens differs between CPU and GPU, so the figure would not be comparable."
         }
       }
+    },
+    "langid-publication-v1": {
+      "role": "langid",
+      "description": "The benchmark set published for language ID (LANGID role) models. Accuracy: open-set top-1 over every label, the macro mean over the FLEURS test languages of scripts/langid/ingest.py, on the first crop_s seconds of each clip without silence trimming, C++ on CPU, every shipped quant; scored by scripts/langid/score.py from one scripts/langid/run.py sweep (all crops), which scripts/langid/compare.py also compares against the reference sweep for the row's agreement. Speed: scripts/langid/bench.py through the Python binding; a sample <clip>-<N>s is the first N seconds of samples/<clip>.wav.",
+      "accuracy": [
+        {
+          "dataset": "fleurs",
+          "split": "test",
+          "languages": "pooled",
+          "metric": "accuracy",
+          "quants": "all-downloads",
+          "batch_size": 1,
+          "timestamps": "none",
+          "backend": "cpu",
+          "crop_s": "5",
+          "trim": "none"
+        }
+      ],
+      "speed": {
+        "quants": "all-downloads",
+        "samples": [
+          "ru-long-10s",
+          "ru-long-30s"
+        ],
+        "iterations": 20,
+        "warmup": 3,
+        "targets": [
+          {
+            "machine": "m4-max",
+            "display": "Apple M4 Max",
+            "backends": [
+              "cpu",
+              "metal"
+            ]
+          },
+          {
+            "machine": "ryzen-4750u",
+            "display": "AMD Ryzen 7 PRO 4750U (Radeon RADV RENOIR)",
+            "backends": [
+              "cpu",
+              "vulkan"
+            ],
+            "cooldown_tctl_c": 55.0
+          }
+        ],
+        "pending": "The published cells were measured on samples/long-45s.wav, since replaced by ru-long.wav, before the CPU kernel work, and the Ryzen rig has none yet. Re-bench both rigs with scripts/langid/bench.py --profile, ingest, delete the long-45s rows, then remove this key to enforce the speed matrix."
+      }
     }
   }
 }
diff --git a/catalog/_schema.json b/catalog/_schema.json
index 0dabdbe26..40e0a42c1 100644
--- a/catalog/_schema.json
+++ b/catalog/_schema.json
@@ -398,6 +398,17 @@
           "mode": {
             "type": "string",
             "description": "Decoding mode for models that publish one metric under several modes (multitalker \"kernel\" / \"masked\" cpWER). Rows with a mode are a separate result set."
+          },
+          "agreement": {
+            "type": "object",
+            "additionalProperties": false,
+            "required": ["n_agree", "n"],
+            "properties": {
+              "n_agree": {"type": "integer", "minimum": 0},
+              "n": {"type": "integer", "minimum": 1},
+              "max_abs_logit_delta": {"type": ["number", "null"], "minimum": 0}
+            },
+            "description": "Language ID only: top-1 decisions of the run this row was scored from that match the reference implementation on the same audio (scripts/langid/compare.py, the port's ship gate), over every crop of the sweep rather than only the scored one, and the largest logit difference seen."
           }
         },
         "allOf": [
diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index 286cbc3f1..a1f831ccf 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -42,9 +42,9 @@
     {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693696}
   ],
   "accuracy_benchmarks": [
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.2,"ci95":[84.1,86.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.2,"ci95":[84.0,86.4],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.2,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05"}
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.2,"ci95":[84.1,86.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":6.5e-05}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.2,"ci95":[84.0,86.4],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05","agreement":{"n_agree":11991,"n":12000,"max_abs_logit_delta":0.098}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.2,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05","agreement":{"n_agree":11703,"n":12000,"max_abs_logit_delta":2.8}}
   ],
   "headline_benchmark": {
     "dataset": "fleurs",
diff --git a/docs/langid.md b/docs/langid.md
index b768e35d9..1e8d1c276 100644
--- a/docs/langid.md
+++ b/docs/langid.md
@@ -7,9 +7,13 @@ shipped family is ecapa_tdnn, SpeechBrain's VoxLingua107 ECAPA-TDNN
 
 Almost all of the difference between a good and a bad integration is in what
 you feed it and what you let it choose from. This page is the protocol, with
-the measured numbers behind each rule. They come from FLEURS `test`, 15
-languages x 200 utterances (`scripts/langid/`), scored with the SpeechBrain
-reference; the C++ engine makes the same decisions on the same audio.
+the measured numbers behind each rule. They are a snapshot of
+`scripts/langid/score.py --md` on the SpeechBrain reference sweeps of FLEURS
+`test`, 15 languages x 200 utterances, untrimmed and trimmed
+(`scripts/langid/`); the C++ engine makes the same decisions on the same
+audio. The shipped GGUFs' own accuracy and reference agreement are rendered
+from the catalog on the
+[model page](models/lang-id-voxlingua107-ecapa.md#accuracy).
 
 ## Minimal use
 
@@ -82,9 +86,7 @@ Accuracy over a user's selection, trimmed crops:
 
 `en+ru` is 99.5% at 3 s while open-set Russian is 54.0%: open-set errors go
 to neighbouring languages (`ru` -> `be`), and a selection without the
-neighbour removes them. A wider but bounded 40-language list lifts the
-untrimmed open-set mean from 67.0 / 85.2 / 91.1 to 77.4 / 92.3 / 96.6 at
-3 / 5 / 10 s.
+neighbour removes them.
 
 If the user has exactly one language enabled, don't call language ID at all.
 
@@ -105,9 +107,6 @@ operating point:
 | open (107) | 0.5 | 97.7 | 92.6 |
 | open (107) | 0.7 | 88.8 | 95.9 |
 | open (107) | 0.9 | 77.1 | 99.0 |
-| 40-language list | 0.5 | 98.7 | 97.3 |
-| 40-language list | 0.7 | 95.9 | 98.6 |
-| 40-language list | 0.9 | 92.0 | 99.4 |
 
 The median top probability is 0.999 when the model is right and 0.650 when it
 is wrong, so a 0.9 gate is a real signal.
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index a82ce4f95..831254528 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -43,11 +43,11 @@ Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measure
 <!-- /catalog -->
 
 <!-- catalog:prose field=wer.notes -->
-Open-set top-1 accuracy over all 107 labels, mean of 15 FLEURS languages
-(200 test utterances each), on the first 5 s of each clip without silence
-trimming. Accuracy is higher with an allowed set and with trimmed speech;
-see docs/langid.md. The C++ F32 port makes the same decision as the
-SpeechBrain reference on all 12000 FLEURS decisions (3 / 5 / 10 s / full).
+Open-set top-1 accuracy over all 107 labels, the mean over 15 FLEURS
+languages, on the first 5 s of each clip without silence trimming.
+Accuracy is higher with an allowed set and with trimmed speech; see
+docs/langid.md. Agreement with the SpeechBrain reference, per GGUF, is on
+the transcribe.cpp model page.
 <!-- /catalog -->
 
 Only near-reference tiers ship (F32 / F16 / Q8_0): the decision is an argmax
@@ -55,37 +55,56 @@ over 107 logits, and the k-quant tiers would save a few MB at most.
 
 ## Accuracy
 
-FLEURS `test`, 15 languages x 200 utterances, crops from the start of each
-clip (no silence trimming), C++ F32 on CPU. Top-1 accuracy, %:
+<!-- catalog:agreement -->
+| GGUF | Top-1 accuracy (95% CI) | Top-1 agreement with SpeechBrain | Max abs logit difference |
+| --- | ---: | ---: | ---: |
+| F32  | 85.20% (84.10-86.50) | 12000 / 12000 | 6.5e-05 |
+| F16  | 85.20% (84.00-86.40) | 11991 / 12000 | 0.098 |
+| Q8_0 | 86.30% (85.20-87.50) | 11703 / 12000 | 2.8 |
+
+Measured at transcribe.cpp `62522202` on 2026-10-05.
+<!-- /catalog -->
+
+Accuracy is the headline above: open set, first 5 s of each clip, no silence
+trimming, C++ on CPU. Agreement counts the scored run's top-1 decisions that
+match the SpeechBrain reference on the same audio, over every crop of the
+sweep (3 / 5 / 10 s / full). It is the ship gate for F32
+(`scripts/langid/compare.py`: every disagreement must be a reviewed near-tie)
+and the evidence for the quants. F16's flips are all near-ties (top-two
+margin under 0.025 logit). Q8_0 is a download format: transcribe.cpp widens
+its weights to F16 at load, so it computes like F16 (F16's memory) and its
+accuracy matches F32 within the confidence interval; its flips are the 8-bit
+weights alone, on decisions F32 also finds uncertain.
+
+### Snapshot: crops and decision spaces
+
+A snapshot of `scripts/langid/score.py --md` on the C++ sweeps behind the
+rows above (transcribe.cpp `62522202`, CPU; FLEURS `test`, 15 languages x 200
+utterances, crops from the start of each clip, no silence trimming). It is
+not rendered from the catalog; regenerate it with the commands under
+Reproduction. Top-1 accuracy, %.
+
+Open set (all 107 labels) at the other crops; 5 s is the catalog row above:
+
+| GGUF | 3 s | 10 s | full |
+|---|---|---|---|
+| F32 | 67.0 | 91.1 | 91.4 |
+| F16 | 67.0 | 91.1 | 91.5 |
+| Q8_0 | 67.2 | 92.0 | 92.3 |
+
+Restricted to a selection, F32:
 
 | decision space | 3 s | 5 s | 10 s | full |
 |---|---|---|---|---|
-| open set (all 107 labels) | 67.0 | 85.2 | 91.1 | 91.4 |
-| a 40-language dictation list | 77.4 | 92.3 | 96.6 | 96.6 |
 | en+ru | 98.8 | 100.0 | 100.0 | 100.0 |
 | en+fr+de+es | 88.6 | 94.1 | 98.9 | 99.4 |
 | cs+sk | 83.0 | 92.2 | 96.8 | 96.5 |
 | id+ms | 87.8 | 89.5 | 89.5 | 88.8 |
 
-Per-language tables, confidence / coverage and confusions:
-`uv run scripts/langid/score.py <run>.jsonl --md <run>.md`.
-
-Agreement with the SpeechBrain reference (top-1, the same 12000 decisions:
-3000 clips x 3 / 5 / 10 s / full):
-
-| GGUF | agreement | max abs logit difference | open set 3 / 5 / 10 s / full |
-|---|---|---|---|
-| F32 | 12000 / 12000 | 6.5e-05 | 67.0 / 85.2 / 91.1 / 91.4 |
-| F16 | 11991 / 12000 | 0.098 | 67.0 / 85.2 / 91.1 / 91.5 |
-| Q8_0 | 11703 / 12000 | 2.8 | 67.2 / 86.3 / 92.0 / 92.3 |
-
-F16's nine flips are all near-ties (top-two margin under 0.025 logit). Q8_0
-is a download format: transcribe.cpp widens its weights to F16 at load, so it
-computes like F16 (45 MB resident) and its accuracy matches F32 within the
-confidence interval. Its flips are the 8-bit weights alone, on decisions F32
-also finds uncertain. Computed in Q8_0, as langid.cpp does, ggml also rounds
-the activations to 8 bits; that moves 6.9% of decisions (93.1% agreement,
-65.4 / 84.3 / 90.9 / 91.4).
+Computed in Q8_0 instead, as langid.cpp does, ggml also rounds the
+activations to 8 bits; that moves 6.9% of decisions (93.1% agreement, open
+set 65.4 / 84.3 / 90.9 / 91.4). Per-language tables, confidence / coverage
+and confusions: `uv run scripts/langid/score.py <run>.jsonl --md <run>.md`.
 
 ## Quick Start
 
@@ -115,42 +134,49 @@ accepted as aliases). Match them against your ASR model's
 
 ## Performance
 
+Measured with `scripts/langid/bench.py` through the Python binding
+(`tools/transcribe-bench` is ASR-only), on the first N seconds of one clip;
+CPU uses the library default thread count.
+
 ### Apple M4 Max
 
-Compute latency (mel + encode), median of 20 warm runs, transcribe.cpp
-`62522202`, `scripts/langid/bench.py`. CPU uses the library default thread
-count (8).
-
-| Backend | Audio | F32 | F16 | Q8_0 |
-|---|---|---:|---:|---:|
-| Metal | 3 s | 6.6 ms | 6.4 ms | 6.1 ms |
-| Metal | 5 s | 9.4 ms | 9.5 ms | 9.3 ms |
-| Metal | 10 s | 14.3 ms | 14.1 ms | 14.2 ms |
-| Metal | 30 s | 37.1 ms | 35.7 ms | 36.2 ms |
-| CPU | 3 s | 40.1 ms | 25.1 ms | 22.9 ms |
-| CPU | 5 s | 70.1 ms | 41.4 ms | 37.7 ms |
-| CPU | 10 s | 147.1 ms | 82.5 ms | 74.9 ms |
-| CPU | 30 s | 450.9 ms | 251.5 ms | 229.5 ms |
+<!-- catalog:perf machine=m4-max dp_ms=1 -->
+Compute latency (mel + encode), speedup over realtime in parentheses.
+
+| Backend | Sample               |               F32 |                F16 |               Q8_0 |
+| ------- | -------------------- | ----------------: | -----------------: | -----------------: |
+| Metal   | long-45s-10s (10.0s) | 14.3 ms (699.79×) |  14.1 ms (709.72×) |  14.2 ms (703.23×) |
+| Metal   | long-45s-30s (30.0s) | 37.1 ms (808.41×) |  35.6 ms (841.51×) |  36.2 ms (828.04×) |
+| CPU     | long-45s-10s (10.0s) | 147.1 ms (67.97×) |  82.5 ms (121.26×) |  74.9 ms (133.58×) |
+| CPU     | long-45s-30s (30.0s) | 450.9 ms (66.53×) | 251.5 ms (119.27×) | 229.5 ms (130.70×) |
+
+Apple M4 Max: transcribe.cpp `62522202` on 2026-10-05.
+<!-- /catalog -->
 
 `AUTO` resolves like every other family (the GPU when there is one). Metal is
-5-10x faster here; CPU is still well under real time and avoids contending
-with an ASR model on the GPU, so load with `TRANSCRIBE_BACKEND_CPU` when that
-matters. Not yet measured on the AMD Ryzen 7 PRO 4750U publication rig, or on
-Vulkan / CUDA.
+several times faster here; CPU is still well under real time and avoids
+contending with an ASR model on the GPU, so load with
+`TRANSCRIBE_BACKEND_CPU` when that matters.
+
+The `long-45s` cells are medians of 20 warm runs on `samples/long-45s.wav`,
+which has since been replaced by `samples/ru-long.wav`, and the CPU ones
+predate the current CPU kernels. Both publication rigs are due a re-bench
+(the AMD Ryzen 7 PRO 4750U has no published cells yet):
 
 ```bash
-uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py \
-  --library build-shared/src/libtranscribe.dylib \
-  --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-{F32,F16,Q8_0}.gguf \
-  --backends cpu,metal --durations 3,5,10,30
+uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py --profile \
+  --library build-shared/src/libtranscribe.dylib
+uv run scripts/catalog/ingest_perf.py
+uv run scripts/catalog/render.py
 ```
 
 ## Numerical Validation
 
 transcribe.cpp is validated tensor-by-tensor against SpeechBrain 1.1.1 on
-eight committed FLEURS clips (`samples/fleurs-*.wav`): 17 stage tensors
-(front end, every encoder block, pooling, embedding, logits) within family
-tolerance, and the top-1 label equal to the reference on every clip. The
+eight committed FLEURS clips (`samples/fleurs-*.wav`): every stage tensor
+gated in `tests/tolerances/ecapa_tdnn.json` (front end, every encoder block,
+pooling, embedding, logits) within tolerance on the calibration host (Apple
+Silicon), and the top-1 label equal to the reference on every clip. The
 dataset gate is the FLEURS decision parity above.
 
 | Field | Value |
@@ -159,7 +185,7 @@ dataset gate is the FLEURS decision parity above.
 | Dump script | `scripts/dump_reference_ecapa_tdnn_speechbrain.py` |
 | Manifest | `tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json` |
 | Command | `uv run scripts/validate.py all --family ecapa_tdnn` |
-| Dataset gate | `scripts/langid/compare.py` (FLEURS, 12000 decisions) |
+| Dataset gate | `scripts/langid/compare.py` (FLEURS, every crop; see Accuracy) |
 
 ## Known Limitations
 
@@ -207,6 +233,10 @@ uv run scripts/validate.py all --family ecapa_tdnn
 
 ### Accuracy acceptance
 
+One reference sweep, then one C++ sweep per shipped GGUF (shown for F32;
+repeat with F16 and Q8_0). `compare.py` is the gate; `score.py --json` and
+`compare.py --json` are what `ingest_accuracy.py` reads, named after the GGUF.
+
 ```bash
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine speechbrain \
@@ -219,5 +249,11 @@ uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp \
   --manifest samples/langid/fleurs-*.manifest.jsonl --crops 3,5,10,full \
   --out reports/langid/cpp-f32-untrimmed.jsonl
 uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl \
-  reports/langid/cpp-f32-untrimmed.jsonl
+  reports/langid/cpp-f32-untrimmed.jsonl \
+  --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
+uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \
+  --md reports/langid/cpp-f32-untrimmed.md \
+  --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
+uv run scripts/catalog/ingest_accuracy.py --models lang-id-voxlingua107-ecapa
+uv run scripts/catalog/render.py
 ```
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index 979b9bdb5..4caa066d9 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -15,13 +15,14 @@ top-k (`docs/langid.md`).
 
 Acceptance: tensor parity on eight FLEURS clips (`validate.py`), and top-1
 decision parity with SpeechBrain on FLEURS (15 languages x 200 utterances x
-3 / 5 / 10 s / full = 12000 decisions): **12000 / 12000**, max abs logit
-difference 6.5e-05 (C++ F32, CPU). Shipped matrix: F32 + F16 + Q8_0;
-F16 agrees on 11991 / 12000 (near-ties only), Q8_0 on 11703 / 12000 at F32
-accuracy. The loader widens Q8_0 weights to F16 (`widen_q8_0_weights` in
+3 / 5 / 10 s / full crops, C++ F32 on CPU), where every disagreement must be
+a reviewed near-tie. Shipped matrix: F32 + F16 + Q8_0. Each GGUF's agreement
+and accuracy live in the catalog and are rendered on
+[the model page](../../models/lang-id-voxlingua107-ecapa.md#accuracy). The
+loader widens Q8_0 weights to F16 (`widen_q8_0_weights` in
 `src/arch/ecapa_tdnn/model.cpp`): computed in Q8_0, ggml also rounds the
-activations to 8 bits and agreement drops to 11175 / 12000 (langid.cpp's
-Q8_0 measured the same 93.2%), 1.6 points open-set at 3 s.
+activations to 8 bits and agreement drops by several points (the model
+page's accuracy snapshot has the figures).
 
 ## Identity
 
@@ -88,15 +89,24 @@ Accuracy and decision parity (FLEURS from the HF cache):
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine speechbrain ... --out reports/langid/ref-speechbrain-untrimmed.jsonl
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp --library build-shared/src/libtranscribe.dylib ... --out reports/langid/cpp-f32-untrimmed.jsonl
-uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl reports/langid/cpp-f32-untrimmed.jsonl
-uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl --md reports/langid/cpp-f32-untrimmed.md
+uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl reports/langid/cpp-f32-untrimmed.jsonl \
+  --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
+uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl --md reports/langid/cpp-f32-untrimmed.md \
+  --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
+uv run scripts/catalog/ingest_accuracy.py --models lang-id-voxlingua107-ecapa
 ```
 
+The catalog rows follow the `langid-publication-v1` profile
+(`catalog/_benchmark_profiles.json`): the open-set mean on the 5 s untrimmed
+crop of each shipped GGUF's sweep, with that sweep's agreement.
+
 `compare.py` fails on any disagreement that is not on a reviewed near-tie
 list (`--near-ties`), and refuses runs whose labels, recipe, manifest hashes
 or checkpoint revision differ.
 
-Benchmarks: `scripts/langid/bench.py` (`tools/transcribe-bench` is ASR-only).
+Benchmarks: `scripts/langid/bench.py --profile` (`tools/transcribe-bench` is
+ASR-only) writes bench-driver reports under `reports/perf/`, which
+`scripts/catalog/ingest_perf.py` folds into the catalog.
 
 ## Capability Validation
 
@@ -104,7 +114,7 @@ Benchmarks: `scripts/langid/bench.py` (`tools/transcribe-bench` is ASR-only).
 |------------|------|----------------|---------------------|--------|--------|
 | Language ID | open set | `build/bin/transcribe-cli -m models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf samples/fleurs-ja.wav` | `language: ja` | MUST PASS | PASS |
 | Language ID | allowed set | `... --allow en,de samples/fleurs-de.wav` | `language: de`, 2 candidates | MUST PASS | PASS |
-| Crop | 30 s window | `transcribe_ecapa_tdnn_real_smoke` (`long-45s.wav`) | `audio_ms == 30000` | MUST PASS | PASS |
+| Crop | 30 s window | `transcribe_ecapa_tdnn_real_smoke` (`ru-long.wav`) | `audio_ms == 30000` | MUST PASS | PASS |
 | Minimum length | 500 ms | `transcribe_ecapa_tdnn_smoke`, `transcribe_langid_dispatch_unit` | `INPUT_TOO_SHORT` below 500 ms | MUST PASS | PASS |
 | Transcribe / translate / timestamps / streaming | n/a | `transcribe_session_init` | `UNSUPPORTED_ROLE` | OUT OF SCOPE — not an ASR model | SKIP — not exposed by runtime |
 | Batch (offline) | n/a | `transcribe-cli --batch` | refused: ASR-only | OUT OF SCOPE — no batch entry points for new roles in v1 | ACCEPTED GAP — one clip per call |
@@ -130,8 +140,8 @@ Benchmarks: `scripts/langid/bench.py` (`tools/transcribe-bench` is ASR-only).
   future repack path must filter on the row count or langid.cpp's abandon
   path disables repacking for the whole file. The performance-core thread
   default did not help (8 threads ties or beats 12).
-- `AUTO` placement is unchanged (GPU first). Metal is 5-10x faster than CPU
-  on the M4 Max; CPU stays well under real time.
+- `AUTO` placement is unchanged (GPU first). Metal against CPU latency is in
+  the model page's Performance section.
 - Q8_0 conv kernels stay F32 (the quantizer's Conv bucket is F32 in every
   preset), so the Q8_0 file is 26.7 MB against langid.cpp's 24.1 MB, and
   `cls.out.weight` is Q8_0 where langid.cpp kept it F16.
diff --git a/samples/README.md b/samples/README.md
index c76bea08e..ca60cd5a4 100644
--- a/samples/README.md
+++ b/samples/README.md
@@ -65,10 +65,6 @@ split, licensed **CC-BY-4.0**.
 | `fleurs-ru.wav` | 7.92 s | `ru_ru` | 1917 |
 | `fleurs-id.wav` | 8.58 s | `id_id` | 1909 |
 | `short-800ms.wav` | 0.80 s | `en_us` | 1904 (first 800 ms) |
-| `long-45s.wav` | 52.28 s | `en_us` | 1904, 1675, 1950, 1728, 1972, 1938 (concatenated) |
-
-`long-45s.wav` is longer than its name says; it only has to exceed the 30 s
-language ID window, and the name is kept so it matches langid.cpp's records.
 
 ## Everything else
 
diff --git a/samples/long-45s.wav b/samples/long-45s.wav
deleted file mode 100644
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GIT binary patch
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diff --git a/scripts/catalog/check.py b/scripts/catalog/check.py
index a0facf246..82c59b287 100755
--- a/scripts/catalog/check.py
+++ b/scripts/catalog/check.py
@@ -74,11 +74,37 @@ def integrity_pass(records: dict) -> int:
         for r in rec.get("speed_benchmarks", []):
             if r.get("machine"):
                 machines[r["machine"]].add(name)
+        # A number edited by hand rarely keeps its siblings in step. Reports
+        # are not checked in, so these self-consistency checks are what
+        # catch an edit that no ingest produced.
+        drifted = [r for r in rec.get("speed_benchmarks", [])
+                   if r.get("total_ms") and not xrt_consistent(r)]
+        outside = [r for r in rec.get("accuracy_benchmarks", [])
+                   if None not in (r.get("ci95") or [None])
+                   and not r["ci95"][0] - 0.01 <= common.row_pct(r) <= r["ci95"][1] + 0.01]
+        disagree = [r for r in rec.get("accuracy_benchmarks", [])
+                    if r.get("agreement") and r["agreement"]["n_agree"] > r["agreement"]["n"]]
+        for rows, what in ((drifted, "speed row(s) whose xrt_compute is not "
+                                     "sample_duration_s / total_ms"),
+                           (outside, "accuracy row(s) whose value lies outside its ci95"),
+                           (disagree, "accuracy row(s) with agreement n_agree > n")):
+            if rows:
+                bad += 1
+                print(f"  FAIL {name}: {len(rows)} {what}")
     print(f"integrity  {len(records) - bad}/{len(records)} clean; "
           f"{len(machines)} machine slug(s): {', '.join(sorted(machines))}")
     return bad
 
 
+def xrt_consistent(row: dict) -> bool:
+    """xrt_compute matches sample_duration_s / total_ms, allowing for
+    total_ms stored to 0.1 ms and xRT to 0.01."""
+    duration, total = row["sample_duration_s"], row["total_ms"]
+    low = duration / ((total + 0.05) / 1000) - 0.005
+    high = duration / (max(total - 0.05, 1e-6) / 1000) + 0.005
+    return low <= row["xrt_compute"] <= high
+
+
 def pairing_pass(records: dict, selected: bool = False) -> int:
     """Catalog records and card specs pair exactly; docs may be shared.
 
@@ -116,19 +142,26 @@ def pairing_pass(records: dict, selected: bool = False) -> int:
 
 
 def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> int:
-    """Check publication matrices, including explicit legacy accuracy rows."""
+    """Check publication matrices, including explicit legacy accuracy rows.
+
+    Each record is held to its role's profile (language ID has its own). A
+    profile whose speed matrix declares `pending` reports that half as TODO
+    even when enforcing, until the bench it names lands."""
     try:
-        resolved_id, profile = profiles.load_profile(profile_id)
+        profiles.load_profile(profile_id)
     except (OSError, ValueError, json.JSONDecodeError) as exc:
         print(f"publication FAIL: {exc}")
         return 1
 
     problems = 0
-    totals = collections.Counter()
+    totals: dict[str, collections.Counter] = collections.defaultdict(collections.Counter)
     for name, record in records.items():
+        resolved_id, profile = profiles.profile_for(record, profile_id)
         # An ASR publication profile does not apply to standalone diarizers.
-        if not record.get("capabilities", {}).get("transcribe", {}).get("supported"):
+        if not profile.get("role") and \
+                not record.get("capabilities", {}).get("transcribe", {}).get("supported"):
             continue
+        pending = profile["speed"].get("pending")
         accuracy_raw = profiles.expected_accuracy(record, profile)
         speed_raw = profiles.expected_speed(record, profile)
         accuracy = profiles.apply_exceptions(record, "accuracy", accuracy_raw)
@@ -177,7 +210,7 @@ def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> in
                     # Preserve pre-profile rows that were already published,
                     # even when their quant was not selected by today's
                     # publication matrix. They are archive data, not drift.
-                    totals["accuracy_archived"] += 1
+                    totals[resolved_id]["accuracy_archived"] += 1
                 else:
                     accuracy_extra.add(key)
                 continue
@@ -193,9 +226,9 @@ def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> in
         per_model["accuracy_extra"] = len(accuracy_extra)
         per_model["accuracy_duplicate"] = sum(
             count - 1 for count in accuracy_counts.values() if count > 1)
-        totals["accuracy_required"] += len(expected_keys)
+        totals[resolved_id]["accuracy_required"] += len(expected_keys)
         for suffix in ("missing", "invalid", "extra", "duplicate"):
-            totals[f"accuracy_{suffix}"] += per_model[f"accuracy_{suffix}"]
+            totals[resolved_id][f"accuracy_{suffix}"] += per_model[f"accuracy_{suffix}"]
 
         # Both published samples are required for each quant selected by the
         # profile on every machine/backend target.
@@ -215,21 +248,28 @@ def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> in
         per_model["speed_extra"] = len(speed_actual - speed_expected)
         per_model["speed_duplicate"] = sum(
             count - 1 for count in speed_counts.values() if count > 1)
-        totals["speed_required"] += len(speed_expected)
+        totals[resolved_id]["speed_required"] += len(speed_expected)
         for suffix in ("missing", "invalid", "extra", "duplicate"):
-            totals[f"speed_{suffix}"] += per_model[f"speed_{suffix}"]
+            totals[resolved_id][f"speed_{suffix}"] += per_model[f"speed_{suffix}"]
 
-        count = sum(per_model.values())
-        if count:
-            problems += count
-            details = ", ".join(f"{key}={value}" for key, value in per_model.items() if value)
+        held = {key: value for key, value in per_model.items()
+                if value and not (pending and key.startswith("speed_"))}
+        waiting = {key: value for key, value in per_model.items()
+                   if value and key not in held}
+        if held:
+            problems += sum(held.values())
+            details = ", ".join(f"{key}={value}" for key, value in held.items())
             print(f"  {'FAIL' if enforce else 'TODO'} {name}: {details}")
+        if waiting:
+            details = ", ".join(f"{key}={value}" for key, value in waiting.items())
+            print(f"  TODO {name}: {details} (pending: {pending})")
 
-    print(f"publication {resolved_id}: accuracy {totals['accuracy_required']} required, "
-          f"{totals['accuracy_missing']} missing, {totals['accuracy_invalid']} invalid, "
-          f"{totals['accuracy_extra']} extra, {totals['accuracy_archived']} archived legacy; speed "
-          f"{totals['speed_required']} required, {totals['speed_missing']} missing, "
-          f"{totals['speed_invalid']} invalid, {totals['speed_extra']} extra")
+    for resolved_id, total in sorted(totals.items()):
+        print(f"publication {resolved_id}: accuracy {total['accuracy_required']} required, "
+              f"{total['accuracy_missing']} missing, {total['accuracy_invalid']} invalid, "
+              f"{total['accuracy_extra']} extra, {total['accuracy_archived']} archived legacy; speed "
+              f"{total['speed_required']} required, {total['speed_missing']} missing, "
+              f"{total['speed_invalid']} invalid, {total['speed_extra']} extra")
     if problems and not enforce:
         print("            audit only; pass --publication-profile to enforce this gate")
     return problems if enforce else 0
diff --git a/scripts/catalog/db.py b/scripts/catalog/db.py
index 6e4d83576..532de458c 100755
--- a/scripts/catalog/db.py
+++ b/scripts/catalog/db.py
@@ -118,7 +118,10 @@
     utts_over_50pct INTEGER,
     publication_profile TEXT,
     scoring TEXT,
-    mode TEXT
+    mode TEXT,
+    agreement_n_agree INTEGER,
+    agreement_n INTEGER,
+    agreement_max_abs_logit_delta REAL
 );
 CREATE UNIQUE INDEX accuracy_identity ON accuracy(
     dataset_id, variant, quant, metric,
@@ -238,7 +241,7 @@ def build(records: dict[str, dict], out: pathlib.Path) -> dict[str, int]:
                 (variant, item["quant"], item["filename"], item["size_bytes"])
                 for item in record.get("downloads", [])])
             con.executemany(
-                "INSERT INTO accuracy VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", [
+                "INSERT INTO accuracy VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", [
                     (dataset_id(row), variant, row["quant"], row["metric"],
                      row.get("language_hint"), row.get("backend"), row.get("err_pct"),
                      row.get("acc_pct"),
@@ -250,7 +253,10 @@ def build(records: dict[str, dict], out: pathlib.Path) -> dict[str, int]:
                      (row.get("errors") or {}).get("del"),
                      (row.get("errors") or {}).get("ins"), row.get("empty_hyp"),
                      row.get("utts_over_50pct"), row.get("publication_profile"),
-                     row.get("scoring"), row.get("mode"))
+                     row.get("scoring"), row.get("mode"),
+                     (row.get("agreement") or {}).get("n_agree"),
+                     (row.get("agreement") or {}).get("n"),
+                     (row.get("agreement") or {}).get("max_abs_logit_delta"))
                     for row in record.get("accuracy_benchmarks", [])])
             con.executemany(
                 "INSERT INTO speed VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", [
diff --git a/scripts/catalog/ingest_accuracy.py b/scripts/catalog/ingest_accuracy.py
index 85a85691c..990c76edb 100755
--- a/scripts/catalog/ingest_accuracy.py
+++ b/scripts/catalog/ingest_accuracy.py
@@ -10,6 +10,13 @@
     for f in reports/wer/*.jsonl; do uv run scripts/wer/score.py "$f"; done
     uv run scripts/catalog/ingest_accuracy.py --dry-run
     uv run scripts/catalog/ingest_accuracy.py
+
+Language ID records follow their own profile (catalog/_benchmark_profiles.json
+`roles`). Their scores come from scripts/langid/score.py --json and
+compare.py --json under reports/langid/, named after the GGUF like a WER
+score (`<gguf stem>.fleurs-mul.score.json`, `.agreement.json`); the profile
+names the crop and trim, and the importer checks the sweep's own recipe
+against them instead of a stamp.
 """
 from __future__ import annotations
 
@@ -23,6 +30,8 @@
 import profiles  # noqa: E402
 
 REPORTS = common.REPO / "reports" / "wer"
+LANGID_REPORTS = common.REPO / "reports" / "langid"
+LANGID_SCORE = "transcribe-langid-score-v1"
 
 
 def score_path(record: dict, cell: dict, reports: pathlib.Path) -> pathlib.Path:
@@ -67,17 +76,68 @@ def row_from_score(cell: dict, score: dict, profile_id: str) -> dict:
     }
 
 
+def langid_row(record: dict, cell: dict, score: dict, agreement: dict | None,
+               profile_id: str) -> tuple[dict | None, list[str]]:
+    """The catalog row for one language ID cell, or why the score cannot be
+    it. The sweep's header travels in the score, so the recipe is checked
+    here rather than trusted from a stamp."""
+    filename = next(item["filename"] for item in record["downloads"]
+                    if item["quant"] == cell["quant"])
+    crop = (score.get("crops") or {}).get(cell["crop_s"])
+    reasons = []
+    if score.get("schema") != LANGID_SCORE:
+        reasons.append(f"schema={score.get('schema')!r}")
+    if score.get("engine") != "cpp":
+        reasons.append(f"engine={score.get('engine')!r}")
+    if pathlib.PurePath(str(score.get("model", ""))).name != filename:
+        reasons.append(f"model={score.get('model')!r}")
+    for field in ("dataset", "split", "language", "backend", "trim"):
+        if score.get(field) != cell.get(field):
+            reasons.append(f"{field}={score.get(field)!r}")
+    if crop is None:
+        reasons.append(f"no {cell['crop_s']} s crop")
+    if not score.get("engine_sha"):
+        reasons.append("engine_sha is empty")
+    if agreement is not None and agreement.get("run") != score.get("run"):
+        reasons.append(f"agreement is for {agreement.get('run')!r}, not {score.get('run')!r}")
+    if reasons:
+        return None, reasons
+    row = {
+        "dataset": cell["dataset"],
+        "split": cell["split"],
+        "language": cell["language"],
+        "backend": cell["backend"],
+        "quant": cell["quant"],
+        "metric": cell["metric"],
+        "acc_pct": crop["acc_pct"],
+        "ci95": crop["ci95"],
+        "n_utts": crop["n"],
+        "batch_size": cell["batch_size"],
+        "timestamps": cell["timestamps"],
+        "engine_sha": score["engine_sha"],
+        "publication_profile": profile_id,
+        "measured_on": (score.get("created") or "")[:10] or None,
+    }
+    if agreement is not None:
+        row["agreement"] = {
+            "n_agree": agreement["n_agree"],
+            "n": agreement["n"],
+            "max_abs_logit_delta": float(f"{agreement['max_abs_logit_delta']:.2g}"),
+        }
+    return row, []
+
+
 def main() -> int:
     parser = argparse.ArgumentParser()
-    parser.add_argument("--reports", default=str(REPORTS))
+    parser.add_argument("--reports", default=None,
+                        help="score directory (default: reports/wer, and "
+                             "reports/langid for language ID records)")
     parser.add_argument("--profile", default=None)
     parser.add_argument("--models", default="",
                         help="comma-separated variants (default: all)")
     parser.add_argument("--dry-run", action="store_true")
     args = parser.parse_args()
 
-    profile_id, profile = profiles.load_profile(args.profile)
-    reports = pathlib.Path(args.reports)
     selected = {item.strip() for item in args.models.split(",") if item.strip()}
     records = common.load_records()
     unknown = selected - records.keys()
@@ -86,41 +146,58 @@ def main() -> int:
         return 2
 
     added = replaced = rejected = missing = unstamped = 0
+    used: set[str] = set()
     for variant, record in records.items():
         if selected and variant not in selected:
             continue
+        profile_id, profile = profiles.profile_for(record, args.profile)
+        langid = profile.get("role") == "langid"
+        reports = pathlib.Path(args.reports) if args.reports else (
+            LANGID_REPORTS if langid else REPORTS)
         path = common.CATALOG_DIR / f"{variant}.json"
         rows = record.get("accuracy_benchmarks", [])
         changed = False
         expected = profiles.apply_exceptions(
             record, "accuracy", profiles.expected_accuracy(record, profile))
+        if expected:
+            used.add(profile_id)
         for cell in expected:
             source_path = score_path(record, cell, reports)
             if not source_path.exists():
                 missing += 1
                 continue
             score = json.loads(source_path.read_text())
-            recipe = score.get("recipe") or {}
-            covered = any(profiles.profile_key(row) == profiles.profile_key(cell)
-                          or (row.get("measurement_provenance") == "legacy-published"
-                              and profiles.accuracy_core_key(row) == profiles.accuracy_core_key(cell))
-                          for row in rows)
-            if not recipe.get("publication_profile") and covered:
-                # A score from before profile stamping, for a cell the catalog
-                # already publishes: superseded history, not a problem.
-                unstamped += 1
-                continue
-            reasons = []
-            if recipe.get("publication_profile") != profile_id:
-                reasons.append(f"profile={recipe.get('publication_profile')!r}")
-            if score.get("metric") != cell["metric"]:
-                reasons.append(f"metric={score.get('metric')!r}")
-            if score.get("timestamps") != cell["timestamps"]:
-                reasons.append(f"timestamps={score.get('timestamps')!r}")
-            if recipe.get("backend") != cell["backend"]:
-                reasons.append(f"backend={recipe.get('backend')!r}")
-            if not score.get("engine_sha"):
-                reasons.append("engine_sha is empty")
+            if langid:
+                agreement_path = source_path.with_name(
+                    source_path.name.replace(".score.json", ".agreement.json"))
+                agreement = (json.loads(agreement_path.read_text())
+                             if agreement_path.exists() else None)
+                new_row, reasons = langid_row(record, cell, score, agreement, profile_id)
+                if not reasons and agreement is None:
+                    print(f"  note {source_path.name}: no {agreement_path.name}; "
+                          f"the row carries no reference agreement")
+            else:
+                new_row, reasons = None, []
+                recipe = score.get("recipe") or {}
+                covered = any(profiles.profile_key(row) == profiles.profile_key(cell)
+                              or (row.get("measurement_provenance") == "legacy-published"
+                                  and profiles.accuracy_core_key(row) == profiles.accuracy_core_key(cell))
+                              for row in rows)
+                if not recipe.get("publication_profile") and covered:
+                    # A score from before profile stamping, for a cell the catalog
+                    # already publishes: superseded history, not a problem.
+                    unstamped += 1
+                    continue
+                if recipe.get("publication_profile") != profile_id:
+                    reasons.append(f"profile={recipe.get('publication_profile')!r}")
+                if score.get("metric") != cell["metric"]:
+                    reasons.append(f"metric={score.get('metric')!r}")
+                if score.get("timestamps") != cell["timestamps"]:
+                    reasons.append(f"timestamps={score.get('timestamps')!r}")
+                if recipe.get("backend") != cell["backend"]:
+                    reasons.append(f"backend={recipe.get('backend')!r}")
+                if not score.get("engine_sha"):
+                    reasons.append("engine_sha is empty")
             if reasons:
                 rejected += 1
                 print(f"  reject {source_path.name}: {', '.join(reasons)}")
@@ -138,7 +215,7 @@ def main() -> int:
                 indices = [index for index, row in enumerate(rows)
                            if row.get("measurement_provenance") == "legacy-published"
                            and profiles.accuracy_core_key(row) == core]
-            new_row = row_from_score(cell, score, profile_id)
+            new_row = new_row or row_from_score(cell, score, profile_id)
             if indices:
                 first = indices[0]
                 if rows[first] == new_row and len(indices) == 1:
@@ -154,7 +231,7 @@ def main() -> int:
         if changed and not args.dry_run:
             common.write_record(path, record)
 
-    print(f"profile {profile_id}: {added} added, {replaced} replaced, "
+    print(f"profile {', '.join(sorted(used)) or '-'}: {added} added, {replaced} replaced, "
           f"{rejected} rejected, {unstamped} unstamped score(s) for already published "
           f"cells skipped, {missing} score file(s) absent")
     if args.dry_run:
diff --git a/scripts/catalog/ingest_perf.py b/scripts/catalog/ingest_perf.py
index 1c4c62370..fb488dcee 100755
--- a/scripts/catalog/ingest_perf.py
+++ b/scripts/catalog/ingest_perf.py
@@ -5,7 +5,9 @@
 """Fold bench driver reports into the catalog's speed_benchmarks rows.
 
 `scripts/bench/run.py` writes one report per (variant, backend) under
-reports/perf/<machine-slug>/, and reports/ is gitignored -- so the latency
+reports/perf/<machine-slug>/ (`scripts/langid/bench.py` writes the same shape
+for language ID, which transcribe-bench cannot run), and reports/ is
+gitignored -- so the latency
 breakdown only exists on the machine that measured it. This is the hop that
 moves it into the catalog, where it is durable and publishable.
 
@@ -112,7 +114,9 @@ def mean(field: str):
             # the canonical backend; the driver records that at the top level.
             "backend": (report.get("backend") or run.get("backend", "")).lower(),
             "quant": quant,
-            "sample": pathlib.PurePosixPath(run.get("sample_path", "")).stem,
+            # The driver names a cell after its clip; scripts/langid/bench.py
+            # scores several lengths of one clip and names each.
+            "sample": run.get("sample") or pathlib.PurePosixPath(run.get("sample_path", "")).stem,
             "sample_duration_s": duration,
             "total_ms": round(total, 1),
             # xrt is recomputed from the unrounded mean rather than carried
@@ -140,25 +144,26 @@ def collect(reports_dir: pathlib.Path) -> tuple[dict, list[str]]:
     best: dict[tuple, dict] = {}
     superseded, unreadable, experiments = collections.Counter(), [], 0
     for path in sorted(reports_dir.glob("*/*.json")):
+        # Reports normally live under the repo, but --reports can name a
+        # staging directory elsewhere (or a relative one); the label is only
+        # for the operator, so fall back to the path as given.
+        try:
+            label = str(path.resolve().relative_to(common.REPO))
+        except ValueError:
+            label = str(path)
         try:
             report = json.loads(path.read_text())
         except (json.JSONDecodeError, UnicodeDecodeError):
-            unreadable.append(str(path.relative_to(common.REPO)))
+            unreadable.append(label)
             continue
         if not isinstance(report, dict) or report.get("schema") != "transcribe-bench-driver-v1":
             kind = report.get("schema") if isinstance(report, dict) else type(report).__name__
-            unreadable.append(f"{path.relative_to(common.REPO)} (schema {kind!r})")
+            unreadable.append(f"{label} (schema {kind!r})")
             continue
         if not publishable(report):
             experiments += 1
             continue
-        # Reports normally live under the repo, but --reports can name a
-        # staging directory elsewhere (or a relative one); the label is only
-        # for the operator, so fall back to the path as given.
-        try:
-            report["_file"] = str(path.resolve().relative_to(common.REPO))
-        except ValueError:
-            report["_file"] = str(path)
+        report["_file"] = label
         for row in cells(report):
             key = (row["variant"], row["machine"], row["backend"], row["quant"], row["sample"])
             previous = best.get(key)
@@ -214,7 +219,6 @@ def main() -> int:
     for note in notes:
         print(f"  note: {note}")
 
-    profile_id, profile = profiles.load_profile()
     filled = updated = added = matched = 0
     drift, refused, unmatched = [], [], []
     for variant, record in common.load_records().items():
@@ -261,6 +265,7 @@ def main() -> int:
         # Profile runs can create rows; the old importer could only refresh
         # placeholders, which made a newly required quant impossible to ingest
         # without first hand-authoring empty catalog cells.
+        profile_id, profile = profiles.profile_for(record)
         expected = profiles.apply_exceptions(
             record, "speed", profiles.expected_speed(record, profile))
         expected_keys = {
diff --git a/scripts/catalog/profiles.py b/scripts/catalog/profiles.py
index 6d926da0a..939a7322c 100644
--- a/scripts/catalog/profiles.py
+++ b/scripts/catalog/profiles.py
@@ -42,6 +42,10 @@ def load_profiles(path: pathlib.Path = PROFILE_PATH) -> dict:
     default = data.get("default")
     if default not in data["profiles"]:
         raise ValueError(f"{path}: default profile {default!r} is not defined")
+    for role, profile_id in (data.get("roles") or {}).items():
+        if data["profiles"].get(profile_id, {}).get("role") != role:
+            raise ValueError(f"{path}: roles.{role} must name a defined profile "
+                             f"whose role is {role!r}")
     return data
 
 
@@ -57,6 +61,30 @@ def load_profile(profile_id: str | None = None) -> tuple[str, dict]:
         ) from exc
 
 
+def profile_for(record: dict, profile_id: str | None = None) -> tuple[str, dict]:
+    """The profile that governs one record: `profile_id` when it is written
+    for the record's role, else the profile its role names in `roles`, else
+    the default. ASR and diarize records share the default; language ID has
+    a profile of its own, since a top-1 accuracy over a pooled set and a
+    classifier's latency are not cells of the ASR matrix."""
+    data = load_profiles()
+    if profile_id:
+        named = load_profile(profile_id)[1]
+        if governs(named, record):
+            return profile_id, named
+    chosen = data.get("roles", {}).get(record.get("role", "asr"), data["default"])
+    return chosen, data["profiles"][chosen]
+
+
+def governs(profile: dict, record: dict) -> bool:
+    """A profile with a `role` governs only records of that role; a profile
+    without one governs every record no role-specific profile claims."""
+    role = record.get("role", "asr")
+    if profile.get("role"):
+        return profile["role"] == role
+    return role not in load_profiles().get("roles", {})
+
+
 def canonical_machine(slug: str) -> str:
     return MACHINE_ALIASES.get(slug, slug)
 
@@ -117,7 +145,10 @@ def _quants(spec: str | list[str], record: dict) -> list[str]:
 def expected_accuracy(record: dict, profile: dict) -> list[dict]:
     """Expand a profile into publication accuracy cells for one model."""
     cells: list[dict] = []
-    if not (record.get("capabilities", {}).get("transcribe", {}).get("supported")):
+    if not governs(profile, record):
+        return cells
+    if not profile.get("role") and not (
+            record.get("capabilities", {}).get("transcribe", {}).get("supported")):
         return cells
     for suite in profile.get("accuracy", []):
         selector = suite["languages"]
@@ -125,6 +156,9 @@ def expected_accuracy(record: dict, profile: dict) -> list[dict]:
             languages = ["en"] if "en" in fleurs_languages(record) else []
         elif selector == "supported-intersect-fleurs":
             languages = fleurs_languages(record)
+        elif selector == "pooled":
+            # One result over every evaluated language at once (language ID).
+            languages = ["mul"]
         else:
             raise ValueError(f"unknown language selector {selector!r}")
         for language in languages:
@@ -133,14 +167,19 @@ def expected_accuracy(record: dict, profile: dict) -> list[dict]:
                     "dataset": suite["dataset"],
                     "split": suite["split"],
                     "language": language,
-                    "runtime_language": runtime_language(record, language),
+                    "runtime_language": (None if selector == "pooled"
+                                         else runtime_language(record, language)),
                     "quant": quant,
-                    "metric": "cer" if language in CER_LANGUAGES else "wer",
+                    "metric": suite.get("metric") or (
+                        "cer" if language in CER_LANGUAGES else "wer"),
                     "batch_size": suite["batch_size"],
                     "sort_by_length": suite.get("sort_by_length", False),
                     "timestamps": suite["timestamps"],
                     "gpu": suite.get("gpu"),
                     "backend": suite.get("backend"),
+                    # Language ID's crop and trim: which slice of a run.py
+                    # sweep the published number is.
+                    **{key: suite[key] for key in ("crop_s", "trim") if key in suite},
                 })
     return cells
 
@@ -174,6 +213,8 @@ def all_speed_samples(profile: dict, records: dict[str, dict]) -> list[str]:
 
 def expected_speed(record: dict, profile: dict) -> list[dict]:
     """Expand the exact publication speed matrix for one model."""
+    if not governs(profile, record):
+        return []
     spec = profile["speed"]
     samples = speed_samples(record, profile)
     cells: list[dict] = []
diff --git a/scripts/catalog/render.py b/scripts/catalog/render.py
index b1f54eafd..8e4ecfc94 100755
--- a/scripts/catalog/render.py
+++ b/scripts/catalog/render.py
@@ -16,8 +16,9 @@
     <!-- /catalog -->
 
 Blocks: `downloads`, `perf machine=<slug>`, `accuracy` (one table per
-dataset split beyond the headline), `recipe` (the mechanical WER sentence
-from the headline rows), `pin` (licence, upstream and validation pins),
+dataset split beyond the headline), `agreement` (language ID: headline
+accuracy and reference agreement per GGUF), `recipe` (the mechanical WER
+sentence from the headline rows), `pin` (licence, upstream and validation pins),
 `intro` (upstream link plus the card spec's `summary`), `prose
 field=wer.notes` (any `|` text field of the spec, dotted path), `family variants=a,b,c` (a roll-up row per variant, for
 family pages), and `family-index` (the root README's supported-models table,
@@ -157,13 +158,15 @@ def ordered(index: int, override: str | None, rank) -> list[str]:
     table = common.render_table(["Backend", "Sample"] + quants,
                                 ["l", "l"] + ["r"] * len(quants), body,
                                 rule_fill=True, max_pad=20)
-    return perf_methodology(rows) + [""] + table + [""] + perf_provenance(machine, rows)
+    return (perf_methodology(record, rows) + [""] + table + [""]
+            + perf_provenance(record, machine, rows))
 
 
-def perf_methodology(rows: dict) -> list[str]:
+def perf_methodology(record: dict, rows: dict) -> list[str]:
     """What a cell is. Iterations and warmup are claimed only for rows that
     name the profile they were measured under."""
-    line = "Compute latency (mel + encode + decode), speedup over realtime in parentheses"
+    stages = "mel + encode" if record.get("role") == "langid" else "mel + encode + decode"
+    line = f"Compute latency ({stages}), speedup over realtime in parentheses"
     ids = sorted({row["publication_profile"] for row in rows.values()
                   if row.get("publication_profile")})
     claims = []
@@ -176,9 +179,9 @@ def perf_methodology(rows: dict) -> list[str]:
     return [line + "."]
 
 
-def perf_provenance(machine: str, rows: dict) -> list[str]:
+def perf_provenance(record: dict, machine: str, rows: dict) -> list[str]:
     """Where the numbers came from: machine, engine commit, date."""
-    _, profile = profiles.load_profile()
+    _, profile = profiles.profile_for(record)
     display = profiles.machine_display(profile, machine)
     builds: dict[tuple, int] = {}
     for row in rows.values():
@@ -331,6 +334,37 @@ def block_accuracy(record: dict, attrs: dict[str, str]) -> list[str]:
     return out
 
 
+def block_agreement(record: dict, attrs: dict[str, str]) -> list[str]:
+    """Language ID: per shipped GGUF, the headline accuracy with its interval
+    and the scored run's top-1 agreement with the reference (the ship gate),
+    then which build measured it."""
+    rows = common.headline_rows(record)
+    reference = (spec_for(record).get("validation") or {}).get("reference", "reference")
+    body = []
+    for item in record.get("downloads", []):
+        row = rows.get(item["quant"])
+        if row is None:
+            continue
+        agreement = row.get("agreement") or {}
+        lo, hi = row["ci95"]
+        delta = agreement.get("max_abs_logit_delta")
+        body.append([item["quant"],
+                     common.fmt_err(row) + ("" if lo is None else f" ({lo:.2f}-{hi:.2f})"),
+                     f"{agreement['n_agree']} / {agreement['n']}" if agreement else "-",
+                     "-" if delta is None else f"{delta:.2g}"])
+    if not any(cells[2] != "-" for cells in body):
+        raise RenderError("no headline row carries an agreement")
+    table = common.render_table(
+        ["GGUF", f"{common.metric_label(common.headline(record)['metric'])} (95% CI)",
+         f"Top-1 agreement with {reference}", "Max abs logit difference"],
+        ["l", "r", "r", "r"], body)
+    builds = sorted({(row["engine_sha"], row.get("measured_on") or "")
+                     for row in rows.values() if row.get("engine_sha")})
+    line = "Measured at " + "; ".join(
+        f"transcribe.cpp `{sha}`" + (f" on {date}" if date else "") for sha, date in builds) + "."
+    return table + ["", line] if builds else table
+
+
 def block_family(records: dict[str, dict], attrs: dict[str, str]) -> list[str]:
     """A family roll-up: one row per variant, headline number at one quant."""
     names = [name for name in attrs.get("variants", "").split(",") if name]
@@ -400,7 +434,7 @@ def block_family_index(records: dict[str, dict], attrs: dict[str, str]) -> list[
 
 BLOCKS = {"downloads": block_downloads, "perf": block_perf,
           "intro": block_intro, "prose": block_prose, "accuracy": block_accuracy,
-          "recipe": block_recipe, "pin": block_pin}
+          "recipe": block_recipe, "pin": block_pin, "agreement": block_agreement}
 
 
 # --------------------------------------------------------------------------
diff --git a/scripts/catalog/test_db_mapping.py b/scripts/catalog/test_db_mapping.py
index 44a23f23a..04ddf46eb 100644
--- a/scripts/catalog/test_db_mapping.py
+++ b/scripts/catalog/test_db_mapping.py
@@ -16,7 +16,8 @@
 
 # Row properties that are flattened or renamed rather than stored one to one.
 ACCURACY_MAPPED = {"dataset": "dataset_id", "split": "dataset_id", "language": "dataset_id",
-                   "ci95": "ci_lo/ci_hi", "errors": "substitutions/deletions/insertions"}
+                   "ci95": "ci_lo/ci_hi", "errors": "substitutions/deletions/insertions",
+                   "agreement": "agreement_n_agree/agreement_n/agreement_max_abs_logit_delta"}
 SPEED_MAPPED = {}
 
 
diff --git a/scripts/dump_reference_ecapa_tdnn_speechbrain.py b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
index 6792f3e9c..be7f48c06 100644
--- a/scripts/dump_reference_ecapa_tdnn_speechbrain.py
+++ b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
@@ -15,7 +15,7 @@
       scripts/dump_reference_ecapa_tdnn_speechbrain.py encoder \
       --model speechbrain/lang-id-voxlingua107-ecapa \
       --audio samples/fleurs-en.wav \
-      --out build/validate/ecapa_tdnn/voxlingua107/fleurs-en/ref
+      --out build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/ref
 
 Writes:
     <name>.f32        raw little-endian float32, row-major
@@ -408,9 +408,8 @@ def cmd_encoder(args: argparse.Namespace) -> int:
 
     # SpeechBrain 1.1.1's Filterbank builds its [201, 60] matrix inside
     # forward() from `f_central` / `band`; there is no `fbank_matrix`
-    # attribute or buffer to read (notes/PLAN.md section 1 assumes one — see the
-    # oracle report). Wrapping the real method captures exactly the matrix
-    # this forward pass used, which is stronger than recomputing it.
+    # attribute or buffer to read. Wrapping the real method captures exactly
+    # the matrix this forward pass used, which is stronger than recomputing it.
     fbanks = fe.compute_fbanks
     captured_filters: dict[str, Any] = {}
     original_create = fbanks._create_fbank_matrix
@@ -448,8 +447,8 @@ def capture_create(f_central_mat, band_mat):
         raise SystemExit(
             f"error: frame count mismatch: fe.mel has T={T}, but "
             f"floor({n_samples}/{HOP}) + 1 = {expected_T}. The C++ front end "
-            f"derives T from this formula; a mismatch means the STFT "
-            f"framing assumption in notes/PLAN.md section 1.1 is wrong."
+            f"derives T from this formula; a mismatch means its STFT "
+            f"framing assumption (center=True, hop {HOP}) is wrong."
         )
     print(f"frames: T={T} == floor({n_samples}/{HOP}) + 1  [ok]")
 
@@ -520,8 +519,9 @@ def dump(name: str, array: np.ndarray, stage: str) -> None:
     expected_window = torch.hamming_window(WIN)
     if not torch.equal(window.to(torch.float32).cpu(), expected_window):
         raise SystemExit(
-            "error: STFT window is not torch.hamming_window(400); notes/PLAN.md "
-            "section 1.1 (periodic Hamming) does not describe this checkpoint"
+            "error: STFT window is not torch.hamming_window(400); the periodic "
+            "Hamming window the C++ front end builds does not describe this "
+            "checkpoint"
         )
     dump("fe.window", vec(window, WIN, name="fe.window"), "frontend")
 
diff --git a/scripts/ecapa_numpy_reference.py b/scripts/ecapa_numpy_reference.py
index 01feb5368..bbf60cb36 100755
--- a/scripts/ecapa_numpy_reference.py
+++ b/scripts/ecapa_numpy_reference.py
@@ -31,11 +31,11 @@
     uv run scripts/ecapa_numpy_reference.py \
       --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
       --audio samples/fleurs-en.wav \
-      --out build/validate/ecapa_tdnn/voxlingua107/fleurs-en/numpy
+      --out build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/numpy
 
     uv run scripts/compare_tensors.py \
-      build/validate/ecapa_tdnn/voxlingua107/fleurs-en/numpy \
-      build/validate/ecapa_tdnn/voxlingua107/fleurs-en/ref \
+      build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/numpy \
+      build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/ref \
       --tolerances tests/tolerances/ecapa_tdnn.json
 
 Writes `<name>.f32` + `<name>.json` for every stage tensor in the dump contract, plus `prediction.json` in the same shape the SpeechBrain dumper
diff --git a/scripts/envs/ecapa_tdnn/pyproject.toml b/scripts/envs/ecapa_tdnn/pyproject.toml
index 2711774de..0c2ce682b 100644
--- a/scripts/envs/ecapa_tdnn/pyproject.toml
+++ b/scripts/envs/ecapa_tdnn/pyproject.toml
@@ -11,11 +11,11 @@ dependencies = [
     # version-specific, and so is the arithmetic inside them.
     # tests/tolerances/ecapa_tdnn.json was measured against THIS version
     # (recorded as reference.revision in
-    # tests/golden/ecapa_tdnn/voxlingua107.manifest.json); a silent bump can
+    # tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json); a silent bump can
     # move both the hook points and the numbers.
     "speechbrain==1.1.1",
-    # CPU torch. The parity regime is fp32 on CPU with one thread
-    # (notes/PLAN.md section 5); no CUDA/Metal build is needed or wanted here.
+    # CPU torch. The parity regime is fp32 on CPU with one thread; no
+    # CUDA/Metal build is needed or wanted here.
     "torch==2.13.0",
     "torchaudio==2.11.0",
     "numpy>=1.26",
diff --git a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
index 3de665aad..906174190 100644
--- a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
+++ b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
@@ -62,8 +62,8 @@ usage: |
 
 wer:
   notes: |
-    Open-set top-1 accuracy over all 107 labels, mean of 15 FLEURS languages
-    (200 test utterances each), on the first 5 s of each clip without silence
-    trimming. Accuracy is higher with an allowed set and with trimmed speech;
-    see docs/langid.md. The C++ F32 port makes the same decision as the
-    SpeechBrain reference on all 12000 FLEURS decisions (3 / 5 / 10 s / full).
+    Open-set top-1 accuracy over all 107 labels, the mean over 15 FLEURS
+    languages, on the first 5 s of each clip without silence trimming.
+    Accuracy is higher with an allowed set and with trimmed speech; see
+    docs/langid.md. Agreement with the SpeechBrain reference, per GGUF, is on
+    the transcribe.cpp model page.
diff --git a/scripts/langid/bench.py b/scripts/langid/bench.py
index 72a95b0f9..8573d98a1 100644
--- a/scripts/langid/bench.py
+++ b/scripts/langid/bench.py
@@ -1,28 +1,41 @@
 #!/usr/bin/env python3
 """
 bench.py - language ID latency through the Python binding: backend x GGUF x
-scored duration, median / p90 of the native mel + encode time over repeated
-runs on one warm session (tools/transcribe-bench is ASR-only).
+scored duration, the native mel + encode time over repeated runs on one warm
+session (tools/transcribe-bench is ASR-only).
 
 Usage:
+  # the publication cells for this machine (catalog/_benchmark_profiles.json)
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py --profile \\
+      --library build-shared/src/libtranscribe.dylib
+  uv run scripts/catalog/ingest_perf.py
+
+  # an experiment
   uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py \\
       --library build-shared/src/libtranscribe.dylib \\
       --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-{F32,F16,Q8_0}.gguf \\
-      --backends cpu,metal --durations 3,5,10,30 --out reports/langid/bench-m4-max.json
+      --backends cpu,metal --durations 3,5,10,30
+
+Audio: the first N seconds of samples/<clip>.wav (default ru-long, 33.8 s of
+Russian), so every duration scores real speech; the cell's sample is named
+`<clip>-<N>s`. The session's max_audio_ms is 60000, so nothing is cropped
+away.
 
-Audio: the first N seconds of samples/long-45s.wav (52 s of FLEURS English),
-so every duration scores real speech. The session's max_audio_ms is 60000,
-so nothing is cropped away.
+Writes one report per backend to reports/perf/<machine-slug>/
+<name>_<variant>_<backend>.json in the bench driver's shape
+(scripts/bench/run.py), which scripts/catalog/ingest_perf.py folds into the
+catalog. As there, only a --profile run is eligible: it takes its GGUFs,
+samples, backends, iterations, warmup and thermal gate from the profile.
 """
 
 from __future__ import annotations
 
 import argparse
+import importlib.util
 import json
 import os
-import platform
+import re
 import statistics
-import subprocess
 import sys
 import time
 from pathlib import Path
@@ -30,71 +43,174 @@
 import numpy as np
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
+SAMPLE_RE = re.compile(r"^(?P<clip>.+)-(?P<seconds>\d+(?:\.\d+)?)s$")
+
+
+def bench_driver():
+    """scripts/bench/run.py, for its machine detection and thermal gate.
+    Loaded by path: this directory has a run.py of its own."""
+    spec = importlib.util.spec_from_file_location(
+        "bench_driver", REPO_ROOT / "scripts" / "bench" / "run.py")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module  # its dataclasses look their module up
+    spec.loader.exec_module(module)
+    return module
+
+
+def seconds_label(seconds: float) -> str:
+    return f"{seconds:g}s"
+
+
+def summary(values: list[float]) -> dict:
+    ordered = sorted(values)
+    return {"mean": statistics.fmean(values), "median": statistics.median(values),
+            "p90": ordered[int(0.9 * (len(ordered) - 1))]}
 
 
 def main(argv: list[str] | None = None) -> int:
     p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--gguf", nargs="+", type=Path, required=True)
-    p.add_argument("--backends", default="cpu,metal")
-    p.add_argument("--durations", default="3,5,10,30")
+    p.add_argument("--profile", nargs="?", const="", default=None,
+                   help="run this machine's publication cells (optionally name "
+                        "the profile; default: the variant's role profile)")
+    p.add_argument("--variant", default="lang-id-voxlingua107-ecapa",
+                   help="catalog variant (default %(default)s)")
+    p.add_argument("--gguf", nargs="+", type=Path, default=None,
+                   help="default with --profile: the variant's downloads under models/")
+    p.add_argument("--backends", default=None, help="default cpu,metal")
+    p.add_argument("--sample", default=None, help="clip under samples/ (default ru-long)")
+    p.add_argument("--durations", default=None, help="default 3,5,10,30")
     p.add_argument("--threads", type=int, default=0, help="0 = library default")
-    p.add_argument("--warmup", type=int, default=3)
-    p.add_argument("--repeat", type=int, default=20)
+    p.add_argument("--warmup", type=int, default=None, help="default 3")
+    p.add_argument("--repeat", type=int, default=None, help="default 20")
     p.add_argument("--library", type=Path, default=None)
-    p.add_argument("--out", type=Path, default=None)
+    p.add_argument("--name", default=None,
+                   help="report filename prefix (default: a timestamp, or "
+                        "<variant>-publication with --profile)")
+    p.add_argument("--out-dir", type=Path, default=REPO_ROOT / "reports" / "perf")
     args = p.parse_args(argv)
 
+    driver = bench_driver()
+    profiles = driver.benchmark_profiles
+    machine = driver.detect_machine()
+    record = driver.catalog_common.load_record(args.variant)
+    profile_id, cooldown_c = None, 0.0
+    if args.profile is not None:
+        conflicting = [flag for flag, value in (
+            ("--gguf", args.gguf), ("--backends", args.backends), ("--sample", args.sample),
+            ("--durations", args.durations), ("--warmup", args.warmup),
+            ("--repeat", args.repeat)) if value is not None]
+        if conflicting:
+            p.error(f"--profile supplies {', '.join(conflicting)}; do not override it")
+        profile_id, profile = profiles.profile_for(record, args.profile or None)
+        target = profiles.target_for_machine(profile, machine["slug"])
+        if target is None:
+            p.error(f"machine {machine['slug']!r} is not a target in profile {profile_id}")
+        cells = [cell for cell in profiles.apply_exceptions(
+                     record, "speed", profiles.expected_speed(record, profile))
+                 if cell["machine"] == target["machine"]]
+        quants = {cell["quant"] for cell in cells}
+        args.gguf = [REPO_ROOT / "models" / args.variant / item["filename"]
+                     for item in record["downloads"] if item["quant"] in quants]
+        args.backends = ",".join(dict.fromkeys(cell["backend"] for cell in cells))
+        samples = [SAMPLE_RE.match(name) for name in
+                   dict.fromkeys(cell["sample"] for cell in cells)]
+        if not all(samples) or len({m["clip"] for m in samples}) != 1:
+            p.error(f"profile samples must be <clip>-<N>s on one clip: "
+                    f"{sorted({cell['sample'] for cell in cells})}")
+        args.sample = samples[0]["clip"]
+        args.durations = ",".join(m["seconds"] for m in samples)
+        args.warmup = int(profile["speed"]["warmup"])
+        args.repeat = int(profile["speed"]["iterations"])
+        cooldown_c = float(target.get("cooldown_tctl_c", 0.0))
+    elif not args.gguf:
+        p.error("--gguf is required without --profile")
+    backends = (args.backends or "cpu,metal").split(",")
+    clip = args.sample or "ru-long"
+    durations = [float(d) for d in (args.durations or "3,5,10,30").split(",")]
+    warmup = 3 if args.warmup is None else args.warmup
+    repeat = 20 if args.repeat is None else args.repeat
+    missing = [str(path) for path in args.gguf if not path.exists()]
+    if missing:
+        p.error(f"no such GGUF: {', '.join(missing)}")
+
     if args.library is not None:
         os.environ["TRANSCRIBE_LIBRARY"] = str(args.library.resolve())
     sys.path.insert(0, str(REPO_ROOT / "bindings" / "python" / "src"))
     import soundfile as sf
     import transcribe_cpp as t
 
-    pcm, sr = sf.read(str(REPO_ROOT / "samples" / "long-45s.wav"), dtype="float32")
+    sample_path = REPO_ROOT / "samples" / f"{clip}.wav"
+    pcm, sr = sf.read(str(sample_path), dtype="float32")
     assert sr == 16000
-    durations = [float(d) for d in args.durations.split(",")]
-    rows = []
-    for gguf in args.gguf:
-        for backend in args.backends.split(","):
+    too_long = [d for d in durations if d * 16000 > pcm.size]
+    if too_long:
+        p.error(f"{sample_path.name} is {pcm.size / 16000:.1f} s; cannot score {too_long} s")
+
+    timestamp = driver.now_utc_iso()
+    commit = t.native_commit()
+    name = args.name or (f"{args.variant}-publication" if profile_id
+                         else driver.timestamp_for_filename(timestamp))
+    for backend in backends:
+        runs = []
+        for gguf in args.gguf:
             t0 = time.perf_counter()
             model = t.Model(str(gguf), backend=backend)
             load_ms = (time.perf_counter() - t0) * 1000
             lid = model.langid_session(n_threads=args.threads, max_audio_ms=60000)
             for d in durations:
-                clip = np.ascontiguousarray(pcm[: int(d * 16000)])
-                for _ in range(args.warmup):
-                    lid.run(clip)
-                compute, wall = [], []
-                for _ in range(args.repeat):
+                driver.cooldown_wait(cooldown_c, 300.0, 10.0)
+                audio = np.ascontiguousarray(pcm[: int(d * 16000)])
+                for _ in range(warmup):
+                    lid.run(audio)
+                per_iter = []
+                for _ in range(repeat):
                     w0 = time.perf_counter()
-                    lid.run(clip)
-                    wall.append((time.perf_counter() - w0) * 1000)
+                    lid.run(audio)
+                    wall = (time.perf_counter() - w0) * 1000
                     tm = lid.timings
-                    compute.append(tm.mel_ms + tm.encode_ms)
-                compute.sort()
-                row = {
-                    "gguf": gguf.name, "backend": backend, "bound": model.backend,
-                    "duration_s": d, "threads": args.threads,
-                    "median_ms": round(statistics.median(compute), 2),
-                    "p90_ms": round(compute[int(0.9 * (len(compute) - 1))], 2),
-                    "wall_median_ms": round(statistics.median(wall), 2),
+                    per_iter.append({"mel_ms": tm.mel_ms, "encode_ms": tm.encode_ms,
+                                     "total_ms": tm.mel_ms + tm.encode_ms, "wall_ms": wall})
+                try:
+                    model_path = str(gguf.resolve().relative_to(REPO_ROOT))
+                except ValueError:
+                    model_path = str(gguf)
+                run = {
+                    "model_path": model_path,
+                    "sample": f"{clip}-{seconds_label(d)}",
+                    "sample_path": str(sample_path.relative_to(REPO_ROOT)),
+                    "sample_duration_s": d,
+                    "backend": model.backend,
+                    "threads": args.threads,
                     "load_ms": round(load_ms, 1),
+                    "per_iter": per_iter,
+                    "summary": {field: summary([it[field] for it in per_iter])
+                                for field in ("mel_ms", "encode_ms", "total_ms", "wall_ms")},
                 }
-                rows.append(row)
-                print(f"{gguf.name:42} {backend:6} {d:5.0f}s  median {row['median_ms']:8.2f} ms  "
-                      f"p90 {row['p90_ms']:8.2f}  wall {row['wall_median_ms']:8.2f}", flush=True)
+                runs.append(run)
+                total = run["summary"]["total_ms"]
+                print(f"{gguf.name:42} {backend:6} {d:5.0f}s  mean {total['mean']:8.2f} ms  "
+                      f"median {total['median']:8.2f}  p90 {total['p90']:8.2f}", flush=True)
             lid.close()
             model.close()
 
-    if args.out:
-        commit = subprocess.run(["git", "rev-parse", "--short", "HEAD"], cwd=REPO_ROOT,
-                                capture_output=True, text=True).stdout.strip()
-        args.out.parent.mkdir(parents=True, exist_ok=True)
-        args.out.write_text(json.dumps({
-            "machine": platform.processor() or platform.machine(),
-            "engine_sha": commit, "warmup": args.warmup, "repeat": args.repeat,
-            "audio": "samples/long-45s.wav (first N s)", "rows": rows}, indent=2) + "\n")
-        print(f"wrote {args.out}")
+        out = args.out_dir / machine["slug"] / f"{driver.slugify(name)}_{args.variant}_{backend}.json"
+        out.parent.mkdir(parents=True, exist_ok=True)
+        out.write_text(json.dumps({
+            "schema": "transcribe-bench-driver-v1",
+            "timestamp": timestamp,
+            "name": name,
+            "publication_profile": profile_id,
+            "machine": machine,
+            # The build the native library reports, which is what ran.
+            "git_sha": commit if commit != "unknown" else driver.get_git_sha(REPO_ROOT),
+            "variant": args.variant,
+            "backend": backend,
+            "iters": repeat,
+            "warmup": warmup,
+            "tool": "scripts/langid/bench.py",
+            "runs": runs,
+        }, indent=2) + "\n")
+        print(f"wrote {out}")
     return 0
 
 
diff --git a/scripts/langid/compare.py b/scripts/langid/compare.py
index 6f4026ac7..00d8f296a 100755
--- a/scripts/langid/compare.py
+++ b/scripts/langid/compare.py
@@ -20,7 +20,8 @@
 Usage:
     uv run scripts/langid/compare.py \\
         reports/langid/ref-speechbrain-untrimmed.jsonl \\
-        reports/langid/cpp-f32-untrimmed.jsonl
+        reports/langid/cpp-f32-untrimmed.jsonl \\
+        --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
 
 Rows are joined on (id, crop_s, trim). Both runs must cover the same keys;
 a key present in only one side is reported and counted as a failure, because
@@ -97,6 +98,10 @@ def main(argv: list[str] | None = None) -> int:
     p.add_argument("--max-listed", type=int, default=MAX_LISTED_DISAGREEMENTS)
     p.add_argument("--near-ties", type=Path, default=None,
                    help="JSON list of reviewed near-tie disagreements")
+    p.add_argument("--json", type=Path, default=None,
+                   help="also write the agreement for scripts/catalog/"
+                        "ingest_accuracy.py; name it <gguf stem>.fleurs-mul."
+                        "agreement.json under reports/langid/")
     args = p.parse_args(argv)
     near_ties: dict[tuple, dict] = {}
     if args.near_ties is not None:
@@ -243,17 +248,49 @@ def main(argv: list[str] | None = None) -> int:
 
     if only_a or only_b:
         print("FAIL: the two runs do not cover the same rows")
-        return 1
-    if unlisted:
+        status = 1
+    elif unlisted:
         print(f"FAIL: {len(unlisted)} disagreements are not reviewed near-ties "
               f"(add them to --near-ties only after listening): "
               f"{[d['key'] for d in unlisted[:10]]}")
-        return 1
-    if rate < args.gate:
+        status = 1
+    elif rate < args.gate:
         print(f"FAIL: agreement {100 * rate:.4f}% < {100 * args.gate:.2f}%")
-        return 1
-    print(f"PASS: agreement {100 * rate:.4f}% >= {100 * args.gate:.2f}%")
-    return 0
+        status = 1
+    else:
+        print(f"PASS: agreement {100 * rate:.4f}% >= {100 * args.gate:.2f}%")
+        status = 0
+
+    if args.json:
+        # The catalog records every shipped GGUF's agreement, gate or not:
+        # only F32 is held to the gate, the quants' counts are the evidence
+        # for shipping them.
+        args.json.parent.mkdir(parents=True, exist_ok=True)
+        args.json.write_text(json.dumps({
+            "schema": "transcribe-langid-agreement-v1",
+            "reference": ha["engine"],
+            "reference_run": rel(args.a),
+            "run": rel(args.b),
+            "model": hb["model"],
+            "n": n,
+            "n_agree": n_agree,
+            "max_abs_logit_delta": abs_deltas[-1],
+            "disagreements": len(disagreements),
+            "unlisted": len(unlisted),
+            "same_rows": not (only_a or only_b),
+            "gate": args.gate,
+            "passed": status == 0,
+        }, indent=2) + "\n")
+        print(f"wrote {args.json}")
+    return status
+
+
+def rel(path: Path) -> str:
+    """Repo-relative when possible, matching score.py's `run`."""
+    try:
+        return str(path.resolve().relative_to(Path(__file__).resolve().parents[2]))
+    except ValueError:
+        return str(path)
 
 
 if __name__ == "__main__":
diff --git a/scripts/langid/ingest.py b/scripts/langid/ingest.py
index dd4acdb36..87d3e4a5b 100755
--- a/scripts/langid/ingest.py
+++ b/scripts/langid/ingest.py
@@ -15,9 +15,10 @@
   samples/langid/fleurs-<code>/<id>.wav        16-bit PCM mono 16 kHz
   samples/langid/fleurs-<code>.manifest.jsonl  {"id","audio","language","duration_s",...}
 
-Selection rule: the first N utterances in parquet order whose decoded
-duration is >= 1 s. Parquet order is the only ordering; no shuffling, no
-hashing, so the same cache always produces the same manifest.
+Only the FLEURS `test` split is read. Selection rule: the first N
+utterances in parquet order whose decoded duration is >= 1 s. Parquet order
+is the only ordering; no shuffling, no hashing, so the same cache always
+produces the same manifest.
 
 Utterance ids are `fleurs-<code>-<NNNN>` where NNNN is the index among the
 selected rows, NOT the FLEURS sentence id: FLEURS records the same sentence
@@ -48,9 +49,10 @@
 DATASET = "google/fleurs"
 LICENCE = "CC-BY-4.0"
 
-# FLEURS config -> VoxLingua107 label code, in the dataset gate's order. `es_419` is Latin-American Spanish,
-# `cmn_hans_cn` is Mandarin in simplified Han, `nb_no` is Bokmal (the label
-# set spells Norwegian `no`); VoxLingua107 has one label for each.
+# FLEURS config -> VoxLingua107 label code, in the dataset gate's order.
+# `es_419` is Latin-American Spanish, `cmn_hans_cn` is Mandarin in simplified
+# Han, `nb_no` is Bokmal (the label set spells Norwegian `no`); VoxLingua107
+# has one label for each.
 FLEURS_LANGUAGES: list[tuple[str, str]] = [
     ("en_us", "en"),
     ("cmn_hans_cn", "zh"),
@@ -67,7 +69,6 @@
     ("da_dk", "da"),
     ("id_id", "id"),
     ("ms_my", "ms"),
-    ("it_it", "it"),
 ]
 
 CODE_TO_CONFIG = {code: config for config, code in FLEURS_LANGUAGES}
@@ -90,7 +91,7 @@ def fleurs_parquet(config: str) -> Path:
     return Path(matches[-1])
 
 
-def iter_rows(path: Path, split: str):
+def iter_rows(path: Path):
     """Yield parquet rows in file order without materialising the table.
 
     FLEURS test splits carry ~400-900 utterances of ~12 s each; a whole-table
@@ -161,7 +162,7 @@ def ingest_language(config: str, code: str, args) -> dict:
     entries: list[dict] = []
     n_short = 0
     n_resampled = 0
-    for row_index, row in enumerate(iter_rows(parquet, args.split)):
+    for row_index, row in enumerate(iter_rows(parquet)):
         if len(entries) >= args.n:
             break
         pcm, sr = decode_audio(row)
@@ -181,7 +182,7 @@ def ingest_language(config: str, code: str, args) -> dict:
             "duration_s": round(pcm.size / SAMPLE_RATE, 3),
             "fleurs_id": int(row["id"]),
             "config": config,
-            "split": args.split,
+            "split": SPLIT,
             "row_index": row_index,
             "dataset": DATASET,
             "licence": LICENCE,
@@ -269,7 +270,6 @@ def main(argv: list[str] | None = None) -> int:
     fp = sub.add_parser("fleurs", help="FLEURS from the local HF parquet cache")
     fp.add_argument("--lang", required=True,
                     help="VoxLingua107 code, comma-separated list, or 'all'")
-    fp.add_argument("--split", default=SPLIT, help=f"dataset split (default: {SPLIT})")
     fp.add_argument("--n", type=int, default=DEFAULT_N,
                     help=f"utterances per language (default: {DEFAULT_N})")
     fp.add_argument("--force", action="store_true",
diff --git a/scripts/langid/run.py b/scripts/langid/run.py
index 371b037d1..8bb5f10cf 100755
--- a/scripts/langid/run.py
+++ b/scripts/langid/run.py
@@ -23,14 +23,13 @@
   line n   {"id","language","crop_s","trim","audio_s","logits":[107],"top1","top1_prob"}
 
 All 107 raw logits are stored per (utterance, crop) so every restricted
-selection can be scored offline by score.py from a single sweep
-. `top1`/`top1_prob` are the open-set decision, kept as a
-convenience; score.py recomputes everything from `logits`.
+selection can be scored offline by score.py from a single sweep.
+`top1`/`top1_prob` are the open-set decision, kept as a convenience;
+score.py recomputes everything from `logits`.
 
 Crop semantics
 --------------
-`--trim none`  : the first N seconds of the raw clip. This is what the
-                 langid.cpp original evaluation measured, so "3 s" is roughly
+`--trim none`  : the first N seconds of the raw clip, so "3 s" is roughly
                  2 s of speech on FLEURS (the clips open with silence).
 `--trim energy`: drop everything before the first 20 ms frame whose RMS
                  exceeds 5% of the clip's maximum frame RMS, then take the
@@ -169,8 +168,7 @@ def materialise_crops(rows: list[dict], crop: str | int, trim: str) -> list[tupl
 
 
 class _DumperArgs:
-    """Argument shim for the dumper's loader (same pattern as
-    make_samples_fleurs.py)."""
+    """Argument shim for the dumper's loader."""
 
     def __init__(self, model: str, revision: str, threads: int) -> None:
         self.model = model
diff --git a/scripts/langid/score.py b/scripts/langid/score.py
index e18c1224a..2a6803090 100755
--- a/scripts/langid/score.py
+++ b/scripts/langid/score.py
@@ -15,32 +15,28 @@
     uv run scripts/langid/score.py reports/langid/ref-speechbrain-untrimmed.jsonl \\
         --md reports/langid/ref-speechbrain-untrimmed.md
 
-    # docs/accuracy.md is written from both reference recipes at once
-    uv run scripts/langid/score.py \\
-        reports/langid/ref-speechbrain-untrimmed.jsonl \\
-        reports/langid/ref-speechbrain-trimmed.jsonl \\
-        --docs docs/accuracy.md
+    # the catalog's accuracy row for one shipped GGUF (ingest_accuracy.py)
+    uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \\
+        --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 
 Tables produced for each run:
   (a) open-set top-1 accuracy per language and mean, per crop, with a 95%
       bootstrap CI on the mean;
-  (b) "handy40" restricted accuracy (logits masked to the 40-language set,
-      softmax over the mask, argmax), per crop;
-  (c) each restricted selection's accuracy over that selection's utterances;
-  (d) confidence: median top-prob when right vs wrong, and coverage /
-      precision at thresholds 0.5 / 0.7 / 0.9, on full clips, open-set and
-      handy40;
-  (e) the 10 confusion pairs with the most open-set errors.
+  (b) each restricted selection's accuracy over that selection's utterances
+      (logits masked to the selection, softmax over the mask, argmax);
+  (c) confidence: median top-prob when right vs wrong, and coverage /
+      precision at thresholds 0.5 / 0.7 / 0.9, open-set, on full clips;
+  (d) the 10 confusion pairs with the most open-set errors.
 
 Correctness is strict code equality. `nn` is not credited for `no`, `be` is
 not credited for `ru`, `jw` is not credited for `id`: those label artefacts
-are exactly what the restricted spaces are supposed to remove, and crediting
-them would erase the effect the tables are measuring.
+are exactly what the restricted selections are supposed to remove, and
+crediting them would erase the effect the tables are measuring.
 
-The mean is the MACRO mean over languages (each language weighs 1/15,
-matching langid.cpp's original ONNX evaluation). The bootstrap resamples utterances within each
-language and re-averages the per-language accuracies, so the CI describes
-the same statistic the point estimate does.
+The mean is the MACRO mean over languages (each language weighs 1/15). The
+bootstrap resamples utterances within each language and re-averages the
+per-language accuracies, so the CI describes the same statistic the point
+estimate does.
 """
 
 from __future__ import annotations
@@ -109,7 +105,7 @@ def softmax_masked(logits: np.ndarray, mask_idx: np.ndarray) -> tuple[np.ndarray
 
     `logits` is [N, 107]; `mask_idx` selects the allowed columns. The softmax
     is taken over the allowed columns only, which is what
-    langid_candidate.prob is.
+    transcribe_langid_candidate.p is.
     """
     z = logits[:, mask_idx]
     z = z - z.max(axis=1, keepdims=True)
@@ -143,44 +139,13 @@ def resolve(codes: list[str], where: str) -> list[str]:
             raise SystemExit(f"error: {where}: duplicate codes after aliasing: {dupes}")
         return out
 
-    restricted = {}
-    for name, entry in spec.get("restricted", {}).items():
-        codes = resolve(entry["codes"], f"restricted.{name}")
-        restricted[name] = {
-            "label": entry.get("label", name),
-            "placeholder": bool(entry.get("placeholder")),
-            "codes": codes,
-        }
     selections = []
     for entry in spec.get("selections", []):
         selections.append({
             "name": entry["name"],
             "codes": resolve(entry["codes"], f"selections.{entry['name']}"),
         })
-    return {"aliases": aliases, "restricted": restricted, "selections": selections,
-            "path": path}
-
-
-def check_handy40(entry: dict, eval_languages: list[str]) -> list[str]:
-    """Sanity assertions on the 40-language set, returned as warnings.
-
-    An eval language missing from the set would score 0 by construction, and
-    keeping `be` / `nn` / `jw` would defeat the whole point of the table
-    (langid.cpp's original ONNX evaluation: restriction removes exactly those label artefacts).
-    """
-    warnings = []
-    codes = entry["codes"]
-    if len(codes) != 40:
-        warnings.append(f"handy40 has {len(codes)} codes, not 40")
-    missing = [c for c in eval_languages if c not in codes]
-    if missing:
-        warnings.append(f"handy40 omits evaluated languages {missing}; they "
-                        f"score 0 by construction")
-    artefacts = [c for c in ("be", "nn", "jw") if c in codes]
-    if artefacts:
-        warnings.append(f"handy40 contains the label artefacts {artefacts} that "
-                        f"the restricted table exists to remove")
-    return warnings
+    return {"aliases": aliases, "selections": selections, "path": path}
 
 
 # ---------------------------------------------------------------------------
@@ -264,7 +229,7 @@ def md_table(header: list[str], rows: list[list[str]]) -> list[str]:
     return out
 
 
-def section_setup(run: Run, sel: dict, warnings: list[str]) -> list[str]:
+def section_setup(run: Run, sel: dict) -> list[str]:
     h = run.header
     r = h["recipe"]
     lines = [f"## Setup", ""]
@@ -291,26 +256,28 @@ def section_setup(run: Run, sel: dict, warnings: list[str]) -> list[str]:
     lines.append(f"- Correctness is strict label-code equality "
                  f"(`nn` is not credited for `no`, `be` not for `ru`, `jw` not for `id`)")
     lines.append("")
-    if warnings:
-        lines.append("> **Warnings**")
-        for w in warnings:
-            lines.append(f"> - {w}")
-        lines.append("")
     return lines
 
 
-def section_openset(run: Run) -> tuple[list[str], dict]:
-    langs_order = run.languages
-    rows = []
+def openset(run: Run) -> tuple[dict[str, dict[str, float]], dict[str, tuple[float, float, float]]]:
+    """Per crop: open-set accuracy per language, and the macro mean with its
+    bootstrap CI as (mean, lo, hi)."""
     means: dict[str, tuple[float, float, float]] = {}
     per_crop_acc: dict[str, dict[str, float]] = {}
     for crop in run.crops:
         d = run.by_crop[crop]
         pred = np.array(run.labels)[np.argmax(d["logits"], axis=1)]
-        acc, mean = per_language_accuracy(pred, d["lang"], langs_order)
-        lo, hi = bootstrap_macro_ci(pred == d["lang"], d["lang"], langs_order)
+        acc, mean = per_language_accuracy(pred, d["lang"], run.languages)
+        lo, hi = bootstrap_macro_ci(pred == d["lang"], d["lang"], run.languages)
         means[crop] = (mean, lo, hi)
         per_crop_acc[crop] = acc
+    return per_crop_acc, means
+
+
+def section_openset(run: Run) -> list[str]:
+    langs_order = run.languages
+    rows = []
+    per_crop_acc, means = openset(run)
 
     for lg in langs_order:
         rows.append([lg] + [pct(per_crop_acc[c][lg]) for c in run.crops])
@@ -319,117 +286,67 @@ def section_openset(run: Run) -> tuple[list[str], dict]:
 
     lines = ["## (a) Open-set top-1 accuracy (all 107 labels), %", ""]
     lines += md_table(["lang"] + [crop_head(c) for c in run.crops], rows)
-    return lines, {"per_language": per_crop_acc, "mean": means}
+    return lines
 
 
-def section_restricted(run: Run, sel: dict) -> tuple[list[str], dict]:
-    lines = []
-    out = {}
-    for name, entry in sel["restricted"].items():
-        idx = np.array([run.index_of[c] for c in entry["codes"]])
-        rows = []
-        per_crop_acc = {}
-        means = {}
-        for crop in run.crops:
-            d = run.by_crop[crop]
-            arg, _p = softmax_masked(d["logits"], idx)
-            pred = np.array(run.labels)[arg]
-            acc, mean = per_language_accuracy(pred, d["lang"], run.languages)
-            lo, hi = bootstrap_macro_ci(pred == d["lang"], d["lang"], run.languages)
-            per_crop_acc[crop] = acc
-            means[crop] = (mean, lo, hi)
-        for lg in run.languages:
-            rows.append([lg] + [pct(per_crop_acc[c][lg]) for c in run.crops])
-        rows.append(["**mean**"] + [f"**{pct(means[c][0])}**" for c in run.crops])
-        rows.append(["95% CI"] + [f"{pct(means[c][1])}-{pct(means[c][2])}" for c in run.crops])
-        tag = " (PLACEHOLDER list)" if entry["placeholder"] else ""
-        lines += [f"## (b) Restricted to {entry['label']}"
-                  f" - {len(entry['codes'])} labels{tag}, %", ""]
-        lines += [f"`{' '.join(entry['codes'])}`", ""]
-        lines += md_table(["lang"] + [crop_head(c) for c in run.crops],
-                          rows)
-        out[name] = {"per_language": per_crop_acc, "mean": means,
-                     "codes": entry["codes"]}
-    return lines, out
-
-
-def section_selections(run: Run, sel: dict) -> tuple[list[str], dict]:
+def section_selections(run: Run, sel: dict) -> list[str]:
     rows = []
-    out = {}
     for entry in sel["selections"]:
         codes = entry["codes"]
         idx = np.array([run.index_of[c] for c in codes])
         cells = []
-        per_crop = {}
         for crop in run.crops:
             d = run.by_crop[crop]
             m = np.isin(d["lang"], codes)
             if not m.any():
                 cells.append("-")
-                per_crop[crop] = float("nan")
                 continue
             arg, _p = softmax_masked(d["logits"][m], idx)
             pred = np.array(run.labels)[arg]
-            acc = float((pred == d["lang"][m]).mean())
-            per_crop[crop] = acc
-            cells.append(pct(acc))
+            cells.append(pct(float((pred == d["lang"][m]).mean())))
         n = int(np.isin(run.by_crop[run.crops[0]]["lang"], codes).sum())
         rows.append([entry["name"], str(n)] + cells)
-        out[entry["name"]] = per_crop
-    lines = ["## (c) Restricted to a user's selection "
+    lines = ["## (b) Restricted to a user's selection "
              "(accuracy over that selection's utterances), %", ""]
     lines += md_table(["selection", "n utts"] +
                       [crop_head(c) for c in run.crops], rows)
-    return lines, out
+    return lines
 
 
-def section_confidence(run: Run, sel: dict) -> tuple[list[str], dict]:
+def section_confidence(run: Run) -> list[str]:
     """Median top-prob right vs wrong, and coverage/precision at thresholds.
 
-    Reported on the `full` crop (the realistic case: the caller has the whole
-    utterance) in two decision spaces - open-set and handy40. `coverage` is
-    the share of decisions kept, `precision` the share of kept decisions that
-    are right; a threshold is only useful when it trades a little of the
-    first for a lot of the second.
+    Reported open-set on the `full` crop (the realistic case: the caller has
+    the whole utterance). `coverage` is the share of decisions kept,
+    `precision` the share of kept decisions that are right; a threshold is
+    only useful when it trades a little of the first for a lot of the second.
     """
     crop = "full" if "full" in run.crops else run.crops[-1]
     d = run.by_crop[crop]
-    spaces: list[tuple[str, np.ndarray]] = [
-        ("open (107)", np.arange(len(run.labels)))]
-    for name, entry in sel["restricted"].items():
-        spaces.append((f"{name} ({len(entry['codes'])})",
-                       np.array([run.index_of[c] for c in entry["codes"]])))
-
-    rows = []
+    space_name = f"open ({len(run.labels)})"
+    arg, prob = softmax_masked(d["logits"], np.arange(len(run.labels)))
+    pred = np.array(run.labels)[arg]
+    right = pred == d["lang"]
+    med_r = float(np.median(prob[right])) if right.any() else float("nan")
+    med_w = float(np.median(prob[~right])) if (~right).any() else float("nan")
+    rows = [[space_name, f"{med_r:.3f}", f"{med_w:.3f}",
+             f"{pct(float(right.mean()))}"]]
     thr_rows = []
-    out = {}
-    for space_name, idx in spaces:
-        arg, prob = softmax_masked(d["logits"], idx)
-        pred = np.array(run.labels)[arg]
-        right = pred == d["lang"]
-        med_r = float(np.median(prob[right])) if right.any() else float("nan")
-        med_w = float(np.median(prob[~right])) if (~right).any() else float("nan")
-        rows.append([space_name, f"{med_r:.3f}", f"{med_w:.3f}",
-                     f"{pct(float(right.mean()))}"])
-        per_thr = {}
-        for t in THRESHOLDS:
-            keep = prob >= t
-            cov = float(keep.mean())
-            prec = float(right[keep].mean()) if keep.any() else float("nan")
-            thr_rows.append([space_name, f"{t:.1f}", pct(cov), pct(prec)])
-            per_thr[t] = (cov, prec)
-        out[space_name] = {"median_right": med_r, "median_wrong": med_w,
-                           "thresholds": per_thr}
-
-    lines = [f"## (d) Confidence, `{crop}` clips", ""]
+    for t in THRESHOLDS:
+        keep = prob >= t
+        cov = float(keep.mean())
+        prec = float(right[keep].mean()) if keep.any() else float("nan")
+        thr_rows.append([space_name, f"{t:.1f}", pct(cov), pct(prec)])
+
+    lines = [f"## (c) Confidence, `{crop}` clips", ""]
     lines += md_table(["decision space", "median top-prob right",
                        "median top-prob wrong", "accuracy %"], rows)
     lines += md_table(["decision space", "threshold", "coverage %",
                        "precision %"], thr_rows)
-    return lines, out
+    return lines
 
 
-def section_confusions(run: Run) -> tuple[list[str], list]:
+def section_confusions(run: Run) -> list[str]:
     counts: dict[tuple[str, str], Counter] = defaultdict(Counter)
     for crop in run.crops:
         d = run.by_crop[crop]
@@ -442,85 +359,58 @@ def section_confusions(run: Run) -> tuple[list[str], list]:
     for (t, p), per_crop in ranked:
         rows.append([f"{t} -> {p}", str(sum(per_crop.values()))] +
                     [str(per_crop.get(c, 0)) for c in run.crops])
-    lines = [f"## (e) Top {TOP_CONFUSIONS} open-set confusion pairs "
+    lines = [f"## (d) Top {TOP_CONFUSIONS} open-set confusion pairs "
              f"(errors, all crops)", ""]
     lines += md_table(["true -> predicted", "total"] +
                       [crop_head(c) for c in run.crops], rows)
-    return lines, [(k, dict(v)) for k, v in ranked]
+    return lines
 
 
 # ---------------------------------------------------------------------------
-# langid.cpp original-evaluation comparison
+# Machine-readable headline (scripts/catalog/ingest_accuracy.py)
 # ---------------------------------------------------------------------------
 
-# langid.cpp's first evaluation, measured on an ONNX export of the same checkpoint,
-# untrimmed crops from the start of the clip. Reproduced here so score.py can
-# print the delta instead of a human eyeballing two documents.
-PLAN_V1_OPEN = {"3": 0.67, "5": 0.85, "10": 0.91, "full": 0.91}
-PLAN_V1_HANDY40 = {"3": 0.77, "5": 0.92, "10": 0.965, "full": 0.966}
-PLAN_V1_SELECTIONS = {
-    "en+zh":       {"3": 0.90, "5": 0.99,   "10": 1.000},
-    "en+es+pt":    {"3": 0.98, "5": 0.995,  "10": 1.000},
-    "es+pt":       {"3": 0.99, "5": 0.995,  "10": 1.000},
-    "ja+ko":       {"3": 0.88, "5": 0.98,   "10": 1.000},
-    "en+ru":       {"3": 0.99, "5": 1.00,   "10": 1.000},
-    "no+da":       {"3": 0.96, "5": 0.998,  "10": 1.000},
-    "en+de+ja":    {"3": 0.82, "5": 0.95,   "10": 0.998},
-    "en+fr+de+es": {"3": 0.89, "5": 0.94,   "10": 0.990},
-    "de+fr+ja":    {"3": 0.80, "5": 0.91,   "10": 0.985},
-    "cs+sk":       {"3": 0.83, "5": 0.92,   "10": 0.970},
-    "id+ms":       {"3": 0.88, "5": 0.895,  "10": 0.895},
-}
-
-
-def section_planv1(run: Run, openset: dict, restricted: dict,
-                   selections: dict) -> list[str]:
-    lines = ["## Delta vs langid.cpp's original ONNX evaluation (ONNX export of the same "
-             "checkpoint, untrimmed)", ""]
-    if run.header["recipe"]["trim"] != "none":
-        lines.append("langid.cpp's original numbers are untrimmed; this run uses "
-                     f"`--trim {run.header['recipe']['trim']}`, so the deltas "
-                     "below are a recipe difference, not a discrepancy.")
-        lines.append("")
-    rows = []
-    for crop in run.crops:
-        ref = PLAN_V1_OPEN.get(crop)
-        got = openset["mean"][crop][0]
-        rows.append(["all 107", crop, pct(ref) if ref else "-", pct(got),
-                     f"{100 * (got - ref):+.1f}" if ref else "-"])
-    if "handy40" in restricted:
-        for crop in run.crops:
-            ref = PLAN_V1_HANDY40.get(crop)
-            got = restricted["handy40"]["mean"][crop][0]
-            rows.append(["Handy's 40*", crop, pct(ref) if ref else "-", pct(got),
-                         f"{100 * (got - ref):+.1f}" if ref else "-"])
-    lines += md_table(["decision space", "crop", "original %", "this run %", "delta pp"],
-                      rows)
-    if "handy40" in restricted:
-        lines.append("\\* langid.cpp's original 40-language set is not recorded anywhere; "
-                     "this row compares against the placeholder list in "
-                     "`scripts/langid/selections.json`, so a delta of a point or "
-                     "two is expected from the set alone.")
-        lines.append("")
-
-    rows = []
-    crops_cmp = [c for c in run.crops if c in ("3", "5", "10")]
-    for name, ref in PLAN_V1_SELECTIONS.items():
-        got = selections.get(name)
-        if got is None:
-            continue
-        cells = []
-        for c in crops_cmp:
-            if c in ref and got.get(c) == got.get(c):
-                cells += [pct(ref[c]), pct(got[c]), f"{100 * (got[c] - ref[c]):+.1f}"]
-            else:
-                cells += ["-", "-", "-"]
-        rows.append([name] + cells)
-    head = ["selection"]
-    for c in crops_cmp:
-        head += [f"v1 {c}s", f"now {c}s", f"d {c}s"]
-    lines += md_table(head, rows)
-    return lines
+SCORE_SCHEMA = "transcribe-langid-score-v1"
+
+
+def dataset_of(recipe: dict) -> str | None:
+    """`fleurs` when every manifest is a scripts/langid/ingest.py FLEURS one
+    (`fleurs-<code>.manifest.jsonl`, test split only), else None."""
+    names = [Path(m).name for m in recipe.get("manifests", [])]
+    return "fleurs" if names and all(n.startswith("fleurs-") for n in names) else None
+
+
+def score_json(run: Run) -> dict:
+    """The open-set macro mean per crop, with what produced it. The catalog
+    takes one crop of this (the publication profile's) as a row; the run's
+    header is carried so the importer can check the recipe itself."""
+    h, r = run.header, run.header["recipe"]
+    _, means = openset(run)
+    return {
+        "schema": SCORE_SCHEMA,
+        "run": rel(run.path),
+        "engine": h["engine"],
+        "model": h["model"],
+        # The build the native library reports, not the checkout scoring it.
+        "engine_sha": r.get("native_commit") if r.get("native_commit") != "unknown" else None,
+        "backend": r.get("backend"),
+        "trim": r["trim"],
+        "dataset": dataset_of(r),
+        "split": "test",
+        "language": "mul",
+        "languages": run.languages,
+        "decision_space": f"open ({len(run.labels)})",
+        "created": h.get("created"),
+        "bootstrap": {"n": N_BOOT, "seed": BOOT_SEED},
+        "crops": {
+            crop: {
+                "n": int(run.by_crop[crop]["lang"].size),
+                "acc_pct": round(100.0 * mean, 2),
+                "ci95": [round(100.0 * lo, 2), round(100.0 * hi, 2)],
+            }
+            for crop, (mean, lo, hi) in means.items()
+        },
+    }
 
 
 # ---------------------------------------------------------------------------
@@ -528,119 +418,13 @@ def section_planv1(run: Run, openset: dict, restricted: dict,
 # ---------------------------------------------------------------------------
 
 
-def score_run(run: Run, sel: dict) -> tuple[list[str], dict]:
-    warnings = []
-    if "handy40" in sel["restricted"]:
-        warnings += check_handy40(sel["restricted"]["handy40"], run.languages)
-        if sel["restricted"]["handy40"]["placeholder"]:
-            warnings.append(
-                "`handy40` is a PLACEHOLDER 40-language list, "
-                "not the real Handy selection. Replace `restricted.handy40.codes` "
-                "in scripts/langid/selections.json and re-run score.py; run.py does "
-                "not need to run again.")
-
+def score_run(run: Run, sel: dict) -> list[str]:
     lines = [f"# Language-ID accuracy - `{run.name}`", ""]
-    lines += section_setup(run, sel, warnings)
-    a, openset = section_openset(run)
-    b, restricted = section_restricted(run, sel)
-    c, selections = section_selections(run, sel)
-    d, confidence = section_confidence(run, sel)
-    e, confusions = section_confusions(run)
-    lines += a + b + c + d + e
-    lines += section_planv1(run, openset, restricted, selections)
-    summary = {"open": openset, "restricted": restricted, "selections": selections,
-               "confidence": confidence, "confusions": confusions,
-               "warnings": warnings}
-    return lines, summary
-
-
-def docs_accuracy(runs: list[tuple[Run, dict, list[str]]], sel: dict,
-                  gate_note: str | None = None) -> list[str]:
-    """docs/accuracy.md: the two reference recipes side by side."""
-    first = runs[0][0]
-    r = first.header["recipe"]
-    lines = [
-        "# Accuracy",
-        "",
-        "> **These are the SpeechBrain reference numbers, not a separate "
-        "measurement of langid.cpp.**",
-        "> The C++ F32 result is required to agree with the reference on "
-        ">= 99.9% of decisions over this same corpus.",
-    ]
-    if gate_note:
-        lines.append(f"> {gate_note}")
-    else:
-        lines.append("> That gate has not been measured yet; until it is, treat "
-                     "this page as the reference's numbers only.")
-    lines += [
-        "",
-        "## Setup",
-        "",
-        f"- Model: `{first.header['model']}` @ `{r.get('revision')}` "
-        f"(speechbrain {r.get('speechbrain')}, torch {r.get('torch')}, CPU, fp32, "
-        f"batch {r.get('batch_size')}).",
-        f"- Dataset: FLEURS `test`, {len(first.languages)} languages "
-        f"(`{' '.join(first.languages)}`), the first "
-        f"{r['n_utterances'] // max(len(first.languages), 1)} utterances of at "
-        f"least 1 s in parquet order per language = {r['n_utterances']} clips, "
-        f"mean ~12 s.",
-        f"- Crops: {', '.join(c + ' s' for c in first.crops if c != 'full')} and "
-        f"the full clip. A clip shorter than the crop is scored whole.",
-        "- Two trim recipes:",
-        "  - **untrimmed** - the crop starts at sample 0 of the raw clip. This "
-        "is what langid.cpp's original ONNX evaluation measured; FLEURS clips open with silence, so "
-        "\"3 s\" is roughly 2 s of speech.",
-        "  - **trimmed** - leading audio is dropped up to the first 20 ms frame "
-        "whose RMS exceeds 5% of the clip's loudest frame, then the crop is "
-        "taken. This is the usage protocol langid.cpp asks callers to follow "
-        "(feed speech, not the raw capture), so it is the honest number for a "
-        "dictation app.",
-        "- Correctness is strict label-code equality. `nn` is not credited for "
-        "`no`, `be` not for `ru`, `jw` not for `id`.",
-        "- The mean is the macro mean over languages; the CI is a "
-        f"{N_BOOT}-resample stratified bootstrap, seed {BOOT_SEED}.",
-        "- Generated by `scripts/langid/score.py` from "
-        + ", ".join(f"`{rel(ru.path)}`" for ru, _s, _l in runs)
-        + ".",
-        "",
-    ]
-
-    crops = first.crops
-    lines += ["## Top-1 accuracy, mean over the "
-              f"{len(first.languages)} languages, %", ""]
-    rows = []
-    for ru, summary, label in runs:
-        rows.append([f"all 107 ({label})"] +
-                    [pct(summary["open"]["mean"][c][0]) for c in crops])
-    for ru, summary, label in runs:
-        if "handy40" in summary["restricted"]:
-            rows.append([f"handy40* ({label})"] +
-                        [pct(summary["restricted"]["handy40"]["mean"][c][0])
-                         for c in crops])
-    lines += md_table(["decision space"] +
-                      [crop_head(c) for c in crops], rows)
-    lines.append("\\* `handy40` is a **placeholder** 40-language dictation list "
-                 ", not the real Handy selection; see "
-                 "`scripts/langid/selections.json`.")
-    lines.append("")
-
-    lines += ["## Restricted to a user's selection, %", ""]
-    head = ["selection"]
-    for _ru, _s, label in runs:
-        head += [f"{label} {crop_head(c)}" for c in crops]
-    rows = []
-    for entry in sel["selections"]:
-        row = [entry["name"]]
-        for _ru, summary, _label in runs:
-            row += [pct(summary["selections"][entry["name"]].get(c, float("nan")))
-                    for c in crops]
-        rows.append(row)
-    lines += md_table(head, rows)
-
-    lines += ["## Per-run detail", ""]
-    for ru, _s, label in runs:
-        lines.append(f"- **{label}**: `reports/langid/{ru.name}.md`")
-    lines.append("")
+    lines += section_setup(run, sel)
+    lines += section_openset(run)
+    lines += section_selections(run, sel)
+    lines += section_confidence(run)
+    lines += section_confusions(run)
     return lines
 
 
@@ -651,44 +435,35 @@ def main(argv: list[str] | None = None) -> int:
     p.add_argument("--selections", type=Path,
                    default=Path(__file__).resolve().parent / "selections.json")
     p.add_argument("--md", type=Path,
-                   help="write the first run's markdown report here")
-    p.add_argument("--docs", type=Path,
-                   help="write a combined docs/accuracy.md from every run given")
-    p.add_argument("--gate-note", default=None,
-                   help="one sentence recording the measured dataset-gate "
-                        "result; goes into --docs verbatim. Passing a claim "
-                        "compare.py did not produce is how this page starts "
-                        "lying, so quote its output.")
+                   help="write the first run's markdown report here (later "
+                        "runs go next to it as <run name>.md)")
+    p.add_argument("--json", type=Path,
+                   help="write the first run's headline (open-set accuracy "
+                        "per crop, with provenance) here, for "
+                        "scripts/catalog/ingest_accuracy.py; name it "
+                        "<gguf stem>.fleurs-mul.score.json under reports/langid/")
     args = p.parse_args(argv)
+    if args.json and len(args.runs) > 1:
+        p.error("--json takes exactly one run")
 
     loaded = [Run(r) for r in args.runs]
     sel = load_selections(args.selections, loaded[0].labels)
+    scored = [(run, score_run(run, sel)) for run in loaded]
 
-    scored = []
-    for run in loaded:
-        lines, summary = score_run(run, sel)
-        label = run.header["recipe"]["trim"]
-        label = "untrimmed" if label == "none" else "trimmed"
-        scored.append((run, summary, label, lines))
-
-    text = "\n".join(scored[0][3]) + "\n"
+    text = "\n".join(scored[0][1]) + "\n"
     sys.stdout.write(text)
     if args.md:
         args.md.parent.mkdir(parents=True, exist_ok=True)
         args.md.write_text(text)
         print(f"wrote {args.md}", file=sys.stderr)
-    for run, _summary, _label, lines in scored[1:]:
-        if args.md:
+        for run, lines in scored[1:]:
             alt = args.md.with_name(f"{run.name}.md")
             alt.write_text("\n".join(lines) + "\n")
             print(f"wrote {alt}", file=sys.stderr)
-
-    if args.docs:
-        doc = docs_accuracy([(r, s, l) for r, s, l, _ln in scored], sel,
-                            args.gate_note)
-        args.docs.parent.mkdir(parents=True, exist_ok=True)
-        args.docs.write_text("\n".join(doc) + "\n")
-        print(f"wrote {args.docs}", file=sys.stderr)
+    if args.json:
+        args.json.parent.mkdir(parents=True, exist_ok=True)
+        args.json.write_text(json.dumps(score_json(loaded[0]), indent=2) + "\n")
+        print(f"wrote {args.json}", file=sys.stderr)
     return 0
 
 
diff --git a/scripts/langid/selections.json b/scripts/langid/selections.json
index 15204d1ef..2eb2d0ce0 100644
--- a/scripts/langid/selections.json
+++ b/scripts/langid/selections.json
@@ -4,15 +4,7 @@
     "Decision spaces scored by scripts/langid/score.py from a single run.py sweep.",
     "Codes are VoxLingua107 label codes; `aliases` maps the spellings a caller",
     "is likely to use onto them (the same aliases the GGUF carries).",
-    "",
-    "handy40 IS A PLACEHOLDER. The real Handy language list",
-    "was not available when this was written, so it is a plausible 40-language",
-    "dictation set: the 15 evaluated languages plus 25 widely-dictated ones.",
-    "Replace `handy40.codes` with the real list and re-run score.py; nothing",
-    "else has to change, because run.py stores all 107 logits per utterance.",
-    "",
-    "The 11 pair/triple selections are langid.cpp's original evaluation table,",
-    "with `nb+da` written `no+da`: VoxLingua107 spells Norwegian Bokmal `no`."
+    "VoxLingua107 spells Norwegian Bokmal `no`, so the Norwegian/Danish pair is `no+da`."
   ],
   "aliases": {
     "he": "iw",
@@ -20,25 +12,13 @@
     "fil": "tl",
     "nb": "no"
   },
-  "restricted": {
-    "handy40": {
-      "label": "Handy's 40 (placeholder)",
-      "placeholder": true,
-      "codes": [
-        "en", "zh", "de", "fr", "es", "pt", "ja", "ko", "ru", "cs",
-        "sk", "no", "da", "id", "ms", "it", "nl", "pl", "tr", "sv",
-        "fi", "el", "hu", "ro", "uk", "bg", "hr", "sr", "vi", "th",
-        "hi", "ar", "he", "fa", "ta", "bn", "ca", "gl", "sl", "ur"
-      ]
-    }
-  },
   "selections": [
     { "name": "en+zh",       "codes": ["en", "zh"] },
     { "name": "en+es+pt",    "codes": ["en", "es", "pt"] },
     { "name": "es+pt",       "codes": ["es", "pt"] },
     { "name": "ja+ko",       "codes": ["ja", "ko"] },
     { "name": "en+ru",       "codes": ["en", "ru"] },
-    { "name": "no+da",       "codes": ["no", "da"], "plan_v1_name": "nb+da" },
+    { "name": "no+da",       "codes": ["no", "da"] },
     { "name": "en+de+ja",    "codes": ["en", "de", "ja"] },
     { "name": "en+fr+de+es", "codes": ["en", "fr", "de", "es"] },
     { "name": "de+fr+ja",    "codes": ["de", "fr", "ja"] },
diff --git a/scripts/lib/ecapa_numpy.py b/scripts/lib/ecapa_numpy.py
index 61d49df15..8ab0d8a7a 100644
--- a/scripts/lib/ecapa_numpy.py
+++ b/scripts/lib/ecapa_numpy.py
@@ -47,7 +47,7 @@
 
 import numpy as np
 import gguf
-from gguf import GGMLQuantizationType, GGUFValueType
+from gguf import GGMLQuantizationType
 
 
 class EcapaNumpy:
@@ -62,10 +62,7 @@ def __init__(self, gguf_path: str | Path) -> None:
         for name, field in reader.fields.items():
             if not field.types:
                 continue
-            if field.types[0] == GGUFValueType.ARRAY:
-                self.meta[name] = field.contents()
-            else:
-                self.meta[name] = field.contents()
+            self.meta[name] = field.contents()
 
         arch = self.meta.get("general.architecture")
         if arch != "ecapa_tdnn":
@@ -173,8 +170,9 @@ def _bn(x: np.ndarray, scale: np.ndarray, shift: np.ndarray) -> np.ndarray:
         """`y = x * scale + shift`, the converter's affine form of BatchNorm.
 
         Broadcasts over time. The scale/shift pair already absorbed
-        gamma/beta/running_mean/running_var/eps at conversion time
-        (notes/DECISIONS.md D08 explains why this cannot fold into the conv).
+        gamma/beta/running_mean/running_var/eps at conversion time. It cannot
+        fold into the conv: a TDNNBlock is conv -> ReLU -> BN, and the ReLU
+        sits between them.
         """
         return (x * scale + shift).astype(np.float32)
 
diff --git a/scripts/lib/quant_policy.py b/scripts/lib/quant_policy.py
index 72f4e5956..43561c003 100644
--- a/scripts/lib/quant_policy.py
+++ b/scripts/lib/quant_policy.py
@@ -49,9 +49,8 @@
     # are slower than Q8_0 on CPU. Ship only the near-reference tiers.
     "sortformer": ("F16", "Q8_0"),
     # ecapa_tdnn (language ID): a 21M-parameter model whose decision is an
-    # argmax over 107 logits; Q8_0 already costs ~7 points of agreement with
-    # the reference on FLEURS for langid.cpp's Python-written file (re-measure
-    # on the transcribe-quantize output), and the K tiers would
+    # argmax over 107 logits. Q8_0 ships for its download size (the loader
+    # widens it to F16, see src/arch/ecapa_tdnn/model.cpp); the K tiers would
     # save a few MB at most. Ship only the near-reference tiers.
     "ecapa_tdnn": ("F16", "Q8_0"),
 }
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index b4307e6cb..b8c1c0203 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -67,6 +67,8 @@ add_library(transcribe
     arch/sortformer/model.cpp
     arch/sortformer/stream.cpp
     arch/sortformer/weights.cpp
+    arch/ecapa_tdnn/cpu_gemm.cpp
+    arch/ecapa_tdnn/cpu_ops.cpp
     arch/ecapa_tdnn/graph.cpp
     arch/ecapa_tdnn/mel.cpp
     arch/ecapa_tdnn/model.cpp
@@ -291,6 +293,14 @@ else()
     set_source_files_properties(third_party/miniz/miniz.c PROPERTIES COMPILE_OPTIONS "-w")
 endif()
 
+# ecapa_tdnn's fused CPU kernels round every multiply and add separately so
+# they reproduce the stock ggml op chain they replace; GCC/Clang contract
+# a*b+c into an FMA by default (-ffp-contract=fast), which changes the bits.
+if(NOT MSVC)
+    set_source_files_properties(arch/ecapa_tdnn/cpu_gemm.cpp arch/ecapa_tdnn/cpu_ops.cpp
+        PROPERTIES COMPILE_OPTIONS "-ffp-contract=off")
+endif()
+
 if(WIN32)
     # Windows toolchains hide M_PI and friends behind _USE_MATH_DEFINES
     # (used by the mel frontends and Whisper's bin loader). MSVC and MinGW
diff --git a/src/arch/ecapa_tdnn/cpu_gemm.cpp b/src/arch/ecapa_tdnn/cpu_gemm.cpp
new file mode 100644
index 000000000..a74bc6fc0
--- /dev/null
+++ b/src/arch/ecapa_tdnn/cpu_gemm.cpp
@@ -0,0 +1,426 @@
+// arch/ecapa_tdnn/cpu_gemm.cpp - see cpu_gemm.h.
+//
+// Kernel: a 16 x NR register tile (two 8-float rows of accumulators per
+// output column, NR <= 6 columns): per k it widens 16 packed weights, then
+// broadcasts one activation value per column and issues two FMAs per column.
+// The activations are read in place (each output column's K values are
+// contiguous in ggml's [K, N] layout), so nothing is packed at run time.
+//
+// Work split: the output is cut into row blocks of kRowBlock rows and column
+// blocks sized so one block's activations stay in L2; blocks are dealt to
+// the ggml threads round robin.
+//
+// Built with -ffp-contract=off (src/CMakeLists.txt): the epilogue's multiply
+// and add must round separately, as the stock ggml_mul / ggml_add do.
+
+#include "cpu_gemm.h"
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstring>
+#include <vector>
+
+#if (defined(__x86_64__) || defined(__i386__)) && (defined(__GNUC__) || defined(__clang__))
+#    define ECAPA_GEMM_X86 1
+#    include <immintrin.h>
+#else
+#    define ECAPA_GEMM_X86 0
+#endif
+
+namespace transcribe::ecapa_tdnn::gemm {
+
+namespace {
+
+#if ECAPA_GEMM_X86
+
+#    define ECAPA_TARGET __attribute__((target("avx2,fma,f16c")))
+
+constexpr int     kNR = 6;    // output columns per micro-tile
+constexpr int64_t kKC = 256;  // k block: 16 x 256 F16 weights = 8 KiB in L1
+constexpr int64_t kNC = 192;  // column block: 256 x 192 packed F32 = 192 KiB in L2
+
+// 16 x 6 micro-tile over k in [0, kc): a = packed weight panel at k0
+// (16 values per k), bp = packed activations (6 values per k). Writes the
+// raw partial sums to acc[12] (two __m256 per column); the caller loads /
+// stores / applies the epilogue. The accumulators are named locals so the
+// compiler keeps all twelve in registers through the k loop.
+#    define ECAPA_FMA6(W0, W1, BP)                          \
+        do {                                                \
+            __m256 xb = _mm256_broadcast_ss((BP) + 0);      \
+            a00       = _mm256_fmadd_ps(W0, xb, a00);       \
+            a01       = _mm256_fmadd_ps(W1, xb, a01);       \
+            xb        = _mm256_broadcast_ss((BP) + 1);      \
+            a10       = _mm256_fmadd_ps(W0, xb, a10);       \
+            a11       = _mm256_fmadd_ps(W1, xb, a11);       \
+            xb        = _mm256_broadcast_ss((BP) + 2);      \
+            a20       = _mm256_fmadd_ps(W0, xb, a20);       \
+            a21       = _mm256_fmadd_ps(W1, xb, a21);       \
+            xb        = _mm256_broadcast_ss((BP) + 3);      \
+            a30       = _mm256_fmadd_ps(W0, xb, a30);       \
+            a31       = _mm256_fmadd_ps(W1, xb, a31);       \
+            xb        = _mm256_broadcast_ss((BP) + 4);      \
+            a40       = _mm256_fmadd_ps(W0, xb, a40);       \
+            a41       = _mm256_fmadd_ps(W1, xb, a41);       \
+            xb        = _mm256_broadcast_ss((BP) + 5);      \
+            a50       = _mm256_fmadd_ps(W0, xb, a50);       \
+            a51       = _mm256_fmadd_ps(W1, xb, a51);       \
+        } while (0)
+
+template <bool F16>
+ECAPA_TARGET void tile_core(const void * a, const float * bp, int64_t kc, __m256 * acc) {
+    __m256 a00 = acc[0], a01 = acc[1], a10 = acc[2], a11 = acc[3], a20 = acc[4], a21 = acc[5];
+    __m256 a30 = acc[6], a31 = acc[7], a40 = acc[8], a41 = acc[9], a50 = acc[10], a51 = acc[11];
+    if constexpr (F16) {
+        const uint16_t * ap = static_cast<const uint16_t *>(a);
+        for (int64_t k = 0; k < kc; ++k) {
+            const __m256 w0 = _mm256_cvtph_ps(_mm_loadu_si128(reinterpret_cast<const __m128i *>(ap)));
+            const __m256 w1 = _mm256_cvtph_ps(_mm_loadu_si128(reinterpret_cast<const __m128i *>(ap + 8)));
+            ECAPA_FMA6(w0, w1, bp);
+            ap += 16;
+            bp += kNR;
+        }
+    } else {
+        const float * ap = static_cast<const float *>(a);
+        for (int64_t k = 0; k < kc; ++k) {
+            const __m256 w0 = _mm256_loadu_ps(ap);
+            const __m256 w1 = _mm256_loadu_ps(ap + 8);
+            ECAPA_FMA6(w0, w1, bp);
+            ap += 16;
+            bp += kNR;
+        }
+    }
+    acc[0] = a00, acc[1] = a01, acc[2] = a10, acc[3] = a11, acc[4] = a20, acc[5] = a21;
+    acc[6] = a30, acc[7] = a31, acc[8] = a40, acc[9] = a41, acc[10] = a50, acc[11] = a51;
+}
+
+#    undef ECAPA_FMA6
+
+template <bool F16>
+ECAPA_TARGET void tile(const void *  a,
+                       const float * bp,
+                       int64_t       kc,
+                       float *       y,
+                       size_t        ldy,
+                       int           n_valid,
+                       bool          first,
+                       bool          last,
+                       Epilogue      ep,
+                       const float * b,
+                       const float * s,
+                       const float * sh) {
+    alignas(32) __m256 acc[2 * kNR];
+    for (int n = 0; n < kNR; ++n) {
+        if (first || n >= n_valid) {
+            acc[2 * n]     = _mm256_setzero_ps();
+            acc[2 * n + 1] = _mm256_setzero_ps();
+        } else {
+            acc[2 * n]     = _mm256_loadu_ps(y + static_cast<size_t>(n) * ldy);
+            acc[2 * n + 1] = _mm256_loadu_ps(y + static_cast<size_t>(n) * ldy + 8);
+        }
+    }
+    tile_core<F16>(a, bp, kc, acc);
+
+    if (last && ep != Epilogue::None) {
+        const __m256 zero = _mm256_setzero_ps();
+        const __m256 s0   = _mm256_loadu_ps(s);
+        const __m256 s1   = _mm256_loadu_ps(s + 8);
+        const __m256 h0   = _mm256_loadu_ps(sh);
+        const __m256 h1   = _mm256_loadu_ps(sh + 8);
+        for (int n = 0; n < kNR; ++n) {
+            __m256 v0 = acc[2 * n];
+            __m256 v1 = acc[2 * n + 1];
+            if (b != nullptr) {
+                v0 = _mm256_add_ps(v0, _mm256_loadu_ps(b));
+                v1 = _mm256_add_ps(v1, _mm256_loadu_ps(b + 8));
+            }
+            v0             = _mm256_max_ps(v0, zero);
+            v1             = _mm256_max_ps(v1, zero);
+            acc[2 * n]     = _mm256_add_ps(_mm256_mul_ps(v0, s0), h0);
+            acc[2 * n + 1] = _mm256_add_ps(_mm256_mul_ps(v1, s1), h1);
+        }
+    }
+    for (int n = 0; n < n_valid; ++n) {
+        float * yc = y + static_cast<size_t>(n) * ldy;
+        _mm256_storeu_ps(yc, acc[2 * n]);
+        _mm256_storeu_ps(yc + 8, acc[2 * n + 1]);
+        if (last && ep == Epilogue::ReluBnTanh) {
+            for (int r = 0; r < kPanel; ++r) {
+                yc[r] = std::tanh(yc[r]);
+            }
+        }
+    }
+}
+
+// Pack columns [j0, j0 + nc) x k [k0, k0 + kc) of x (column j's K values at
+// x + j*ldx) into kNR-wide k-major micro-panels, zero filling past nc.
+void pack_x(const float * x, size_t ldx, int64_t j0, int64_t nc, int64_t k0, int64_t kc, float * bp) {
+    const int64_t n_panels = (nc + kNR - 1) / kNR;
+    for (int64_t p = 0; p < n_panels; ++p) {
+        float * dst = bp + static_cast<size_t>(p * kc * kNR);
+        for (int n = 0; n < kNR; ++n) {
+            const int64_t j = p * kNR + n;
+            if (j >= nc) {
+                for (int64_t k = 0; k < kc; ++k) {
+                    dst[static_cast<size_t>(k * kNR + n)] = 0.0f;
+                }
+                continue;
+            }
+            const float * src = x + static_cast<size_t>(j0 + j) * ldx + static_cast<size_t>(k0);
+            for (int64_t k = 0; k < kc; ++k) {
+                dst[static_cast<size_t>(k * kNR + n)] = src[k];
+            }
+        }
+    }
+}
+
+// Work split: the output is cut into (row block x column block) jobs dealt
+// to the threads round robin. A row block's weight slice for one k block
+// (kMB x kKC F16 = 128 KiB) and the packed activations of one column block
+// (kKC x <= kNC F32 = 192 KiB) both stay in L2 while the micro-tiles sweep
+// them. The k order inside every output element is fixed (segments, then k
+// blocks, then k), so the result does not depend on the thread count.
+constexpr int64_t kMB = 256;
+
+// Claim the next job of thread `owner`'s range [owner*per, ...). A slot
+// packs (generation << 32 | jobs taken); a stale generation reads as zero.
+int64_t claim(Desc & d, int owner, uint32_t gen, int64_t limit) {
+    std::atomic<uint64_t> & a   = d.slot[owner];
+    uint64_t                cur = a.load(std::memory_order_relaxed);
+    for (;;) {
+        const uint32_t g     = static_cast<uint32_t>(cur >> 32);
+        const int64_t  taken = g == gen ? static_cast<int64_t>(cur & 0xffffffffu) : 0;
+        if (taken >= limit) {
+            return -1;
+        }
+        const uint64_t next = (static_cast<uint64_t>(gen) << 32) | static_cast<uint64_t>(taken + 1);
+        if (a.compare_exchange_weak(cur, next, std::memory_order_relaxed)) {
+            return taken;
+        }
+    }
+}
+
+void gemm_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    Desc & d = *static_cast<Desc *>(ud);
+    if (nth > Desc::kMaxThreads) {
+        nth = Desc::kMaxThreads;
+        if (ith >= nth) {
+            return;
+        }
+    }
+    // The generation advances once per compute: the last thread to arrive
+    // in compute n bumps it for compute n + 1. Threads of one compute all
+    // read the same value because nobody arrives before every thread has
+    // read it (the read happens before this thread's own arrival).
+    const uint32_t gen = d.generation.load(std::memory_order_acquire) + 1;
+    const int64_t M = d.M;
+    const int64_t N = d.N;
+
+    const int64_t mb   = std::min(kMB, M);
+    const int64_t n_rb = (M + mb - 1) / mb;
+    int64_t       n_cb = (N + kNC - 1) / kNC;
+    if (n_rb * n_cb < 4 * nth) {
+        // Too few jobs to keep every thread busy: narrow the column blocks.
+        n_cb = std::min<int64_t>((N + kNR - 1) / kNR, (4 * nth + n_rb - 1) / n_rb);
+    }
+    int64_t cb_w = (N + n_cb - 1) / n_cb;
+    cb_w         = (cb_w + kNR - 1) / kNR * kNR;
+    n_cb         = (N + cb_w - 1) / cb_w;
+
+    const float * bias  = d.b_src >= 0 ? static_cast<const float *>(dst->src[d.b_src]->data) : nullptr;
+    const float * scale = d.scale_src >= 0 ? static_cast<const float *>(dst->src[d.scale_src]->data) : nullptr;
+    const float * shift = d.shift_src >= 0 ? static_cast<const float *>(dst->src[d.shift_src]->data) : nullptr;
+    const size_t  ldy   = dst->nb[1] / sizeof(float);
+    float *       y0    = static_cast<float *>(dst->data);
+
+    int64_t n_blocks = 0;  // k blocks over all segments
+    for (int s = 0; s < d.n_segs; ++s) {
+        n_blocks += (dst->src[d.segs[s].w_src]->ne[0] + kKC - 1) / kKC;
+    }
+
+    thread_local std::vector<float> bp;
+    bp.resize(static_cast<size_t>(std::max(cb_w, int64_t{ kNR }) * kKC));
+
+    const int64_t n_jobs = n_rb * n_cb;
+    const int64_t per    = (n_jobs + nth - 1) / nth;
+
+    // Own range first, then steal from the others in ring order.
+    auto next_job = [&]() -> int64_t {
+        for (int r = 0; r < nth; ++r) {
+            const int     owner = (ith + r) % nth;
+            const int64_t lo    = owner * per;
+            const int64_t lim   = std::min(per, n_jobs - lo);
+            if (lim <= 0) {
+                continue;
+            }
+            const int64_t k = claim(d, owner, gen, lim);
+            if (k >= 0) {
+                return lo + k;
+            }
+        }
+        return -1;
+    };
+
+    for (int64_t job = next_job(); job >= 0; job = next_job()) {
+        const int64_t rb       = job % n_rb;
+        const int64_t cb       = job / n_rb;
+        const int64_t i0       = rb * mb;
+        const int64_t i1       = std::min(M, i0 + mb);
+        const int64_t j0       = cb * cb_w;
+        const int64_t ncb      = std::min(cb_w, N - j0);
+        const int64_t n_panels = (ncb + kNR - 1) / kNR;
+
+        int64_t blk = 0;
+        for (int s = 0; s < d.n_segs; ++s) {
+            const ggml_tensor * w   = dst->src[d.segs[s].w_src];
+            const ggml_tensor * x   = dst->src[d.segs[s].x_src];
+            const int64_t       K   = w->ne[0];
+            const bool          f16 = w->type == GGML_TYPE_F16;
+            const size_t        esz = ggml_type_size(w->type);
+            const size_t        ldx = x->nb[1] / sizeof(float);
+            const float * xs = static_cast<const float *>(x->data) + static_cast<size_t>(d.segs[s].x_row0) * ldx;
+            const char *  wm = static_cast<const char *>(w->data) + static_cast<size_t>(d.segs[s].mat) * M * K * esz;
+
+            for (int64_t k0 = 0; k0 < K; k0 += kKC, ++blk) {
+                const int64_t kc    = std::min(kKC, K - k0);
+                const bool    first = blk == 0;
+                const bool    last  = blk == n_blocks - 1;
+                pack_x(xs, ldx, j0, ncb, k0, kc, bp.data());
+
+                for (int64_t i = i0; i < i1; i += kPanel) {
+                    const char *  a  = wm + (static_cast<size_t>(i) * K + static_cast<size_t>(k0) * kPanel) * esz;
+                    const float * bb = bias ? bias + i : nullptr;
+                    const float * ss = scale ? scale + i : nullptr;
+                    const float * hh = shift ? shift + i : nullptr;
+                    for (int64_t p = 0; p < n_panels; ++p) {
+                        const int64_t jj = j0 + p * kNR;
+                        const int     nv = static_cast<int>(std::min<int64_t>(kNR, j0 + ncb - jj));
+                        float *       y  = y0 + static_cast<size_t>(jj) * ldy + static_cast<size_t>(i);
+                        const float * pb = bp.data() + static_cast<size_t>(p * kc * kNR);
+                        if (f16) {
+                            tile<true>(a, pb, kc, y, ldy, nv, first, last, d.ep, bb, ss, hh);
+                        } else {
+                            tile<false>(a, pb, kc, y, ldy, nv, first, last, d.ep, bb, ss, hh);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    if (d.arrived.fetch_add(1, std::memory_order_acq_rel) + 1 == nth) {
+        d.arrived.store(0, std::memory_order_relaxed);
+        d.generation.store(gen, std::memory_order_release);
+    }
+}
+
+bool cpu_has_kernel() {
+    __builtin_cpu_init();
+    return __builtin_cpu_supports("avx2") && __builtin_cpu_supports("fma") && __builtin_cpu_supports("f16c");
+}
+
+#endif  // ECAPA_GEMM_X86
+
+}  // namespace
+
+bool available() {
+#if ECAPA_GEMM_X86
+    static const bool ok = cpu_has_kernel();
+    return ok;
+#else
+    return false;
+#endif
+}
+
+bool pack_weight(ggml_tensor * t) {
+    if (t == nullptr || (t->type != GGML_TYPE_F32 && t->type != GGML_TYPE_F16) || !ggml_is_contiguous(t)) {
+        return false;
+    }
+    const int64_t K = t->ne[0];
+    const int64_t M = t->ne[1];
+    const int64_t Q = t->ne[2] * t->ne[3];
+    if (M % kPanel != 0 || K <= 0) {
+        return false;
+    }
+    const size_t         esz = ggml_type_size(t->type);
+    std::vector<uint8_t> src(ggml_nbytes(t));
+    std::vector<uint8_t> dst(src.size());
+    ggml_backend_tensor_get(t, src.data(), 0, src.size());
+    for (int64_t q = 0; q < Q; ++q) {
+        const uint8_t * sm = src.data() + static_cast<size_t>(q * M * K) * esz;
+        uint8_t *       dm = dst.data() + static_cast<size_t>(q * M * K) * esz;
+        for (int64_t i = 0; i < M; ++i) {
+            for (int64_t k = 0; k < K; ++k) {
+                const size_t from = static_cast<size_t>(i * K + k);
+                const size_t to   = static_cast<size_t>((i / kPanel) * K * kPanel + k * kPanel + (i % kPanel));
+                std::memcpy(dm + to * esz, sm + from * esz, esz);
+            }
+        }
+    }
+    ggml_backend_tensor_set(t, dst.data(), 0, dst.size());
+    return true;
+}
+
+ggml_tensor * mul_mat(ggml_context *  ctx,
+                      Arena &         arena,
+                      const Segment * segs,
+                      int             n_segs,
+                      int64_t         N,
+                      Epilogue        ep,
+                      ggml_tensor *   b,
+                      ggml_tensor *   scale,
+                      ggml_tensor *   shift) {
+#if ECAPA_GEMM_X86
+    if (n_segs < 1 || n_segs > kMaxSegs) {
+        return nullptr;
+    }
+    Desc & d   = arena.emplace_back();
+    d.n_segs   = n_segs;
+    d.M        = segs[0].w->ne[1];
+    d.N        = N;
+    d.ep       = ep;
+
+    ggml_tensor * args[GGML_MAX_SRC] = {};
+    int           n                  = 0;
+    auto          add                = [&](ggml_tensor * t) {
+        for (int i = 0; i < n; ++i) {
+            if (args[i] == t) {
+                return i;
+            }
+        }
+        args[n] = t;
+        return n++;
+    };
+    for (int s = 0; s < n_segs; ++s) {
+        d.segs[s].w_src  = add(segs[s].w);
+        d.segs[s].mat    = segs[s].mat;
+        d.segs[s].x_src  = add(segs[s].x);
+        d.segs[s].x_row0 = segs[s].x_row0;
+    }
+    if (b != nullptr) {
+        d.b_src = add(b);
+    }
+    if (scale != nullptr) {
+        d.scale_src = add(scale);
+    }
+    if (shift != nullptr) {
+        d.shift_src = add(shift);
+    }
+
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, d.M, N, 1, 1, args, n, gemm_fn, GGML_N_TASKS_MAX, &d);
+#else
+    (void) ctx;
+    (void) arena;
+    (void) segs;
+    (void) n_segs;
+    (void) N;
+    (void) ep;
+    (void) b;
+    (void) scale;
+    (void) shift;
+    return nullptr;
+#endif
+}
+
+}  // namespace transcribe::ecapa_tdnn::gemm
diff --git a/src/arch/ecapa_tdnn/cpu_gemm.h b/src/arch/ecapa_tdnn/cpu_gemm.h
new file mode 100644
index 000000000..670820abd
--- /dev/null
+++ b/src/arch/ecapa_tdnn/cpu_gemm.h
@@ -0,0 +1,113 @@
+// arch/ecapa_tdnn/cpu_gemm.h - x86 AVX2 GEMM for the ECAPA-TDNN CPU graph.
+//
+// INTERNAL to src/arch/ecapa_tdnn/. Optional: available() is false on
+// non-x86 builds and on x86 CPUs without AVX2 + FMA + F16C, and the graph
+// then uses ggml_mul_mat.
+//
+// Why: on AVX2, ggml's F16 x F32 matmul rounds the activations to F16 and
+// widens both operands inside its innermost loop, which runs ~1.7x slower
+// than its F32 path. This kernel keeps the weights F16 in memory (repacked
+// once at load into 16-row panels), widens them in registers, keeps the
+// activations F32, and accumulates in F32. It also fuses what the graph
+// would otherwise do in separate passes over the output:
+//   - several (weight, activation) segments summed into one output, which
+//     covers both the MFA stage (three blocks) and a dilated k-tap
+//     convolution (one segment per tap, the activation shifted by k*d rows);
+//   - the TDNN epilogue max(acc + b, 0) * scale + shift, optional tanh.
+//
+// Every output element is one F32 FMA chain over k in segment order, so the
+// result does not depend on the thread count.
+
+#pragma once
+
+#include <atomic>
+#include <cstddef>
+#include <cstdint>
+#include <deque>
+
+struct ggml_context;
+struct ggml_tensor;
+
+namespace transcribe::ecapa_tdnn::gemm {
+
+// True when this build and this CPU can run the kernel.
+bool available();
+
+// Rows per weight panel. A weight with M output rows is usable when
+// M % kPanel == 0.
+constexpr int kPanel = 16;
+
+// Most segments one mul_mat sums: the MFA stage's three blocks, or a k-tap
+// convolution with k <= 3. A model with wider Res2Net kernels keeps those
+// weights on ggml_mul_mat (model.cpp only packs them when k <= kMaxSegs).
+constexpr int kMaxSegs = 3;
+
+// Repack a weight in place into panel-major order. `t` is a ggml weight
+// ne = [K, M, n_mats] (rows of K contiguous, F32 or F16), on a host buffer.
+// Matrix q's row i, column k moves to
+//   q*M*K + (i / 16)*K*16 + k*16 + (i % 16)          (elements)
+// After this only the GEMM may read `t`. Returns false (and leaves `t`
+// untouched) if the type or shape is unsupported.
+bool pack_weight(ggml_tensor * t);
+
+enum class Epilogue : int32_t {
+    None    = 0,  // y = acc
+    ReluBn  = 1,  // y = max(acc + b, 0) * scale + shift (b optional)
+    ReluBnTanh = 2,
+};
+
+// One operand pair: rows [0, M) of packed weight matrix `mat` of `w` times
+// the activation `x` ne = [K, *] starting at row `x_row0`.
+struct Segment {
+    ggml_tensor * w      = nullptr;
+    int           mat    = 0;
+    ggml_tensor * x      = nullptr;
+    int64_t       x_row0 = 0;
+};
+
+// Per-node descriptor, read by the kernel at compute time. Owned by the
+// caller's Arena, which must outlive the graph's computation (a deque, so
+// growing it never moves an existing descriptor).
+struct Desc {
+    struct Seg {
+        int     w_src  = -1;  // dst->src index of the packed weight
+        int     mat    = 0;
+        int     x_src  = -1;  // dst->src index of the activation
+        int64_t x_row0 = 0;
+    };
+
+    Seg      segs[kMaxSegs];
+    int      n_segs    = 0;
+    int64_t  M         = 0;
+    int64_t  N         = 0;
+    Epilogue ep        = Epilogue::None;
+    int      b_src     = -1;
+    int      scale_src = -1;
+    int      shift_src = -1;
+
+    // Work stealing between the threads of one compute (cpu_gemm.cpp).
+    // Slot t holds thread t's progress through its own job range, tagged
+    // with the compute generation so a reused graph starts clean without a
+    // reset pass (custom ops have no barrier to reset behind).
+    static constexpr int kMaxThreads = 256;
+    std::atomic<uint64_t> slot[kMaxThreads] = {};
+    std::atomic<uint32_t> generation{ 0 };
+    std::atomic<int32_t>  arrived{ 0 };
+};
+
+using Arena = std::deque<Desc>;
+
+// y[M, N] = epilogue(sum over segments of W_s @ x_s[x_row0 : x_row0 + N]).
+// Returns a new [M, N] tensor. b / scale / shift are F32 vectors ne = [M]
+// (b may be null; all null for Epilogue::None). 1..kMaxSegs segments.
+ggml_tensor * mul_mat(ggml_context *  ctx,
+                      Arena &         arena,
+                      const Segment * segs,
+                      int             n_segs,
+                      int64_t         N,
+                      Epilogue        ep,
+                      ggml_tensor *   b,
+                      ggml_tensor *   scale,
+                      ggml_tensor *   shift);
+
+}  // namespace transcribe::ecapa_tdnn::gemm
diff --git a/src/arch/ecapa_tdnn/cpu_ops.cpp b/src/arch/ecapa_tdnn/cpu_ops.cpp
new file mode 100644
index 000000000..332c08d64
--- /dev/null
+++ b/src/arch/ecapa_tdnn/cpu_ops.cpp
@@ -0,0 +1,669 @@
+// arch/ecapa_tdnn/cpu_ops.cpp - fused CPU kernels; see cpu_ops.h.
+//
+// Threading: elementwise kernels split the frame axis (rows of [C, T]);
+// time reductions split the channel axis into blocks, so every thread walks
+// all T rows over its own channels and each channel's accumulation order is
+// the same sequential-over-t order as ggml's reductions, for any thread
+// count.
+
+#include "cpu_ops.h"
+
+#include "ggml.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <vector>
+
+#if (defined(__x86_64__) || defined(__i386__)) && (defined(__GNUC__) || defined(__clang__))
+#    define ECAPA_OPS_X86 1
+#    include <immintrin.h>
+#else
+#    define ECAPA_OPS_X86 0
+#endif
+
+namespace transcribe::ecapa_tdnn::cpu {
+
+namespace {
+
+#if ECAPA_OPS_X86
+#    define ECAPA_AVX2 __attribute__((target("avx2,fma")))
+
+// ggml's AVX2 expf (ggml-cpu/vec.h ggml_v_expf, ARM optimized-routines
+// polynomial, max error 1.45 ulp): ggml's own soft_max uses it on AVX2
+// builds, so this keeps the attention softmax on the arithmetic the stock
+// graph had.
+ECAPA_AVX2 inline __m256 v_expf(__m256 x) {
+    const __m256  r = _mm256_set1_ps(0x1.8p23f);
+    const __m256  z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r);
+    const __m256  n = _mm256_sub_ps(z, r);
+    const __m256  b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f),
+                                       _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x));
+    const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23);
+    const __m256  k = _mm256_castsi256_ps(_mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1))));
+    const __m256i c =
+        _mm256_castps_si256(_mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(126), _CMP_GT_OQ));
+    const __m256 u = _mm256_mul_ps(b, b);
+    const __m256 j = _mm256_fmadd_ps(
+        _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, _mm256_set1_ps(0x1.573e2ep-5f)), u,
+                        _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, _mm256_set1_ps(0x1.fffdb6p-2f))),
+        u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b));
+    if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) {
+        return _mm256_fmadd_ps(j, k, k);
+    }
+    const __m256i g  = _mm256_and_si256(_mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)),
+                                        _mm256_set1_epi32(static_cast<int>(0x82000000u)));
+    const __m256  s1 = _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000)));
+    const __m256  s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g));
+    const __m256i d =
+        _mm256_castps_si256(_mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(192), _CMP_GT_OQ));
+    return _mm256_or_ps(
+        _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)),
+        _mm256_andnot_ps(_mm256_castsi256_ps(d),
+                         _mm256_or_ps(_mm256_and_ps(_mm256_castsi256_ps(c), _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)),
+                                      _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k)))));
+}
+
+bool cpu_has_avx2_fma() {
+    __builtin_cpu_init();
+    return __builtin_cpu_supports("avx2") && __builtin_cpu_supports("fma");
+}
+
+// r[c] = exp(r[c] - mx[c]) for c in [0, n), returning nothing; sums go to sum.
+ECAPA_AVX2 void exp_row_avx2(float * r, const float * mx, double * sum, size_t n) {
+    size_t c = 0;
+    for (; c + 8 <= n; c += 8) {
+        const __m256 e = v_expf(_mm256_sub_ps(_mm256_loadu_ps(r + c), _mm256_loadu_ps(mx + c)));
+        _mm256_storeu_ps(r + c, e);
+        alignas(32) float ev[8];
+        _mm256_store_ps(ev, e);
+        for (int i = 0; i < 8; ++i) {
+            sum[c + static_cast<size_t>(i)] += static_cast<double>(ev[i]);
+        }
+    }
+    for (; c < n; ++c) {
+        const float e = std::exp(r[c] - mx[c]);
+        r[c]          = e;
+        sum[c] += static_cast<double>(e);
+    }
+}
+#endif
+
+void exp_row(float * r, const float * mx, double * sum, size_t n) {
+#if ECAPA_OPS_X86
+    static const bool avx2 = cpu_has_avx2_fma();
+    if (avx2) {
+        exp_row_avx2(r, mx, sum, n);
+        return;
+    }
+#endif
+    for (size_t c = 0; c < n; ++c) {
+        const float e = std::exp(r[c] - mx[c]);
+        r[c]          = e;
+        sum[c] += static_cast<double>(e);
+    }
+}
+
+// Per-node flags passed through the custom op's userdata. Static storage:
+// the scheduler reads userdata at compute time, long after graph build.
+struct EpilogueParams {
+    bool has_bias;
+    bool tanh_after;
+};
+
+const float * f32(const ggml_tensor * t) {
+    return static_cast<const float *>(t->data);
+}
+
+float * f32_mut(ggml_tensor * t) {
+    return static_cast<float *>(t->data);
+}
+
+size_t row_floats(const ggml_tensor * t) {
+    return t->nb[1] / sizeof(float);
+}
+
+void thread_rows(int64_t n, int ith, int nth, int64_t & r0, int64_t & r1) {
+    const int64_t per = (n + nth - 1) / nth;
+    r0                = std::min<int64_t>(per * ith, n);
+    r1                = std::min<int64_t>(r0 + per, n);
+}
+
+// Time reductions walk all T rows over a channel block. Blocks of
+// kChanBlock channels (1 KiB per row) are dealt to the threads round robin:
+// wide enough that each row read is a few whole cache lines, narrow enough
+// that 3072 channels still spread over 8+ threads.
+constexpr int64_t kChanBlock = 256;
+
+template <typename F>
+void for_channel_blocks(int64_t C, int ith, int nth, F && f) {
+    for (int64_t c0 = static_cast<int64_t>(ith) * kChanBlock; c0 < C; c0 += static_cast<int64_t>(nth) * kChanBlock) {
+        f(c0, std::min(C, c0 + kChanBlock));
+    }
+}
+
+// y = max(x + b, 0) * s + sh  (and tanh) over rows [r0, r1) of a [C, T]
+// activation. The add / max / mul / add sequence is evaluated in float in
+// the same order as ggml's add -> relu -> mul -> add chain, so the result is
+// bit-identical to the stock ops.
+void epilogue_rows(const float * in,
+                   size_t        in_stride,
+                   float *       out,
+                   size_t        out_stride,
+                   int64_t       C,
+                   int64_t       r0,
+                   int64_t       r1,
+                   const float * b,
+                   const float * s,
+                   const float * sh,
+                   bool          tanh_after) {
+    for (int64_t t = r0; t < r1; ++t) {
+        const float * x = in + static_cast<size_t>(t) * in_stride;
+        float *       y = out + static_cast<size_t>(t) * out_stride;
+        if (b != nullptr) {
+            for (int64_t c = 0; c < C; ++c) {
+                float v = x[c] + b[c];
+                v       = v > 0.0f ? v : 0.0f;
+                y[c]    = v * s[c] + sh[c];
+            }
+        } else {
+            for (int64_t c = 0; c < C; ++c) {
+                const float v = x[c] > 0.0f ? x[c] : 0.0f;
+                y[c]          = v * s[c] + sh[c];
+            }
+        }
+        if (tanh_after) {
+            for (int64_t c = 0; c < C; ++c) {
+                y[c] = std::tanh(y[c]);
+            }
+        }
+    }
+}
+
+// dst is a view of src[0] (in place). src: [z, (b), scale, shift].
+void epilogue_inplace_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    const auto *  p     = static_cast<const EpilogueParams *>(ud);
+    const int64_t C     = dst->ne[0];
+    int           i     = 1;
+    const float * b     = p->has_bias ? f32(dst->src[i++]) : nullptr;
+    const float * s     = f32(dst->src[i++]);
+    const float * sh    = f32(dst->src[i++]);
+    int64_t       r0    = 0;
+    int64_t       r1    = 0;
+    thread_rows(dst->ne[1], ith, nth, r0, r1);
+    epilogue_rows(f32(dst), row_floats(dst), f32_mut(dst), row_floats(dst), C, r0, r1, b, s, sh, p->tanh_after);
+}
+
+// dst is a view of src[0]; src: [z0, z1, z2, b, scale, shift].
+void epilogue_sum3_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    (void) ud;
+    const int64_t C    = dst->ne[0];
+    const float * z1   = f32(dst->src[1]);
+    const float * z2   = f32(dst->src[2]);
+    const float * b    = f32(dst->src[3]);
+    const float * s    = f32(dst->src[4]);
+    const float * sh   = f32(dst->src[5]);
+    float *       y    = f32_mut(dst);
+    const size_t  st   = row_floats(dst);
+    const size_t  st1  = row_floats(dst->src[1]);
+    const size_t  st2  = row_floats(dst->src[2]);
+    int64_t       r0   = 0;
+    int64_t       r1   = 0;
+    thread_rows(dst->ne[1], ith, nth, r0, r1);
+    for (int64_t t = r0; t < r1; ++t) {
+        float *       yr  = y + static_cast<size_t>(t) * st;
+        const float * z1r = z1 + static_cast<size_t>(t) * st1;
+        const float * z2r = z2 + static_cast<size_t>(t) * st2;
+        for (int64_t c = 0; c < C; ++c) {
+            // ((z0 + z1) + z2) + b: the stock graph's add order.
+            float v = yr[c] + z1r[c];
+            v       = v + z2r[c];
+            v       = v + b[c];
+            v       = v > 0.0f ? v : 0.0f;
+            yr[c]   = v * s[c] + sh[c];
+        }
+    }
+}
+
+// dst [C]; src: [x [C, T]].
+void time_mean_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    (void) ud;
+    const ggml_tensor * x  = dst->src[0];
+    const int64_t       T  = x->ne[1];
+    const size_t        st = row_floats(x);
+    float *             y  = f32_mut(dst);
+    double              acc[kChanBlock];
+    for_channel_blocks(x->ne[0], ith, nth, [&](int64_t c0, int64_t c1) {
+        const int64_t n = c1 - c0;
+        std::fill(acc, acc + n, 0.0);
+        for (int64_t t = 0; t < T; ++t) {
+            const float * r = f32(x) + static_cast<size_t>(t) * st + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                acc[c] += static_cast<double>(r[c]);
+            }
+        }
+        for (int64_t c = 0; c < n; ++c) {
+            // ggml_mean: (float) sum, then /= (float) n.
+            y[c0 + c] = static_cast<float>(acc[c]) / static_cast<float>(T);
+        }
+    });
+}
+
+// dst [C, T]; src: [x [C, T], s [C], res [C, T]].
+void scale_add_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    (void) ud;
+    const ggml_tensor * x   = dst->src[0];
+    const float *       s   = f32(dst->src[1]);
+    const ggml_tensor * res = dst->src[2];
+    const int64_t       C   = dst->ne[0];
+    int64_t             r0  = 0;
+    int64_t             r1  = 0;
+    thread_rows(dst->ne[1], ith, nth, r0, r1);
+    for (int64_t t = r0; t < r1; ++t) {
+        const float * xr = f32(x) + static_cast<size_t>(t) * row_floats(x);
+        const float * rr = f32(res) + static_cast<size_t>(t) * row_floats(res);
+        float *       yr = f32_mut(dst) + static_cast<size_t>(t) * row_floats(dst);
+        for (int64_t c = 0; c < C; ++c) {
+            const float v = xr[c] * s[c];
+            yr[c]         = v + rr[c];
+        }
+    }
+}
+
+// dst [2C] = [mean, sqrt(max(var, eps))]; src: [x [C, T]]; ud: const float * eps.
+void time_stats_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    const float         eps = *static_cast<const float *>(ud);
+    const ggml_tensor * x   = dst->src[0];
+    const int64_t       C   = x->ne[0];
+    const int64_t       T   = x->ne[1];
+    const size_t        st  = row_floats(x);
+    float *             y   = f32_mut(dst);
+    double              acc[kChanBlock];
+    float               mean[kChanBlock];
+    for_channel_blocks(C, ith, nth, [&](int64_t c0, int64_t c1) {
+        const int64_t n = c1 - c0;
+        std::fill(acc, acc + n, 0.0);
+        for (int64_t t = 0; t < T; ++t) {
+            const float * r = f32(x) + static_cast<size_t>(t) * st + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                acc[c] += static_cast<double>(r[c]);
+            }
+        }
+        for (int64_t c = 0; c < n; ++c) {
+            mean[c] = static_cast<float>(acc[c]) / static_cast<float>(T);
+            acc[c]  = 0.0;
+        }
+        for (int64_t t = 0; t < T; ++t) {
+            const float * r = f32(x) + static_cast<size_t>(t) * st + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                const float d = r[c] - mean[c];
+                acc[c] += static_cast<double>(d * d);
+            }
+        }
+        for (int64_t c = 0; c < n; ++c) {
+            float var     = static_cast<float>(acc[c]) / static_cast<float>(T);
+            var           = var < eps ? eps : var;
+            y[c0 + c]     = mean[c];
+            y[C + c0 + c] = std::sqrt(var);
+        }
+    });
+}
+
+// In place over src[0] = logits [C, T]: softmax over t of (logits + bias)
+// per channel. src: [logits, bias].
+void softmax_time_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    (void) ud;
+    const float * b  = f32(dst->src[1]);
+    const int64_t T  = dst->ne[1];
+    const size_t  st = row_floats(dst);
+    float *       l  = f32_mut(dst);
+    float         mx[kChanBlock];
+    double        sum[kChanBlock];
+    float         inv[kChanBlock];
+    for_channel_blocks(dst->ne[0], ith, nth, [&](int64_t c0, int64_t c1) {
+        const int64_t n = c1 - c0;
+        std::fill(mx, mx + n, -INFINITY);
+        std::fill(sum, sum + n, 0.0);
+        for (int64_t t = 0; t < T; ++t) {
+            float * r = l + static_cast<size_t>(t) * st + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                const float v = r[c] + b[c0 + c];
+                r[c]          = v;
+                mx[c]         = v > mx[c] ? v : mx[c];
+            }
+        }
+        for (int64_t t = 0; t < T; ++t) {
+            exp_row(l + static_cast<size_t>(t) * st + c0, mx, sum, static_cast<size_t>(n));
+        }
+        for (int64_t c = 0; c < n; ++c) {
+            inv[c] = static_cast<float>(1.0 / sum[c]);  // ggml_vec_scale_f32(dp, 1.0/sum)
+        }
+        for (int64_t t = 0; t < T; ++t) {
+            float * r = l + static_cast<size_t>(t) * st + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                r[c] *= inv[c];
+            }
+        }
+    });
+}
+
+// dst [2C] = [mu, sigma]; src: [x [C, T], w [C, T]]; ud: const float * eps.
+void weighted_stats_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    const float         eps = *static_cast<const float *>(ud);
+    const ggml_tensor * x   = dst->src[0];
+    const ggml_tensor * w   = dst->src[1];
+    const int64_t       C   = x->ne[0];
+    const int64_t       T   = x->ne[1];
+    const size_t        sx  = row_floats(x);
+    const size_t        sw  = row_floats(w);
+    float *             y   = f32_mut(dst);
+    double              acc[kChanBlock];
+    float               mu[kChanBlock];
+    for_channel_blocks(C, ith, nth, [&](int64_t c0, int64_t c1) {
+        const int64_t n = c1 - c0;
+        std::fill(acc, acc + n, 0.0);
+        for (int64_t t = 0; t < T; ++t) {
+            const float * xr = f32(x) + static_cast<size_t>(t) * sx + c0;
+            const float * wr = f32(w) + static_cast<size_t>(t) * sw + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                acc[c] += static_cast<double>(wr[c] * xr[c]);
+            }
+        }
+        for (int64_t c = 0; c < n; ++c) {
+            mu[c]  = static_cast<float>(acc[c]);
+            acc[c] = 0.0;
+        }
+        for (int64_t t = 0; t < T; ++t) {
+            const float * xr = f32(x) + static_cast<size_t>(t) * sx + c0;
+            const float * wr = f32(w) + static_cast<size_t>(t) * sw + c0;
+            for (int64_t c = 0; c < n; ++c) {
+                const float d = xr[c] - mu[c];
+                acc[c] += static_cast<double>(wr[c] * (d * d));
+            }
+        }
+        for (int64_t c = 0; c < n; ++c) {
+            float sig     = static_cast<float>(acc[c]);
+            sig           = sig < eps ? eps : sig;
+            y[c0 + c]     = mu[c];
+            y[C + c0 + c] = std::sqrt(sig);
+        }
+    });
+}
+
+// Res2Net step parameters, packed into the custom op's userdata pointer
+// (they are small integers, and this keeps the graph free of side storage).
+struct Res2Params {
+    bool has_z;
+    int  next;  // -1 = no next chunk
+    int  kernel;
+    int  dilation;
+};
+
+void * pack_res2(const Res2Params & p) {
+    const uintptr_t v = (p.has_z ? 1u : 0u) | (static_cast<uintptr_t>(p.next + 1) & 0xffu) << 1 |
+                        (static_cast<uintptr_t>(p.kernel) & 0xffu) << 9 |
+                        (static_cast<uintptr_t>(p.dilation) & 0xffu) << 17;
+    return reinterpret_cast<void *>(v);
+}
+
+Res2Params unpack_res2(const void * ud) {
+    const uintptr_t v = reinterpret_cast<uintptr_t>(ud);
+    return { (v & 1u) != 0, static_cast<int>((v >> 1) & 0xffu) - 1, static_cast<int>((v >> 9) & 0xffu),
+             static_cast<int>((v >> 17) & 0xffu) };
+}
+
+// src: [h, (z, b, scale, shift)].
+void res2_step_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    const Res2Params    p   = unpack_res2(ud);
+    const ggml_tensor * h   = dst->src[0];
+    const int64_t       w   = dst->ne[0];
+    const int64_t       T   = h->ne[1];
+    const int64_t       pad = static_cast<int64_t>(p.dilation) * (p.kernel - 1) / 2;
+    const int64_t       n_pad_rows = p.next >= 0 ? T + 2 * pad : 0;
+    const int64_t       n_rows     = n_pad_rows + (p.has_z ? T : 0);
+    const size_t        sd         = row_floats(dst);
+    const size_t        sh_        = row_floats(h);
+
+    const float * z  = nullptr;
+    const float * b  = nullptr;
+    const float * s  = nullptr;
+    const float * sh = nullptr;
+    size_t        sz = 0;
+    if (p.has_z) {
+        z  = f32(dst->src[1]);
+        sz = row_floats(dst->src[1]);
+        b  = f32(dst->src[2]);
+        s  = f32(dst->src[3]);
+        sh = f32(dst->src[4]);
+    }
+
+    // y(t) into out[0..w): ((Z_0 + Z_1) + ... + Z_{K-1}) + b, ReLU, BN.
+    auto y_at = [&](int64_t t, float * out) {
+        const float * r0 = z + static_cast<size_t>(t) * sz;
+        for (int64_t c = 0; c < w; ++c) {
+            out[c] = r0[c];
+        }
+        for (int k = 1; k < p.kernel; ++k) {
+            const float * rk = z + static_cast<size_t>(t + static_cast<int64_t>(k) * p.dilation) * sz +
+                               static_cast<size_t>(k) * static_cast<size_t>(w);
+            for (int64_t c = 0; c < w; ++c) {
+                out[c] += rk[c];
+            }
+        }
+        for (int64_t c = 0; c < w; ++c) {
+            float v = out[c] + b[c];
+            v       = v > 0.0f ? v : 0.0f;
+            out[c]  = v * s[c] + sh[c];
+        }
+    };
+
+    std::vector<float> tmp(static_cast<size_t>(w));
+    int64_t            r0 = 0;
+    int64_t            r1 = 0;
+    thread_rows(n_rows, ith, nth, r0, r1);
+    for (int64_t r = r0; r < r1; ++r) {
+        float * out = f32_mut(dst) + static_cast<size_t>(r) * sd;
+        if (r >= n_pad_rows) {
+            y_at(r - n_pad_rows, out);
+            continue;
+        }
+        // torch "reflect" (edge not repeated); T > pad is checked at build.
+        int64_t t = r - pad;
+        if (t < 0) {
+            t = -t;
+        } else if (t >= T) {
+            t = 2 * (T - 1) - t;
+        }
+        const float * hr = f32(h) + static_cast<size_t>(t) * sh_ + static_cast<size_t>(p.next) * static_cast<size_t>(w);
+        if (p.has_z) {
+            y_at(t, tmp.data());
+            for (int64_t c = 0; c < w; ++c) {
+                out[c] = hr[c] + tmp[static_cast<size_t>(c)];
+            }
+        } else {
+            for (int64_t c = 0; c < w; ++c) {
+                out[c] = hr[c];
+            }
+        }
+    }
+}
+
+// dst [w, T + 2p] = reflect(h[next] + y); src: [h, y]; ud: packed params.
+void res2_pad_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    const Res2Params    p   = unpack_res2(ud);
+    const ggml_tensor * h   = dst->src[0];
+    const ggml_tensor * y   = dst->src[1];
+    const int64_t       w   = dst->ne[0];
+    const int64_t       T   = h->ne[1];
+    const int64_t       pad = static_cast<int64_t>(p.dilation) * (p.kernel - 1) / 2;
+    int64_t             r0  = 0;
+    int64_t             r1  = 0;
+    thread_rows(dst->ne[1], ith, nth, r0, r1);
+    for (int64_t r = r0; r < r1; ++r) {
+        int64_t t = r - pad;  // torch "reflect", edge not repeated
+        if (t < 0) {
+            t = -t;
+        } else if (t >= T) {
+            t = 2 * (T - 1) - t;
+        }
+        const float * hr = f32(h) + static_cast<size_t>(t) * row_floats(h) + static_cast<size_t>(p.next * w);
+        const float * yr = f32(y) + static_cast<size_t>(t) * row_floats(y);
+        float *       o  = f32_mut(dst) + static_cast<size_t>(r) * row_floats(dst);
+        for (int64_t c = 0; c < w; ++c) {
+            o[c] = hr[c] + yr[c];
+        }
+    }
+}
+
+// dst [C, T]; src: [h, y_1 .. y_n] (y_i are [w, T] views).
+void res2_gather_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
+    (void) ud;
+    const ggml_tensor * h  = dst->src[0];
+    const int64_t       C  = dst->ne[0];
+    int                 n  = 0;
+    while (n + 1 < GGML_MAX_SRC && dst->src[n + 1] != nullptr) {
+        ++n;
+    }
+    const int64_t w  = dst->src[1]->ne[0];
+    int64_t       r0 = 0;
+    int64_t       r1 = 0;
+    thread_rows(dst->ne[1], ith, nth, r0, r1);
+    for (int64_t t = r0; t < r1; ++t) {
+        float *       out = f32_mut(dst) + static_cast<size_t>(t) * row_floats(dst);
+        const float * hr  = f32(h) + static_cast<size_t>(t) * row_floats(h);
+        std::copy(hr, hr + w, out);
+        for (int i = 1; i <= n; ++i) {
+            const ggml_tensor * y  = dst->src[i];
+            const float *       yr = f32(y) + static_cast<size_t>(t) * row_floats(y);
+            std::copy(yr, yr + w, out + static_cast<size_t>(i) * static_cast<size_t>(w));
+        }
+        (void) C;
+    }
+}
+
+const EpilogueParams k_ep_bias{ true, false };
+const EpilogueParams k_ep_bias_tanh{ true, true };
+const EpilogueParams k_ep_nobias{ false, false };
+const EpilogueParams k_ep_nobias_tanh{ false, true };
+
+void * ud(const void * p) {
+    return const_cast<void *>(p);
+}
+
+}  // namespace
+
+ggml_tensor * epilogue(ggml_context * ctx,
+                       ggml_tensor *  z,
+                       ggml_tensor *  b,
+                       ggml_tensor *  scale,
+                       ggml_tensor *  shift,
+                       bool           tanh_after,
+                       bool           inplace) {
+    const EpilogueParams * p = b != nullptr ? (tanh_after ? &k_ep_bias_tanh : &k_ep_bias)
+                                            : (tanh_after ? &k_ep_nobias_tanh : &k_ep_nobias);
+    ggml_tensor *          args[3];
+    int                    n = 0;
+    if (b != nullptr) {
+        args[n++] = b;
+    }
+    args[n++] = scale;
+    args[n++] = shift;
+    if (!inplace) {
+        z = ggml_cont(ctx, z);
+    }
+    return ggml_custom_inplace(ctx, z, args, n, epilogue_inplace_fn, GGML_N_TASKS_MAX, ud(p));
+}
+
+ggml_tensor * epilogue_sum3(ggml_context * ctx,
+                            ggml_tensor *  z0,
+                            ggml_tensor *  z1,
+                            ggml_tensor *  z2,
+                            ggml_tensor *  b,
+                            ggml_tensor *  scale,
+                            ggml_tensor *  shift,
+                            bool           inplace) {
+    ggml_tensor * args[5] = { z1, z2, b, scale, shift };
+    if (!inplace) {
+        z0 = ggml_cont(ctx, z0);
+    }
+    return ggml_custom_inplace(ctx, z0, args, 5, epilogue_sum3_fn, GGML_N_TASKS_MAX, nullptr);
+}
+
+ggml_tensor * time_mean(ggml_context * ctx, ggml_tensor * x) {
+    ggml_tensor * args[1] = { x };
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, x->ne[0], 1, 1, 1, args, 1, time_mean_fn, GGML_N_TASKS_MAX, nullptr);
+}
+
+ggml_tensor * scale_add(ggml_context * ctx, ggml_tensor * x, ggml_tensor * s, ggml_tensor * res) {
+    ggml_tensor * args[3] = { x, s, res };
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, x->ne[0], x->ne[1], 1, 1, args, 3, scale_add_fn, GGML_N_TASKS_MAX,
+                          nullptr);
+}
+
+ggml_tensor * time_stats(ggml_context * ctx, ggml_tensor * x, const float * eps) {
+    ggml_tensor * args[1] = { x };
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 1, time_stats_fn, GGML_N_TASKS_MAX,
+                          ud(eps));
+}
+
+ggml_tensor * attn_stats(ggml_context * ctx, ggml_tensor * x, ggml_tensor * logits, ggml_tensor * bias,
+                         const float * eps) {
+    ggml_tensor * sm_args[1] = { bias };
+    ggml_tensor * w = ggml_custom_inplace(ctx, logits, sm_args, 1, softmax_time_fn, GGML_N_TASKS_MAX, nullptr);
+    ggml_tensor * args[2] = { x, w };
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 2, weighted_stats_fn, GGML_N_TASKS_MAX,
+                          ud(eps));
+}
+
+Res2Step res2_step(ggml_context * ctx,
+                   ggml_tensor *  h,
+                   int            next,
+                   int            width,
+                   ggml_tensor *  z,
+                   ggml_tensor *  b,
+                   ggml_tensor *  scale,
+                   ggml_tensor *  shift,
+                   int            kernel,
+                   int            dilation) {
+    Res2Step      r;
+    const int64_t T          = h->ne[1];
+    const int64_t pad        = static_cast<int64_t>(dilation) * (kernel - 1) / 2;
+    const int64_t n_pad_rows = next >= 0 ? T + 2 * pad : 0;
+    const int64_t n_rows     = n_pad_rows + (z != nullptr ? T : 0);
+
+    ggml_tensor * args[5] = { h, z, b, scale, shift };
+    const int     n_args  = z != nullptr ? 5 : 1;
+    r.out = ggml_custom_4d(ctx, GGML_TYPE_F32, width, n_rows, 1, 1, args, n_args, res2_step_fn, GGML_N_TASKS_MAX,
+                           pack_res2({ z != nullptr, next, kernel, dilation }));
+    if (next >= 0) {
+        r.pad = ggml_view_2d(ctx, r.out, width, n_pad_rows, r.out->nb[1], 0);
+    }
+    if (z != nullptr) {
+        r.y = ggml_view_2d(ctx, r.out, width, T, r.out->nb[1], static_cast<size_t>(n_pad_rows) * r.out->nb[1]);
+    }
+    return r;
+}
+
+Res2Step res2_pad(ggml_context * ctx, ggml_tensor * h, int next, int width, ggml_tensor * y, int kernel, int dilation) {
+    Res2Step      r;
+    const int64_t T       = h->ne[1];
+    const int64_t pad     = static_cast<int64_t>(dilation) * (kernel - 1) / 2;
+    ggml_tensor * args[2] = { h, y };
+    r.out = ggml_custom_4d(ctx, GGML_TYPE_F32, width, T + 2 * pad, 1, 1, args, 2, res2_pad_fn, GGML_N_TASKS_MAX,
+                           pack_res2({ false, next, kernel, dilation }));
+    r.pad = r.out;
+    return r;
+}
+
+ggml_tensor * res2_gather(ggml_context * ctx, ggml_tensor * h, ggml_tensor * const * ys, int n_ys) {
+    ggml_tensor * args[GGML_MAX_SRC - 1] = {};
+    args[0]                              = h;
+    for (int i = 0; i < n_ys; ++i) {
+        args[i + 1] = ys[i];
+    }
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, h->ne[0], h->ne[1], 1, 1, args, n_ys + 1, res2_gather_fn,
+                          GGML_N_TASKS_MAX, nullptr);
+}
+
+}  // namespace transcribe::ecapa_tdnn::cpu
diff --git a/src/arch/ecapa_tdnn/cpu_ops.h b/src/arch/ecapa_tdnn/cpu_ops.h
new file mode 100644
index 000000000..b90db423c
--- /dev/null
+++ b/src/arch/ecapa_tdnn/cpu_ops.h
@@ -0,0 +1,98 @@
+// arch/ecapa_tdnn/cpu_ops.h - fused CPU kernels for the ECAPA-TDNN graph.
+//
+// INTERNAL to src/arch/ecapa_tdnn/. Used only when the graph runs on the
+// ggml CPU backend (graph.cpp's `cpu_ops` build); every other backend keeps
+// the stock-op graph. Each builder returns a GGML_OP_CUSTOM node that runs on
+// all of the scheduler's CPU threads.
+//
+// Why these exist: with stock ggml ops the activations are channel-innermost
+// ne = [C, T], so every reduction over time needs a transposed copy first,
+// and ggml's CPU MEAN / SUM_ROWS run on one thread. The epilogue chain
+// (bias -> ReLU -> BN scale -> BN shift) is four full passes over the
+// tensor, each with a thread barrier. The kernels below do each of those in
+// one multithreaded pass over the [C, T] layout, with the same float
+// arithmetic per element and the same sequential double accumulation over
+// time that ggml's reductions use (time_mean / time_stats are bit-identical
+// to transpose + ggml_mean).
+//
+// Every input must be F32 and contiguous; vectors are ne = [C].
+
+#pragma once
+
+struct ggml_context;
+struct ggml_tensor;
+
+namespace transcribe::ecapa_tdnn::cpu {
+
+// y = max(z + b, 0) * scale + shift, then tanh(y) if `tanh_after`.
+// z is ne = [C, T]; b may be null. In place over z when `inplace`.
+ggml_tensor * epilogue(ggml_context * ctx,
+                       ggml_tensor *  z,
+                       ggml_tensor *  b,
+                       ggml_tensor *  scale,
+                       ggml_tensor *  shift,
+                       bool           tanh_after,
+                       bool           inplace);
+
+// y = max(z0 + z1 + z2 + b, 0) * scale + shift (the MFA stage's three
+// per-block products). In place over z0 when `inplace`.
+ggml_tensor * epilogue_sum3(ggml_context * ctx,
+                            ggml_tensor *  z0,
+                            ggml_tensor *  z1,
+                            ggml_tensor *  z2,
+                            ggml_tensor *  b,
+                            ggml_tensor *  scale,
+                            ggml_tensor *  shift,
+                            bool           inplace);
+
+// Mean over time of x ne = [C, T], returned as ne = [C].
+ggml_tensor * time_mean(ggml_context * ctx, ggml_tensor * x);
+
+// y = x * s + res: x, res ne = [C, T], s ne = [C]. New tensor.
+ggml_tensor * scale_add(ggml_context * ctx, ggml_tensor * x, ggml_tensor * s, ggml_tensor * res);
+
+// Uniform statistics over time of x ne = [C, T]: ne = [2C] holding
+// [mean, sqrt(max(var, *eps))], biased variance. `eps` must outlive compute.
+ggml_tensor * time_stats(ggml_context * ctx, ggml_tensor * x, const float * eps);
+
+// Attentive statistics: per channel c, w = softmax_t(logits[c, t] + bias[c]),
+// mu = sum_t w x, sigma = sqrt(max(sum_t w (x - mu)^2, *eps)). x and logits
+// ne = [C, T], bias ne = [C]; returns ne = [2C] = [mu, sigma].
+ggml_tensor * attn_stats(ggml_context * ctx, ggml_tensor * x, ggml_tensor * logits, ggml_tensor * bias,
+                         const float * eps);
+
+// One Res2Net step. Chunk i of the block (i >= 1) is
+//   y_i = BN(ReLU(sum_k Z[k*w + c, t + k*d] + b))
+// where Z = W_stack @ pad_i is the stacked-tap product ne = [K*w, T + 2p]
+// of the reflect-padded chunk input (p = d*(K-1)/2). The step computes y_i
+// and, when there is a next chunk, that chunk's reflect-padded input
+//   pad_{i+1} = reflect(h[next] + y_i)            ne = [w, T + 2p]
+// in one pass. The first step of a block has no Z: it only pads h[1].
+//
+// The returned tensor packs both, rows [0, T + 2p) = pad (when next >= 0)
+// followed by T rows of y (when z != nullptr); use res2_pad / res2_y.
+struct Res2Step {
+    ggml_tensor * out = nullptr;
+    ggml_tensor * pad = nullptr;  // view, ne = [w, T + 2p], contiguous
+    ggml_tensor * y   = nullptr;  // view, ne = [w, T]
+};
+
+Res2Step res2_step(ggml_context * ctx,
+                   ggml_tensor *  h,      // block input [C, T]
+                   int            next,   // chunk index of h to pad next, or -1
+                   int            width,  // chunk width w
+                   ggml_tensor *  z,      // [K*w, T + 2p] or nullptr
+                   ggml_tensor *  b,
+                   ggml_tensor *  scale,
+                   ggml_tensor *  shift,
+                   int            kernel,
+                   int            dilation);
+
+// pad_{next} = reflect(h[next] + y) for a GEMM-produced y ne = [w, T]:
+// returns a Res2Step whose `pad` is set (out == pad).
+Res2Step res2_pad(ggml_context * ctx, ggml_tensor * h, int next, int width, ggml_tensor * y, int kernel, int dilation);
+
+// Res2Net output [C, T]: chunk 0 from h, chunk i from ys[i - 1] (each [w, T]).
+ggml_tensor * res2_gather(ggml_context * ctx, ggml_tensor * h, ggml_tensor * const * ys, int n_ys);
+
+}  // namespace transcribe::ecapa_tdnn::cpu
diff --git a/src/arch/ecapa_tdnn/ecapa_tdnn.h b/src/arch/ecapa_tdnn/ecapa_tdnn.h
index 18bac9b5e..269dba060 100644
--- a/src/arch/ecapa_tdnn/ecapa_tdnn.h
+++ b/src/arch/ecapa_tdnn/ecapa_tdnn.h
@@ -13,6 +13,7 @@
 
 #pragma once
 
+#include "cpu_gemm.h"
 #include "mel.h"
 #include "transcribe-backend.h"
 #include "transcribe-langid.h"
@@ -43,6 +44,23 @@ struct Model final : public transcribe_model {
     ggml_backend_buffer_t backend_buffer = nullptr;
     BackendPlan           plan;
 
+    // Load-time derived weights (Weights::blk0_w_im2col): their own metadata
+    // context and backend buffer, since ctx_meta is sized for the file.
+    ggml_context *        ctx_derived    = nullptr;
+    ggml_backend_buffer_t derived_buffer = nullptr;
+
+    // True when the graph uses the fused CPU kernels (cpu_ops.h): the
+    // primary backend is the ggml CPU backend. Fixed at load.
+    bool cpu_ops = false;
+
+    // Which weights were repacked at load for the AVX2 GEMM (cpu_gemm.h);
+    // packed weights are only readable by that GEMM. Fixed at load.
+    //   gemm_conv: the k>1 kernels (stage 0's im2col weight, Res2Net taps)
+    //   gemm_lin:  the T-wide 1x1 weights (tdnn1/tdnn2, mfa, asp.tdnn.x,
+    //              asp.attn); only when they are F16
+    bool gemm_conv = false;
+    bool gemm_lin  = false;
+
     // Built at load from stt.langid.labels.*; immutable after.
     LangidLabels labels;
 
@@ -58,6 +76,7 @@ struct Model final : public transcribe_model {
 struct Session final : public transcribe_langid_session {
     // Host scratch, reused across calls.
     std::vector<float>   mel_buf;                // [T * n_mels], frame-major
+    std::vector<float>   im2col_buf;             // [T * blk0_cols], frame-major
     std::vector<int32_t> idx_buf[kNumSeBlocks];  // reflect indices, [T + 2p]
 
     // Metadata arena for the per-call graph build, handed to ggml_init as
@@ -66,8 +85,9 @@ struct Session final : public transcribe_langid_session {
     // mem_buffer.
     std::vector<uint8_t> graph_arena;
 
-    // One-shot TRANSCRIBE_ECAPA_GRAPH_STATS reporting latch (see model.cpp).
-    bool logged_graph_stats = false;
+    // GEMM node descriptors of the current graph (cpu_gemm.h). Cleared with
+    // the compute context at the start of every run.
+    gemm::Arena gemm_arena;
 };
 
 extern const Arch arch;
diff --git a/src/arch/ecapa_tdnn/graph.cpp b/src/arch/ecapa_tdnn/graph.cpp
index 175e61c2d..98c044800 100644
--- a/src/arch/ecapa_tdnn/graph.cpp
+++ b/src/arch/ecapa_tdnn/graph.cpp
@@ -7,10 +7,19 @@
 // and the ASP / MFA input concatenations split into per-operand weight
 // blocks.
 //
-// Layout: every activation is ggml ne = [C, T]. Reductions over time need
-// ggml's ne[0], so they run on a contiguous transposed copy [T, C] and the
-// result is reshaped back. The three SE transposes and two ASP transposes
-// per pass are negligible next to the 1x1 convolutions.
+// Two graphs share this file, selected by build_graph's `cpu_ops`:
+//   - the stock-op graph (GPU backends, and any multi-backend schedule):
+//     plain ggml ops; reductions over time run on a contiguous transposed
+//     copy [T, C] because ggml reduces over ne[0].
+//   - the CPU graph (ggml CPU backend only): the same math through the
+//     fused kernels in cpu_ops.h (epilogues, time reductions on [C, T]
+//     without transposes, Res2Net without concats) and, where the model
+//     packed its weights, the AVX2 GEMM in cpu_gemm.h with the epilogue
+//     fused into the store. On a Ryzen 7 4750U this is ~2.4x faster than
+//     the stock graph for the F16 file at 8 threads.
+// Stage 0 is one im2col matmul in both (see build_blk0_im2col).
+//
+// Layout: every activation is ggml ne = [C, T].
 //
 // Helper contract (named / linear / bn_affine / reflect_rows / conv_taps):
 // an activation is ggml `ne = [C, T]`, CHANNEL-INNERMOST, frames are rows.
@@ -19,7 +28,8 @@
 //   - a bias / BatchNorm vector `[C]` a free broadcast over T,
 //   - reflect padding a single `ggml_get_rows` over the frame axis,
 //   - a dilated k>1 convolution a sum of matmuls against row-slices of the
-//     padded activation, with no im2col and no transposes.
+//     padded activation, with no transposes (stage 0 alone runs as one
+//     matmul over a host-built im2col; see build_blk0_im2col).
 // Time-axis reductions (means, softmax, weighted sums) act on ggml's ne[0],
 // so the caller transposes to `[T, C]` first.
 //
@@ -33,6 +43,7 @@
 
 #include "graph.h"
 
+#include "cpu_ops.h"
 #include "ecapa_tdnn.h"
 #include "ggml.h"
 #include "transcribe-debug.h"
@@ -115,8 +126,7 @@ ggml_tensor * conv_taps(ggml_context * ctx, ggml_tensor * xpad, ggml_tensor * w,
 }
 
 // Nodes reserved in the cgraph. The real graph is 566 nodes for the published
-// configuration and is independent of T (TRANSCRIBE_ECAPA_GRAPH_STATS
-// reports it).
+// configuration and is independent of T.
 constexpr size_t kGraphSize = 2048;
 
 // Name a tensor, mark it as a graph output so the scheduler cannot reuse its
@@ -125,6 +135,11 @@ constexpr size_t kGraphSize = 2048;
 void mark_dump(ggml_tensor *& slot, ggml_tensor * t, const char * name) {
     named(t, name);
     debug::mark_tensor_for_dump(t);
+    if (t->view_src != nullptr) {
+        // In-place CPU kernels return a view; the allocator frees the view's
+        // backing tensor, so that is what has to be kept alive.
+        debug::mark_tensor_for_dump(t->view_src);
+    }
     slot = t;
 }
 
@@ -207,11 +222,239 @@ ggml_tensor * se_block(ggml_context * ctx, const SeBlock & se, ggml_tensor * x)
     return ggml_mul(ctx, x, s);
 }
 
+// ---- CPU-backend variants (cpu_ops.h) -------------------------------------
+//
+// Same math as the stock helpers above; see cpu_ops.h for why they exist.
+
+// F32 accumulation for F16 weights (see linear()).
+ggml_tensor * mul_mat_acc(ggml_context * ctx, ggml_tensor * w, ggml_tensor * x) {
+    ggml_tensor * y = ggml_mul_mat(ctx, w, x);
+    if (w->type == GGML_TYPE_F16) {
+        ggml_prec_set_acc(y, GGML_PREC_F32);
+    }
+    return y;
+}
+
+// TDNNBlock k=1: with packed weights one GEMM with the bias/ReLU/BN fused
+// into its store; otherwise ggml_mul_mat plus one fused epilogue pass.
+ggml_tensor * tdnn_1x1_cpu(ggml_context * ctx, gemm::Arena * ar, const TdnnLayer & l, ggml_tensor * x) {
+    if (ar != nullptr) {
+        const gemm::Segment sg{ l.w, 0, x, 0 };
+        return gemm::mul_mat(ctx, *ar, &sg, 1, x->ne[1], gemm::Epilogue::ReluBn, l.b, l.bn.scale, l.bn.shift);
+    }
+    return cpu::epilogue(ctx, mul_mat_acc(ctx, l.w, x), l.b, l.bn.scale, l.bn.shift, false, true);
+}
+
+// Res2Net on the CPU. Each sub-conv is ONE matmul of its stacked taps
+// (the [w, K*w] view of the tap-major [w, w, K] weight) against the padded
+// chunk input, and one fused kernel that sums the shifted tap rows, applies
+// the epilogue, and builds the next chunk's padded input; the output is
+// assembled once at the end instead of through S - 1 growing concats.
+//
+// With packed weights (the GEMM path) the stacked-tap trick is replaced by a
+// true dilated conv: one GEMM with one segment per tap, tap k reading the
+// padded input k*d rows further on, with the epilogue fused into the store.
+// The padded input for the next chunk (h[next] + y, reflect padded) is then
+// a small pad kernel.
+ggml_tensor * res2net_cpu(ggml_context *         ctx,
+                          gemm::Arena *          ar,
+                          const SeRes2NetBlock & blk,
+                          ggml_tensor *          h,
+                          int                    kernel,
+                          int                    dilation,
+                          int                    chunk) {
+    ggml_tensor * ys[kRes2NetSubs] = {};
+    const int64_t T                = h->ne[1];
+
+    cpu::Res2Step st = cpu::res2_step(ctx, h, /*next=*/1, chunk, nullptr, nullptr, nullptr, nullptr, kernel, dilation);
+    for (int j = 0; j < kRes2NetSubs; ++j) {
+        const Res2Sub & s  = blk.res2[j];
+        const int       nx = (j + 2 < kRes2NetScale) ? j + 2 : -1;
+        if (ar != nullptr) {
+            gemm::Segment sg[gemm::kMaxSegs];
+            for (int k = 0; k < kernel; ++k) {
+                sg[k] = { s.w, k, st.pad, static_cast<int64_t>(k) * dilation };
+            }
+            ys[j] = gemm::mul_mat(ctx, *ar, sg, kernel, T, gemm::Epilogue::ReluBn, s.b, s.bn.scale, s.bn.shift);
+            if (nx >= 0) {
+                st = cpu::res2_pad(ctx, h, nx, chunk, ys[j], kernel, dilation);
+            }
+            continue;
+        }
+        ggml_tensor * ws = ggml_reshape_2d(ctx, s.w, chunk, chunk * kernel);  // [w, K*w], tap-major rows
+        ggml_tensor * z  = mul_mat_acc(ctx, ws, st.pad);                      // [K*w, T + 2p]
+        st               = cpu::res2_step(ctx, h, nx, chunk, z, s.b, s.bn.scale, s.bn.shift, kernel, dilation);
+        ys[j]            = st.y;
+    }
+    return cpu::res2_gather(ctx, h, ys, kRes2NetSubs);
+}
+
+// SEBlock fused with the residual add: x * s + residual.
+ggml_tensor * se_block_cpu(ggml_context * ctx, const SeBlock & se, ggml_tensor * x, ggml_tensor * residual) {
+    ggml_tensor * s = cpu::time_mean(ctx, x);
+    s               = ggml_relu(ctx, linear(ctx, se.c1_w, s, se.c1_b));
+    s               = ggml_sigmoid(ctx, linear(ctx, se.c2_w, s, se.c2_b));
+    return cpu::scale_add(ctx, x, s, residual);
+}
+
+// MFA + attentive statistics pooling, stock ops. Returns pooled [2*Cm].
+ggml_tensor * mfa_asp(ggml_context *        ctx,
+                      GraphBuild &          gb,
+                      const HParams &       hp,
+                      const Weights &       w,
+                      ggml_tensor * const * blk_out,
+                      int                   Cm) {
+    ggml_tensor * pooled = nullptr;
+    // ---- MFA: 1x1 over concat(blk1, blk2, blk3), split into three blocks --
+    ggml_tensor * m      = nullptr;
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        ggml_tensor * part = ggml_mul_mat(ctx, w.mfa_w[i], blk_out[i]);
+        if (w.mfa_w[i]->type == GGML_TYPE_F16) {
+            ggml_prec_set_acc(part, GGML_PREC_F32);
+        }
+        m = (m == nullptr) ? part : ggml_add(ctx, m, part);
+    }
+    m = ggml_add(ctx, m, w.mfa_b);
+    m = ggml_relu(ctx, m);
+    m = bn_affine(ctx, m, w.mfa_bn.scale, w.mfa_bn.shift);
+    mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
+
+    // ---- attentive statistics pooling ------------------------------------
+    //
+    // mean / std are the utterance statistics over T with the uniform
+    // (unpadded) weighting SpeechBrain uses when wav_lens is all-ones; the
+    // variance is the biased 1/T one, and the clamp before the sqrt is
+    // SpeechBrain's `.clamp(eps)` and must not be dropped.
+    ggml_tensor * mT   = ggml_cont(ctx, ggml_transpose(ctx, m));  // [T, Cm]
+    ggml_tensor * mean = ggml_mean(ctx, mT);                      // [1, Cm]
+    ggml_tensor * var  = ggml_mean(ctx, ggml_sqr(ctx, ggml_sub(ctx, mT, mean)));
+    ggml_tensor * sd   = ggml_sqrt(ctx, ggml_clamp(ctx, var, hp.asp_eps, FLT_MAX));
+
+    ggml_tensor * mean1 = ggml_reshape_1d(ctx, mean, Cm);
+    ggml_tensor * sd1   = ggml_reshape_1d(ctx, sd, Cm);
+
+    // The context term wm@mean + ws@std + b is a [att] vector broadcast over
+    // T, so the 3*Cm -> att convolution costs one Cm x T matmul instead of
+    // three.
+    ggml_tensor * cvec = ggml_add(ctx, linear(ctx, w.asp_wm, mean1, nullptr), linear(ctx, w.asp_ws, sd1, nullptr));
+    cvec               = ggml_add(ctx, cvec, w.asp_b);
+
+    ggml_tensor * a = ggml_add(ctx, linear(ctx, w.asp_wx, m, nullptr), cvec);  // [att, T]
+    a               = ggml_relu(ctx, a);
+    a               = bn_affine(ctx, a, w.asp_bn.scale, w.asp_bn.shift);
+    a               = ggml_tanh(ctx, a);
+
+    ggml_tensor * al = linear(ctx, w.asp_attn_w, a, w.asp_attn_b);  // [Cm, T]
+    // Marked BEFORE the transpose: ggml ne = [Cm, T] lands on disk as
+    // [T, Cm], the orientation the reference dumps.
+    mark_dump(gb.dumps.asp_attn_logits, al, "enc.asp.attn_logits");
+
+    ggml_tensor * alT  = ggml_cont(ctx, ggml_transpose(ctx, al));     // [T, Cm]
+    ggml_tensor * attn = ggml_soft_max(ctx, alT);                     // over ne[0] = T
+
+    ggml_tensor * mu  = ggml_sum_rows(ctx, ggml_mul(ctx, attn, mT));  // [1, Cm]
+    ggml_tensor * dev = ggml_sqr(ctx, ggml_sub(ctx, mT, mu));
+    ggml_tensor * sig = ggml_sum_rows(ctx, ggml_mul(ctx, attn, dev));
+    sig               = ggml_sqrt(ctx, ggml_clamp(ctx, sig, hp.asp_eps, FLT_MAX));
+
+    pooled = ggml_concat(ctx, ggml_reshape_1d(ctx, mu, Cm), ggml_reshape_1d(ctx, sig, Cm), /*dim=*/0);  // [2*Cm]
+    mark_dump(gb.dumps.asp_out, pooled, "enc.asp.out");
+
+    return pooled;
+}
+
+// MFA + attentive statistics pooling on the CPU: the three MFA products and
+// their epilogue are one fused pass, and every reduction over time runs on
+// the [C, T] layout directly (no transposes). Returns pooled [2*Cm].
+ggml_tensor * mfa_asp_cpu(ggml_context *        ctx,
+                          gemm::Arena *         ar,
+                          GraphBuild &          gb,
+                          const HParams &       hp,
+                          const Weights &       w,
+                          ggml_tensor * const * blk_out) {
+    const int     Cm = hp.c_mfa();
+    const int64_t T  = blk_out[0]->ne[1];
+
+    ggml_tensor * m = nullptr;
+    if (ar != nullptr) {
+        // All three products accumulate in one GEMM (three segments).
+        const gemm::Segment sg[kNumSeBlocks] = {
+            { w.mfa_w[0], 0, blk_out[0], 0 },
+            { w.mfa_w[1], 0, blk_out[1], 0 },
+            { w.mfa_w[2], 0, blk_out[2], 0 }
+        };
+        m = gemm::mul_mat(ctx, *ar, sg, kNumSeBlocks, T, gemm::Epilogue::ReluBn, w.mfa_b, w.mfa_bn.scale,
+                          w.mfa_bn.shift);
+    } else {
+        m = cpu::epilogue_sum3(ctx, mul_mat_acc(ctx, w.mfa_w[0], blk_out[0]), mul_mat_acc(ctx, w.mfa_w[1], blk_out[1]),
+                               mul_mat_acc(ctx, w.mfa_w[2], blk_out[2]), w.mfa_b, w.mfa_bn.scale, w.mfa_bn.shift, true);
+    }
+    mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
+
+    // [mean, std] over T, then the context term wm@mean + ws@std + b.
+    ggml_tensor * st    = cpu::time_stats(ctx, m, &hp.asp_eps);
+    ggml_tensor * mean1 = ggml_view_1d(ctx, st, Cm, 0);
+    ggml_tensor * sd1   = ggml_view_1d(ctx, st, Cm, static_cast<size_t>(Cm) * sizeof(float));
+    ggml_tensor * cvec  = ggml_add(ctx, linear(ctx, w.asp_wm, mean1, nullptr), linear(ctx, w.asp_ws, sd1, nullptr));
+    cvec                = ggml_add(ctx, cvec, w.asp_b);
+
+    // a = tanh(BN(ReLU(wx@m + cvec))): cvec plays the bias.
+    ggml_tensor * a  = nullptr;
+    ggml_tensor * al = nullptr;
+    if (ar != nullptr) {
+        const gemm::Segment sx{ w.asp_wx, 0, m, 0 };
+        a = gemm::mul_mat(ctx, *ar, &sx, 1, T, gemm::Epilogue::ReluBnTanh, cvec, w.asp_bn.scale, w.asp_bn.shift);
+        const gemm::Segment sa{ w.asp_attn_w, 0, a, 0 };
+        al = gemm::mul_mat(ctx, *ar, &sa, 1, T, gemm::Epilogue::None, nullptr, nullptr, nullptr);
+    } else {
+        a  = cpu::epilogue(ctx, mul_mat_acc(ctx, w.asp_wx, m), cvec, w.asp_bn.scale, w.asp_bn.shift,
+                           /*tanh_after=*/true, true);
+        al = mul_mat_acc(ctx, w.asp_attn_w, a);
+    }
+    // al: attention logits [Cm, T] WITHOUT the bias; attn_stats adds it per
+    // channel before the softmax over time. The dump point needs the biased
+    // logits, so the dump build adds it explicitly on a side branch.
+    if (debug::enabled()) {
+        mark_dump(gb.dumps.asp_attn_logits, ggml_add(ctx, al, w.asp_attn_b), "enc.asp.attn_logits");
+        ggml_build_forward_expand(gb.graph, gb.dumps.asp_attn_logits);
+    }
+    ggml_tensor * pooled = cpu::attn_stats(ctx, m, al, w.asp_attn_b, &hp.asp_eps);  // [2*Cm] = [mu, sigma]
+    mark_dump(gb.dumps.asp_out, pooled, "enc.asp.out");
+    return pooled;
+}
+
 }  // namespace
 
-GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
+void build_blk0_im2col(const HParams & hp, const float * mel, int T, std::vector<float> & out) {
+    const int    n_mels = hp.mel_n_mels;
+    const int    K      = hp.kernel_sizes[0];
+    const int    d      = hp.dilations[0];
+    const int    p      = hp.pad(0);
+    const size_t cols   = static_cast<size_t>(hp.blk0_cols());
+    out.assign(static_cast<size_t>(T) * cols, 0.0f);
+    for (int t = 0; t < T; ++t) {
+        float * row = out.data() + static_cast<size_t>(t) * cols;
+        for (int k = 0; k < K; ++k) {
+            int src = t + k * d - p;  // torch "reflect", edge not repeated
+            if (src < 0) {
+                src = -src;
+            } else if (src >= T) {
+                src = 2 * (T - 1) - src;
+            }
+            const float * m = mel + static_cast<size_t>(src) * static_cast<size_t>(n_mels);
+            std::copy(m, m + n_mels, row + static_cast<size_t>(k) * static_cast<size_t>(n_mels));
+        }
+    }
+}
+
+GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_ops, gemm::Arena * arena) {
     GraphBuild gb{};
 
+    // Per weight group: the arena when that group was packed, else null
+    // (null routes the group through ggml_mul_mat).
+    gemm::Arena * ar_conv = cpu_ops && model.gemm_conv ? arena : nullptr;
+    gemm::Arena * ar_lin  = cpu_ops && model.gemm_lin ? arena : nullptr;
+
     const HParams & hp = model.hparams;
     const Weights & w  = model.weights;
 
@@ -221,7 +464,6 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
         return gb;
     }
 
-    const int C     = hp.c_block();
     const int Cm    = hp.c_mfa();
     const int chunk = hp.c_chunk();
 
@@ -242,22 +484,36 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
     }
 
     // ---- inputs ----------------------------------------------------------
-    gb.mel_in = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.mel_n_mels, T);
-    named(gb.mel_in, "fe.mel.in");
-    ggml_set_input(gb.mel_in);
-
-    for (int i = 0; i < kNumSeBlocks; ++i) {
-        char name[32];
-        std::snprintf(name, sizeof(name), "reflect.idx.%d", i);
-        gb.idx[i] = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, T + 2 * hp.pad(i + 1));
-        named(gb.idx[i], name);
-        ggml_set_input(gb.idx[i]);
+    gb.blk0_in = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.blk0_cols(), T);
+    named(gb.blk0_in, "fe.mel.im2col");
+    ggml_set_input(gb.blk0_in);
+
+    if (!cpu_ops) {
+        for (int i = 0; i < kNumSeBlocks; ++i) {
+            char name[32];
+            std::snprintf(name, sizeof(name), "reflect.idx.%d", i);
+            gb.idx[i] = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, T + 2 * hp.pad(i + 1));
+            named(gb.idx[i], name);
+            ggml_set_input(gb.idx[i]);
+        }
     }
 
     // ---- stage 0: TDNNBlock n_mels -> C ----------------------------------
-    // read_hparams guarantees pad(0) == pad(1), so idx[0] is the right gather.
-    ggml_tensor * x =
-        tdnn_conv(ctx, reflect_rows(ctx, gb.mel_in, gb.idx[0]), w.blk0_w, w.blk0_b, w.blk0_bn, hp.dilations[0], T);
+    // One matmul over the host-built im2col (K0 taps x n_mels, zero padded to
+    // a multiple of 32): n_mels = 60 alone is not a multiple of any CPU
+    // tinyBLAS tile, and ggml's fallback GEMM for that shape is ~15x slower.
+    ggml_tensor * x = nullptr;
+    if (ar_conv != nullptr) {
+        const gemm::Segment sg{ w.blk0_w_im2col, 0, gb.blk0_in, 0 };
+        x = gemm::mul_mat(ctx, *ar_conv, &sg, 1, T, gemm::Epilogue::ReluBn, w.blk0_b, w.blk0_bn.scale, w.blk0_bn.shift);
+    } else if (cpu_ops) {
+        x = cpu::epilogue(ctx, mul_mat_acc(ctx, w.blk0_w_im2col, gb.blk0_in), w.blk0_b, w.blk0_bn.scale,
+                          w.blk0_bn.shift, false, true);
+    } else {
+        x = linear(ctx, w.blk0_w_im2col, gb.blk0_in, w.blk0_b);
+        x = ggml_relu(ctx, x);
+        x = bn_affine(ctx, x, w.blk0_bn.scale, w.blk0_bn.shift);
+    }
     mark_dump(gb.dumps.blk0_out, x, "enc.blk.0.out");
 
     // ---- stages 1..3: SERes2Net ------------------------------------------
@@ -268,86 +524,42 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
 
         ggml_tensor * residual = x;
 
-        ggml_tensor * h = tdnn_1x1(ctx, blk.tdnn1, x);
+        ggml_tensor * h = cpu_ops ? tdnn_1x1_cpu(ctx, ar_lin, blk.tdnn1, x) : tdnn_1x1(ctx, blk.tdnn1, x);
         if (i == 0) {
             mark_dump(gb.dumps.blk1_tdnn1_out, h, "enc.blk.1.tdnn1.out");
         }
 
-        h = res2net(ctx, blk, h, gb.idx[i], d, T, chunk);
+        if (cpu_ops) {
+            h = res2net_cpu(ctx, ar_conv, blk, h, hp.kernel_sizes[static_cast<size_t>(i + 1)], d, chunk);
+        } else {
+            h = res2net(ctx, blk, h, gb.idx[i], d, T, chunk);
+        }
         if (i == 0) {
             mark_dump(gb.dumps.blk1_res2_out, h, "enc.blk.1.res2.out");
         }
 
-        h = tdnn_1x1(ctx, blk.tdnn2, h);
+        h = cpu_ops ? tdnn_1x1_cpu(ctx, ar_lin, blk.tdnn2, h) : tdnn_1x1(ctx, blk.tdnn2, h);
 
-        h = se_block(ctx, blk.se, h);
-        if (i == 0) {
-            mark_dump(gb.dumps.blk1_se_out, h, "enc.blk.1.se.out");
+        if (cpu_ops) {
+            // The SE output and the residual add are one kernel here, so the
+            // enc.blk.1.se.out dump point does not exist in this graph.
+            x = se_block_cpu(ctx, blk.se, h, residual);
+        } else {
+            h = se_block(ctx, blk.se, h);
+            if (i == 0) {
+                mark_dump(gb.dumps.blk1_se_out, h, "enc.blk.1.se.out");
+            }
+            x = ggml_add(ctx, h, residual);
         }
 
-        x = ggml_add(ctx, h, residual);
-
         char name[32];
         std::snprintf(name, sizeof(name), "enc.blk.%d.out", i + 1);
         mark_dump(gb.dumps.blk_out[i], x, name);
         blk_out[i] = x;
     }
 
-    // ---- MFA: 1x1 over concat(blk1, blk2, blk3), split into three blocks --
-    ggml_tensor * m = nullptr;
-    for (int i = 0; i < kNumSeBlocks; ++i) {
-        ggml_tensor * part = ggml_mul_mat(ctx, w.mfa_w[i], blk_out[i]);
-        if (w.mfa_w[i]->type == GGML_TYPE_F16) {
-            ggml_prec_set_acc(part, GGML_PREC_F32);
-        }
-        m = (m == nullptr) ? part : ggml_add(ctx, m, part);
-    }
-    m = ggml_add(ctx, m, w.mfa_b);
-    m = ggml_relu(ctx, m);
-    m = bn_affine(ctx, m, w.mfa_bn.scale, w.mfa_bn.shift);
-    mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
-
-    // ---- attentive statistics pooling ------------------------------------
-    //
-    // mean / std are the utterance statistics over T with the uniform
-    // (unpadded) weighting SpeechBrain uses when wav_lens is all-ones; the
-    // variance is the biased 1/T one, and the clamp before the sqrt is
-    // SpeechBrain's `.clamp(eps)` and must not be dropped.
-    ggml_tensor * mT   = ggml_cont(ctx, ggml_transpose(ctx, m));  // [T, Cm]
-    ggml_tensor * mean = ggml_mean(ctx, mT);                      // [1, Cm]
-    ggml_tensor * var  = ggml_mean(ctx, ggml_sqr(ctx, ggml_sub(ctx, mT, mean)));
-    ggml_tensor * sd   = ggml_sqrt(ctx, ggml_clamp(ctx, var, hp.asp_eps, FLT_MAX));
-
-    ggml_tensor * mean1 = ggml_reshape_1d(ctx, mean, Cm);
-    ggml_tensor * sd1   = ggml_reshape_1d(ctx, sd, Cm);
-
-    // The context term wm@mean + ws@std + b is a [att] vector broadcast over
-    // T, so the 3*Cm -> att convolution costs one Cm x T matmul instead of
-    // three.
-    ggml_tensor * cvec = ggml_add(ctx, linear(ctx, w.asp_wm, mean1, nullptr), linear(ctx, w.asp_ws, sd1, nullptr));
-    cvec               = ggml_add(ctx, cvec, w.asp_b);
-
-    ggml_tensor * a = ggml_add(ctx, linear(ctx, w.asp_wx, m, nullptr), cvec);  // [att, T]
-    a               = ggml_relu(ctx, a);
-    a               = bn_affine(ctx, a, w.asp_bn.scale, w.asp_bn.shift);
-    a               = ggml_tanh(ctx, a);
-
-    ggml_tensor * al = linear(ctx, w.asp_attn_w, a, w.asp_attn_b);  // [Cm, T]
-    // Marked BEFORE the transpose: ggml ne = [Cm, T] lands on disk as
-    // [T, Cm], the orientation the reference dumps.
-    mark_dump(gb.dumps.asp_attn_logits, al, "enc.asp.attn_logits");
-
-    ggml_tensor * alT  = ggml_cont(ctx, ggml_transpose(ctx, al));     // [T, Cm]
-    ggml_tensor * attn = ggml_soft_max(ctx, alT);                     // over ne[0] = T
-
-    ggml_tensor * mu  = ggml_sum_rows(ctx, ggml_mul(ctx, attn, mT));  // [1, Cm]
-    ggml_tensor * dev = ggml_sqr(ctx, ggml_sub(ctx, mT, mu));
-    ggml_tensor * sig = ggml_sum_rows(ctx, ggml_mul(ctx, attn, dev));
-    sig               = ggml_sqrt(ctx, ggml_clamp(ctx, sig, hp.asp_eps, FLT_MAX));
-
     ggml_tensor * pooled =
-        ggml_concat(ctx, ggml_reshape_1d(ctx, mu, Cm), ggml_reshape_1d(ctx, sig, Cm), /*dim=*/0);  // [2*Cm]
-    mark_dump(gb.dumps.asp_out, pooled, "enc.asp.out");
+        cpu_ops ? mfa_asp_cpu(ctx, ar_lin, gb, hp, w, blk_out) : mfa_asp(ctx, gb, hp, w, blk_out, Cm);
 
     // ---- embedding head (asp_bn folded into fc) ---------------------------
     ggml_tensor * emb = linear(ctx, w.fc_w, pooled, w.fc_b);
@@ -369,7 +581,6 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
     ggml_set_output(gb.logits);
     ggml_build_forward_expand(gb.graph, gb.logits);
 
-    (void) C;
     return gb;
 }
 
diff --git a/src/arch/ecapa_tdnn/graph.h b/src/arch/ecapa_tdnn/graph.h
index d0d4db339..c88578153 100644
--- a/src/arch/ecapa_tdnn/graph.h
+++ b/src/arch/ecapa_tdnn/graph.h
@@ -9,9 +9,12 @@
 
 #pragma once
 
+#include "cpu_gemm.h"
 #include "ggml.h"
 #include "weights.h"
 
+#include <vector>
+
 struct ggml_context;
 struct ggml_cgraph;
 struct ggml_tensor;
@@ -45,13 +48,14 @@ struct Dumps {
 };
 
 struct GraphBuild {
-    // Log-mel input, ne = [n_mels, T] f32. The caller uploads the front end's
-    // frame-major buffer verbatim (frame t's n_mels values contiguous).
-    ggml_tensor * mel_in = nullptr;
+    // Stage-0 input: the host-built im2col of the reflect-padded log-mel,
+    // ne = [blk0_cols, T] f32. Row t holds mel_pad[t + k*d0] for k = 0..K0-1
+    // back to back, then zeros up to blk0_cols (build_blk0_im2col).
+    ggml_tensor * blk0_in = nullptr;
 
     // Reflect-padding gather indices, one per SERes2Net block, I32
-    // ne = [T + 2*pad(i+1)]. Stage 0 reuses idx[0] (read_hparams enforces
-    // pad(0) == pad(1)).
+    // ne = [T + 2*pad(i+1)]. Only the stock-op graph uses them; the CPU
+    // graph pads inside its Res2Net kernel and leaves these null.
     ggml_tensor * idx[kNumSeBlocks] = { nullptr, nullptr, nullptr };
 
     // Output.
@@ -63,7 +67,16 @@ struct GraphBuild {
 };
 
 // Build the full forward graph for T frames into `ctx` (a fresh no_alloc
-// context). Returns a build with `graph == nullptr` on invalid input.
-GraphBuild build_graph(ggml_context * ctx, const Model & model, int T);
+// context). `cpu_ops` selects the fused CPU kernels (cpu_ops.h); it must only
+// be set when every node runs on the ggml CPU backend. Weights the model
+// packed for the AVX2 GEMM (Model::gemm_conv / gemm_lin) are routed through
+// it; `arena` holds those nodes' descriptors and must outlive the compute.
+// Returns a build with `graph == nullptr` on invalid input.
+GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_ops, gemm::Arena * arena);
+
+// Fill `out` ([T, hp.blk0_cols()] frame-major, i.e. ggml ne = [cols, T])
+// with the stage-0 im2col of the frame-major log-mel `mel` [T, n_mels]:
+// out[t, k*n_mels + m] = mel[reflect(t + k*d0 - p0), m], zeros past K0*n_mels.
+void build_blk0_im2col(const HParams & hp, const float * mel, int T, std::vector<float> & out);
 
 }  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/mel.cpp b/src/arch/ecapa_tdnn/mel.cpp
index 922f40479..9ef459ac5 100644
--- a/src/arch/ecapa_tdnn/mel.cpp
+++ b/src/arch/ecapa_tdnn/mel.cpp
@@ -44,12 +44,19 @@ namespace {
 void dft_naive_f32(const float * in, int N, const float * cos_lut, const float * sin_lut, int lut_size, float * out) {
     const int stride = lut_size / N;
     for (int k = 0; k < N; ++k) {
-        float re = 0.0f;
-        float im = 0.0f;
+        float     re   = 0.0f;
+        float     im   = 0.0f;
+        // idx = (k * n * stride) % lut_size, advanced incrementally (same
+        // indices, same accumulation order, no division per term).
+        const int step = (k * stride) % lut_size;
+        int       idx  = 0;
         for (int n = 0; n < N; ++n) {
-            const int idx = (k * n * stride) % lut_size;
             re += in[n] * cos_lut[idx];
             im -= in[n] * sin_lut[idx];
+            idx += step;
+            if (idx >= lut_size) {
+                idx -= lut_size;
+            }
         }
         out[2 * k]     = re;
         out[2 * k + 1] = im;
@@ -180,6 +187,22 @@ MelFrontend::MelFrontend(const MelConfig & cfg) : cfg_(cfg) {
     }
 
     build_twiddle_lut(cfg_.n_fft, cos_lut_, sin_lut_);
+
+    fb_lo_.assign(static_cast<size_t>(cfg_.n_mels), 0);
+    fb_hi_.assign(static_cast<size_t>(cfg_.n_mels), 0);
+    for (int m = 0; m < cfg_.n_mels; ++m) {
+        const float * row = cfg_.filterbank.data() + static_cast<size_t>(m) * static_cast<size_t>(n_freq_);
+        int           lo  = n_freq_;
+        int           hi  = 0;
+        for (int k = 0; k < n_freq_; ++k) {
+            if (row[k] != 0.0f) {
+                lo = std::min(lo, k);
+                hi = k + 1;
+            }
+        }
+        fb_lo_[static_cast<size_t>(m)] = lo < hi ? lo : 0;
+        fb_hi_[static_cast<size_t>(m)] = lo < hi ? hi : 0;
+    }
 }
 
 int MelFrontend::n_frames_for(int64_t n_samples) const {
@@ -275,7 +298,8 @@ transcribe_status MelFrontend::compute(const float *        pcm,
             for (int m = 0; m < n_mels; ++m) {
                 const float * fb_row = cfg_.filterbank.data() + static_cast<size_t>(m) * static_cast<size_t>(n_freq);
                 double        sum    = 0.0;
-                for (int k = 0; k < n_freq; ++k) {
+                const int     k_hi   = fb_hi_[static_cast<size_t>(m)];
+                for (int k = fb_lo_[static_cast<size_t>(m)]; k < k_hi; ++k) {
                     sum += static_cast<double>(fb_row[k]) * static_cast<double>(power[static_cast<size_t>(k)]);
                 }
                 // SpeechBrain Filterbank.log_mel: 10*log10(max(x, amin))
diff --git a/src/arch/ecapa_tdnn/mel.h b/src/arch/ecapa_tdnn/mel.h
index e24cca04d..b9ef6765d 100644
--- a/src/arch/ecapa_tdnn/mel.h
+++ b/src/arch/ecapa_tdnn/mel.h
@@ -157,6 +157,13 @@ class MelFrontend {
     // and avoid a per-load cast in the inner butterfly.
     std::vector<float> cos_lut_;
     std::vector<float> sin_lut_;
+
+    // Nonzero support of each filterbank row: [fb_lo_[m], fb_hi_[m]) holds
+    // every nonzero weight of mel m (SpeechBrain's triangles cover a few
+    // bins each). The mel matmul only visits that range; the skipped terms
+    // are exact zeros, so the fp64 sum is unchanged.
+    std::vector<int> fb_lo_;
+    std::vector<int> fb_hi_;
 };
 
 // --- test hook ---------------------------------------------------------
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index 8d2bf9812..fa3665cdf 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -17,7 +17,6 @@
 #include "transcribe-backend.h"
 #include "transcribe-batch-util.h"
 #include "transcribe-debug.h"
-#include "transcribe-env.h"
 #include "transcribe-load-common.h"
 #include "transcribe-loader.h"
 #include "transcribe-log.h"
@@ -30,6 +29,7 @@
 #include <cmath>
 #include <cstddef>
 #include <cstdint>
+#include <cstring>
 #include <fstream>
 #include <ios>
 #include <memory>
@@ -52,9 +52,8 @@ constexpr const char k_default_variant[] = "lang-id-voxlingua107-ecapa";
 constexpr size_t k_sched_graph_size = 2048;
 
 // Metadata arena for one per-call graph build. The published configuration
-// uses 0.36 MiB of it (TRANSCRIBE_ECAPA_GRAPH_STATS reports the figure; the
-// graph is independent of T); the rest is headroom so a future graph change
-// cannot silently truncate.
+// uses 0.36 MiB of it (the graph is independent of T); the rest is headroom
+// so a future graph change cannot silently truncate.
 constexpr size_t k_compute_ctx_bytes = 4u * 1024u * 1024u;
 
 int64_t now_us() {
@@ -116,11 +115,9 @@ transcribe_status read_labels(const gguf_context * gguf, LangidLabels & out) {
 // Q8_0 is a download format here, not a compute format: every Q8_0 weight is
 // widened to F16 at load. ggml's Q8_0 matmuls also quantize the ACTIVATIONS
 // to 8 bits, and on FLEURS that, not the 8-bit weights, is what moves the
-// decision: computed in Q8_0 the published file agrees with SpeechBrain on
-// 93.1% of 12000 top-1 decisions; the same weights computed in F16 agree on
-// 97.5% and score at F32's accuracy. The cost is F16 memory for those weights
-// (45 MB instead of 27 MB resident) and F16 speed, which is no slower than
-// Q8_0 on any backend measured without the CPU repack path.
+// decision; the same weights computed in F16 score at F32's accuracy
+// (agreement figures: docs/models/lang-id-voxlingua107-ecapa.md). The cost
+// is F16 memory for those weights, and F16 speed.
 //
 // Retype in place: the tensors come from the no_alloc gguf context and have
 // no data or views yet, so only type and strides change. Returns the count.
@@ -205,6 +202,110 @@ transcribe_status stream_weights(const std::string & path, const gguf_context *
     return TRANSCRIBE_OK;
 }
 
+// Build Weights::blk0_w_im2col from blk0_w: the tap-major [n_mels, C, K0]
+// kernel regrouped to [K0*n_mels, C] (column k*n_mels + m of output row c is
+// tap k, mel m) and zero padded to hp.blk0_cols(). Same type as blk0_w.
+transcribe_status build_derived_weights(Model & m) {
+    const HParams & hp     = m.hparams;
+    ggml_tensor *   src    = m.weights.blk0_w;
+    const int64_t   n_mels = hp.mel_n_mels;
+    const int64_t   C      = hp.c_block();
+    const int64_t   K      = hp.kernel_sizes[0];
+    const int64_t   cols   = hp.blk0_cols();
+
+    ggml_init_params ip{};
+    ip.mem_size   = 4 * ggml_tensor_overhead();
+    ip.no_alloc   = true;
+    m.ctx_derived = ggml_init(ip);
+    if (m.ctx_derived == nullptr) {
+        return TRANSCRIBE_ERR_OOM;
+    }
+    ggml_tensor * dst = ggml_new_tensor_2d(m.ctx_derived, src->type, cols, C);
+    ggml_set_name(dst, "blk.0.conv.weight.im2col");
+    m.derived_buffer = ggml_backend_alloc_ctx_tensors(m.ctx_derived, m.plan.primary);
+    if (m.derived_buffer == nullptr) {
+        return TRANSCRIBE_ERR_OOM;
+    }
+    ggml_backend_buffer_set_usage(m.derived_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+
+    const size_t         esz = ggml_type_size(src->type);  // F32 / F16, block size 1
+    std::vector<uint8_t> in(ggml_nbytes(src));
+    std::vector<uint8_t> out(ggml_nbytes(dst), 0);         // zero bits are 0.0 in F32 and F16
+    ggml_backend_tensor_get(src, in.data(), 0, in.size());
+    for (int64_t k = 0; k < K; ++k) {
+        for (int64_t c = 0; c < C; ++c) {
+            const uint8_t * s = in.data() + static_cast<size_t>(k) * src->nb[2] + static_cast<size_t>(c) * src->nb[1];
+            uint8_t * d = out.data() + static_cast<size_t>(c) * dst->nb[1] + static_cast<size_t>(k * n_mels) * esz;
+            std::memcpy(d, s, static_cast<size_t>(n_mels) * esz);
+        }
+    }
+    ggml_backend_tensor_set(dst, out.data(), 0, out.size());
+    m.weights.blk0_w_im2col = dst;
+    return TRANSCRIBE_OK;
+}
+
+// Repack weights for the AVX2 GEMM (cpu_gemm.h), in two all-or-nothing
+// groups the graph routes as a unit:
+//   - the k>1 kernels (F32 in every shipped file): this GEMM beats ggml's on
+//     their small shapes, and stage 0's K = 320 is not a tinyBLAS shape;
+//   - the T-wide 1x1 weights, only when F16 (F16 files, and Q8_0 after
+//     widening): ggml widens F16 operands in its inner loop, ~1.7x slower.
+//     For F32 1x1 weights ggml's tinyBLAS is as fast as this GEMM.
+// A group is skipped (left on ggml_mul_mat) unless every weight fits the
+// GEMM: F16/F32, contiguous, rows a multiple of the panel, and at most
+// kMaxSegs matrices (one GEMM segment per tap).
+transcribe_status pack_gemm_weights(Model & m) {
+    if (!gemm::available()) {
+        return TRANSCRIBE_OK;
+    }
+    Weights & w = m.weights;
+
+    auto fits = [](const std::vector<ggml_tensor *> & ts, bool need_f16) {
+        for (const ggml_tensor * t : ts) {
+            if (t->ne[1] % gemm::kPanel != 0 || t->ne[2] * t->ne[3] > gemm::kMaxSegs || !ggml_is_contiguous(t) ||
+                (need_f16 && t->type != GGML_TYPE_F16) || (t->type != GGML_TYPE_F16 && t->type != GGML_TYPE_F32)) {
+                return false;
+            }
+        }
+        return true;
+    };
+    // After fits() this cannot fail; if it ever does, some weights are
+    // already packed and unreadable by ggml_mul_mat, so the load fails.
+    auto pack = [&](const std::vector<ggml_tensor *> & ts) {
+        for (ggml_tensor * t : ts) {
+            if (!gemm::pack_weight(t)) {
+                log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: GEMM weight repack failed for %s", kTag, t->name);
+                return false;
+            }
+        }
+        return true;
+    };
+
+    std::vector<ggml_tensor *> conv{ w.blk0_w_im2col };
+    std::vector<ggml_tensor *> lin;
+    for (SeRes2NetBlock & b : w.blocks) {
+        for (Res2Sub & r : b.res2) {
+            conv.push_back(r.w);
+        }
+        lin.push_back(b.tdnn1.w);
+        lin.push_back(b.tdnn2.w);
+    }
+    for (ggml_tensor * t : w.mfa_w) {
+        lin.push_back(t);
+    }
+    lin.push_back(w.asp_wx);
+    lin.push_back(w.asp_attn_w);
+
+    m.gemm_conv = fits(conv, /*need_f16=*/false);
+    m.gemm_lin  = fits(lin, /*need_f16=*/true);
+    if ((m.gemm_conv && !pack(conv)) || (m.gemm_lin && !pack(lin))) {
+        return TRANSCRIBE_ERR_BACKEND;
+    }
+    log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: AVX2 GEMM for %s%s%s", kTag, m.gemm_conv ? "conv" : "",
+            m.gemm_conv && m.gemm_lin ? " + " : "", m.gemm_lin ? "F16 1x1" : (m.gemm_conv ? "" : "nothing"));
+    return TRANSCRIBE_OK;
+}
+
 // ---------------------------------------------------------------------------
 // load
 // ---------------------------------------------------------------------------
@@ -308,6 +409,20 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
     if (auto st = stream_weights(loader.path(), guard.ctx, m->ctx_meta); st != TRANSCRIBE_OK) {
         return st;
     }
+    if (auto st = build_derived_weights(*m); st != TRANSCRIBE_OK) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: derived weight allocation failed", kTag);
+        return st;
+    }
+
+    // Fused CPU kernels only when the whole graph runs on the CPU backend:
+    // a custom op has no GPU implementation, and the scheduler would bounce
+    // every one of them back to the host.
+    m->cpu_ops = m->plan.primary_kind == BackendKind::Cpu && m->plan.scheduler_list.size() == 1;
+    if (m->cpu_ops) {
+        if (auto st = pack_gemm_weights(*m); st != TRANSCRIBE_OK) {
+            return st;
+        }
+    }
 
     m->roles = TRANSCRIBE_ROLE_LANGID;
     // The abort callback is honored (transcribe_langid_set_abort_callback).
@@ -353,9 +468,21 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
                              "frontend");
     }
 
-    // ---- reflect-padding indices ------------------------------------------
-    for (int i = 0; i < kNumSeBlocks; ++i) {
-        build_reflect_indices(T, hp.pad(i + 1), s.idx_buf[i]);
+    // reflect_rows / the Res2Net kernel need T > pad (the 500 ms LANGID
+    // minimum gives T >= 51 frames; the largest pad here is 4).
+    for (int i = 0; i < kNumStages - 1; ++i) {
+        if (T <= hp.pad(i)) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: T=%d is too short for reflect padding %d", kTag, T, hp.pad(i));
+            return TRANSCRIBE_ERR_INPUT_TOO_SHORT;
+        }
+    }
+
+    // ---- stage-0 im2col and reflect-padding indices ------------------------
+    build_blk0_im2col(hp, s.mel_buf.data(), T, s.im2col_buf);
+    if (!m.cpu_ops) {
+        for (int i = 0; i < kNumSeBlocks; ++i) {
+            build_reflect_indices(T, hp.pad(i + 1), s.idx_buf[i]);
+        }
     }
 
     // ---- graph -------------------------------------------------------------
@@ -366,6 +493,7 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
         ggml_free(s.compute_ctx);
         s.compute_ctx = nullptr;
     }
+    s.gemm_arena.clear();
     {
         if (s.graph_arena.size() != k_compute_ctx_bytes) {
             s.graph_arena.resize(k_compute_ctx_bytes);
@@ -381,7 +509,7 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
         }
     }
 
-    GraphBuild gb = build_graph(s.compute_ctx, m, T);
+    GraphBuild gb = build_graph(s.compute_ctx, m, T, m.cpu_ops, &s.gemm_arena);
     if (gb.graph == nullptr || gb.logits == nullptr) {
         return TRANSCRIBE_ERR_GGUF;
     }
@@ -405,30 +533,13 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
         return TRANSCRIBE_ERR_OOM;
     }
 
-    // ---- graph stats (TRANSCRIBE_ECAPA_GRAPH_STATS=1) ------------------------
-    //
-    // One line per session, after the first allocation: the node count, how
-    // much of the metadata arena the build used, the number of scheduler
-    // splits (more than one means an op fell back to another backend), and
-    // each backend's compute buffer.
-    if (!s.logged_graph_stats && env::flag("TRANSCRIBE_ECAPA_GRAPH_STATS")) {
-        s.logged_graph_stats = true;
-        log_msg(TRANSCRIBE_LOG_LEVEL_DEBUG, "%s: graph T=%d nodes=%d arena=%.2f/%.2f MiB splits=%d threads=%d", kTag, T,
-                ggml_graph_n_nodes(gb.graph), static_cast<double>(ggml_used_mem(s.compute_ctx)) / (1024.0 * 1024.0),
-                static_cast<double>(s.graph_arena.size()) / (1024.0 * 1024.0), ggml_backend_sched_get_n_splits(s.sched),
-                n_threads);
-        for (ggml_backend_t be : m.plan.scheduler_list) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_DEBUG, "%s:   sched buffer [%s] %.2f MiB", kTag, ggml_backend_name(be),
-                    static_cast<double>(ggml_backend_sched_get_buffer_size(s.sched, be)) / (1024.0 * 1024.0));
-        }
-    }
-
     // ---- inputs -------------------------------------------------------------
-    // The front end writes frame-major [T, n_mels], byte-identical to ggml
-    // ne = [n_mels, T].
-    ggml_backend_tensor_set(gb.mel_in, s.mel_buf.data(), 0, s.mel_buf.size() * sizeof(float));
+    // Frame-major [T, cols], byte-identical to ggml ne = [cols, T].
+    ggml_backend_tensor_set(gb.blk0_in, s.im2col_buf.data(), 0, s.im2col_buf.size() * sizeof(float));
     for (int i = 0; i < kNumSeBlocks; ++i) {
-        ggml_backend_tensor_set(gb.idx[i], s.idx_buf[i].data(), 0, s.idx_buf[i].size() * sizeof(int32_t));
+        if (gb.idx[i] != nullptr) {
+            ggml_backend_tensor_set(gb.idx[i], s.idx_buf[i].data(), 0, s.idx_buf[i].size() * sizeof(int32_t));
+        }
     }
 
     // ---- compute -------------------------------------------------------------
@@ -458,7 +569,7 @@ void dump_stages(const GraphBuild & gb) {
     try_dump("enc.blk.0.out", gb.dumps.blk0_out, "encoder");
     try_dump("enc.blk.1.tdnn1.out", gb.dumps.blk1_tdnn1_out, "encoder");
     try_dump("enc.blk.1.res2.out", gb.dumps.blk1_res2_out, "encoder");
-    try_dump("enc.blk.1.se.out", gb.dumps.blk1_se_out, "encoder");
+    try_dump("enc.blk.1.se.out", gb.dumps.blk1_se_out, "encoder");  // stock-op graph only
     try_dump("enc.blk.1.out", gb.dumps.blk_out[0], "encoder");
     try_dump("enc.blk.2.out", gb.dumps.blk_out[1], "encoder");
     try_dump("enc.blk.3.out", gb.dumps.blk_out[2], "encoder");
@@ -545,6 +656,12 @@ Model::~Model() {
     if (backend_buffer != nullptr) {
         safe_buffer_free(backend_buffer);
     }
+    if (ctx_derived != nullptr) {
+        ggml_free(ctx_derived);
+    }
+    if (derived_buffer != nullptr) {
+        safe_buffer_free(derived_buffer);
+    }
     for (auto it = plan.scheduler_list.rbegin(); it != plan.scheduler_list.rend(); ++it) {
         safe_backend_free(*it);
     }
diff --git a/src/arch/ecapa_tdnn/weights.cpp b/src/arch/ecapa_tdnn/weights.cpp
index 30bf28e0f..b45681633 100644
--- a/src/arch/ecapa_tdnn/weights.cpp
+++ b/src/arch/ecapa_tdnn/weights.cpp
@@ -206,18 +206,6 @@ transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
         return TRANSCRIBE_ERR_GGUF;
     }
 
-    // Stage 0 reuses the reflect-index tensor built for the first SERes2Net
-    // block (the graph carries one index input per SERes2Net block), so the
-    // two padding widths have to agree. They do for every published
-    // configuration: k=5 d=1 and k=3 d=2 both give p = 2.
-    if (hp.pad(0) != hp.pad(1)) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
-                "%s: stage 0 reflect padding (%d) differs from stage 1 (%d); this build "
-                "shares one reflect-index input between them",
-                kTag, hp.pad(0), hp.pad(1));
-        return TRANSCRIBE_ERR_GGUF;
-    }
-
     for (const int32_t v : { hp.se_channels, hp.attention_channels, hp.embedding_dim, hp.classifier_hidden }) {
         if (v <= 0 || v > kMaxChannels) {
             log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: se/attention/embedding/hidden dimensions out of range", kTag);
diff --git a/src/arch/ecapa_tdnn/weights.h b/src/arch/ecapa_tdnn/weights.h
index ffb309263..1a29fd08a 100644
--- a/src/arch/ecapa_tdnn/weights.h
+++ b/src/arch/ecapa_tdnn/weights.h
@@ -119,6 +119,13 @@ struct HParams {
 
     int32_t n_freq() const { return mel_n_fft / 2 + 1; }
 
+    // Inner width of the stage-0 im2col matmul: K0 * n_mels rounded up to a
+    // multiple of 32 (every CPU tinyBLAS tile width divides it), zero padded.
+    int32_t blk0_cols() const {
+        const int32_t n = kernel_sizes.empty() ? 0 : kernel_sizes[0] * mel_n_mels;
+        return (n + 31) / 32 * 32;
+    }
+
     // Reflect padding on each side of stage `i`: d * (k - 1) / 2, the
     // "same"-padding width SpeechBrain's Conv1d uses.
     int32_t pad(int i) const {
@@ -168,8 +175,13 @@ struct Weights {
     ggml_tensor * mel_filters = nullptr;  // ne=[n_freq, n_mels]
 
     // Stage 0: plain TDNNBlock, n_mels -> C, k = kernel_sizes[0].
-    ggml_tensor * blk0_w = nullptr;  // ne=[n_mels, C, K0]
-    ggml_tensor * blk0_b = nullptr;  // [C]
+    ggml_tensor * blk0_w        = nullptr;  // ne=[n_mels, C, K0]
+    // Derived at load (model.cpp, not in the GGUF): blk0_w regrouped as one
+    // [K0 * n_mels, C] matrix, zero-padded on the inner axis to
+    // HParams::blk0_cols(), so stage 0 is a single aligned matmul against
+    // the host-built im2col of the mel. Same type as blk0_w.
+    ggml_tensor * blk0_w_im2col = nullptr;  // ne=[blk0_cols, C]
+    ggml_tensor * blk0_b        = nullptr;  // [C]
     BnAffine      blk0_bn;
 
     // Stages 1..3.
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 807ea3d09..d8490aa60 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -107,7 +107,8 @@ set(TRANSCRIBE_FIXTURE_FILES
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_labels.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf)
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_k5.gguf)
 
 find_program(TRANSCRIBE_UV uv)
 
@@ -423,6 +424,16 @@ foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize
     add_test(NAME transcribe_${_t}_unit COMMAND transcribe_${_t}_unit)
 endforeach()
 
+# ecapa_tdnn CPU kernels (AVX2 GEMM, fused reductions) against scalar
+# references, at several thread counts. The GEMM part skips (RC 77) where the
+# CPU or build has no AVX2 kernel.
+add_executable(transcribe_ecapa_tdnn_gemm_unit ecapa_tdnn_gemm_unit.cpp)
+target_link_libraries(transcribe_ecapa_tdnn_gemm_unit PRIVATE transcribe ggml)
+target_include_directories(transcribe_ecapa_tdnn_gemm_unit PRIVATE ${CMAKE_SOURCE_DIR}/src)
+transcribe_apply_warnings(transcribe_ecapa_tdnn_gemm_unit)
+add_test(NAME transcribe_ecapa_tdnn_gemm_unit COMMAND transcribe_ecapa_tdnn_gemm_unit)
+set_tests_properties(transcribe_ecapa_tdnn_gemm_unit PROPERTIES SKIP_RETURN_CODE 77)
+
 # -----------------------------------------------------------------------------
 # Dispatcher-level streaming entry-point behavior
 # -----------------------------------------------------------------------------
diff --git a/tests/ecapa_tdnn_gemm_unit.cpp b/tests/ecapa_tdnn_gemm_unit.cpp
new file mode 100644
index 000000000..2b2433703
--- /dev/null
+++ b/tests/ecapa_tdnn_gemm_unit.cpp
@@ -0,0 +1,282 @@
+// ecapa_tdnn_gemm_unit.cpp - the ecapa_tdnn CPU kernels against scalar
+// references: the AVX2 GEMM (src/arch/ecapa_tdnn/cpu_gemm.h) and the fused
+// time reductions / softmax (cpu_ops.h).
+//
+// GEMM cases cover what the graph uses: F16 and F32 weights, 1..3 segments
+// with row offsets (a dilated conv's taps), every epilogue,
+// odd N (partial 6-wide tiles), K not a multiple of the k block, and 1..16
+// threads; every result must be bit-identical across thread counts and
+// within FMA-rounding distance of an fp64 reference. Skips (RC 77) when the
+// CPU or build has no AVX2 kernel.
+
+#include "arch/ecapa_tdnn/cpu_gemm.h"
+#include "arch/ecapa_tdnn/cpu_ops.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#include "ggml-cpu.h"
+#include "ggml.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <random>
+#include <vector>
+
+using namespace transcribe::ecapa_tdnn;
+
+namespace {
+
+int g_fail = 0;
+
+#define CHECK(cond, ...)                                       \
+    do {                                                       \
+        if (!(cond)) {                                         \
+            std::fprintf(stderr, "FAIL %s:%d: ", __FILE__, __LINE__); \
+            std::fprintf(stderr, __VA_ARGS__);                 \
+            std::fprintf(stderr, "\n");                        \
+            ++g_fail;                                          \
+        }                                                      \
+    } while (0)
+
+struct Case {
+    const char *    name;
+    ggml_type       wtype;
+    int64_t         M, K, N;
+    int             n_segs;      // segments share one weight tensor (n_mats = n_segs)
+    int64_t         row_step;    // segment s reads x from row s * row_step
+    gemm::Epilogue  ep;
+    bool            bias;
+};
+
+std::vector<float> rnd(size_t n, std::mt19937 & rng, float scale) {
+    std::normal_distribution<float> nd(0.0f, scale);
+    std::vector<float>              v(n);
+    for (auto & x : v) {
+        x = nd(rng);
+    }
+    return v;
+}
+
+bool run_case(const Case & c) {
+    std::mt19937  rng(1234);
+    const int64_t Q      = c.n_segs;
+    const int64_t x_rows = c.N + (c.n_segs - 1) * c.row_step;
+
+    std::vector<float> wf = rnd(static_cast<size_t>(Q * c.M * c.K), rng, 0.05f);
+    std::vector<float> xf = rnd(static_cast<size_t>(x_rows * c.K), rng, 1.0f);
+    std::vector<float> bf = rnd(static_cast<size_t>(c.M), rng, 0.3f);
+    std::vector<float> sf = rnd(static_cast<size_t>(c.M), rng, 1.0f);
+    std::vector<float> hf = rnd(static_cast<size_t>(c.M), rng, 0.3f);
+
+    // Weights as the kernel sees them: round through F16 when F16.
+    std::vector<ggml_fp16_t> wh;
+    if (c.wtype == GGML_TYPE_F16) {
+        wh.resize(wf.size());
+        ggml_fp32_to_fp16_row(wf.data(), wh.data(), static_cast<int64_t>(wf.size()));
+        ggml_fp16_to_fp32_row(wh.data(), wf.data(), static_cast<int64_t>(wf.size()));
+    }
+
+    // fp64 reference.
+    std::vector<double> ref(static_cast<size_t>(c.M * c.N));
+    for (int64_t j = 0; j < c.N; ++j) {
+        for (int64_t i = 0; i < c.M; ++i) {
+            double acc = 0.0;
+            for (int64_t s = 0; s < Q; ++s) {
+                const float * w = wf.data() + static_cast<size_t>((s * c.M + i) * c.K);
+                const float * x = xf.data() + static_cast<size_t>((j + s * c.row_step) * c.K);
+                for (int64_t k = 0; k < c.K; ++k) {
+                    acc += static_cast<double>(w[k]) * static_cast<double>(x[k]);
+                }
+            }
+            if (c.ep != gemm::Epilogue::None) {
+                double v = acc + (c.bias ? bf[static_cast<size_t>(i)] : 0.0);
+                v        = v > 0.0 ? v : 0.0;
+                v        = v * sf[static_cast<size_t>(i)] + hf[static_cast<size_t>(i)];
+                if (c.ep == gemm::Epilogue::ReluBnTanh) {
+                    v = std::tanh(v);
+                }
+                acc = v;
+            }
+            ref[static_cast<size_t>(j * c.M + i)] = acc;
+        }
+    }
+
+    std::vector<float> first;
+    bool               ok = true;
+    for (int nt : { 1, 3, 8, 16 }) {
+        ggml_backend_t be = ggml_backend_cpu_init();
+        ggml_backend_cpu_set_n_threads(be, nt);
+        ggml_init_params ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
+        ggml_context *   ctx = ggml_init(ip);
+        ggml_tensor *    w   = ggml_new_tensor_3d(ctx, c.wtype, c.K, c.M, Q);
+        ggml_tensor *    x   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c.K, x_rows);
+        ggml_tensor *    b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
+        ggml_tensor *    s   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
+        ggml_tensor *    h   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
+        ggml_backend_buffer_t wbuf = ggml_backend_alloc_ctx_tensors(ctx, be);
+        if (c.wtype == GGML_TYPE_F16) {
+            ggml_backend_tensor_set(w, wh.data(), 0, ggml_nbytes(w));
+        } else {
+            ggml_backend_tensor_set(w, wf.data(), 0, ggml_nbytes(w));
+        }
+        ggml_backend_tensor_set(x, xf.data(), 0, ggml_nbytes(x));
+        ggml_backend_tensor_set(b, bf.data(), 0, ggml_nbytes(b));
+        ggml_backend_tensor_set(s, sf.data(), 0, ggml_nbytes(s));
+        ggml_backend_tensor_set(h, hf.data(), 0, ggml_nbytes(h));
+        CHECK(gemm::pack_weight(w), "%s: pack_weight failed", c.name);
+
+        gemm::Arena   arena;
+        gemm::Segment segs[gemm::kMaxSegs];
+        for (int i = 0; i < c.n_segs; ++i) {
+            segs[i] = { w, i, x, i * c.row_step };
+        }
+        const bool    epi = c.ep != gemm::Epilogue::None;
+        ggml_tensor * y   = gemm::mul_mat(ctx, arena, segs, c.n_segs, c.N, c.ep, epi && c.bias ? b : nullptr,
+                                          epi ? s : nullptr, epi ? h : nullptr);
+        ggml_cgraph * g   = ggml_new_graph(ctx);
+        ggml_build_forward_expand(g, y);
+        ggml_gallocr_t ga = ggml_gallocr_new(ggml_backend_get_default_buffer_type(be));
+        CHECK(ggml_gallocr_alloc_graph(ga, g), "%s: graph alloc failed", c.name);
+        // Run twice: the second compute exercises the work-stealing state
+        // left behind by the first.
+        for (int rep = 0; rep < 2; ++rep) {
+            CHECK(ggml_backend_graph_compute(be, g) == GGML_STATUS_SUCCESS, "%s: compute failed", c.name);
+        }
+
+        std::vector<float> out(static_cast<size_t>(c.M * c.N));
+        ggml_backend_tensor_get(y, out.data(), 0, ggml_nbytes(y));
+
+        double worst = 0.0;
+        for (int64_t j = 0; j < c.N; ++j) {
+            for (int64_t i = 0; i < c.M; ++i) {
+                const double got = out[static_cast<size_t>(j * c.M + i)];
+                const double r   = ref[static_cast<size_t>(j * c.M + i)];
+                worst            = std::fmax(worst, std::fabs(got - r) / (1.0 + std::fabs(r)));
+            }
+        }
+        CHECK(worst < 1e-4, "%s t=%d: max rel err %.3e", c.name, nt, worst);
+        ok &= worst < 1e-4;
+        if (first.empty()) {
+            first = out;
+        } else {
+            const bool same = std::memcmp(first.data(), out.data(), out.size() * sizeof(float)) == 0;
+            CHECK(same, "%s t=%d: result differs from t=1", c.name, nt);
+            ok &= same;
+        }
+
+        ggml_gallocr_free(ga);
+        ggml_backend_buffer_free(wbuf);
+        ggml_free(ctx);
+        ggml_backend_free(be);
+    }
+    return ok;
+}
+
+// time_stats / attn_stats against fp64 references on a [C, T] tensor.
+void run_reductions() {
+    const int64_t      C = 600, T = 517;  // C not a multiple of the 256 channel block
+    std::mt19937       rng(99);
+    std::vector<float> xf = rnd(static_cast<size_t>(C * T), rng, 1.0f);
+    std::vector<float> lf = rnd(static_cast<size_t>(C * T), rng, 2.0f);
+    std::vector<float> bf = rnd(static_cast<size_t>(C), rng, 1.0f);
+    static const float eps = 1e-12f;
+
+    for (int nt : { 1, 5, 8 }) {
+        ggml_backend_t be = ggml_backend_cpu_init();
+        ggml_backend_cpu_set_n_threads(be, nt);
+        ggml_init_params ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
+        ggml_context *   ctx = ggml_init(ip);
+        ggml_tensor *    x   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
+        ggml_tensor *    l   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
+        ggml_tensor *    b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, C);
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, be);
+        ggml_backend_tensor_set(x, xf.data(), 0, ggml_nbytes(x));
+        ggml_backend_tensor_set(l, lf.data(), 0, ggml_nbytes(l));
+        ggml_backend_tensor_set(b, bf.data(), 0, ggml_nbytes(b));
+
+        ggml_tensor * st = cpu::time_stats(ctx, x, &eps);
+        ggml_tensor * at = cpu::attn_stats(ctx, x, l, b, &eps);
+        ggml_tensor * mn = cpu::time_mean(ctx, x);
+        ggml_cgraph * g  = ggml_new_graph(ctx);
+        ggml_build_forward_expand(g, st);
+        ggml_build_forward_expand(g, at);
+        ggml_build_forward_expand(g, mn);
+        ggml_gallocr_t ga = ggml_gallocr_new(ggml_backend_get_default_buffer_type(be));
+        ggml_gallocr_alloc_graph(ga, g);
+        CHECK(ggml_backend_graph_compute(be, g) == GGML_STATUS_SUCCESS, "reductions: compute failed");
+
+        std::vector<float> vst(static_cast<size_t>(2 * C)), vat(static_cast<size_t>(2 * C)), vmn(static_cast<size_t>(C));
+        ggml_backend_tensor_get(st, vst.data(), 0, ggml_nbytes(st));
+        ggml_backend_tensor_get(at, vat.data(), 0, ggml_nbytes(at));
+        ggml_backend_tensor_get(mn, vmn.data(), 0, ggml_nbytes(mn));
+
+        double e_st = 0, e_at = 0, e_mn = 0;
+        for (int64_t c = 0; c < C; ++c) {
+            double s = 0, q = 0, mx = -1e30, z = 0, mu = 0, sg = 0;
+            for (int64_t t = 0; t < T; ++t) {
+                s += xf[static_cast<size_t>(t * C + c)];
+            }
+            const double mean = s / T;
+            for (int64_t t = 0; t < T; ++t) {
+                const double d = xf[static_cast<size_t>(t * C + c)] - mean;
+                q += d * d;
+                mx = std::fmax(mx, static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)]);
+            }
+            for (int64_t t = 0; t < T; ++t) {
+                z += std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx);
+            }
+            for (int64_t t = 0; t < T; ++t) {
+                const double w =
+                    std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx) / z;
+                mu += w * xf[static_cast<size_t>(t * C + c)];
+            }
+            for (int64_t t = 0; t < T; ++t) {
+                const double w =
+                    std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx) / z;
+                const double d = xf[static_cast<size_t>(t * C + c)] - mu;
+                sg += w * d * d;
+            }
+            e_mn = std::fmax(e_mn, std::fabs(vmn[static_cast<size_t>(c)] - mean));
+            e_st = std::fmax(e_st, std::fabs(vst[static_cast<size_t>(c)] - mean));
+            e_st = std::fmax(e_st, std::fabs(vst[static_cast<size_t>(C + c)] - std::sqrt(q / T)));
+            e_at = std::fmax(e_at, std::fabs(vat[static_cast<size_t>(c)] - mu));
+            e_at = std::fmax(e_at, std::fabs(vat[static_cast<size_t>(C + c)] - std::sqrt(sg)));
+        }
+        CHECK(e_mn < 1e-5 && e_st < 1e-5 && e_at < 1e-4, "reductions t=%d: mean %.2e stats %.2e attn %.2e", nt, e_mn,
+              e_st, e_at);
+
+        ggml_gallocr_free(ga);
+        ggml_backend_buffer_free(buf);
+        ggml_free(ctx);
+        ggml_backend_free(be);
+    }
+}
+
+}  // namespace
+
+int main() {
+    run_reductions();
+
+    if (!gemm::available()) {
+        std::printf("ecapa_tdnn_gemm_unit: no AVX2 GEMM on this CPU/build; reductions ok=%d, GEMM skipped\n",
+                    g_fail == 0);
+        return g_fail == 0 ? 77 : 1;
+    }
+
+    const Case cases[] = {
+        { "f16 1x1 relu-bn", GGML_TYPE_F16, 64, 96, 37, 1, 0, gemm::Epilogue::ReluBn, true },
+        { "f16 mfa 3-seg", GGML_TYPE_F16, 48, 300, 61, 3, 0, gemm::Epilogue::ReluBn, true },
+        { "f16 none tanh-free", GGML_TYPE_F16, 32, 513, 13, 1, 0, gemm::Epilogue::None, false },
+        { "f16 tanh", GGML_TYPE_F16, 16, 40, 7, 1, 0, gemm::Epilogue::ReluBnTanh, true },
+        { "f32 dilated conv 3-tap", GGML_TYPE_F32, 32, 24, 50, 3, 4, gemm::Epilogue::ReluBn, true },
+        { "f32 im2col no-bias", GGML_TYPE_F32, 16, 320, 101, 1, 0, gemm::Epilogue::ReluBn, false },
+        { "f16 N=1", GGML_TYPE_F16, 16, 33, 1, 2, 1, gemm::Epilogue::None, false },
+    };
+    for (const Case & c : cases) {
+        run_case(c);
+    }
+    if (g_fail == 0) {
+        std::printf("ecapa_tdnn_gemm_unit: ok\n");
+    }
+    return g_fail == 0 ? 0 : 1;
+}
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
index 339704cb0..f33771524 100644
--- a/tests/ecapa_tdnn_real_smoke.cpp
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -121,7 +121,7 @@ int main() {
     // still runs.
     float   p        = 0.0f;
     int64_t audio_ms = 0;
-    top1(s, "long-45s.wav", &p, &audio_ms);
+    top1(s, "ru-long.wav", &p, &audio_ms);
     CHECK(audio_ms == 30000);
     top1(s, "short-800ms.wav", &p, &audio_ms);
     CHECK(audio_ms == 800 && std::isfinite(p));
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index 0a2992b7f..8c6ae9082 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -3,7 +3,7 @@
 // runs the same against static, shared and installed builds.
 //
 // arch_ecapa_tdnn_minimal.gguf (tests/fixtures/make_gguf_fixtures.py) is a
-// 1/32-width model with the exact metadata and tensor contract of the real
+// 1/8-width model with the exact metadata and tensor contract of the real
 // VoxLingua107 file, so this drives the production loader, front end, ggml
 // graph and role dispatcher over the same code path the real checkpoint
 // uses. It asserts structure and invariants (statuses, orderings,
@@ -51,11 +51,32 @@ std::vector<float> noise(size_t n, uint32_t seed) {
     return out;
 }
 
+// The loader logs which weight groups it moved onto the AVX2 CPU GEMM
+// ("AVX2 GEMM for conv + F16 1x1"); the last such line, for the checks below.
+std::string g_gemm_log;
+
+void capture_log(transcribe_log_level /*level*/, const char * msg, void * /*userdata*/) {
+    if (msg != nullptr && std::strstr(msg, "AVX2 GEMM for") != nullptr) {
+        g_gemm_log = msg;
+    }
+}
+
+// Whether this CPU runs the AVX2 GEMM (the same test cpu_gemm.cpp makes).
+bool cpu_has_gemm() {
+#if (defined(__x86_64__) || defined(__i386__)) && (defined(__GNUC__) || defined(__clang__))
+    __builtin_cpu_init();
+    return __builtin_cpu_supports("avx2") && __builtin_cpu_supports("fma") && __builtin_cpu_supports("f16c");
+#else
+    return false;
+#endif
+}
+
 std::string fixture(const char * name) {
     return std::string(TRANSCRIBE_TEST_FIXTURES_DIR) + "/" + name;
 }
 
 transcribe_model * load_cpu(const char * name, transcribe_status * st_out = nullptr) {
+    g_gemm_log.clear();
     transcribe_model_load_params mp;
     transcribe_model_load_params_init(&mp);
     mp.backend                 = TRANSCRIBE_BACKEND_CPU;
@@ -235,7 +256,11 @@ void test_bad_labels() {
 // Q8_0 weights are widened to F16 at load: a Q8_0 model must give exactly
 // the logits of the same model stored as F16 holding the dequantized values.
 void test_q8_0_widened_to_f16() {
-    transcribe_model * q8  = load_cpu("arch_ecapa_tdnn_q8_0.gguf");
+    transcribe_model * q8 = load_cpu("arch_ecapa_tdnn_q8_0.gguf");
+    if (cpu_has_gemm()) {
+        // Widened to F16, every 1x1 weight takes the GEMM too.
+        CHECK(g_gemm_log.find("conv + F16 1x1") != std::string::npos);
+    }
     transcribe_model * ref = load_cpu("arch_ecapa_tdnn_q8_0_as_f16.gguf");
     CHECK(q8 != nullptr && ref != nullptr);
     if (q8 == nullptr || ref == nullptr) {
@@ -260,10 +285,33 @@ void test_q8_0_widened_to_f16() {
     transcribe_model_free(ref);
 }
 
+// Res2Net kernels wider than the GEMM's segment limit: the loader keeps
+// those weights on ggml_mul_mat (it used to route them to the GEMM and
+// overflow its per-tap segment array) and the model still runs.
+void test_wide_kernel_fallback() {
+    transcribe_status  st = TRANSCRIBE_OK;
+    transcribe_model * m  = load_cpu("arch_ecapa_tdnn_k5.gguf", &st);
+    CHECK(st == TRANSCRIBE_OK && m != nullptr);
+    if (m == nullptr) {
+        return;
+    }
+    if (cpu_has_gemm()) {
+        CHECK(!g_gemm_log.empty() && g_gemm_log.find("conv") == std::string::npos);
+    }
+    const std::vector<float>    pcm = noise(16000 * 3, 7);
+    transcribe_langid_session * s   = open_session(m, 4);
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    for (const auto & c : candidates_of(s)) {
+        CHECK(std::isfinite(c.logit));
+    }
+    transcribe_langid_session_free(s);
+    transcribe_model_free(m);
+}
+
 }  // namespace
 
 int main() {
-    transcribe_log_set(nullptr, nullptr);
+    transcribe_log_set(capture_log, nullptr);
 
     transcribe_status  st = TRANSCRIBE_OK;
     transcribe_model * m  = load_cpu("arch_ecapa_tdnn_minimal.gguf", &st);
@@ -271,6 +319,12 @@ int main() {
         std::fprintf(stderr, "FAIL: load arch_ecapa_tdnn_minimal.gguf: %s\n", transcribe_status_string(st));
         return EXIT_FAILURE;
     }
+    if (cpu_has_gemm()) {
+        // The toy model is wide enough for the GEMM, so the graph tests below
+        // (thread invariance in particular) run the GEMM path; F32 1x1 weights
+        // stay on ggml_mul_mat by design.
+        CHECK(g_gemm_log.find("AVX2 GEMM for conv") != std::string::npos);
+    }
     test_model_surface(m);
     test_run(m);
     test_thread_invariance(m);
@@ -278,6 +332,7 @@ int main() {
 
     test_bad_labels();
     test_q8_0_widened_to_f16();
+    test_wide_kernel_fallback();
 
     if (g_failures != 0) {
         std::fprintf(stderr, "%d failure(s)\n", g_failures);
diff --git a/tests/fixtures/make_gguf_fixtures.py b/tests/fixtures/make_gguf_fixtures.py
index 7fbc9a8f1..897aa8f2a 100644
--- a/tests/fixtures/make_gguf_fixtures.py
+++ b/tests/fixtures/make_gguf_fixtures.py
@@ -1373,20 +1373,22 @@ def _qwen3_asr_tensors() -> list[Tensor]:
 # ---------------------------------------------------------------------------
 #
 # The same metadata and tensor contract scripts/convert-ecapa_tdnn.py writes
-# for speechbrain/lang-id-voxlingua107-ecapa, at 1/32 width: channels
-# [32]*4 + [96], the real kernel sizes / dilations / res2net scale, a real
+# for speechbrain/lang-id-voxlingua107-ecapa, at 1/8 width: channels
+# [128]*4 + [384] and 32 attention channels, the narrowest that still puts
+# every weight on the AVX2 CPU GEMM (16-row panels; Res2Net chunk = 16, and
+# 2-D weights Q8_0-eligible in the Q8_0 fixtures), the real kernel sizes / dilations / res2net scale, a real
 # 60 x 201 front end, five labels aa..ee plus the alias xx=aa. Weights are
 # small seeded pseudo-random values so a forward pass stays well inside float
 # range; tests assert structure and invariants, never specific values.
 
 import random as _random
 
-ECAPA_CHANNELS     = [32, 32, 32, 32, 96]
+ECAPA_CHANNELS     = [128, 128, 128, 128, 384]
 ECAPA_KERNELS      = [5, 3, 3, 3, 1]
 ECAPA_DILATIONS    = [1, 2, 3, 4, 1]
 ECAPA_SCALE        = 8
 ECAPA_SE           = 8
-ECAPA_ATT          = 8
+ECAPA_ATT          = 32
 ECAPA_EMB          = 16
 ECAPA_HID          = 16
 ECAPA_N_MELS       = 60
@@ -1395,7 +1397,8 @@ def _qwen3_asr_tensors() -> list[Tensor]:
 ECAPA_LABEL_NAMES  = ["Alpha", "Bravo", "Charlie", "Delta", "Echo"]
 
 
-def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str]) -> list[bytes]:
+def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str],
+                           kernels: list[int]) -> list[bytes]:
     return [
         _pack_kv_string("stt.frontend.type", "speechbrain_fbank"),
         _pack_kv_uint32("stt.frontend.sample_rate", 16000),
@@ -1410,7 +1413,7 @@ def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str
         _pack_kv_string("stt.frontend.normalize", "sentence_mean"),
         _pack_kv_uint32("stt.ecapa_tdnn.format_version", 1),
         _pack_kv_array_int32("stt.ecapa_tdnn.channels", ECAPA_CHANNELS),
-        _pack_kv_array_int32("stt.ecapa_tdnn.kernel_sizes", ECAPA_KERNELS),
+        _pack_kv_array_int32("stt.ecapa_tdnn.kernel_sizes", kernels),
         _pack_kv_array_int32("stt.ecapa_tdnn.dilations", ECAPA_DILATIONS),
         _pack_kv_uint32("stt.ecapa_tdnn.res2net_scale", ECAPA_SCALE),
         _pack_kv_uint32("stt.ecapa_tdnn.se_channels", ECAPA_SE),
@@ -1425,7 +1428,7 @@ def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str
     ]
 
 
-def _ecapa_tdnn_tensors(n_labels: int) -> list[Tensor]:
+def _ecapa_tdnn_tensors(n_labels: int, kernels: list[int]) -> list[Tensor]:
     rng = _random.Random(0)
     out: list[Tensor] = []
 
@@ -1460,11 +1463,11 @@ def tdnn(prefix: str, ic: int, oc: int, k: int, conv: bool) -> None:
     add("frontend.mel_filterbank", [ECAPA_N_FREQ, ECAPA_N_MELS], fb)
 
     c, cm, chunk = ECAPA_CHANNELS[0], ECAPA_CHANNELS[-1], ECAPA_CHANNELS[0] // ECAPA_SCALE
-    tdnn("blk.0", ECAPA_N_MELS, c, ECAPA_KERNELS[0], conv=True)
+    tdnn("blk.0", ECAPA_N_MELS, c, kernels[0], conv=True)
     for i in (1, 2, 3):
         tdnn(f"blk.{i}.tdnn1", c, c, 1, conv=False)
         for j in range(ECAPA_SCALE - 1):
-            tdnn(f"blk.{i}.res2.{j}", chunk, chunk, ECAPA_KERNELS[i], conv=True)
+            tdnn(f"blk.{i}.res2.{j}", chunk, chunk, kernels[i], conv=True)
         tdnn(f"blk.{i}.tdnn2", c, c, 1, conv=False)
         weight(f"blk.{i}.se.c1.weight", [c, ECAPA_SE])
         vec(f"blk.{i}.se.c1.bias", ECAPA_SE, 0.0, 0.05)
@@ -1525,8 +1528,9 @@ def _ecapa_tdnn_q8_0(tensors: list[Tensor], as_f16: bool) -> list[Tensor]:
     return out
 
 
-def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "") -> bytes:
-    tensors = _ecapa_tdnn_tensors(len(codes))
+def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "",
+                     kernels: list[int] = ECAPA_KERNELS) -> bytes:
+    tensors = _ecapa_tdnn_tensors(len(codes), kernels)
     if q8_0:
         tensors = _ecapa_tdnn_q8_0(tensors, as_f16=(q8_0 == "as_f16"))
     return _build_full_gguf(
@@ -1534,7 +1538,7 @@ def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_
         [
             _pack_kv_string("general.architecture", "ecapa_tdnn"),
             _pack_kv_string("stt.variant", "ecapa-tdnn-toy"),
-            *_ecapa_tdnn_hparams_kv(codes, names, aliases),
+            *_ecapa_tdnn_hparams_kv(codes, names, aliases, kernels),
         ],
         tensors,
     )
@@ -1968,6 +1972,11 @@ def emit_fixtures(out_dir: Path) -> None:
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="q8_0"))
     _write(out_dir / "arch_ecapa_tdnn_q8_0_as_f16.gguf",
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="as_f16"))
+    # Res2Net kernels wider than the CPU GEMM's segment limit (3): the loader
+    # must keep those weights on ggml_mul_mat rather than overflow the GEMM's
+    # per-tap segment array.
+    _write(out_dir / "arch_ecapa_tdnn_k5.gguf",
+           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], kernels=[5, 5, 5, 5, 1]))
 
 
 def main(argv: list[str]) -> int:

From 16d46bc2404758ff38fa235de8a5167120e3c955 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Tue, 6 Oct 2026 16:05:49 +0800
Subject: [PATCH 07/38] vulkan gpu work

---
 src/arch/ecapa_tdnn/graph.cpp   | 116 ++++++++++++++++++++------------
 src/arch/ecapa_tdnn/graph.h     |   4 ++
 src/arch/ecapa_tdnn/model.cpp   |   9 ++-
 tests/ecapa_tdnn_real_smoke.cpp |  76 ++++++++++++++++++++-
 4 files changed, 160 insertions(+), 45 deletions(-)

diff --git a/src/arch/ecapa_tdnn/graph.cpp b/src/arch/ecapa_tdnn/graph.cpp
index 98c044800..9c815d6af 100644
--- a/src/arch/ecapa_tdnn/graph.cpp
+++ b/src/arch/ecapa_tdnn/graph.cpp
@@ -125,7 +125,7 @@ ggml_tensor * conv_taps(ggml_context * ctx, ggml_tensor * xpad, ggml_tensor * w,
     return acc;
 }
 
-// Nodes reserved in the cgraph. The real graph is 566 nodes for the published
+// Nodes reserved in the cgraph. The real graph is 571 nodes for the published
 // configuration and is independent of T.
 constexpr size_t kGraphSize = 2048;
 
@@ -179,38 +179,39 @@ ggml_tensor * mean_over_time(ggml_context * ctx, ggml_tensor * x) {
 //   yi = B(i-1)(xi + y(i-1))    i = 2 .. S-1
 //   out = concat(y0 .. y(S-1))
 //
-// Chunk views are strided (stride C, width C/S), so each needs a ggml_cont
-// before it can serve as a matmul operand.
+// The output is assembled in h itself: chunk 0 already holds y0, and each yi
+// is written over chunk i once it has been read, so there is no concat
+// chain. The write is a set_rows into h viewed as [C/S, S, T] (row i of each
+// frame is chunk i; chunk_ids holds 0..S-1), which touches only that chunk;
+// ggml_set would rewrite all of h on some backends. Chunk i is read through
+// the previous write's result, which orders the read after it. The strided
+// chunk views feed get_rows / add directly; both accept a row stride, so no
+// ggml_cont either.
 ggml_tensor * res2net(ggml_context *         ctx,
                       const SeRes2NetBlock & blk,
                       ggml_tensor *          h,
                       ggml_tensor *          idx,
+                      ggml_tensor *          chunk_ids,
                       int                    dilation,
                       int                    T,
                       int                    chunk) {
     const size_t chunk_bytes = static_cast<size_t>(chunk) * ggml_element_size(h);
 
-    ggml_tensor * acc  = nullptr;  // concatenated output so far
+    ggml_tensor * out  = ggml_reshape_3d(ctx, h, chunk, kRes2NetScale, T);
     ggml_tensor * prev = nullptr;  // y(i-1)
 
-    for (int i = 0; i < kRes2NetScale; ++i) {
-        ggml_tensor * ci =
-            ggml_cont(ctx, ggml_view_2d(ctx, h, chunk, T, h->nb[1], static_cast<size_t>(i) * chunk_bytes));
-
-        ggml_tensor * yi = nullptr;
-        if (i == 0) {
-            yi = ci;
-        } else {
-            ggml_tensor *   in = (i == 1) ? ci : ggml_add(ctx, ci, prev);
-            const Res2Sub & s  = blk.res2[i - 1];
-            yi                 = tdnn_conv(ctx, reflect_rows(ctx, in, idx), s.w, s.b, s.bn, dilation, T);
-        }
+    for (int i = 1; i < kRes2NetScale; ++i) {
+        ggml_tensor *   ci = ggml_view_2d(ctx, out, chunk, T, out->nb[2], static_cast<size_t>(i) * chunk_bytes);
+        ggml_tensor *   in = (i == 1) ? ci : ggml_add(ctx, ci, prev);
+        const Res2Sub & s  = blk.res2[i - 1];
+        ggml_tensor *   yi = tdnn_conv(ctx, reflect_rows(ctx, in, idx), s.w, s.b, s.bn, dilation, T);
 
+        out  = ggml_set_rows(ctx, out, ggml_reshape_3d(ctx, yi, chunk, 1, T),
+                             ggml_view_1d(ctx, chunk_ids, 1, static_cast<size_t>(i) * sizeof(int32_t)));
         prev = yi;
-        acc  = (acc == nullptr) ? yi : ggml_concat(ctx, acc, yi, 0);
     }
 
-    return acc;
+    return ggml_reshape_2d(ctx, out, h->ne[0], T);
 }
 
 // SpeechBrain SEBlock: s = mean_T(x) -> 1x1 -> ReLU -> 1x1 -> sigmoid, then
@@ -304,19 +305,23 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
                       const Weights &       w,
                       ggml_tensor * const * blk_out,
                       int                   Cm) {
-    ggml_tensor * pooled = nullptr;
+    const int64_t T = blk_out[0]->ne[1];
+
     // ---- MFA: 1x1 over concat(blk1, blk2, blk3), split into three blocks --
-    ggml_tensor * m      = nullptr;
+    // All three products are expanded before the adds, and the chain starts
+    // from the last block's product, so both adds wait on it and sit next to
+    // each other where Vulkan fuses them into one pass. (Starting from block
+    // 1's product lets the Vulkan graph reorderer hoist that add into block
+    // 3, which splits the pair.)
+    ggml_tensor * part[kNumSeBlocks] = {};
     for (int i = 0; i < kNumSeBlocks; ++i) {
-        ggml_tensor * part = ggml_mul_mat(ctx, w.mfa_w[i], blk_out[i]);
-        if (w.mfa_w[i]->type == GGML_TYPE_F16) {
-            ggml_prec_set_acc(part, GGML_PREC_F32);
-        }
-        m = (m == nullptr) ? part : ggml_add(ctx, m, part);
+        part[i] = linear(ctx, w.mfa_w[i], blk_out[i], nullptr);
+        ggml_build_forward_expand(gb.graph, part[i]);
     }
-    m = ggml_add(ctx, m, w.mfa_b);
-    m = ggml_relu(ctx, m);
-    m = bn_affine(ctx, m, w.mfa_bn.scale, w.mfa_bn.shift);
+    ggml_tensor * m = ggml_add(ctx, ggml_add(ctx, part[2], part[0]), part[1]);
+    m               = ggml_add(ctx, m, w.mfa_b);
+    m               = ggml_relu(ctx, m);
+    m               = bn_affine(ctx, m, w.mfa_bn.scale, w.mfa_bn.shift);
     mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
 
     // ---- attentive statistics pooling ------------------------------------
@@ -325,9 +330,14 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     // (unpadded) weighting SpeechBrain uses when wav_lens is all-ones; the
     // variance is the biased 1/T one, and the clamp before the sqrt is
     // SpeechBrain's `.clamp(eps)` and must not be dropped.
+    //
+    // Every statistic works on d = m - mean, so the attention-weighted ones
+    // below reuse d and d^2 instead of making their own full-size passes.
     ggml_tensor * mT   = ggml_cont(ctx, ggml_transpose(ctx, m));  // [T, Cm]
     ggml_tensor * mean = ggml_mean(ctx, mT);                      // [1, Cm]
-    ggml_tensor * var  = ggml_mean(ctx, ggml_sqr(ctx, ggml_sub(ctx, mT, mean)));
+    ggml_tensor * d    = ggml_sub(ctx, mT, mean);                 // [T, Cm]
+    ggml_tensor * d2   = ggml_sqr(ctx, d);
+    ggml_tensor * var  = ggml_mean(ctx, d2);
     ggml_tensor * sd   = ggml_sqrt(ctx, ggml_clamp(ctx, var, hp.asp_eps, FLT_MAX));
 
     ggml_tensor * mean1 = ggml_reshape_1d(ctx, mean, Cm);
@@ -344,20 +354,34 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     a               = bn_affine(ctx, a, w.asp_bn.scale, w.asp_bn.shift);
     a               = ggml_tanh(ctx, a);
 
-    ggml_tensor * al = linear(ctx, w.asp_attn_w, a, w.asp_attn_b);  // [Cm, T]
-    // Marked BEFORE the transpose: ggml ne = [Cm, T] lands on disk as
-    // [T, Cm], the orientation the reference dumps.
-    mark_dump(gb.dumps.asp_attn_logits, al, "enc.asp.attn_logits");
-
-    ggml_tensor * alT  = ggml_cont(ctx, ggml_transpose(ctx, al));     // [T, Cm]
-    ggml_tensor * attn = ggml_soft_max(ctx, alT);                     // over ne[0] = T
+    // Attention logits WITHOUT the per-channel bias: the softmax runs over T
+    // within each channel, so a per-channel constant cancels out of it. The
+    // dump point needs the biased logits, so the dump build adds the bias on
+    // a side branch, marked BEFORE any transpose: ggml ne = [Cm, T] lands on
+    // disk as [T, Cm], the orientation the reference dumps.
+    ggml_tensor * al = linear(ctx, w.asp_attn_w, a, nullptr);  // [Cm, T]
+    if (debug::enabled()) {
+        mark_dump(gb.dumps.asp_attn_logits, ggml_add(ctx, al, w.asp_attn_b), "enc.asp.attn_logits");
+        ggml_build_forward_expand(gb.graph, gb.dumps.asp_attn_logits);
+    }
 
-    ggml_tensor * mu  = ggml_sum_rows(ctx, ggml_mul(ctx, attn, mT));  // [1, Cm]
-    ggml_tensor * dev = ggml_sqr(ctx, ggml_sub(ctx, mT, mu));
-    ggml_tensor * sig = ggml_sum_rows(ctx, ggml_mul(ctx, attn, dev));
-    sig               = ggml_sqrt(ctx, ggml_clamp(ctx, sig, hp.asp_eps, FLT_MAX));
+    ggml_tensor * alT  = ggml_cont(ctx, ggml_transpose(ctx, al));  // [T, Cm]
+    ggml_tensor * attn = ggml_soft_max(ctx, alT);                  // over ne[0] = T
+
+    // Weighted moments about the uniform mean, each a per-channel dot product
+    // over T (a batched mat-vec, one batch per channel; no product tensor):
+    //   e1 = sum_t attn d,  e2 = sum_t attn d^2
+    //   mu = mean + e1,     sigma^2 = sum_t attn (m - mu)^2 = e2 - e1^2
+    // (sum_t attn = 1).
+    ggml_tensor * attn3 = ggml_reshape_3d(ctx, attn, T, 1, Cm);
+    ggml_tensor * e1    = ggml_reshape_2d(ctx, ggml_mul_mat(ctx, attn3, ggml_reshape_3d(ctx, d, T, 1, Cm)), 1, Cm);
+    ggml_tensor * e2    = ggml_reshape_2d(ctx, ggml_mul_mat(ctx, attn3, ggml_reshape_3d(ctx, d2, T, 1, Cm)), 1, Cm);
+    ggml_tensor * mu    = ggml_add(ctx, mean, e1);  // [1, Cm]
+    ggml_tensor * sig   = ggml_sub(ctx, e2, ggml_sqr(ctx, e1));
+    sig                 = ggml_sqrt(ctx, ggml_clamp(ctx, sig, hp.asp_eps, FLT_MAX));
 
-    pooled = ggml_concat(ctx, ggml_reshape_1d(ctx, mu, Cm), ggml_reshape_1d(ctx, sig, Cm), /*dim=*/0);  // [2*Cm]
+    ggml_tensor * pooled =
+        ggml_concat(ctx, ggml_reshape_1d(ctx, mu, Cm), ggml_reshape_1d(ctx, sig, Cm), /*dim=*/0);  // [2*Cm]
     mark_dump(gb.dumps.asp_out, pooled, "enc.asp.out");
 
     return pooled;
@@ -496,6 +520,9 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_
             named(gb.idx[i], name);
             ggml_set_input(gb.idx[i]);
         }
+        gb.chunk_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, kRes2NetScale);
+        named(gb.chunk_ids, "res2.chunk_ids");
+        ggml_set_input(gb.chunk_ids);
     }
 
     // ---- stage 0: TDNNBlock n_mels -> C ----------------------------------
@@ -532,7 +559,12 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_
         if (cpu_ops) {
             h = res2net_cpu(ctx, ar_conv, blk, h, hp.kernel_sizes[static_cast<size_t>(i + 1)], d, chunk);
         } else {
-            h = res2net(ctx, blk, h, gb.idx[i], d, T, chunk);
+            // The stock Res2Net writes its output over its input, so a dump
+            // build hands it a copy to keep the tdnn1 dump intact.
+            if (i == 0 && debug::enabled()) {
+                h = ggml_cont(ctx, h);
+            }
+            h = res2net(ctx, blk, h, gb.idx[i], gb.chunk_ids, d, T, chunk);
         }
         if (i == 0) {
             mark_dump(gb.dumps.blk1_res2_out, h, "enc.blk.1.res2.out");
diff --git a/src/arch/ecapa_tdnn/graph.h b/src/arch/ecapa_tdnn/graph.h
index c88578153..a5f0566bc 100644
--- a/src/arch/ecapa_tdnn/graph.h
+++ b/src/arch/ecapa_tdnn/graph.h
@@ -58,6 +58,10 @@ struct GraphBuild {
     // graph pads inside its Res2Net kernel and leaves these null.
     ggml_tensor * idx[kNumSeBlocks] = { nullptr, nullptr, nullptr };
 
+    // Res2Net chunk numbers 0..kRes2NetScale-1, I32. Only the stock-op graph
+    // uses them (set_rows targets when writing a chunk's output in place).
+    ggml_tensor * chunk_ids = nullptr;
+
     // Output.
     ggml_tensor * logits = nullptr;  // ne = [n_labels]
 
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index fa3665cdf..7412bbf1b 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -47,7 +47,7 @@ constexpr const char * kTag = "ecapa_tdnn";
 constexpr const char k_default_variant[] = "lang-id-voxlingua107-ecapa";
 
 // Nodes reserved in the scheduler. Must be >= the graph's node count; see
-// graph.cpp's kGraphSize. The built graph is 566 nodes and is independent of
+// graph.cpp's kGraphSize. The built graph is 571 nodes and is independent of
 // T, so this is ~3.5x headroom.
 constexpr size_t k_sched_graph_size = 2048;
 
@@ -541,6 +541,13 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
             ggml_backend_tensor_set(gb.idx[i], s.idx_buf[i].data(), 0, s.idx_buf[i].size() * sizeof(int32_t));
         }
     }
+    if (gb.chunk_ids != nullptr) {
+        int32_t ids[kRes2NetScale];
+        for (int i = 0; i < kRes2NetScale; ++i) {
+            ids[i] = i;
+        }
+        ggml_backend_tensor_set(gb.chunk_ids, ids, 0, sizeof(ids));
+    }
 
     // ---- compute -------------------------------------------------------------
     const int64_t t_enc_start = now_us();
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
index f33771524..c5274e717 100644
--- a/tests/ecapa_tdnn_real_smoke.cpp
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -1,7 +1,9 @@
 // ecapa_tdnn_real_smoke.cpp - the real VoxLingua107 ECAPA-TDNN GGUF through
 // the public LANGID API: label table, aliases, top-1 on committed FLEURS
-// clips, the crop on a long clip, and a short clip just over the minimum.
-// Gated by TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip). CPU backend.
+// clips, the crop on a long clip, and a short clip just over the minimum,
+// all on the CPU backend; then, when a GPU backend loads, the stock-op GPU
+// graph against the CPU graph's logits. Gated by TRANSCRIBE_ECAPA_TDNN_GGUF
+// (RC 77 skip).
 
 #include "transcribe.h"
 #include "transcribe/langid.h"
@@ -61,6 +63,74 @@ std::string top1(transcribe_langid_session * s, const char * wav, float * p_out,
     return c.code != nullptr ? c.code : "";
 }
 
+// Every label's logit for one clip, by label index; empty when the run fails.
+std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
+    std::vector<float> pcm;
+    if (!load_sample(wav, pcm) ||
+        transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) != TRANSCRIBE_OK) {
+        return {};
+    }
+    transcribe_langid_result r;
+    transcribe_langid_result_init(&r);
+    transcribe_langid_get_result(s, &r);
+    std::vector<float> out(static_cast<size_t>(r.n_candidates), 0.0f);
+    for (int i = 0; i < r.n_candidates; ++i) {
+        transcribe_langid_candidate c;
+        transcribe_langid_candidate_init(&c);
+        transcribe_langid_get_candidate(s, i, &c);
+        if (c.index >= 0 && c.index < r.n_candidates) {
+            out[static_cast<size_t>(c.index)] = c.logit;
+        }
+    }
+    return out;
+}
+
+// The GPU backends run the stock-op graph, which no other test reaches.
+// Their matmuls are not bit-exact F32 (Vulkan on an AMD iGPU lands ~1e-2 off
+// the CPU logits), so this bounds the drift rather than demanding parity.
+void check_gpu_matches_cpu(const char * path, transcribe_model * cpu_model) {
+    const transcribe_backend_request gpus[] = { TRANSCRIBE_BACKEND_VULKAN, TRANSCRIBE_BACKEND_METAL };
+    transcribe_model *               gm     = nullptr;
+    for (const transcribe_backend_request b : gpus) {
+        transcribe_model_load_params mp;
+        transcribe_model_load_params_init(&mp);
+        mp.backend = b;
+        if (transcribe_model_load_file(path, &mp, &gm) == TRANSCRIBE_OK) {
+            break;
+        }
+        gm = nullptr;
+    }
+    if (gm == nullptr) {
+        std::fprintf(stderr, "ecapa_tdnn_real_smoke: no GPU backend; skipping the GPU graph check.\n");
+        return;
+    }
+
+    transcribe_langid_session * cs = nullptr;
+    transcribe_langid_session * gs = nullptr;
+    CHECK(transcribe_langid_session_init(cpu_model, nullptr, &cs) == TRANSCRIBE_OK);
+    CHECK(transcribe_langid_session_init(gm, nullptr, &gs) == TRANSCRIBE_OK);
+    for (const char * wav : { "fleurs-en.wav", "fleurs-zh.wav", "ru-long.wav" }) {
+        const std::vector<float> a = all_logits(cs, wav);
+        const std::vector<float> b = all_logits(gs, wav);
+        if (a.empty() || a.size() != b.size()) {
+            std::fprintf(stderr, "FAIL: %s: GPU run (%zu logits) vs CPU (%zu)\n", wav, b.size(), a.size());
+            ++g_failures;
+            continue;
+        }
+        float max_diff = 0.0f;
+        for (size_t i = 0; i < a.size(); ++i) {
+            max_diff = std::fmax(max_diff, std::fabs(a[i] - b[i]));
+        }
+        if (!(max_diff < 0.05f)) {
+            std::fprintf(stderr, "FAIL: %s: GPU logits differ from CPU by %.4f\n", wav, max_diff);
+            ++g_failures;
+        }
+    }
+    transcribe_langid_session_free(gs);
+    transcribe_langid_session_free(cs);
+    transcribe_model_free(gm);
+}
+
 }  // namespace
 
 int main() {
@@ -127,6 +197,8 @@ int main() {
     CHECK(audio_ms == 800 && std::isfinite(p));
 
     transcribe_langid_session_free(s);
+
+    check_gpu_matches_cpu(path, m);
     transcribe_model_free(m);
 
     if (g_failures != 0) {

From d836d01a6c7709c1a802cad66f58959e237035a5 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Tue, 6 Oct 2026 18:01:44 +0800
Subject: [PATCH 08/38] langid: merge fixes for the ecapa_tdnn CPU/GPU kernel
 work

---
 catalog/lang-id-voxlingua107-ecapa.json   |   6 +-
 docs/models/lang-id-voxlingua107-ecapa.md |  29 ++++---
 docs/porting/families/ecapa_tdnn.md       |   3 +-
 scripts/catalog/ingest_accuracy.py        |  37 +++++---
 scripts/langid/compare.py                 |   9 ++
 src/arch/ecapa_tdnn/cpu_gemm.cpp          |  61 +++++++------
 src/arch/ecapa_tdnn/cpu_gemm.h            |   6 +-
 src/arch/ecapa_tdnn/cpu_ops.cpp           | 101 ++++++++++++----------
 src/arch/ecapa_tdnn/cpu_ops.h             |   7 +-
 src/arch/ecapa_tdnn/graph.cpp             |  24 +++--
 src/arch/ecapa_tdnn/model.cpp             |   2 +-
 tests/CMakeLists.txt                      |   5 +-
 tests/ecapa_tdnn_gemm_unit.cpp            |  91 ++++++++++---------
 tests/ecapa_tdnn_real_smoke.cpp           |  27 +++++-
 14 files changed, 244 insertions(+), 164 deletions(-)

diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index a1f831ccf..5998ea859 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -42,9 +42,9 @@
     {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693696}
   ],
   "accuracy_benchmarks": [
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.2,"ci95":[84.1,86.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":6.5e-05}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.2,"ci95":[84.0,86.4],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05","agreement":{"n_agree":11991,"n":12000,"max_abs_logit_delta":0.098}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.2,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"62522202","measured_on":"2026-10-05","agreement":{"n_agree":11703,"n":12000,"max_abs_logit_delta":2.8}}
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"16d46bc2","publication_profile":"langid-publication-v1","measured_on":"2026-10-06","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.4e-05}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"16d46bc2","publication_profile":"langid-publication-v1","measured_on":"2026-10-06","agreement":{"n_agree":11984,"n":12000,"max_abs_logit_delta":0.094}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.27,"ci95":[85.13,87.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"16d46bc2","publication_profile":"langid-publication-v1","measured_on":"2026-10-06","agreement":{"n_agree":11704,"n":12000,"max_abs_logit_delta":2.9}}
   ],
   "headline_benchmark": {
     "dataset": "fleurs",
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 831254528..ee962bf5a 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -33,13 +33,13 @@ Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface
 <!-- catalog:downloads -->
 | Quantization | Download |  Size | Top-1 accuracy (FLEURS multilingual) |
 | --- | --- | ---: | ---: |
-| F32          | [lang-id-voxlingua107-ecapa-F32.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F32.gguf) | 85 MB | 85.20% |
-| F16          | [lang-id-voxlingua107-ecapa-F16.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F16.gguf) | 45 MB | 85.20% |
-| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 86.30% |
+| F32          | [lang-id-voxlingua107-ecapa-F32.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F32.gguf) | 85 MB | 85.23% |
+| F16          | [lang-id-voxlingua107-ecapa-F16.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F16.gguf) | 45 MB | 85.17% |
+| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 86.27% |
 <!-- /catalog -->
 
 <!-- catalog:recipe -->
-Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `62522202` on 2026-10-05.
+Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `16d46bc2` on 2026-10-06.
 <!-- /catalog -->
 
 <!-- catalog:prose field=wer.notes -->
@@ -58,11 +58,11 @@ over 107 logits, and the k-quant tiers would save a few MB at most.
 <!-- catalog:agreement -->
 | GGUF | Top-1 accuracy (95% CI) | Top-1 agreement with SpeechBrain | Max abs logit difference |
 | --- | ---: | ---: | ---: |
-| F32  | 85.20% (84.10-86.50) | 12000 / 12000 | 6.5e-05 |
-| F16  | 85.20% (84.00-86.40) | 11991 / 12000 | 0.098 |
-| Q8_0 | 86.30% (85.20-87.50) | 11703 / 12000 | 2.8 |
+| F32  | 85.23% (84.07-86.47) | 12000 / 12000 | 7.4e-05 |
+| F16  | 85.17% (84.00-86.43) | 11984 / 12000 | 0.094 |
+| Q8_0 | 86.27% (85.13-87.47) | 11704 / 12000 | 2.9 |
 
-Measured at transcribe.cpp `62522202` on 2026-10-05.
+Measured at transcribe.cpp `16d46bc2` on 2026-10-06.
 <!-- /catalog -->
 
 Accuracy is the headline above: open set, first 5 s of each clip, no silence
@@ -71,7 +71,7 @@ match the SpeechBrain reference on the same audio, over every crop of the
 sweep (3 / 5 / 10 s / full). It is the ship gate for F32
 (`scripts/langid/compare.py`: every disagreement must be a reviewed near-tie)
 and the evidence for the quants. F16's flips are all near-ties (top-two
-margin under 0.025 logit). Q8_0 is a download format: transcribe.cpp widens
+margin under 0.04 logit). Q8_0 is a download format: transcribe.cpp widens
 its weights to F16 at load, so it computes like F16 (F16's memory) and its
 accuracy matches F32 within the confidence interval; its flips are the 8-bit
 weights alone, on decisions F32 also finds uncertain.
@@ -79,7 +79,7 @@ weights alone, on decisions F32 also finds uncertain.
 ### Snapshot: crops and decision spaces
 
 A snapshot of `scripts/langid/score.py --md` on the C++ sweeps behind the
-rows above (transcribe.cpp `62522202`, CPU; FLEURS `test`, 15 languages x 200
+rows above (transcribe.cpp `16d46bc2`, CPU; FLEURS `test`, 15 languages x 200
 utterances, crops from the start of each clip, no silence trimming). It is
 not rendered from the catalog; regenerate it with the commands under
 Reproduction. Top-1 accuracy, %.
@@ -89,7 +89,7 @@ Open set (all 107 labels) at the other crops; 5 s is the catalog row above:
 | GGUF | 3 s | 10 s | full |
 |---|---|---|---|
 | F32 | 67.0 | 91.1 | 91.4 |
-| F16 | 67.0 | 91.1 | 91.5 |
+| F16 | 66.9 | 91.0 | 91.4 |
 | Q8_0 | 67.2 | 92.0 | 92.3 |
 
 Restricted to a selection, F32:
@@ -234,8 +234,11 @@ uv run scripts/validate.py all --family ecapa_tdnn
 ### Accuracy acceptance
 
 One reference sweep, then one C++ sweep per shipped GGUF (shown for F32;
-repeat with F16 and Q8_0). `compare.py` is the gate; `score.py --json` and
-`compare.py --json` are what `ingest_accuracy.py` reads, named after the GGUF.
+repeat with F16 and Q8_0, passing `--report-only` to `compare.py`: only F32
+is gated). `compare.py` is the gate; `score.py --json` and `compare.py --json`
+are what `ingest_accuracy.py` reads, named after the GGUF, and the importer
+rejects a score without its agreement or over a language set other than
+`scripts/langid/ingest.py`'s.
 
 ```bash
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index 4caa066d9..3a736f715 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -102,7 +102,8 @@ crop of each shipped GGUF's sweep, with that sweep's agreement.
 
 `compare.py` fails on any disagreement that is not on a reviewed near-tie
 list (`--near-ties`), and refuses runs whose labels, recipe, manifest hashes
-or checkpoint revision differ.
+or checkpoint revision differ. Only F32 is gated: run the F16 and Q8_0
+sweeps through it with `--report-only`, which still writes their agreement.
 
 Benchmarks: `scripts/langid/bench.py --profile` (`tools/transcribe-bench` is
 ASR-only) writes bench-driver reports under `reports/perf/`, which
diff --git a/scripts/catalog/ingest_accuracy.py b/scripts/catalog/ingest_accuracy.py
index 990c76edb..a6e081ca5 100755
--- a/scripts/catalog/ingest_accuracy.py
+++ b/scripts/catalog/ingest_accuracy.py
@@ -21,6 +21,7 @@
 from __future__ import annotations
 
 import argparse
+import ast
 import json
 import pathlib
 import sys
@@ -32,6 +33,20 @@
 REPORTS = common.REPO / "reports" / "wer"
 LANGID_REPORTS = common.REPO / "reports" / "langid"
 LANGID_SCORE = "transcribe-langid-score-v1"
+LANGID_INGEST = common.REPO / "scripts" / "langid" / "ingest.py"
+
+
+def fleurs_langid_codes() -> list[str]:
+    """The label codes of scripts/langid/ingest.py's FLEURS_LANGUAGES, the
+    language set the profile's macro mean is over. Read from the source
+    (ingest.py needs numpy, which the catalog scripts do not install)."""
+    tree = ast.parse(LANGID_INGEST.read_text())
+    for node in tree.body:
+        target = node.target if isinstance(node, ast.AnnAssign) else (
+            node.targets[0] if isinstance(node, ast.Assign) else None)
+        if isinstance(target, ast.Name) and target.id == "FLEURS_LANGUAGES":
+            return [code for _config, code in ast.literal_eval(node.value)]
+    raise RuntimeError(f"FLEURS_LANGUAGES not found in {LANGID_INGEST}")
 
 
 def score_path(record: dict, cell: dict, reports: pathlib.Path) -> pathlib.Path:
@@ -98,8 +113,14 @@ def langid_row(record: dict, cell: dict, score: dict, agreement: dict | None,
         reasons.append(f"no {cell['crop_s']} s crop")
     if not score.get("engine_sha"):
         reasons.append("engine_sha is empty")
-    if agreement is not None and agreement.get("run") != score.get("run"):
+    if score.get("dataset") == "fleurs" and sorted(score.get("languages") or []) != sorted(fleurs_langid_codes()):
+        reasons.append(f"languages={score.get('languages')!r} are not scripts/langid/ingest.py's FLEURS set")
+    if agreement is None:
+        reasons.append("no agreement (write it with compare.py --json)")
+    elif agreement.get("run") != score.get("run"):
         reasons.append(f"agreement is for {agreement.get('run')!r}, not {score.get('run')!r}")
+    elif not agreement.get("same_rows", False):
+        reasons.append("agreement run does not cover the reference's rows")
     if reasons:
         return None, reasons
     row = {
@@ -118,12 +139,11 @@ def langid_row(record: dict, cell: dict, score: dict, agreement: dict | None,
         "publication_profile": profile_id,
         "measured_on": (score.get("created") or "")[:10] or None,
     }
-    if agreement is not None:
-        row["agreement"] = {
-            "n_agree": agreement["n_agree"],
-            "n": agreement["n"],
-            "max_abs_logit_delta": float(f"{agreement['max_abs_logit_delta']:.2g}"),
-        }
+    row["agreement"] = {
+        "n_agree": agreement["n_agree"],
+        "n": agreement["n"],
+        "max_abs_logit_delta": float(f"{agreement['max_abs_logit_delta']:.2g}"),
+    }
     return row, []
 
 
@@ -173,9 +193,6 @@ def main() -> int:
                 agreement = (json.loads(agreement_path.read_text())
                              if agreement_path.exists() else None)
                 new_row, reasons = langid_row(record, cell, score, agreement, profile_id)
-                if not reasons and agreement is None:
-                    print(f"  note {source_path.name}: no {agreement_path.name}; "
-                          f"the row carries no reference agreement")
             else:
                 new_row, reasons = None, []
                 recipe = score.get("recipe") or {}
diff --git a/scripts/langid/compare.py b/scripts/langid/compare.py
index 00d8f296a..e56e52b66 100755
--- a/scripts/langid/compare.py
+++ b/scripts/langid/compare.py
@@ -32,6 +32,10 @@
     1  an unlisted disagreement, agreement below the gate, or coverage differs
     2  usage / input / provenance error
 
+Only F32 is held to the gate. For the quants pass --report-only: the
+agreement is still measured, printed and written (--json, with "passed"),
+but only a coverage mismatch exits 1.
+
 Near-tie list (JSON): [{"id", "crop_s", "trim", "a_top1", "b_top1",
 "max_gap", "note"}]. An entry only excuses that exact disagreement while both
 engines' margins stay within max_gap; review every entry by listening.
@@ -98,6 +102,9 @@ def main(argv: list[str] | None = None) -> int:
     p.add_argument("--max-listed", type=int, default=MAX_LISTED_DISAGREEMENTS)
     p.add_argument("--near-ties", type=Path, default=None,
                    help="JSON list of reviewed near-tie disagreements")
+    p.add_argument("--report-only", action="store_true",
+                   help="measure and record agreement without gating on it (the quants); "
+                        "a coverage mismatch still exits 1")
     p.add_argument("--json", type=Path, default=None,
                    help="also write the agreement for scripts/catalog/"
                         "ingest_accuracy.py; name it <gguf stem>.fleurs-mul."
@@ -282,6 +289,8 @@ def main(argv: list[str] | None = None) -> int:
             "passed": status == 0,
         }, indent=2) + "\n")
         print(f"wrote {args.json}")
+    if args.report_only and not (only_a or only_b):
+        return 0
     return status
 
 
diff --git a/src/arch/ecapa_tdnn/cpu_gemm.cpp b/src/arch/ecapa_tdnn/cpu_gemm.cpp
index a74bc6fc0..1584bcaac 100644
--- a/src/arch/ecapa_tdnn/cpu_gemm.cpp
+++ b/src/arch/ecapa_tdnn/cpu_gemm.cpp
@@ -47,30 +47,29 @@ constexpr int64_t kNC = 192;  // column block: 256 x 192 packed F32 = 192 KiB in
 // raw partial sums to acc[12] (two __m256 per column); the caller loads /
 // stores / applies the epilogue. The accumulators are named locals so the
 // compiler keeps all twelve in registers through the k loop.
-#    define ECAPA_FMA6(W0, W1, BP)                          \
-        do {                                                \
-            __m256 xb = _mm256_broadcast_ss((BP) + 0);      \
-            a00       = _mm256_fmadd_ps(W0, xb, a00);       \
-            a01       = _mm256_fmadd_ps(W1, xb, a01);       \
-            xb        = _mm256_broadcast_ss((BP) + 1);      \
-            a10       = _mm256_fmadd_ps(W0, xb, a10);       \
-            a11       = _mm256_fmadd_ps(W1, xb, a11);       \
-            xb        = _mm256_broadcast_ss((BP) + 2);      \
-            a20       = _mm256_fmadd_ps(W0, xb, a20);       \
-            a21       = _mm256_fmadd_ps(W1, xb, a21);       \
-            xb        = _mm256_broadcast_ss((BP) + 3);      \
-            a30       = _mm256_fmadd_ps(W0, xb, a30);       \
-            a31       = _mm256_fmadd_ps(W1, xb, a31);       \
-            xb        = _mm256_broadcast_ss((BP) + 4);      \
-            a40       = _mm256_fmadd_ps(W0, xb, a40);       \
-            a41       = _mm256_fmadd_ps(W1, xb, a41);       \
-            xb        = _mm256_broadcast_ss((BP) + 5);      \
-            a50       = _mm256_fmadd_ps(W0, xb, a50);       \
-            a51       = _mm256_fmadd_ps(W1, xb, a51);       \
+#    define ECAPA_FMA6(W0, W1, BP)                     \
+        do {                                           \
+            __m256 xb = _mm256_broadcast_ss((BP) + 0); \
+            a00       = _mm256_fmadd_ps(W0, xb, a00);  \
+            a01       = _mm256_fmadd_ps(W1, xb, a01);  \
+            xb        = _mm256_broadcast_ss((BP) + 1); \
+            a10       = _mm256_fmadd_ps(W0, xb, a10);  \
+            a11       = _mm256_fmadd_ps(W1, xb, a11);  \
+            xb        = _mm256_broadcast_ss((BP) + 2); \
+            a20       = _mm256_fmadd_ps(W0, xb, a20);  \
+            a21       = _mm256_fmadd_ps(W1, xb, a21);  \
+            xb        = _mm256_broadcast_ss((BP) + 3); \
+            a30       = _mm256_fmadd_ps(W0, xb, a30);  \
+            a31       = _mm256_fmadd_ps(W1, xb, a31);  \
+            xb        = _mm256_broadcast_ss((BP) + 4); \
+            a40       = _mm256_fmadd_ps(W0, xb, a40);  \
+            a41       = _mm256_fmadd_ps(W1, xb, a41);  \
+            xb        = _mm256_broadcast_ss((BP) + 5); \
+            a50       = _mm256_fmadd_ps(W0, xb, a50);  \
+            a51       = _mm256_fmadd_ps(W1, xb, a51);  \
         } while (0)
 
-template <bool F16>
-ECAPA_TARGET void tile_core(const void * a, const float * bp, int64_t kc, __m256 * acc) {
+template <bool F16> ECAPA_TARGET void tile_core(const void * a, const float * bp, int64_t kc, __m256 * acc) {
     __m256 a00 = acc[0], a01 = acc[1], a10 = acc[2], a11 = acc[3], a20 = acc[4], a21 = acc[5];
     __m256 a30 = acc[6], a31 = acc[7], a40 = acc[8], a41 = acc[9], a50 = acc[10], a51 = acc[11];
     if constexpr (F16) {
@@ -215,8 +214,8 @@ void gemm_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
     // read the same value because nobody arrives before every thread has
     // read it (the read happens before this thread's own arrival).
     const uint32_t gen = d.generation.load(std::memory_order_acquire) + 1;
-    const int64_t M = d.M;
-    const int64_t N = d.N;
+    const int64_t  M   = d.M;
+    const int64_t  N   = d.N;
 
     const int64_t mb   = std::min(kMB, M);
     const int64_t n_rb = (M + mb - 1) / mb;
@@ -280,8 +279,8 @@ void gemm_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
             const bool          f16 = w->type == GGML_TYPE_F16;
             const size_t        esz = ggml_type_size(w->type);
             const size_t        ldx = x->nb[1] / sizeof(float);
-            const float * xs = static_cast<const float *>(x->data) + static_cast<size_t>(d.segs[s].x_row0) * ldx;
-            const char *  wm = static_cast<const char *>(w->data) + static_cast<size_t>(d.segs[s].mat) * M * K * esz;
+            const float *       xs  = static_cast<const float *>(x->data) + static_cast<size_t>(d.segs[s].x_row0) * ldx;
+            const char * wm = static_cast<const char *>(w->data) + static_cast<size_t>(d.segs[s].mat) * M * K * esz;
 
             for (int64_t k0 = 0; k0 < K; k0 += kKC, ++blk) {
                 const int64_t kc    = std::min(kKC, K - k0);
@@ -375,11 +374,11 @@ ggml_tensor * mul_mat(ggml_context *  ctx,
     if (n_segs < 1 || n_segs > kMaxSegs) {
         return nullptr;
     }
-    Desc & d   = arena.emplace_back();
-    d.n_segs   = n_segs;
-    d.M        = segs[0].w->ne[1];
-    d.N        = N;
-    d.ep       = ep;
+    Desc & d = arena.emplace_back();
+    d.n_segs = n_segs;
+    d.M      = segs[0].w->ne[1];
+    d.N      = N;
+    d.ep     = ep;
 
     ggml_tensor * args[GGML_MAX_SRC] = {};
     int           n                  = 0;
diff --git a/src/arch/ecapa_tdnn/cpu_gemm.h b/src/arch/ecapa_tdnn/cpu_gemm.h
index 670820abd..f3c58733c 100644
--- a/src/arch/ecapa_tdnn/cpu_gemm.h
+++ b/src/arch/ecapa_tdnn/cpu_gemm.h
@@ -51,8 +51,8 @@ constexpr int kMaxSegs = 3;
 bool pack_weight(ggml_tensor * t);
 
 enum class Epilogue : int32_t {
-    None    = 0,  // y = acc
-    ReluBn  = 1,  // y = max(acc + b, 0) * scale + shift (b optional)
+    None       = 0,  // y = acc
+    ReluBn     = 1,  // y = max(acc + b, 0) * scale + shift (b optional)
     ReluBnTanh = 2,
 };
 
@@ -89,7 +89,7 @@ struct Desc {
     // Slot t holds thread t's progress through its own job range, tagged
     // with the compute generation so a reused graph starts clean without a
     // reset pass (custom ops have no barrier to reset behind).
-    static constexpr int kMaxThreads = 256;
+    static constexpr int  kMaxThreads       = 256;
     std::atomic<uint64_t> slot[kMaxThreads] = {};
     std::atomic<uint32_t> generation{ 0 };
     std::atomic<int32_t>  arrived{ 0 };
diff --git a/src/arch/ecapa_tdnn/cpu_ops.cpp b/src/arch/ecapa_tdnn/cpu_ops.cpp
index 332c08d64..ab495510b 100644
--- a/src/arch/ecapa_tdnn/cpu_ops.cpp
+++ b/src/arch/ecapa_tdnn/cpu_ops.cpp
@@ -35,11 +35,11 @@ namespace {
 // builds, so this keeps the attention softmax on the arithmetic the stock
 // graph had.
 ECAPA_AVX2 inline __m256 v_expf(__m256 x) {
-    const __m256  r = _mm256_set1_ps(0x1.8p23f);
-    const __m256  z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r);
-    const __m256  n = _mm256_sub_ps(z, r);
-    const __m256  b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f),
-                                       _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x));
+    const __m256 r = _mm256_set1_ps(0x1.8p23f);
+    const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r);
+    const __m256 n = _mm256_sub_ps(z, r);
+    const __m256 b =
+        _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x));
     const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23);
     const __m256  k = _mm256_castsi256_ps(_mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1))));
     const __m256i c =
@@ -60,9 +60,10 @@ ECAPA_AVX2 inline __m256 v_expf(__m256 x) {
         _mm256_castps_si256(_mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(192), _CMP_GT_OQ));
     return _mm256_or_ps(
         _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)),
-        _mm256_andnot_ps(_mm256_castsi256_ps(d),
-                         _mm256_or_ps(_mm256_and_ps(_mm256_castsi256_ps(c), _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)),
-                                      _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k)))));
+        _mm256_andnot_ps(
+            _mm256_castsi256_ps(d),
+            _mm256_or_ps(_mm256_and_ps(_mm256_castsi256_ps(c), _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)),
+                         _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k)))));
 }
 
 bool cpu_has_avx2_fma() {
@@ -90,6 +91,9 @@ ECAPA_AVX2 void exp_row_avx2(float * r, const float * mx, double * sum, size_t n
 }
 #endif
 
+// Elsewhere (ARM included) this is libm expf, which is not the NEON
+// ggml_v_expf ggml's soft_max uses there: the softmax is within a few ulp of
+// the stock graph's, not bit-identical.
 void exp_row(float * r, const float * mx, double * sum, size_t n) {
 #if ECAPA_OPS_X86
     static const bool avx2 = cpu_has_avx2_fma();
@@ -136,8 +140,7 @@ void thread_rows(int64_t n, int ith, int nth, int64_t & r0, int64_t & r1) {
 // that 3072 channels still spread over 8+ threads.
 constexpr int64_t kChanBlock = 256;
 
-template <typename F>
-void for_channel_blocks(int64_t C, int ith, int nth, F && f) {
+template <typename F> void for_channel_blocks(int64_t C, int ith, int nth, F && f) {
     for (int64_t c0 = static_cast<int64_t>(ith) * kChanBlock; c0 < C; c0 += static_cast<int64_t>(nth) * kChanBlock) {
         f(c0, std::min(C, c0 + kChanBlock));
     }
@@ -183,14 +186,14 @@ void epilogue_rows(const float * in,
 
 // dst is a view of src[0] (in place). src: [z, (b), scale, shift].
 void epilogue_inplace_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const auto *  p     = static_cast<const EpilogueParams *>(ud);
-    const int64_t C     = dst->ne[0];
-    int           i     = 1;
-    const float * b     = p->has_bias ? f32(dst->src[i++]) : nullptr;
-    const float * s     = f32(dst->src[i++]);
-    const float * sh    = f32(dst->src[i++]);
-    int64_t       r0    = 0;
-    int64_t       r1    = 0;
+    const auto *  p  = static_cast<const EpilogueParams *>(ud);
+    const int64_t C  = dst->ne[0];
+    int           i  = 1;
+    const float * b  = p->has_bias ? f32(dst->src[i++]) : nullptr;
+    const float * s  = f32(dst->src[i++]);
+    const float * sh = f32(dst->src[i++]);
+    int64_t       r0 = 0;
+    int64_t       r1 = 0;
     thread_rows(dst->ne[1], ith, nth, r0, r1);
     epilogue_rows(f32(dst), row_floats(dst), f32_mut(dst), row_floats(dst), C, r0, r1, b, s, sh, p->tanh_after);
 }
@@ -198,18 +201,18 @@ void epilogue_inplace_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
 // dst is a view of src[0]; src: [z0, z1, z2, b, scale, shift].
 void epilogue_sum3_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
     (void) ud;
-    const int64_t C    = dst->ne[0];
-    const float * z1   = f32(dst->src[1]);
-    const float * z2   = f32(dst->src[2]);
-    const float * b    = f32(dst->src[3]);
-    const float * s    = f32(dst->src[4]);
-    const float * sh   = f32(dst->src[5]);
-    float *       y    = f32_mut(dst);
-    const size_t  st   = row_floats(dst);
-    const size_t  st1  = row_floats(dst->src[1]);
-    const size_t  st2  = row_floats(dst->src[2]);
-    int64_t       r0   = 0;
-    int64_t       r1   = 0;
+    const int64_t C   = dst->ne[0];
+    const float * z1  = f32(dst->src[1]);
+    const float * z2  = f32(dst->src[2]);
+    const float * b   = f32(dst->src[3]);
+    const float * s   = f32(dst->src[4]);
+    const float * sh  = f32(dst->src[5]);
+    float *       y   = f32_mut(dst);
+    const size_t  st  = row_floats(dst);
+    const size_t  st1 = row_floats(dst->src[1]);
+    const size_t  st2 = row_floats(dst->src[2]);
+    int64_t       r0  = 0;
+    int64_t       r1  = 0;
     thread_rows(dst->ne[1], ith, nth, r0, r1);
     for (int64_t t = r0; t < r1; ++t) {
         float *       yr  = y + static_cast<size_t>(t) * st;
@@ -415,11 +418,11 @@ Res2Params unpack_res2(const void * ud) {
 
 // src: [h, (z, b, scale, shift)].
 void res2_step_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const Res2Params    p   = unpack_res2(ud);
-    const ggml_tensor * h   = dst->src[0];
-    const int64_t       w   = dst->ne[0];
-    const int64_t       T   = h->ne[1];
-    const int64_t       pad = static_cast<int64_t>(p.dilation) * (p.kernel - 1) / 2;
+    const Res2Params    p          = unpack_res2(ud);
+    const ggml_tensor * h          = dst->src[0];
+    const int64_t       w          = dst->ne[0];
+    const int64_t       T          = h->ne[1];
+    const int64_t       pad        = static_cast<int64_t>(p.dilation) * (p.kernel - 1) / 2;
     const int64_t       n_pad_rows = p.next >= 0 ? T + 2 * pad : 0;
     const int64_t       n_rows     = n_pad_rows + (p.has_z ? T : 0);
     const size_t        sd         = row_floats(dst);
@@ -519,9 +522,9 @@ void res2_pad_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
 // dst [C, T]; src: [h, y_1 .. y_n] (y_i are [w, T] views).
 void res2_gather_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
     (void) ud;
-    const ggml_tensor * h  = dst->src[0];
-    const int64_t       C  = dst->ne[0];
-    int                 n  = 0;
+    const ggml_tensor * h = dst->src[0];
+    const int64_t       C = dst->ne[0];
+    int                 n = 0;
     while (n + 1 < GGML_MAX_SRC && dst->src[n + 1] != nullptr) {
         ++n;
     }
@@ -560,10 +563,10 @@ ggml_tensor * epilogue(ggml_context * ctx,
                        ggml_tensor *  shift,
                        bool           tanh_after,
                        bool           inplace) {
-    const EpilogueParams * p = b != nullptr ? (tanh_after ? &k_ep_bias_tanh : &k_ep_bias)
-                                            : (tanh_after ? &k_ep_nobias_tanh : &k_ep_nobias);
-    ggml_tensor *          args[3];
-    int                    n = 0;
+    const EpilogueParams * p =
+        b != nullptr ? (tanh_after ? &k_ep_bias_tanh : &k_ep_bias) : (tanh_after ? &k_ep_nobias_tanh : &k_ep_nobias);
+    ggml_tensor * args[3];
+    int           n = 0;
     if (b != nullptr) {
         args[n++] = b;
     }
@@ -603,15 +606,17 @@ ggml_tensor * scale_add(ggml_context * ctx, ggml_tensor * x, ggml_tensor * s, gg
 
 ggml_tensor * time_stats(ggml_context * ctx, ggml_tensor * x, const float * eps) {
     ggml_tensor * args[1] = { x };
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 1, time_stats_fn, GGML_N_TASKS_MAX,
-                          ud(eps));
+    return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 1, time_stats_fn, GGML_N_TASKS_MAX, ud(eps));
 }
 
-ggml_tensor * attn_stats(ggml_context * ctx, ggml_tensor * x, ggml_tensor * logits, ggml_tensor * bias,
-                         const float * eps) {
+ggml_tensor * attn_stats(ggml_context * ctx,
+                         ggml_tensor *  x,
+                         ggml_tensor *  logits,
+                         ggml_tensor *  bias,
+                         const float *  eps) {
     ggml_tensor * sm_args[1] = { bias };
-    ggml_tensor * w = ggml_custom_inplace(ctx, logits, sm_args, 1, softmax_time_fn, GGML_N_TASKS_MAX, nullptr);
-    ggml_tensor * args[2] = { x, w };
+    ggml_tensor * w          = ggml_custom_inplace(ctx, logits, sm_args, 1, softmax_time_fn, GGML_N_TASKS_MAX, nullptr);
+    ggml_tensor * args[2]    = { x, w };
     return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 2, weighted_stats_fn, GGML_N_TASKS_MAX,
                           ud(eps));
 }
diff --git a/src/arch/ecapa_tdnn/cpu_ops.h b/src/arch/ecapa_tdnn/cpu_ops.h
index b90db423c..9cfe2f1bb 100644
--- a/src/arch/ecapa_tdnn/cpu_ops.h
+++ b/src/arch/ecapa_tdnn/cpu_ops.h
@@ -58,8 +58,11 @@ ggml_tensor * time_stats(ggml_context * ctx, ggml_tensor * x, const float * eps)
 // Attentive statistics: per channel c, w = softmax_t(logits[c, t] + bias[c]),
 // mu = sum_t w x, sigma = sqrt(max(sum_t w (x - mu)^2, *eps)). x and logits
 // ne = [C, T], bias ne = [C]; returns ne = [2C] = [mu, sigma].
-ggml_tensor * attn_stats(ggml_context * ctx, ggml_tensor * x, ggml_tensor * logits, ggml_tensor * bias,
-                         const float * eps);
+ggml_tensor * attn_stats(ggml_context * ctx,
+                         ggml_tensor *  x,
+                         ggml_tensor *  logits,
+                         ggml_tensor *  bias,
+                         const float *  eps);
 
 // One Res2Net step. Chunk i of the block (i >= 1) is
 //   y_i = BN(ReLU(sum_k Z[k*w + c, t + k*d] + b))
diff --git a/src/arch/ecapa_tdnn/graph.cpp b/src/arch/ecapa_tdnn/graph.cpp
index 9c815d6af..5534fbba7 100644
--- a/src/arch/ecapa_tdnn/graph.cpp
+++ b/src/arch/ecapa_tdnn/graph.cpp
@@ -290,11 +290,20 @@ ggml_tensor * res2net_cpu(ggml_context *         ctx,
     return cpu::res2_gather(ctx, h, ys, kRes2NetSubs);
 }
 
-// SEBlock fused with the residual add: x * s + residual.
-ggml_tensor * se_block_cpu(ggml_context * ctx, const SeBlock & se, ggml_tensor * x, ggml_tensor * residual) {
+// SEBlock fused with the residual add: x * s + residual. The fused kernel
+// never materializes x * s, so a dump build passes `se_out` to get it on a
+// side branch (the caller marks it and adds it to the graph).
+ggml_tensor * se_block_cpu(ggml_context *  ctx,
+                           const SeBlock & se,
+                           ggml_tensor *   x,
+                           ggml_tensor *   residual,
+                           ggml_tensor **  se_out) {
     ggml_tensor * s = cpu::time_mean(ctx, x);
     s               = ggml_relu(ctx, linear(ctx, se.c1_w, s, se.c1_b));
     s               = ggml_sigmoid(ctx, linear(ctx, se.c2_w, s, se.c2_b));
+    if (se_out != nullptr) {
+        *se_out = ggml_mul(ctx, x, s);
+    }
     return cpu::scale_add(ctx, x, s, residual);
 }
 
@@ -573,9 +582,14 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_
         h = cpu_ops ? tdnn_1x1_cpu(ctx, ar_lin, blk.tdnn2, h) : tdnn_1x1(ctx, blk.tdnn2, h);
 
         if (cpu_ops) {
-            // The SE output and the residual add are one kernel here, so the
-            // enc.blk.1.se.out dump point does not exist in this graph.
-            x = se_block_cpu(ctx, blk.se, h, residual);
+            // The SE output and the residual add are one kernel here; a dump
+            // build recomputes enc.blk.1.se.out on a side branch.
+            ggml_tensor * se_out = nullptr;
+            x = se_block_cpu(ctx, blk.se, h, residual, i == 0 && debug::enabled() ? &se_out : nullptr);
+            if (se_out != nullptr) {
+                mark_dump(gb.dumps.blk1_se_out, se_out, "enc.blk.1.se.out");
+                ggml_build_forward_expand(gb.graph, se_out);
+            }
         } else {
             h = se_block(ctx, blk.se, h);
             if (i == 0) {
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index 7412bbf1b..63c67eba3 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -576,7 +576,7 @@ void dump_stages(const GraphBuild & gb) {
     try_dump("enc.blk.0.out", gb.dumps.blk0_out, "encoder");
     try_dump("enc.blk.1.tdnn1.out", gb.dumps.blk1_tdnn1_out, "encoder");
     try_dump("enc.blk.1.res2.out", gb.dumps.blk1_res2_out, "encoder");
-    try_dump("enc.blk.1.se.out", gb.dumps.blk1_se_out, "encoder");  // stock-op graph only
+    try_dump("enc.blk.1.se.out", gb.dumps.blk1_se_out, "encoder");
     try_dump("enc.blk.1.out", gb.dumps.blk_out[0], "encoder");
     try_dump("enc.blk.2.out", gb.dumps.blk_out[1], "encoder");
     try_dump("enc.blk.3.out", gb.dumps.blk_out[2], "encoder");
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index d8490aa60..7b3e528e6 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1394,12 +1394,13 @@ if(TRANSCRIBE_BUILD_REAL_MODEL_TESTS)
         SKIP_RETURN_CODE 77)
 
     # ecapa_tdnn on the LANGID role: label table, aliases, top-1 on the
-    # committed FLEURS clips, the 30 s crop. Gated by
+    # committed FLEURS clips, the 30 s crop, and the GPU graph against the
+    # CPU logits when a GPU backend loads. Gated by
     # TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip).
     add_executable(transcribe_ecapa_tdnn_real_smoke
         ecapa_tdnn_real_smoke.cpp)
     target_link_libraries(transcribe_ecapa_tdnn_real_smoke
-        PRIVATE transcribe transcribe-common-example)
+        PRIVATE transcribe transcribe-common-example ggml)
     target_compile_definitions(transcribe_ecapa_tdnn_real_smoke PRIVATE
         "TRANSCRIBE_TEST_SAMPLES_DIR=\"${CMAKE_SOURCE_DIR}/samples\"")
     transcribe_apply_warnings(transcribe_ecapa_tdnn_real_smoke)
diff --git a/tests/ecapa_tdnn_gemm_unit.cpp b/tests/ecapa_tdnn_gemm_unit.cpp
index 2b2433703..ae7c288a1 100644
--- a/tests/ecapa_tdnn_gemm_unit.cpp
+++ b/tests/ecapa_tdnn_gemm_unit.cpp
@@ -28,24 +28,24 @@ namespace {
 
 int g_fail = 0;
 
-#define CHECK(cond, ...)                                       \
-    do {                                                       \
-        if (!(cond)) {                                         \
+#define CHECK(cond, ...)                                              \
+    do {                                                              \
+        if (!(cond)) {                                                \
             std::fprintf(stderr, "FAIL %s:%d: ", __FILE__, __LINE__); \
-            std::fprintf(stderr, __VA_ARGS__);                 \
-            std::fprintf(stderr, "\n");                        \
-            ++g_fail;                                          \
-        }                                                      \
+            std::fprintf(stderr, __VA_ARGS__);                        \
+            std::fprintf(stderr, "\n");                               \
+            ++g_fail;                                                 \
+        }                                                             \
     } while (0)
 
 struct Case {
-    const char *    name;
-    ggml_type       wtype;
-    int64_t         M, K, N;
-    int             n_segs;      // segments share one weight tensor (n_mats = n_segs)
-    int64_t         row_step;    // segment s reads x from row s * row_step
-    gemm::Epilogue  ep;
-    bool            bias;
+    const char *   name;
+    ggml_type      wtype;
+    int64_t        M, K, N;
+    int            n_segs;    // segments share one weight tensor (n_mats = n_segs)
+    int64_t        row_step;  // segment s reads x from row s * row_step
+    gemm::Epilogue ep;
+    bool           bias;
 };
 
 std::vector<float> rnd(size_t n, std::mt19937 & rng, float scale) {
@@ -106,13 +106,13 @@ bool run_case(const Case & c) {
     for (int nt : { 1, 3, 8, 16 }) {
         ggml_backend_t be = ggml_backend_cpu_init();
         ggml_backend_cpu_set_n_threads(be, nt);
-        ggml_init_params ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
-        ggml_context *   ctx = ggml_init(ip);
-        ggml_tensor *    w   = ggml_new_tensor_3d(ctx, c.wtype, c.K, c.M, Q);
-        ggml_tensor *    x   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c.K, x_rows);
-        ggml_tensor *    b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
-        ggml_tensor *    s   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
-        ggml_tensor *    h   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
+        ggml_init_params      ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
+        ggml_context *        ctx  = ggml_init(ip);
+        ggml_tensor *         w    = ggml_new_tensor_3d(ctx, c.wtype, c.K, c.M, Q);
+        ggml_tensor *         x    = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c.K, x_rows);
+        ggml_tensor *         b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
+        ggml_tensor *         s    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
+        ggml_tensor *         h    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
         ggml_backend_buffer_t wbuf = ggml_backend_alloc_ctx_tensors(ctx, be);
         if (c.wtype == GGML_TYPE_F16) {
             ggml_backend_tensor_set(w, wh.data(), 0, ggml_nbytes(w));
@@ -176,19 +176,19 @@ bool run_case(const Case & c) {
 void run_reductions() {
     const int64_t      C = 600, T = 517;  // C not a multiple of the 256 channel block
     std::mt19937       rng(99);
-    std::vector<float> xf = rnd(static_cast<size_t>(C * T), rng, 1.0f);
-    std::vector<float> lf = rnd(static_cast<size_t>(C * T), rng, 2.0f);
-    std::vector<float> bf = rnd(static_cast<size_t>(C), rng, 1.0f);
+    std::vector<float> xf  = rnd(static_cast<size_t>(C * T), rng, 1.0f);
+    std::vector<float> lf  = rnd(static_cast<size_t>(C * T), rng, 2.0f);
+    std::vector<float> bf  = rnd(static_cast<size_t>(C), rng, 1.0f);
     static const float eps = 1e-12f;
 
     for (int nt : { 1, 5, 8 }) {
         ggml_backend_t be = ggml_backend_cpu_init();
         ggml_backend_cpu_set_n_threads(be, nt);
-        ggml_init_params ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
-        ggml_context *   ctx = ggml_init(ip);
-        ggml_tensor *    x   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
-        ggml_tensor *    l   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
-        ggml_tensor *    b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, C);
+        ggml_init_params      ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
+        ggml_context *        ctx = ggml_init(ip);
+        ggml_tensor *         x   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
+        ggml_tensor *         l   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
+        ggml_tensor *         b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, C);
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, be);
         ggml_backend_tensor_set(x, xf.data(), 0, ggml_nbytes(x));
         ggml_backend_tensor_set(l, lf.data(), 0, ggml_nbytes(l));
@@ -205,7 +205,8 @@ void run_reductions() {
         ggml_gallocr_alloc_graph(ga, g);
         CHECK(ggml_backend_graph_compute(be, g) == GGML_STATUS_SUCCESS, "reductions: compute failed");
 
-        std::vector<float> vst(static_cast<size_t>(2 * C)), vat(static_cast<size_t>(2 * C)), vmn(static_cast<size_t>(C));
+        std::vector<float> vst(static_cast<size_t>(2 * C)), vat(static_cast<size_t>(2 * C)),
+            vmn(static_cast<size_t>(C));
         ggml_backend_tensor_get(st, vst.data(), 0, ggml_nbytes(st));
         ggml_backend_tensor_get(at, vat.data(), 0, ggml_nbytes(at));
         ggml_backend_tensor_get(mn, vmn.data(), 0, ggml_nbytes(mn));
@@ -220,19 +221,23 @@ void run_reductions() {
             for (int64_t t = 0; t < T; ++t) {
                 const double d = xf[static_cast<size_t>(t * C + c)] - mean;
                 q += d * d;
-                mx = std::fmax(mx, static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)]);
+                mx =
+                    std::fmax(mx, static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)]);
             }
             for (int64_t t = 0; t < T; ++t) {
-                z += std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx);
+                z +=
+                    std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx);
             }
             for (int64_t t = 0; t < T; ++t) {
-                const double w =
-                    std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx) / z;
+                const double w = std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) +
+                                          bf[static_cast<size_t>(c)] - mx) /
+                                 z;
                 mu += w * xf[static_cast<size_t>(t * C + c)];
             }
             for (int64_t t = 0; t < T; ++t) {
-                const double w =
-                    std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx) / z;
+                const double w = std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) +
+                                          bf[static_cast<size_t>(c)] - mx) /
+                                 z;
                 const double d = xf[static_cast<size_t>(t * C + c)] - mu;
                 sg += w * d * d;
             }
@@ -264,13 +269,13 @@ int main() {
     }
 
     const Case cases[] = {
-        { "f16 1x1 relu-bn", GGML_TYPE_F16, 64, 96, 37, 1, 0, gemm::Epilogue::ReluBn, true },
-        { "f16 mfa 3-seg", GGML_TYPE_F16, 48, 300, 61, 3, 0, gemm::Epilogue::ReluBn, true },
-        { "f16 none tanh-free", GGML_TYPE_F16, 32, 513, 13, 1, 0, gemm::Epilogue::None, false },
-        { "f16 tanh", GGML_TYPE_F16, 16, 40, 7, 1, 0, gemm::Epilogue::ReluBnTanh, true },
-        { "f32 dilated conv 3-tap", GGML_TYPE_F32, 32, 24, 50, 3, 4, gemm::Epilogue::ReluBn, true },
-        { "f32 im2col no-bias", GGML_TYPE_F32, 16, 320, 101, 1, 0, gemm::Epilogue::ReluBn, false },
-        { "f16 N=1", GGML_TYPE_F16, 16, 33, 1, 2, 1, gemm::Epilogue::None, false },
+        { "f16 1x1 relu-bn",        GGML_TYPE_F16, 64, 96,  37,  1, 0, gemm::Epilogue::ReluBn,     true  },
+        { "f16 mfa 3-seg",          GGML_TYPE_F16, 48, 300, 61,  3, 0, gemm::Epilogue::ReluBn,     true  },
+        { "f16 none tanh-free",     GGML_TYPE_F16, 32, 513, 13,  1, 0, gemm::Epilogue::None,       false },
+        { "f16 tanh",               GGML_TYPE_F16, 16, 40,  7,   1, 0, gemm::Epilogue::ReluBnTanh, true  },
+        { "f32 dilated conv 3-tap", GGML_TYPE_F32, 32, 24,  50,  3, 4, gemm::Epilogue::ReluBn,     true  },
+        { "f32 im2col no-bias",     GGML_TYPE_F32, 16, 320, 101, 1, 0, gemm::Epilogue::ReluBn,     false },
+        { "f16 N=1",                GGML_TYPE_F16, 16, 33,  1,   2, 1, gemm::Epilogue::None,       false },
     };
     for (const Case & c : cases) {
         run_case(c);
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
index c5274e717..c6a29a85a 100644
--- a/tests/ecapa_tdnn_real_smoke.cpp
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -5,6 +5,7 @@
 // graph against the CPU graph's logits. Gated by TRANSCRIBE_ECAPA_TDNN_GGUF
 // (RC 77 skip).
 
+#include "gguf.h"
 #include "transcribe.h"
 #include "transcribe/langid.h"
 #include "wav.h"
@@ -85,10 +86,30 @@ std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
     return out;
 }
 
+// general.file_type of the GGUF (0 = all F32, 1 = F16, 7 = Q8_0), or -1.
+int gguf_file_type(const char * path) {
+    gguf_init_params gp{};
+    gp.no_alloc      = true;
+    gp.ctx           = nullptr;
+    gguf_context * g = gguf_init_from_file(path, gp);
+    if (g == nullptr) {
+        return -1;
+    }
+    const int64_t key = gguf_find_key(g, "general.file_type");
+    const int     ft =
+        key >= 0 && gguf_get_kv_type(g, key) == GGUF_TYPE_UINT32 ? static_cast<int>(gguf_get_val_u32(g, key)) : -1;
+    gguf_free(g);
+    return ft;
+}
+
 // The GPU backends run the stock-op graph, which no other test reaches.
 // Their matmuls are not bit-exact F32 (Vulkan on an AMD iGPU lands ~1e-2 off
-// the CPU logits), so this bounds the drift rather than demanding parity.
+// the CPU logits, Metal ~7e-3), so this bounds the drift rather than
+// demanding parity. Reduced-precision files drift further because the CPU and
+// GPU round F16 operands differently (Metal: up to 0.052 for F16, 0.048 for
+// Q8_0 on these clips), so they get twice that.
 void check_gpu_matches_cpu(const char * path, transcribe_model * cpu_model) {
+    const float                      bound  = gguf_file_type(path) == 0 ? 0.05f : 0.1f;
     const transcribe_backend_request gpus[] = { TRANSCRIBE_BACKEND_VULKAN, TRANSCRIBE_BACKEND_METAL };
     transcribe_model *               gm     = nullptr;
     for (const transcribe_backend_request b : gpus) {
@@ -121,7 +142,9 @@ void check_gpu_matches_cpu(const char * path, transcribe_model * cpu_model) {
         for (size_t i = 0; i < a.size(); ++i) {
             max_diff = std::fmax(max_diff, std::fabs(a[i] - b[i]));
         }
-        if (!(max_diff < 0.05f)) {
+        std::fprintf(stderr, "ecapa_tdnn_real_smoke: %s: GPU vs CPU max |logit diff| %.4f (bound %.2f)\n", wav,
+                     max_diff, bound);
+        if (!(max_diff < bound)) {
             std::fprintf(stderr, "FAIL: %s: GPU logits differ from CPU by %.4f\n", wav, max_diff);
             ++g_failures;
         }

From f56334133ed76c5bcf8b5d9910b9c4a5f8b31c89 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 15:28:22 +0800
Subject: [PATCH 09/38] langid: drop ecapa_tdnn hand-written CPU kernels

The plain ggml graph (the one the GPU backends run) is now the CPU path
too. Removed:

- src/arch/ecapa_tdnn/cpu_ops.{cpp,h}: fused epilogues, time reductions
  on [C, T], Res2Net step/pad/gather, SE scale-add, attentive stats
- src/arch/ecapa_tdnn/cpu_gemm.{cpp,h}: AVX2 GEMM with fused epilogue
  and its load-time weight repacking (pack_gemm_weights)
- the second, CPU-only graph in graph.cpp, Model::cpu_ops / gemm_conv /
  gemm_lin, Session::gemm_arena, and the -ffp-contract=off override
- tests/ecapa_tdnn_gemm_unit.cpp, the GEMM-log checks and the wide-kernel
  fallback test in ecapa_tdnn_smoke.cpp, and the arch_ecapa_tdnn_k5.gguf
  fixture; the toy fixtures go back to 1/32 width

Stage-0 im2col, the GPU graph rework, and Q8_0 -> F16 widening stay.

Kernels recoverable from d836d01a (src/arch/ecapa_tdnn/cpu_gemm.*, cpu_ops.*, tests/ecapa_tdnn_gemm_unit.cpp).
---
 src/CMakeLists.txt                   |  10 -
 src/arch/ecapa_tdnn/cpu_gemm.cpp     | 425 -----------------
 src/arch/ecapa_tdnn/cpu_gemm.h       | 113 -----
 src/arch/ecapa_tdnn/cpu_ops.cpp      | 674 ---------------------------
 src/arch/ecapa_tdnn/cpu_ops.h        | 101 ----
 src/arch/ecapa_tdnn/ecapa_tdnn.h     |  17 -
 src/arch/ecapa_tdnn/graph.cpp        | 247 ++--------
 src/arch/ecapa_tdnn/graph.h          |  16 +-
 src/arch/ecapa_tdnn/model.cpp        |  85 +---
 tests/CMakeLists.txt                 |  13 +-
 tests/ecapa_tdnn_gemm_unit.cpp       | 287 ------------
 tests/ecapa_tdnn_smoke.cpp           |  61 +--
 tests/fixtures/make_gguf_fixtures.py |  33 +-
 13 files changed, 57 insertions(+), 2025 deletions(-)
 delete mode 100644 src/arch/ecapa_tdnn/cpu_gemm.cpp
 delete mode 100644 src/arch/ecapa_tdnn/cpu_gemm.h
 delete mode 100644 src/arch/ecapa_tdnn/cpu_ops.cpp
 delete mode 100644 src/arch/ecapa_tdnn/cpu_ops.h
 delete mode 100644 tests/ecapa_tdnn_gemm_unit.cpp

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index b8c1c0203..b4307e6cb 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -67,8 +67,6 @@ add_library(transcribe
     arch/sortformer/model.cpp
     arch/sortformer/stream.cpp
     arch/sortformer/weights.cpp
-    arch/ecapa_tdnn/cpu_gemm.cpp
-    arch/ecapa_tdnn/cpu_ops.cpp
     arch/ecapa_tdnn/graph.cpp
     arch/ecapa_tdnn/mel.cpp
     arch/ecapa_tdnn/model.cpp
@@ -293,14 +291,6 @@ else()
     set_source_files_properties(third_party/miniz/miniz.c PROPERTIES COMPILE_OPTIONS "-w")
 endif()
 
-# ecapa_tdnn's fused CPU kernels round every multiply and add separately so
-# they reproduce the stock ggml op chain they replace; GCC/Clang contract
-# a*b+c into an FMA by default (-ffp-contract=fast), which changes the bits.
-if(NOT MSVC)
-    set_source_files_properties(arch/ecapa_tdnn/cpu_gemm.cpp arch/ecapa_tdnn/cpu_ops.cpp
-        PROPERTIES COMPILE_OPTIONS "-ffp-contract=off")
-endif()
-
 if(WIN32)
     # Windows toolchains hide M_PI and friends behind _USE_MATH_DEFINES
     # (used by the mel frontends and Whisper's bin loader). MSVC and MinGW
diff --git a/src/arch/ecapa_tdnn/cpu_gemm.cpp b/src/arch/ecapa_tdnn/cpu_gemm.cpp
deleted file mode 100644
index 1584bcaac..000000000
--- a/src/arch/ecapa_tdnn/cpu_gemm.cpp
+++ /dev/null
@@ -1,425 +0,0 @@
-// arch/ecapa_tdnn/cpu_gemm.cpp - see cpu_gemm.h.
-//
-// Kernel: a 16 x NR register tile (two 8-float rows of accumulators per
-// output column, NR <= 6 columns): per k it widens 16 packed weights, then
-// broadcasts one activation value per column and issues two FMAs per column.
-// The activations are read in place (each output column's K values are
-// contiguous in ggml's [K, N] layout), so nothing is packed at run time.
-//
-// Work split: the output is cut into row blocks of kRowBlock rows and column
-// blocks sized so one block's activations stay in L2; blocks are dealt to
-// the ggml threads round robin.
-//
-// Built with -ffp-contract=off (src/CMakeLists.txt): the epilogue's multiply
-// and add must round separately, as the stock ggml_mul / ggml_add do.
-
-#include "cpu_gemm.h"
-
-#include "ggml-backend.h"
-#include "ggml.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstring>
-#include <vector>
-
-#if (defined(__x86_64__) || defined(__i386__)) && (defined(__GNUC__) || defined(__clang__))
-#    define ECAPA_GEMM_X86 1
-#    include <immintrin.h>
-#else
-#    define ECAPA_GEMM_X86 0
-#endif
-
-namespace transcribe::ecapa_tdnn::gemm {
-
-namespace {
-
-#if ECAPA_GEMM_X86
-
-#    define ECAPA_TARGET __attribute__((target("avx2,fma,f16c")))
-
-constexpr int     kNR = 6;    // output columns per micro-tile
-constexpr int64_t kKC = 256;  // k block: 16 x 256 F16 weights = 8 KiB in L1
-constexpr int64_t kNC = 192;  // column block: 256 x 192 packed F32 = 192 KiB in L2
-
-// 16 x 6 micro-tile over k in [0, kc): a = packed weight panel at k0
-// (16 values per k), bp = packed activations (6 values per k). Writes the
-// raw partial sums to acc[12] (two __m256 per column); the caller loads /
-// stores / applies the epilogue. The accumulators are named locals so the
-// compiler keeps all twelve in registers through the k loop.
-#    define ECAPA_FMA6(W0, W1, BP)                     \
-        do {                                           \
-            __m256 xb = _mm256_broadcast_ss((BP) + 0); \
-            a00       = _mm256_fmadd_ps(W0, xb, a00);  \
-            a01       = _mm256_fmadd_ps(W1, xb, a01);  \
-            xb        = _mm256_broadcast_ss((BP) + 1); \
-            a10       = _mm256_fmadd_ps(W0, xb, a10);  \
-            a11       = _mm256_fmadd_ps(W1, xb, a11);  \
-            xb        = _mm256_broadcast_ss((BP) + 2); \
-            a20       = _mm256_fmadd_ps(W0, xb, a20);  \
-            a21       = _mm256_fmadd_ps(W1, xb, a21);  \
-            xb        = _mm256_broadcast_ss((BP) + 3); \
-            a30       = _mm256_fmadd_ps(W0, xb, a30);  \
-            a31       = _mm256_fmadd_ps(W1, xb, a31);  \
-            xb        = _mm256_broadcast_ss((BP) + 4); \
-            a40       = _mm256_fmadd_ps(W0, xb, a40);  \
-            a41       = _mm256_fmadd_ps(W1, xb, a41);  \
-            xb        = _mm256_broadcast_ss((BP) + 5); \
-            a50       = _mm256_fmadd_ps(W0, xb, a50);  \
-            a51       = _mm256_fmadd_ps(W1, xb, a51);  \
-        } while (0)
-
-template <bool F16> ECAPA_TARGET void tile_core(const void * a, const float * bp, int64_t kc, __m256 * acc) {
-    __m256 a00 = acc[0], a01 = acc[1], a10 = acc[2], a11 = acc[3], a20 = acc[4], a21 = acc[5];
-    __m256 a30 = acc[6], a31 = acc[7], a40 = acc[8], a41 = acc[9], a50 = acc[10], a51 = acc[11];
-    if constexpr (F16) {
-        const uint16_t * ap = static_cast<const uint16_t *>(a);
-        for (int64_t k = 0; k < kc; ++k) {
-            const __m256 w0 = _mm256_cvtph_ps(_mm_loadu_si128(reinterpret_cast<const __m128i *>(ap)));
-            const __m256 w1 = _mm256_cvtph_ps(_mm_loadu_si128(reinterpret_cast<const __m128i *>(ap + 8)));
-            ECAPA_FMA6(w0, w1, bp);
-            ap += 16;
-            bp += kNR;
-        }
-    } else {
-        const float * ap = static_cast<const float *>(a);
-        for (int64_t k = 0; k < kc; ++k) {
-            const __m256 w0 = _mm256_loadu_ps(ap);
-            const __m256 w1 = _mm256_loadu_ps(ap + 8);
-            ECAPA_FMA6(w0, w1, bp);
-            ap += 16;
-            bp += kNR;
-        }
-    }
-    acc[0] = a00, acc[1] = a01, acc[2] = a10, acc[3] = a11, acc[4] = a20, acc[5] = a21;
-    acc[6] = a30, acc[7] = a31, acc[8] = a40, acc[9] = a41, acc[10] = a50, acc[11] = a51;
-}
-
-#    undef ECAPA_FMA6
-
-template <bool F16>
-ECAPA_TARGET void tile(const void *  a,
-                       const float * bp,
-                       int64_t       kc,
-                       float *       y,
-                       size_t        ldy,
-                       int           n_valid,
-                       bool          first,
-                       bool          last,
-                       Epilogue      ep,
-                       const float * b,
-                       const float * s,
-                       const float * sh) {
-    alignas(32) __m256 acc[2 * kNR];
-    for (int n = 0; n < kNR; ++n) {
-        if (first || n >= n_valid) {
-            acc[2 * n]     = _mm256_setzero_ps();
-            acc[2 * n + 1] = _mm256_setzero_ps();
-        } else {
-            acc[2 * n]     = _mm256_loadu_ps(y + static_cast<size_t>(n) * ldy);
-            acc[2 * n + 1] = _mm256_loadu_ps(y + static_cast<size_t>(n) * ldy + 8);
-        }
-    }
-    tile_core<F16>(a, bp, kc, acc);
-
-    if (last && ep != Epilogue::None) {
-        const __m256 zero = _mm256_setzero_ps();
-        const __m256 s0   = _mm256_loadu_ps(s);
-        const __m256 s1   = _mm256_loadu_ps(s + 8);
-        const __m256 h0   = _mm256_loadu_ps(sh);
-        const __m256 h1   = _mm256_loadu_ps(sh + 8);
-        for (int n = 0; n < kNR; ++n) {
-            __m256 v0 = acc[2 * n];
-            __m256 v1 = acc[2 * n + 1];
-            if (b != nullptr) {
-                v0 = _mm256_add_ps(v0, _mm256_loadu_ps(b));
-                v1 = _mm256_add_ps(v1, _mm256_loadu_ps(b + 8));
-            }
-            v0             = _mm256_max_ps(v0, zero);
-            v1             = _mm256_max_ps(v1, zero);
-            acc[2 * n]     = _mm256_add_ps(_mm256_mul_ps(v0, s0), h0);
-            acc[2 * n + 1] = _mm256_add_ps(_mm256_mul_ps(v1, s1), h1);
-        }
-    }
-    for (int n = 0; n < n_valid; ++n) {
-        float * yc = y + static_cast<size_t>(n) * ldy;
-        _mm256_storeu_ps(yc, acc[2 * n]);
-        _mm256_storeu_ps(yc + 8, acc[2 * n + 1]);
-        if (last && ep == Epilogue::ReluBnTanh) {
-            for (int r = 0; r < kPanel; ++r) {
-                yc[r] = std::tanh(yc[r]);
-            }
-        }
-    }
-}
-
-// Pack columns [j0, j0 + nc) x k [k0, k0 + kc) of x (column j's K values at
-// x + j*ldx) into kNR-wide k-major micro-panels, zero filling past nc.
-void pack_x(const float * x, size_t ldx, int64_t j0, int64_t nc, int64_t k0, int64_t kc, float * bp) {
-    const int64_t n_panels = (nc + kNR - 1) / kNR;
-    for (int64_t p = 0; p < n_panels; ++p) {
-        float * dst = bp + static_cast<size_t>(p * kc * kNR);
-        for (int n = 0; n < kNR; ++n) {
-            const int64_t j = p * kNR + n;
-            if (j >= nc) {
-                for (int64_t k = 0; k < kc; ++k) {
-                    dst[static_cast<size_t>(k * kNR + n)] = 0.0f;
-                }
-                continue;
-            }
-            const float * src = x + static_cast<size_t>(j0 + j) * ldx + static_cast<size_t>(k0);
-            for (int64_t k = 0; k < kc; ++k) {
-                dst[static_cast<size_t>(k * kNR + n)] = src[k];
-            }
-        }
-    }
-}
-
-// Work split: the output is cut into (row block x column block) jobs dealt
-// to the threads round robin. A row block's weight slice for one k block
-// (kMB x kKC F16 = 128 KiB) and the packed activations of one column block
-// (kKC x <= kNC F32 = 192 KiB) both stay in L2 while the micro-tiles sweep
-// them. The k order inside every output element is fixed (segments, then k
-// blocks, then k), so the result does not depend on the thread count.
-constexpr int64_t kMB = 256;
-
-// Claim the next job of thread `owner`'s range [owner*per, ...). A slot
-// packs (generation << 32 | jobs taken); a stale generation reads as zero.
-int64_t claim(Desc & d, int owner, uint32_t gen, int64_t limit) {
-    std::atomic<uint64_t> & a   = d.slot[owner];
-    uint64_t                cur = a.load(std::memory_order_relaxed);
-    for (;;) {
-        const uint32_t g     = static_cast<uint32_t>(cur >> 32);
-        const int64_t  taken = g == gen ? static_cast<int64_t>(cur & 0xffffffffu) : 0;
-        if (taken >= limit) {
-            return -1;
-        }
-        const uint64_t next = (static_cast<uint64_t>(gen) << 32) | static_cast<uint64_t>(taken + 1);
-        if (a.compare_exchange_weak(cur, next, std::memory_order_relaxed)) {
-            return taken;
-        }
-    }
-}
-
-void gemm_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    Desc & d = *static_cast<Desc *>(ud);
-    if (nth > Desc::kMaxThreads) {
-        nth = Desc::kMaxThreads;
-        if (ith >= nth) {
-            return;
-        }
-    }
-    // The generation advances once per compute: the last thread to arrive
-    // in compute n bumps it for compute n + 1. Threads of one compute all
-    // read the same value because nobody arrives before every thread has
-    // read it (the read happens before this thread's own arrival).
-    const uint32_t gen = d.generation.load(std::memory_order_acquire) + 1;
-    const int64_t  M   = d.M;
-    const int64_t  N   = d.N;
-
-    const int64_t mb   = std::min(kMB, M);
-    const int64_t n_rb = (M + mb - 1) / mb;
-    int64_t       n_cb = (N + kNC - 1) / kNC;
-    if (n_rb * n_cb < 4 * nth) {
-        // Too few jobs to keep every thread busy: narrow the column blocks.
-        n_cb = std::min<int64_t>((N + kNR - 1) / kNR, (4 * nth + n_rb - 1) / n_rb);
-    }
-    int64_t cb_w = (N + n_cb - 1) / n_cb;
-    cb_w         = (cb_w + kNR - 1) / kNR * kNR;
-    n_cb         = (N + cb_w - 1) / cb_w;
-
-    const float * bias  = d.b_src >= 0 ? static_cast<const float *>(dst->src[d.b_src]->data) : nullptr;
-    const float * scale = d.scale_src >= 0 ? static_cast<const float *>(dst->src[d.scale_src]->data) : nullptr;
-    const float * shift = d.shift_src >= 0 ? static_cast<const float *>(dst->src[d.shift_src]->data) : nullptr;
-    const size_t  ldy   = dst->nb[1] / sizeof(float);
-    float *       y0    = static_cast<float *>(dst->data);
-
-    int64_t n_blocks = 0;  // k blocks over all segments
-    for (int s = 0; s < d.n_segs; ++s) {
-        n_blocks += (dst->src[d.segs[s].w_src]->ne[0] + kKC - 1) / kKC;
-    }
-
-    thread_local std::vector<float> bp;
-    bp.resize(static_cast<size_t>(std::max(cb_w, int64_t{ kNR }) * kKC));
-
-    const int64_t n_jobs = n_rb * n_cb;
-    const int64_t per    = (n_jobs + nth - 1) / nth;
-
-    // Own range first, then steal from the others in ring order.
-    auto next_job = [&]() -> int64_t {
-        for (int r = 0; r < nth; ++r) {
-            const int     owner = (ith + r) % nth;
-            const int64_t lo    = owner * per;
-            const int64_t lim   = std::min(per, n_jobs - lo);
-            if (lim <= 0) {
-                continue;
-            }
-            const int64_t k = claim(d, owner, gen, lim);
-            if (k >= 0) {
-                return lo + k;
-            }
-        }
-        return -1;
-    };
-
-    for (int64_t job = next_job(); job >= 0; job = next_job()) {
-        const int64_t rb       = job % n_rb;
-        const int64_t cb       = job / n_rb;
-        const int64_t i0       = rb * mb;
-        const int64_t i1       = std::min(M, i0 + mb);
-        const int64_t j0       = cb * cb_w;
-        const int64_t ncb      = std::min(cb_w, N - j0);
-        const int64_t n_panels = (ncb + kNR - 1) / kNR;
-
-        int64_t blk = 0;
-        for (int s = 0; s < d.n_segs; ++s) {
-            const ggml_tensor * w   = dst->src[d.segs[s].w_src];
-            const ggml_tensor * x   = dst->src[d.segs[s].x_src];
-            const int64_t       K   = w->ne[0];
-            const bool          f16 = w->type == GGML_TYPE_F16;
-            const size_t        esz = ggml_type_size(w->type);
-            const size_t        ldx = x->nb[1] / sizeof(float);
-            const float *       xs  = static_cast<const float *>(x->data) + static_cast<size_t>(d.segs[s].x_row0) * ldx;
-            const char * wm = static_cast<const char *>(w->data) + static_cast<size_t>(d.segs[s].mat) * M * K * esz;
-
-            for (int64_t k0 = 0; k0 < K; k0 += kKC, ++blk) {
-                const int64_t kc    = std::min(kKC, K - k0);
-                const bool    first = blk == 0;
-                const bool    last  = blk == n_blocks - 1;
-                pack_x(xs, ldx, j0, ncb, k0, kc, bp.data());
-
-                for (int64_t i = i0; i < i1; i += kPanel) {
-                    const char *  a  = wm + (static_cast<size_t>(i) * K + static_cast<size_t>(k0) * kPanel) * esz;
-                    const float * bb = bias ? bias + i : nullptr;
-                    const float * ss = scale ? scale + i : nullptr;
-                    const float * hh = shift ? shift + i : nullptr;
-                    for (int64_t p = 0; p < n_panels; ++p) {
-                        const int64_t jj = j0 + p * kNR;
-                        const int     nv = static_cast<int>(std::min<int64_t>(kNR, j0 + ncb - jj));
-                        float *       y  = y0 + static_cast<size_t>(jj) * ldy + static_cast<size_t>(i);
-                        const float * pb = bp.data() + static_cast<size_t>(p * kc * kNR);
-                        if (f16) {
-                            tile<true>(a, pb, kc, y, ldy, nv, first, last, d.ep, bb, ss, hh);
-                        } else {
-                            tile<false>(a, pb, kc, y, ldy, nv, first, last, d.ep, bb, ss, hh);
-                        }
-                    }
-                }
-            }
-        }
-    }
-    if (d.arrived.fetch_add(1, std::memory_order_acq_rel) + 1 == nth) {
-        d.arrived.store(0, std::memory_order_relaxed);
-        d.generation.store(gen, std::memory_order_release);
-    }
-}
-
-bool cpu_has_kernel() {
-    __builtin_cpu_init();
-    return __builtin_cpu_supports("avx2") && __builtin_cpu_supports("fma") && __builtin_cpu_supports("f16c");
-}
-
-#endif  // ECAPA_GEMM_X86
-
-}  // namespace
-
-bool available() {
-#if ECAPA_GEMM_X86
-    static const bool ok = cpu_has_kernel();
-    return ok;
-#else
-    return false;
-#endif
-}
-
-bool pack_weight(ggml_tensor * t) {
-    if (t == nullptr || (t->type != GGML_TYPE_F32 && t->type != GGML_TYPE_F16) || !ggml_is_contiguous(t)) {
-        return false;
-    }
-    const int64_t K = t->ne[0];
-    const int64_t M = t->ne[1];
-    const int64_t Q = t->ne[2] * t->ne[3];
-    if (M % kPanel != 0 || K <= 0) {
-        return false;
-    }
-    const size_t         esz = ggml_type_size(t->type);
-    std::vector<uint8_t> src(ggml_nbytes(t));
-    std::vector<uint8_t> dst(src.size());
-    ggml_backend_tensor_get(t, src.data(), 0, src.size());
-    for (int64_t q = 0; q < Q; ++q) {
-        const uint8_t * sm = src.data() + static_cast<size_t>(q * M * K) * esz;
-        uint8_t *       dm = dst.data() + static_cast<size_t>(q * M * K) * esz;
-        for (int64_t i = 0; i < M; ++i) {
-            for (int64_t k = 0; k < K; ++k) {
-                const size_t from = static_cast<size_t>(i * K + k);
-                const size_t to   = static_cast<size_t>((i / kPanel) * K * kPanel + k * kPanel + (i % kPanel));
-                std::memcpy(dm + to * esz, sm + from * esz, esz);
-            }
-        }
-    }
-    ggml_backend_tensor_set(t, dst.data(), 0, dst.size());
-    return true;
-}
-
-ggml_tensor * mul_mat(ggml_context *  ctx,
-                      Arena &         arena,
-                      const Segment * segs,
-                      int             n_segs,
-                      int64_t         N,
-                      Epilogue        ep,
-                      ggml_tensor *   b,
-                      ggml_tensor *   scale,
-                      ggml_tensor *   shift) {
-#if ECAPA_GEMM_X86
-    if (n_segs < 1 || n_segs > kMaxSegs) {
-        return nullptr;
-    }
-    Desc & d = arena.emplace_back();
-    d.n_segs = n_segs;
-    d.M      = segs[0].w->ne[1];
-    d.N      = N;
-    d.ep     = ep;
-
-    ggml_tensor * args[GGML_MAX_SRC] = {};
-    int           n                  = 0;
-    auto          add                = [&](ggml_tensor * t) {
-        for (int i = 0; i < n; ++i) {
-            if (args[i] == t) {
-                return i;
-            }
-        }
-        args[n] = t;
-        return n++;
-    };
-    for (int s = 0; s < n_segs; ++s) {
-        d.segs[s].w_src  = add(segs[s].w);
-        d.segs[s].mat    = segs[s].mat;
-        d.segs[s].x_src  = add(segs[s].x);
-        d.segs[s].x_row0 = segs[s].x_row0;
-    }
-    if (b != nullptr) {
-        d.b_src = add(b);
-    }
-    if (scale != nullptr) {
-        d.scale_src = add(scale);
-    }
-    if (shift != nullptr) {
-        d.shift_src = add(shift);
-    }
-
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, d.M, N, 1, 1, args, n, gemm_fn, GGML_N_TASKS_MAX, &d);
-#else
-    (void) ctx;
-    (void) arena;
-    (void) segs;
-    (void) n_segs;
-    (void) N;
-    (void) ep;
-    (void) b;
-    (void) scale;
-    (void) shift;
-    return nullptr;
-#endif
-}
-
-}  // namespace transcribe::ecapa_tdnn::gemm
diff --git a/src/arch/ecapa_tdnn/cpu_gemm.h b/src/arch/ecapa_tdnn/cpu_gemm.h
deleted file mode 100644
index f3c58733c..000000000
--- a/src/arch/ecapa_tdnn/cpu_gemm.h
+++ /dev/null
@@ -1,113 +0,0 @@
-// arch/ecapa_tdnn/cpu_gemm.h - x86 AVX2 GEMM for the ECAPA-TDNN CPU graph.
-//
-// INTERNAL to src/arch/ecapa_tdnn/. Optional: available() is false on
-// non-x86 builds and on x86 CPUs without AVX2 + FMA + F16C, and the graph
-// then uses ggml_mul_mat.
-//
-// Why: on AVX2, ggml's F16 x F32 matmul rounds the activations to F16 and
-// widens both operands inside its innermost loop, which runs ~1.7x slower
-// than its F32 path. This kernel keeps the weights F16 in memory (repacked
-// once at load into 16-row panels), widens them in registers, keeps the
-// activations F32, and accumulates in F32. It also fuses what the graph
-// would otherwise do in separate passes over the output:
-//   - several (weight, activation) segments summed into one output, which
-//     covers both the MFA stage (three blocks) and a dilated k-tap
-//     convolution (one segment per tap, the activation shifted by k*d rows);
-//   - the TDNN epilogue max(acc + b, 0) * scale + shift, optional tanh.
-//
-// Every output element is one F32 FMA chain over k in segment order, so the
-// result does not depend on the thread count.
-
-#pragma once
-
-#include <atomic>
-#include <cstddef>
-#include <cstdint>
-#include <deque>
-
-struct ggml_context;
-struct ggml_tensor;
-
-namespace transcribe::ecapa_tdnn::gemm {
-
-// True when this build and this CPU can run the kernel.
-bool available();
-
-// Rows per weight panel. A weight with M output rows is usable when
-// M % kPanel == 0.
-constexpr int kPanel = 16;
-
-// Most segments one mul_mat sums: the MFA stage's three blocks, or a k-tap
-// convolution with k <= 3. A model with wider Res2Net kernels keeps those
-// weights on ggml_mul_mat (model.cpp only packs them when k <= kMaxSegs).
-constexpr int kMaxSegs = 3;
-
-// Repack a weight in place into panel-major order. `t` is a ggml weight
-// ne = [K, M, n_mats] (rows of K contiguous, F32 or F16), on a host buffer.
-// Matrix q's row i, column k moves to
-//   q*M*K + (i / 16)*K*16 + k*16 + (i % 16)          (elements)
-// After this only the GEMM may read `t`. Returns false (and leaves `t`
-// untouched) if the type or shape is unsupported.
-bool pack_weight(ggml_tensor * t);
-
-enum class Epilogue : int32_t {
-    None       = 0,  // y = acc
-    ReluBn     = 1,  // y = max(acc + b, 0) * scale + shift (b optional)
-    ReluBnTanh = 2,
-};
-
-// One operand pair: rows [0, M) of packed weight matrix `mat` of `w` times
-// the activation `x` ne = [K, *] starting at row `x_row0`.
-struct Segment {
-    ggml_tensor * w      = nullptr;
-    int           mat    = 0;
-    ggml_tensor * x      = nullptr;
-    int64_t       x_row0 = 0;
-};
-
-// Per-node descriptor, read by the kernel at compute time. Owned by the
-// caller's Arena, which must outlive the graph's computation (a deque, so
-// growing it never moves an existing descriptor).
-struct Desc {
-    struct Seg {
-        int     w_src  = -1;  // dst->src index of the packed weight
-        int     mat    = 0;
-        int     x_src  = -1;  // dst->src index of the activation
-        int64_t x_row0 = 0;
-    };
-
-    Seg      segs[kMaxSegs];
-    int      n_segs    = 0;
-    int64_t  M         = 0;
-    int64_t  N         = 0;
-    Epilogue ep        = Epilogue::None;
-    int      b_src     = -1;
-    int      scale_src = -1;
-    int      shift_src = -1;
-
-    // Work stealing between the threads of one compute (cpu_gemm.cpp).
-    // Slot t holds thread t's progress through its own job range, tagged
-    // with the compute generation so a reused graph starts clean without a
-    // reset pass (custom ops have no barrier to reset behind).
-    static constexpr int  kMaxThreads       = 256;
-    std::atomic<uint64_t> slot[kMaxThreads] = {};
-    std::atomic<uint32_t> generation{ 0 };
-    std::atomic<int32_t>  arrived{ 0 };
-};
-
-using Arena = std::deque<Desc>;
-
-// y[M, N] = epilogue(sum over segments of W_s @ x_s[x_row0 : x_row0 + N]).
-// Returns a new [M, N] tensor. b / scale / shift are F32 vectors ne = [M]
-// (b may be null; all null for Epilogue::None). 1..kMaxSegs segments.
-ggml_tensor * mul_mat(ggml_context *  ctx,
-                      Arena &         arena,
-                      const Segment * segs,
-                      int             n_segs,
-                      int64_t         N,
-                      Epilogue        ep,
-                      ggml_tensor *   b,
-                      ggml_tensor *   scale,
-                      ggml_tensor *   shift);
-
-}  // namespace transcribe::ecapa_tdnn::gemm
diff --git a/src/arch/ecapa_tdnn/cpu_ops.cpp b/src/arch/ecapa_tdnn/cpu_ops.cpp
deleted file mode 100644
index ab495510b..000000000
--- a/src/arch/ecapa_tdnn/cpu_ops.cpp
+++ /dev/null
@@ -1,674 +0,0 @@
-// arch/ecapa_tdnn/cpu_ops.cpp - fused CPU kernels; see cpu_ops.h.
-//
-// Threading: elementwise kernels split the frame axis (rows of [C, T]);
-// time reductions split the channel axis into blocks, so every thread walks
-// all T rows over its own channels and each channel's accumulation order is
-// the same sequential-over-t order as ggml's reductions, for any thread
-// count.
-
-#include "cpu_ops.h"
-
-#include "ggml.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <cstdint>
-#include <vector>
-
-#if (defined(__x86_64__) || defined(__i386__)) && (defined(__GNUC__) || defined(__clang__))
-#    define ECAPA_OPS_X86 1
-#    include <immintrin.h>
-#else
-#    define ECAPA_OPS_X86 0
-#endif
-
-namespace transcribe::ecapa_tdnn::cpu {
-
-namespace {
-
-#if ECAPA_OPS_X86
-#    define ECAPA_AVX2 __attribute__((target("avx2,fma")))
-
-// ggml's AVX2 expf (ggml-cpu/vec.h ggml_v_expf, ARM optimized-routines
-// polynomial, max error 1.45 ulp): ggml's own soft_max uses it on AVX2
-// builds, so this keeps the attention softmax on the arithmetic the stock
-// graph had.
-ECAPA_AVX2 inline __m256 v_expf(__m256 x) {
-    const __m256 r = _mm256_set1_ps(0x1.8p23f);
-    const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r);
-    const __m256 n = _mm256_sub_ps(z, r);
-    const __m256 b =
-        _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x));
-    const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23);
-    const __m256  k = _mm256_castsi256_ps(_mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1))));
-    const __m256i c =
-        _mm256_castps_si256(_mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(126), _CMP_GT_OQ));
-    const __m256 u = _mm256_mul_ps(b, b);
-    const __m256 j = _mm256_fmadd_ps(
-        _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, _mm256_set1_ps(0x1.573e2ep-5f)), u,
-                        _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, _mm256_set1_ps(0x1.fffdb6p-2f))),
-        u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b));
-    if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) {
-        return _mm256_fmadd_ps(j, k, k);
-    }
-    const __m256i g  = _mm256_and_si256(_mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)),
-                                        _mm256_set1_epi32(static_cast<int>(0x82000000u)));
-    const __m256  s1 = _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000)));
-    const __m256  s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g));
-    const __m256i d =
-        _mm256_castps_si256(_mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(192), _CMP_GT_OQ));
-    return _mm256_or_ps(
-        _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)),
-        _mm256_andnot_ps(
-            _mm256_castsi256_ps(d),
-            _mm256_or_ps(_mm256_and_ps(_mm256_castsi256_ps(c), _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)),
-                         _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k)))));
-}
-
-bool cpu_has_avx2_fma() {
-    __builtin_cpu_init();
-    return __builtin_cpu_supports("avx2") && __builtin_cpu_supports("fma");
-}
-
-// r[c] = exp(r[c] - mx[c]) for c in [0, n), returning nothing; sums go to sum.
-ECAPA_AVX2 void exp_row_avx2(float * r, const float * mx, double * sum, size_t n) {
-    size_t c = 0;
-    for (; c + 8 <= n; c += 8) {
-        const __m256 e = v_expf(_mm256_sub_ps(_mm256_loadu_ps(r + c), _mm256_loadu_ps(mx + c)));
-        _mm256_storeu_ps(r + c, e);
-        alignas(32) float ev[8];
-        _mm256_store_ps(ev, e);
-        for (int i = 0; i < 8; ++i) {
-            sum[c + static_cast<size_t>(i)] += static_cast<double>(ev[i]);
-        }
-    }
-    for (; c < n; ++c) {
-        const float e = std::exp(r[c] - mx[c]);
-        r[c]          = e;
-        sum[c] += static_cast<double>(e);
-    }
-}
-#endif
-
-// Elsewhere (ARM included) this is libm expf, which is not the NEON
-// ggml_v_expf ggml's soft_max uses there: the softmax is within a few ulp of
-// the stock graph's, not bit-identical.
-void exp_row(float * r, const float * mx, double * sum, size_t n) {
-#if ECAPA_OPS_X86
-    static const bool avx2 = cpu_has_avx2_fma();
-    if (avx2) {
-        exp_row_avx2(r, mx, sum, n);
-        return;
-    }
-#endif
-    for (size_t c = 0; c < n; ++c) {
-        const float e = std::exp(r[c] - mx[c]);
-        r[c]          = e;
-        sum[c] += static_cast<double>(e);
-    }
-}
-
-// Per-node flags passed through the custom op's userdata. Static storage:
-// the scheduler reads userdata at compute time, long after graph build.
-struct EpilogueParams {
-    bool has_bias;
-    bool tanh_after;
-};
-
-const float * f32(const ggml_tensor * t) {
-    return static_cast<const float *>(t->data);
-}
-
-float * f32_mut(ggml_tensor * t) {
-    return static_cast<float *>(t->data);
-}
-
-size_t row_floats(const ggml_tensor * t) {
-    return t->nb[1] / sizeof(float);
-}
-
-void thread_rows(int64_t n, int ith, int nth, int64_t & r0, int64_t & r1) {
-    const int64_t per = (n + nth - 1) / nth;
-    r0                = std::min<int64_t>(per * ith, n);
-    r1                = std::min<int64_t>(r0 + per, n);
-}
-
-// Time reductions walk all T rows over a channel block. Blocks of
-// kChanBlock channels (1 KiB per row) are dealt to the threads round robin:
-// wide enough that each row read is a few whole cache lines, narrow enough
-// that 3072 channels still spread over 8+ threads.
-constexpr int64_t kChanBlock = 256;
-
-template <typename F> void for_channel_blocks(int64_t C, int ith, int nth, F && f) {
-    for (int64_t c0 = static_cast<int64_t>(ith) * kChanBlock; c0 < C; c0 += static_cast<int64_t>(nth) * kChanBlock) {
-        f(c0, std::min(C, c0 + kChanBlock));
-    }
-}
-
-// y = max(x + b, 0) * s + sh  (and tanh) over rows [r0, r1) of a [C, T]
-// activation. The add / max / mul / add sequence is evaluated in float in
-// the same order as ggml's add -> relu -> mul -> add chain, so the result is
-// bit-identical to the stock ops.
-void epilogue_rows(const float * in,
-                   size_t        in_stride,
-                   float *       out,
-                   size_t        out_stride,
-                   int64_t       C,
-                   int64_t       r0,
-                   int64_t       r1,
-                   const float * b,
-                   const float * s,
-                   const float * sh,
-                   bool          tanh_after) {
-    for (int64_t t = r0; t < r1; ++t) {
-        const float * x = in + static_cast<size_t>(t) * in_stride;
-        float *       y = out + static_cast<size_t>(t) * out_stride;
-        if (b != nullptr) {
-            for (int64_t c = 0; c < C; ++c) {
-                float v = x[c] + b[c];
-                v       = v > 0.0f ? v : 0.0f;
-                y[c]    = v * s[c] + sh[c];
-            }
-        } else {
-            for (int64_t c = 0; c < C; ++c) {
-                const float v = x[c] > 0.0f ? x[c] : 0.0f;
-                y[c]          = v * s[c] + sh[c];
-            }
-        }
-        if (tanh_after) {
-            for (int64_t c = 0; c < C; ++c) {
-                y[c] = std::tanh(y[c]);
-            }
-        }
-    }
-}
-
-// dst is a view of src[0] (in place). src: [z, (b), scale, shift].
-void epilogue_inplace_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const auto *  p  = static_cast<const EpilogueParams *>(ud);
-    const int64_t C  = dst->ne[0];
-    int           i  = 1;
-    const float * b  = p->has_bias ? f32(dst->src[i++]) : nullptr;
-    const float * s  = f32(dst->src[i++]);
-    const float * sh = f32(dst->src[i++]);
-    int64_t       r0 = 0;
-    int64_t       r1 = 0;
-    thread_rows(dst->ne[1], ith, nth, r0, r1);
-    epilogue_rows(f32(dst), row_floats(dst), f32_mut(dst), row_floats(dst), C, r0, r1, b, s, sh, p->tanh_after);
-}
-
-// dst is a view of src[0]; src: [z0, z1, z2, b, scale, shift].
-void epilogue_sum3_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    (void) ud;
-    const int64_t C   = dst->ne[0];
-    const float * z1  = f32(dst->src[1]);
-    const float * z2  = f32(dst->src[2]);
-    const float * b   = f32(dst->src[3]);
-    const float * s   = f32(dst->src[4]);
-    const float * sh  = f32(dst->src[5]);
-    float *       y   = f32_mut(dst);
-    const size_t  st  = row_floats(dst);
-    const size_t  st1 = row_floats(dst->src[1]);
-    const size_t  st2 = row_floats(dst->src[2]);
-    int64_t       r0  = 0;
-    int64_t       r1  = 0;
-    thread_rows(dst->ne[1], ith, nth, r0, r1);
-    for (int64_t t = r0; t < r1; ++t) {
-        float *       yr  = y + static_cast<size_t>(t) * st;
-        const float * z1r = z1 + static_cast<size_t>(t) * st1;
-        const float * z2r = z2 + static_cast<size_t>(t) * st2;
-        for (int64_t c = 0; c < C; ++c) {
-            // ((z0 + z1) + z2) + b: the stock graph's add order.
-            float v = yr[c] + z1r[c];
-            v       = v + z2r[c];
-            v       = v + b[c];
-            v       = v > 0.0f ? v : 0.0f;
-            yr[c]   = v * s[c] + sh[c];
-        }
-    }
-}
-
-// dst [C]; src: [x [C, T]].
-void time_mean_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    (void) ud;
-    const ggml_tensor * x  = dst->src[0];
-    const int64_t       T  = x->ne[1];
-    const size_t        st = row_floats(x);
-    float *             y  = f32_mut(dst);
-    double              acc[kChanBlock];
-    for_channel_blocks(x->ne[0], ith, nth, [&](int64_t c0, int64_t c1) {
-        const int64_t n = c1 - c0;
-        std::fill(acc, acc + n, 0.0);
-        for (int64_t t = 0; t < T; ++t) {
-            const float * r = f32(x) + static_cast<size_t>(t) * st + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                acc[c] += static_cast<double>(r[c]);
-            }
-        }
-        for (int64_t c = 0; c < n; ++c) {
-            // ggml_mean: (float) sum, then /= (float) n.
-            y[c0 + c] = static_cast<float>(acc[c]) / static_cast<float>(T);
-        }
-    });
-}
-
-// dst [C, T]; src: [x [C, T], s [C], res [C, T]].
-void scale_add_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    (void) ud;
-    const ggml_tensor * x   = dst->src[0];
-    const float *       s   = f32(dst->src[1]);
-    const ggml_tensor * res = dst->src[2];
-    const int64_t       C   = dst->ne[0];
-    int64_t             r0  = 0;
-    int64_t             r1  = 0;
-    thread_rows(dst->ne[1], ith, nth, r0, r1);
-    for (int64_t t = r0; t < r1; ++t) {
-        const float * xr = f32(x) + static_cast<size_t>(t) * row_floats(x);
-        const float * rr = f32(res) + static_cast<size_t>(t) * row_floats(res);
-        float *       yr = f32_mut(dst) + static_cast<size_t>(t) * row_floats(dst);
-        for (int64_t c = 0; c < C; ++c) {
-            const float v = xr[c] * s[c];
-            yr[c]         = v + rr[c];
-        }
-    }
-}
-
-// dst [2C] = [mean, sqrt(max(var, eps))]; src: [x [C, T]]; ud: const float * eps.
-void time_stats_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const float         eps = *static_cast<const float *>(ud);
-    const ggml_tensor * x   = dst->src[0];
-    const int64_t       C   = x->ne[0];
-    const int64_t       T   = x->ne[1];
-    const size_t        st  = row_floats(x);
-    float *             y   = f32_mut(dst);
-    double              acc[kChanBlock];
-    float               mean[kChanBlock];
-    for_channel_blocks(C, ith, nth, [&](int64_t c0, int64_t c1) {
-        const int64_t n = c1 - c0;
-        std::fill(acc, acc + n, 0.0);
-        for (int64_t t = 0; t < T; ++t) {
-            const float * r = f32(x) + static_cast<size_t>(t) * st + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                acc[c] += static_cast<double>(r[c]);
-            }
-        }
-        for (int64_t c = 0; c < n; ++c) {
-            mean[c] = static_cast<float>(acc[c]) / static_cast<float>(T);
-            acc[c]  = 0.0;
-        }
-        for (int64_t t = 0; t < T; ++t) {
-            const float * r = f32(x) + static_cast<size_t>(t) * st + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                const float d = r[c] - mean[c];
-                acc[c] += static_cast<double>(d * d);
-            }
-        }
-        for (int64_t c = 0; c < n; ++c) {
-            float var     = static_cast<float>(acc[c]) / static_cast<float>(T);
-            var           = var < eps ? eps : var;
-            y[c0 + c]     = mean[c];
-            y[C + c0 + c] = std::sqrt(var);
-        }
-    });
-}
-
-// In place over src[0] = logits [C, T]: softmax over t of (logits + bias)
-// per channel. src: [logits, bias].
-void softmax_time_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    (void) ud;
-    const float * b  = f32(dst->src[1]);
-    const int64_t T  = dst->ne[1];
-    const size_t  st = row_floats(dst);
-    float *       l  = f32_mut(dst);
-    float         mx[kChanBlock];
-    double        sum[kChanBlock];
-    float         inv[kChanBlock];
-    for_channel_blocks(dst->ne[0], ith, nth, [&](int64_t c0, int64_t c1) {
-        const int64_t n = c1 - c0;
-        std::fill(mx, mx + n, -INFINITY);
-        std::fill(sum, sum + n, 0.0);
-        for (int64_t t = 0; t < T; ++t) {
-            float * r = l + static_cast<size_t>(t) * st + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                const float v = r[c] + b[c0 + c];
-                r[c]          = v;
-                mx[c]         = v > mx[c] ? v : mx[c];
-            }
-        }
-        for (int64_t t = 0; t < T; ++t) {
-            exp_row(l + static_cast<size_t>(t) * st + c0, mx, sum, static_cast<size_t>(n));
-        }
-        for (int64_t c = 0; c < n; ++c) {
-            inv[c] = static_cast<float>(1.0 / sum[c]);  // ggml_vec_scale_f32(dp, 1.0/sum)
-        }
-        for (int64_t t = 0; t < T; ++t) {
-            float * r = l + static_cast<size_t>(t) * st + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                r[c] *= inv[c];
-            }
-        }
-    });
-}
-
-// dst [2C] = [mu, sigma]; src: [x [C, T], w [C, T]]; ud: const float * eps.
-void weighted_stats_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const float         eps = *static_cast<const float *>(ud);
-    const ggml_tensor * x   = dst->src[0];
-    const ggml_tensor * w   = dst->src[1];
-    const int64_t       C   = x->ne[0];
-    const int64_t       T   = x->ne[1];
-    const size_t        sx  = row_floats(x);
-    const size_t        sw  = row_floats(w);
-    float *             y   = f32_mut(dst);
-    double              acc[kChanBlock];
-    float               mu[kChanBlock];
-    for_channel_blocks(C, ith, nth, [&](int64_t c0, int64_t c1) {
-        const int64_t n = c1 - c0;
-        std::fill(acc, acc + n, 0.0);
-        for (int64_t t = 0; t < T; ++t) {
-            const float * xr = f32(x) + static_cast<size_t>(t) * sx + c0;
-            const float * wr = f32(w) + static_cast<size_t>(t) * sw + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                acc[c] += static_cast<double>(wr[c] * xr[c]);
-            }
-        }
-        for (int64_t c = 0; c < n; ++c) {
-            mu[c]  = static_cast<float>(acc[c]);
-            acc[c] = 0.0;
-        }
-        for (int64_t t = 0; t < T; ++t) {
-            const float * xr = f32(x) + static_cast<size_t>(t) * sx + c0;
-            const float * wr = f32(w) + static_cast<size_t>(t) * sw + c0;
-            for (int64_t c = 0; c < n; ++c) {
-                const float d = xr[c] - mu[c];
-                acc[c] += static_cast<double>(wr[c] * (d * d));
-            }
-        }
-        for (int64_t c = 0; c < n; ++c) {
-            float sig     = static_cast<float>(acc[c]);
-            sig           = sig < eps ? eps : sig;
-            y[c0 + c]     = mu[c];
-            y[C + c0 + c] = std::sqrt(sig);
-        }
-    });
-}
-
-// Res2Net step parameters, packed into the custom op's userdata pointer
-// (they are small integers, and this keeps the graph free of side storage).
-struct Res2Params {
-    bool has_z;
-    int  next;  // -1 = no next chunk
-    int  kernel;
-    int  dilation;
-};
-
-void * pack_res2(const Res2Params & p) {
-    const uintptr_t v = (p.has_z ? 1u : 0u) | (static_cast<uintptr_t>(p.next + 1) & 0xffu) << 1 |
-                        (static_cast<uintptr_t>(p.kernel) & 0xffu) << 9 |
-                        (static_cast<uintptr_t>(p.dilation) & 0xffu) << 17;
-    return reinterpret_cast<void *>(v);
-}
-
-Res2Params unpack_res2(const void * ud) {
-    const uintptr_t v = reinterpret_cast<uintptr_t>(ud);
-    return { (v & 1u) != 0, static_cast<int>((v >> 1) & 0xffu) - 1, static_cast<int>((v >> 9) & 0xffu),
-             static_cast<int>((v >> 17) & 0xffu) };
-}
-
-// src: [h, (z, b, scale, shift)].
-void res2_step_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const Res2Params    p          = unpack_res2(ud);
-    const ggml_tensor * h          = dst->src[0];
-    const int64_t       w          = dst->ne[0];
-    const int64_t       T          = h->ne[1];
-    const int64_t       pad        = static_cast<int64_t>(p.dilation) * (p.kernel - 1) / 2;
-    const int64_t       n_pad_rows = p.next >= 0 ? T + 2 * pad : 0;
-    const int64_t       n_rows     = n_pad_rows + (p.has_z ? T : 0);
-    const size_t        sd         = row_floats(dst);
-    const size_t        sh_        = row_floats(h);
-
-    const float * z  = nullptr;
-    const float * b  = nullptr;
-    const float * s  = nullptr;
-    const float * sh = nullptr;
-    size_t        sz = 0;
-    if (p.has_z) {
-        z  = f32(dst->src[1]);
-        sz = row_floats(dst->src[1]);
-        b  = f32(dst->src[2]);
-        s  = f32(dst->src[3]);
-        sh = f32(dst->src[4]);
-    }
-
-    // y(t) into out[0..w): ((Z_0 + Z_1) + ... + Z_{K-1}) + b, ReLU, BN.
-    auto y_at = [&](int64_t t, float * out) {
-        const float * r0 = z + static_cast<size_t>(t) * sz;
-        for (int64_t c = 0; c < w; ++c) {
-            out[c] = r0[c];
-        }
-        for (int k = 1; k < p.kernel; ++k) {
-            const float * rk = z + static_cast<size_t>(t + static_cast<int64_t>(k) * p.dilation) * sz +
-                               static_cast<size_t>(k) * static_cast<size_t>(w);
-            for (int64_t c = 0; c < w; ++c) {
-                out[c] += rk[c];
-            }
-        }
-        for (int64_t c = 0; c < w; ++c) {
-            float v = out[c] + b[c];
-            v       = v > 0.0f ? v : 0.0f;
-            out[c]  = v * s[c] + sh[c];
-        }
-    };
-
-    std::vector<float> tmp(static_cast<size_t>(w));
-    int64_t            r0 = 0;
-    int64_t            r1 = 0;
-    thread_rows(n_rows, ith, nth, r0, r1);
-    for (int64_t r = r0; r < r1; ++r) {
-        float * out = f32_mut(dst) + static_cast<size_t>(r) * sd;
-        if (r >= n_pad_rows) {
-            y_at(r - n_pad_rows, out);
-            continue;
-        }
-        // torch "reflect" (edge not repeated); T > pad is checked at build.
-        int64_t t = r - pad;
-        if (t < 0) {
-            t = -t;
-        } else if (t >= T) {
-            t = 2 * (T - 1) - t;
-        }
-        const float * hr = f32(h) + static_cast<size_t>(t) * sh_ + static_cast<size_t>(p.next) * static_cast<size_t>(w);
-        if (p.has_z) {
-            y_at(t, tmp.data());
-            for (int64_t c = 0; c < w; ++c) {
-                out[c] = hr[c] + tmp[static_cast<size_t>(c)];
-            }
-        } else {
-            for (int64_t c = 0; c < w; ++c) {
-                out[c] = hr[c];
-            }
-        }
-    }
-}
-
-// dst [w, T + 2p] = reflect(h[next] + y); src: [h, y]; ud: packed params.
-void res2_pad_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    const Res2Params    p   = unpack_res2(ud);
-    const ggml_tensor * h   = dst->src[0];
-    const ggml_tensor * y   = dst->src[1];
-    const int64_t       w   = dst->ne[0];
-    const int64_t       T   = h->ne[1];
-    const int64_t       pad = static_cast<int64_t>(p.dilation) * (p.kernel - 1) / 2;
-    int64_t             r0  = 0;
-    int64_t             r1  = 0;
-    thread_rows(dst->ne[1], ith, nth, r0, r1);
-    for (int64_t r = r0; r < r1; ++r) {
-        int64_t t = r - pad;  // torch "reflect", edge not repeated
-        if (t < 0) {
-            t = -t;
-        } else if (t >= T) {
-            t = 2 * (T - 1) - t;
-        }
-        const float * hr = f32(h) + static_cast<size_t>(t) * row_floats(h) + static_cast<size_t>(p.next * w);
-        const float * yr = f32(y) + static_cast<size_t>(t) * row_floats(y);
-        float *       o  = f32_mut(dst) + static_cast<size_t>(r) * row_floats(dst);
-        for (int64_t c = 0; c < w; ++c) {
-            o[c] = hr[c] + yr[c];
-        }
-    }
-}
-
-// dst [C, T]; src: [h, y_1 .. y_n] (y_i are [w, T] views).
-void res2_gather_fn(ggml_tensor * dst, int ith, int nth, void * ud) {
-    (void) ud;
-    const ggml_tensor * h = dst->src[0];
-    const int64_t       C = dst->ne[0];
-    int                 n = 0;
-    while (n + 1 < GGML_MAX_SRC && dst->src[n + 1] != nullptr) {
-        ++n;
-    }
-    const int64_t w  = dst->src[1]->ne[0];
-    int64_t       r0 = 0;
-    int64_t       r1 = 0;
-    thread_rows(dst->ne[1], ith, nth, r0, r1);
-    for (int64_t t = r0; t < r1; ++t) {
-        float *       out = f32_mut(dst) + static_cast<size_t>(t) * row_floats(dst);
-        const float * hr  = f32(h) + static_cast<size_t>(t) * row_floats(h);
-        std::copy(hr, hr + w, out);
-        for (int i = 1; i <= n; ++i) {
-            const ggml_tensor * y  = dst->src[i];
-            const float *       yr = f32(y) + static_cast<size_t>(t) * row_floats(y);
-            std::copy(yr, yr + w, out + static_cast<size_t>(i) * static_cast<size_t>(w));
-        }
-        (void) C;
-    }
-}
-
-const EpilogueParams k_ep_bias{ true, false };
-const EpilogueParams k_ep_bias_tanh{ true, true };
-const EpilogueParams k_ep_nobias{ false, false };
-const EpilogueParams k_ep_nobias_tanh{ false, true };
-
-void * ud(const void * p) {
-    return const_cast<void *>(p);
-}
-
-}  // namespace
-
-ggml_tensor * epilogue(ggml_context * ctx,
-                       ggml_tensor *  z,
-                       ggml_tensor *  b,
-                       ggml_tensor *  scale,
-                       ggml_tensor *  shift,
-                       bool           tanh_after,
-                       bool           inplace) {
-    const EpilogueParams * p =
-        b != nullptr ? (tanh_after ? &k_ep_bias_tanh : &k_ep_bias) : (tanh_after ? &k_ep_nobias_tanh : &k_ep_nobias);
-    ggml_tensor * args[3];
-    int           n = 0;
-    if (b != nullptr) {
-        args[n++] = b;
-    }
-    args[n++] = scale;
-    args[n++] = shift;
-    if (!inplace) {
-        z = ggml_cont(ctx, z);
-    }
-    return ggml_custom_inplace(ctx, z, args, n, epilogue_inplace_fn, GGML_N_TASKS_MAX, ud(p));
-}
-
-ggml_tensor * epilogue_sum3(ggml_context * ctx,
-                            ggml_tensor *  z0,
-                            ggml_tensor *  z1,
-                            ggml_tensor *  z2,
-                            ggml_tensor *  b,
-                            ggml_tensor *  scale,
-                            ggml_tensor *  shift,
-                            bool           inplace) {
-    ggml_tensor * args[5] = { z1, z2, b, scale, shift };
-    if (!inplace) {
-        z0 = ggml_cont(ctx, z0);
-    }
-    return ggml_custom_inplace(ctx, z0, args, 5, epilogue_sum3_fn, GGML_N_TASKS_MAX, nullptr);
-}
-
-ggml_tensor * time_mean(ggml_context * ctx, ggml_tensor * x) {
-    ggml_tensor * args[1] = { x };
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, x->ne[0], 1, 1, 1, args, 1, time_mean_fn, GGML_N_TASKS_MAX, nullptr);
-}
-
-ggml_tensor * scale_add(ggml_context * ctx, ggml_tensor * x, ggml_tensor * s, ggml_tensor * res) {
-    ggml_tensor * args[3] = { x, s, res };
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, x->ne[0], x->ne[1], 1, 1, args, 3, scale_add_fn, GGML_N_TASKS_MAX,
-                          nullptr);
-}
-
-ggml_tensor * time_stats(ggml_context * ctx, ggml_tensor * x, const float * eps) {
-    ggml_tensor * args[1] = { x };
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 1, time_stats_fn, GGML_N_TASKS_MAX, ud(eps));
-}
-
-ggml_tensor * attn_stats(ggml_context * ctx,
-                         ggml_tensor *  x,
-                         ggml_tensor *  logits,
-                         ggml_tensor *  bias,
-                         const float *  eps) {
-    ggml_tensor * sm_args[1] = { bias };
-    ggml_tensor * w          = ggml_custom_inplace(ctx, logits, sm_args, 1, softmax_time_fn, GGML_N_TASKS_MAX, nullptr);
-    ggml_tensor * args[2]    = { x, w };
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, 2 * x->ne[0], 1, 1, 1, args, 2, weighted_stats_fn, GGML_N_TASKS_MAX,
-                          ud(eps));
-}
-
-Res2Step res2_step(ggml_context * ctx,
-                   ggml_tensor *  h,
-                   int            next,
-                   int            width,
-                   ggml_tensor *  z,
-                   ggml_tensor *  b,
-                   ggml_tensor *  scale,
-                   ggml_tensor *  shift,
-                   int            kernel,
-                   int            dilation) {
-    Res2Step      r;
-    const int64_t T          = h->ne[1];
-    const int64_t pad        = static_cast<int64_t>(dilation) * (kernel - 1) / 2;
-    const int64_t n_pad_rows = next >= 0 ? T + 2 * pad : 0;
-    const int64_t n_rows     = n_pad_rows + (z != nullptr ? T : 0);
-
-    ggml_tensor * args[5] = { h, z, b, scale, shift };
-    const int     n_args  = z != nullptr ? 5 : 1;
-    r.out = ggml_custom_4d(ctx, GGML_TYPE_F32, width, n_rows, 1, 1, args, n_args, res2_step_fn, GGML_N_TASKS_MAX,
-                           pack_res2({ z != nullptr, next, kernel, dilation }));
-    if (next >= 0) {
-        r.pad = ggml_view_2d(ctx, r.out, width, n_pad_rows, r.out->nb[1], 0);
-    }
-    if (z != nullptr) {
-        r.y = ggml_view_2d(ctx, r.out, width, T, r.out->nb[1], static_cast<size_t>(n_pad_rows) * r.out->nb[1]);
-    }
-    return r;
-}
-
-Res2Step res2_pad(ggml_context * ctx, ggml_tensor * h, int next, int width, ggml_tensor * y, int kernel, int dilation) {
-    Res2Step      r;
-    const int64_t T       = h->ne[1];
-    const int64_t pad     = static_cast<int64_t>(dilation) * (kernel - 1) / 2;
-    ggml_tensor * args[2] = { h, y };
-    r.out = ggml_custom_4d(ctx, GGML_TYPE_F32, width, T + 2 * pad, 1, 1, args, 2, res2_pad_fn, GGML_N_TASKS_MAX,
-                           pack_res2({ false, next, kernel, dilation }));
-    r.pad = r.out;
-    return r;
-}
-
-ggml_tensor * res2_gather(ggml_context * ctx, ggml_tensor * h, ggml_tensor * const * ys, int n_ys) {
-    ggml_tensor * args[GGML_MAX_SRC - 1] = {};
-    args[0]                              = h;
-    for (int i = 0; i < n_ys; ++i) {
-        args[i + 1] = ys[i];
-    }
-    return ggml_custom_4d(ctx, GGML_TYPE_F32, h->ne[0], h->ne[1], 1, 1, args, n_ys + 1, res2_gather_fn,
-                          GGML_N_TASKS_MAX, nullptr);
-}
-
-}  // namespace transcribe::ecapa_tdnn::cpu
diff --git a/src/arch/ecapa_tdnn/cpu_ops.h b/src/arch/ecapa_tdnn/cpu_ops.h
deleted file mode 100644
index 9cfe2f1bb..000000000
--- a/src/arch/ecapa_tdnn/cpu_ops.h
+++ /dev/null
@@ -1,101 +0,0 @@
-// arch/ecapa_tdnn/cpu_ops.h - fused CPU kernels for the ECAPA-TDNN graph.
-//
-// INTERNAL to src/arch/ecapa_tdnn/. Used only when the graph runs on the
-// ggml CPU backend (graph.cpp's `cpu_ops` build); every other backend keeps
-// the stock-op graph. Each builder returns a GGML_OP_CUSTOM node that runs on
-// all of the scheduler's CPU threads.
-//
-// Why these exist: with stock ggml ops the activations are channel-innermost
-// ne = [C, T], so every reduction over time needs a transposed copy first,
-// and ggml's CPU MEAN / SUM_ROWS run on one thread. The epilogue chain
-// (bias -> ReLU -> BN scale -> BN shift) is four full passes over the
-// tensor, each with a thread barrier. The kernels below do each of those in
-// one multithreaded pass over the [C, T] layout, with the same float
-// arithmetic per element and the same sequential double accumulation over
-// time that ggml's reductions use (time_mean / time_stats are bit-identical
-// to transpose + ggml_mean).
-//
-// Every input must be F32 and contiguous; vectors are ne = [C].
-
-#pragma once
-
-struct ggml_context;
-struct ggml_tensor;
-
-namespace transcribe::ecapa_tdnn::cpu {
-
-// y = max(z + b, 0) * scale + shift, then tanh(y) if `tanh_after`.
-// z is ne = [C, T]; b may be null. In place over z when `inplace`.
-ggml_tensor * epilogue(ggml_context * ctx,
-                       ggml_tensor *  z,
-                       ggml_tensor *  b,
-                       ggml_tensor *  scale,
-                       ggml_tensor *  shift,
-                       bool           tanh_after,
-                       bool           inplace);
-
-// y = max(z0 + z1 + z2 + b, 0) * scale + shift (the MFA stage's three
-// per-block products). In place over z0 when `inplace`.
-ggml_tensor * epilogue_sum3(ggml_context * ctx,
-                            ggml_tensor *  z0,
-                            ggml_tensor *  z1,
-                            ggml_tensor *  z2,
-                            ggml_tensor *  b,
-                            ggml_tensor *  scale,
-                            ggml_tensor *  shift,
-                            bool           inplace);
-
-// Mean over time of x ne = [C, T], returned as ne = [C].
-ggml_tensor * time_mean(ggml_context * ctx, ggml_tensor * x);
-
-// y = x * s + res: x, res ne = [C, T], s ne = [C]. New tensor.
-ggml_tensor * scale_add(ggml_context * ctx, ggml_tensor * x, ggml_tensor * s, ggml_tensor * res);
-
-// Uniform statistics over time of x ne = [C, T]: ne = [2C] holding
-// [mean, sqrt(max(var, *eps))], biased variance. `eps` must outlive compute.
-ggml_tensor * time_stats(ggml_context * ctx, ggml_tensor * x, const float * eps);
-
-// Attentive statistics: per channel c, w = softmax_t(logits[c, t] + bias[c]),
-// mu = sum_t w x, sigma = sqrt(max(sum_t w (x - mu)^2, *eps)). x and logits
-// ne = [C, T], bias ne = [C]; returns ne = [2C] = [mu, sigma].
-ggml_tensor * attn_stats(ggml_context * ctx,
-                         ggml_tensor *  x,
-                         ggml_tensor *  logits,
-                         ggml_tensor *  bias,
-                         const float *  eps);
-
-// One Res2Net step. Chunk i of the block (i >= 1) is
-//   y_i = BN(ReLU(sum_k Z[k*w + c, t + k*d] + b))
-// where Z = W_stack @ pad_i is the stacked-tap product ne = [K*w, T + 2p]
-// of the reflect-padded chunk input (p = d*(K-1)/2). The step computes y_i
-// and, when there is a next chunk, that chunk's reflect-padded input
-//   pad_{i+1} = reflect(h[next] + y_i)            ne = [w, T + 2p]
-// in one pass. The first step of a block has no Z: it only pads h[1].
-//
-// The returned tensor packs both, rows [0, T + 2p) = pad (when next >= 0)
-// followed by T rows of y (when z != nullptr); use res2_pad / res2_y.
-struct Res2Step {
-    ggml_tensor * out = nullptr;
-    ggml_tensor * pad = nullptr;  // view, ne = [w, T + 2p], contiguous
-    ggml_tensor * y   = nullptr;  // view, ne = [w, T]
-};
-
-Res2Step res2_step(ggml_context * ctx,
-                   ggml_tensor *  h,      // block input [C, T]
-                   int            next,   // chunk index of h to pad next, or -1
-                   int            width,  // chunk width w
-                   ggml_tensor *  z,      // [K*w, T + 2p] or nullptr
-                   ggml_tensor *  b,
-                   ggml_tensor *  scale,
-                   ggml_tensor *  shift,
-                   int            kernel,
-                   int            dilation);
-
-// pad_{next} = reflect(h[next] + y) for a GEMM-produced y ne = [w, T]:
-// returns a Res2Step whose `pad` is set (out == pad).
-Res2Step res2_pad(ggml_context * ctx, ggml_tensor * h, int next, int width, ggml_tensor * y, int kernel, int dilation);
-
-// Res2Net output [C, T]: chunk 0 from h, chunk i from ys[i - 1] (each [w, T]).
-ggml_tensor * res2_gather(ggml_context * ctx, ggml_tensor * h, ggml_tensor * const * ys, int n_ys);
-
-}  // namespace transcribe::ecapa_tdnn::cpu
diff --git a/src/arch/ecapa_tdnn/ecapa_tdnn.h b/src/arch/ecapa_tdnn/ecapa_tdnn.h
index 269dba060..0b056f5be 100644
--- a/src/arch/ecapa_tdnn/ecapa_tdnn.h
+++ b/src/arch/ecapa_tdnn/ecapa_tdnn.h
@@ -13,7 +13,6 @@
 
 #pragma once
 
-#include "cpu_gemm.h"
 #include "mel.h"
 #include "transcribe-backend.h"
 #include "transcribe-langid.h"
@@ -49,18 +48,6 @@ struct Model final : public transcribe_model {
     ggml_context *        ctx_derived    = nullptr;
     ggml_backend_buffer_t derived_buffer = nullptr;
 
-    // True when the graph uses the fused CPU kernels (cpu_ops.h): the
-    // primary backend is the ggml CPU backend. Fixed at load.
-    bool cpu_ops = false;
-
-    // Which weights were repacked at load for the AVX2 GEMM (cpu_gemm.h);
-    // packed weights are only readable by that GEMM. Fixed at load.
-    //   gemm_conv: the k>1 kernels (stage 0's im2col weight, Res2Net taps)
-    //   gemm_lin:  the T-wide 1x1 weights (tdnn1/tdnn2, mfa, asp.tdnn.x,
-    //              asp.attn); only when they are F16
-    bool gemm_conv = false;
-    bool gemm_lin  = false;
-
     // Built at load from stt.langid.labels.*; immutable after.
     LangidLabels labels;
 
@@ -84,10 +71,6 @@ struct Session final : public transcribe_langid_session {
     // run. Outlives compute_ctx's use: ggml_free never touches a borrowed
     // mem_buffer.
     std::vector<uint8_t> graph_arena;
-
-    // GEMM node descriptors of the current graph (cpu_gemm.h). Cleared with
-    // the compute context at the start of every run.
-    gemm::Arena gemm_arena;
 };
 
 extern const Arch arch;
diff --git a/src/arch/ecapa_tdnn/graph.cpp b/src/arch/ecapa_tdnn/graph.cpp
index 5534fbba7..330f3c7ca 100644
--- a/src/arch/ecapa_tdnn/graph.cpp
+++ b/src/arch/ecapa_tdnn/graph.cpp
@@ -7,17 +7,9 @@
 // and the ASP / MFA input concatenations split into per-operand weight
 // blocks.
 //
-// Two graphs share this file, selected by build_graph's `cpu_ops`:
-//   - the stock-op graph (GPU backends, and any multi-backend schedule):
-//     plain ggml ops; reductions over time run on a contiguous transposed
-//     copy [T, C] because ggml reduces over ne[0].
-//   - the CPU graph (ggml CPU backend only): the same math through the
-//     fused kernels in cpu_ops.h (epilogues, time reductions on [C, T]
-//     without transposes, Res2Net without concats) and, where the model
-//     packed its weights, the AVX2 GEMM in cpu_gemm.h with the epilogue
-//     fused into the store. On a Ryzen 7 4750U this is ~2.4x faster than
-//     the stock graph for the F16 file at 8 threads.
-// Stage 0 is one im2col matmul in both (see build_blk0_im2col).
+// One graph of plain ggml ops serves every backend; reductions over time
+// run on a contiguous transposed copy [T, C] because ggml reduces over
+// ne[0]. Stage 0 is one im2col matmul (see build_blk0_im2col).
 //
 // Layout: every activation is ggml ne = [C, T].
 //
@@ -43,7 +35,6 @@
 
 #include "graph.h"
 
-#include "cpu_ops.h"
 #include "ecapa_tdnn.h"
 #include "ggml.h"
 #include "transcribe-debug.h"
@@ -136,8 +127,9 @@ void mark_dump(ggml_tensor *& slot, ggml_tensor * t, const char * name) {
     named(t, name);
     debug::mark_tensor_for_dump(t);
     if (t->view_src != nullptr) {
-        // In-place CPU kernels return a view; the allocator frees the view's
-        // backing tensor, so that is what has to be kept alive.
+        // In-place ops (Res2Net's set_rows) return a view; the allocator
+        // frees the view's backing tensor, so that is what has to be kept
+        // alive.
         debug::mark_tensor_for_dump(t->view_src);
     }
     slot = t;
@@ -223,90 +215,6 @@ ggml_tensor * se_block(ggml_context * ctx, const SeBlock & se, ggml_tensor * x)
     return ggml_mul(ctx, x, s);
 }
 
-// ---- CPU-backend variants (cpu_ops.h) -------------------------------------
-//
-// Same math as the stock helpers above; see cpu_ops.h for why they exist.
-
-// F32 accumulation for F16 weights (see linear()).
-ggml_tensor * mul_mat_acc(ggml_context * ctx, ggml_tensor * w, ggml_tensor * x) {
-    ggml_tensor * y = ggml_mul_mat(ctx, w, x);
-    if (w->type == GGML_TYPE_F16) {
-        ggml_prec_set_acc(y, GGML_PREC_F32);
-    }
-    return y;
-}
-
-// TDNNBlock k=1: with packed weights one GEMM with the bias/ReLU/BN fused
-// into its store; otherwise ggml_mul_mat plus one fused epilogue pass.
-ggml_tensor * tdnn_1x1_cpu(ggml_context * ctx, gemm::Arena * ar, const TdnnLayer & l, ggml_tensor * x) {
-    if (ar != nullptr) {
-        const gemm::Segment sg{ l.w, 0, x, 0 };
-        return gemm::mul_mat(ctx, *ar, &sg, 1, x->ne[1], gemm::Epilogue::ReluBn, l.b, l.bn.scale, l.bn.shift);
-    }
-    return cpu::epilogue(ctx, mul_mat_acc(ctx, l.w, x), l.b, l.bn.scale, l.bn.shift, false, true);
-}
-
-// Res2Net on the CPU. Each sub-conv is ONE matmul of its stacked taps
-// (the [w, K*w] view of the tap-major [w, w, K] weight) against the padded
-// chunk input, and one fused kernel that sums the shifted tap rows, applies
-// the epilogue, and builds the next chunk's padded input; the output is
-// assembled once at the end instead of through S - 1 growing concats.
-//
-// With packed weights (the GEMM path) the stacked-tap trick is replaced by a
-// true dilated conv: one GEMM with one segment per tap, tap k reading the
-// padded input k*d rows further on, with the epilogue fused into the store.
-// The padded input for the next chunk (h[next] + y, reflect padded) is then
-// a small pad kernel.
-ggml_tensor * res2net_cpu(ggml_context *         ctx,
-                          gemm::Arena *          ar,
-                          const SeRes2NetBlock & blk,
-                          ggml_tensor *          h,
-                          int                    kernel,
-                          int                    dilation,
-                          int                    chunk) {
-    ggml_tensor * ys[kRes2NetSubs] = {};
-    const int64_t T                = h->ne[1];
-
-    cpu::Res2Step st = cpu::res2_step(ctx, h, /*next=*/1, chunk, nullptr, nullptr, nullptr, nullptr, kernel, dilation);
-    for (int j = 0; j < kRes2NetSubs; ++j) {
-        const Res2Sub & s  = blk.res2[j];
-        const int       nx = (j + 2 < kRes2NetScale) ? j + 2 : -1;
-        if (ar != nullptr) {
-            gemm::Segment sg[gemm::kMaxSegs];
-            for (int k = 0; k < kernel; ++k) {
-                sg[k] = { s.w, k, st.pad, static_cast<int64_t>(k) * dilation };
-            }
-            ys[j] = gemm::mul_mat(ctx, *ar, sg, kernel, T, gemm::Epilogue::ReluBn, s.b, s.bn.scale, s.bn.shift);
-            if (nx >= 0) {
-                st = cpu::res2_pad(ctx, h, nx, chunk, ys[j], kernel, dilation);
-            }
-            continue;
-        }
-        ggml_tensor * ws = ggml_reshape_2d(ctx, s.w, chunk, chunk * kernel);  // [w, K*w], tap-major rows
-        ggml_tensor * z  = mul_mat_acc(ctx, ws, st.pad);                      // [K*w, T + 2p]
-        st               = cpu::res2_step(ctx, h, nx, chunk, z, s.b, s.bn.scale, s.bn.shift, kernel, dilation);
-        ys[j]            = st.y;
-    }
-    return cpu::res2_gather(ctx, h, ys, kRes2NetSubs);
-}
-
-// SEBlock fused with the residual add: x * s + residual. The fused kernel
-// never materializes x * s, so a dump build passes `se_out` to get it on a
-// side branch (the caller marks it and adds it to the graph).
-ggml_tensor * se_block_cpu(ggml_context *  ctx,
-                           const SeBlock & se,
-                           ggml_tensor *   x,
-                           ggml_tensor *   residual,
-                           ggml_tensor **  se_out) {
-    ggml_tensor * s = cpu::time_mean(ctx, x);
-    s               = ggml_relu(ctx, linear(ctx, se.c1_w, s, se.c1_b));
-    s               = ggml_sigmoid(ctx, linear(ctx, se.c2_w, s, se.c2_b));
-    if (se_out != nullptr) {
-        *se_out = ggml_mul(ctx, x, s);
-    }
-    return cpu::scale_add(ctx, x, s, residual);
-}
-
 // MFA + attentive statistics pooling, stock ops. Returns pooled [2*Cm].
 ggml_tensor * mfa_asp(ggml_context *        ctx,
                       GraphBuild &          gb,
@@ -396,66 +304,6 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     return pooled;
 }
 
-// MFA + attentive statistics pooling on the CPU: the three MFA products and
-// their epilogue are one fused pass, and every reduction over time runs on
-// the [C, T] layout directly (no transposes). Returns pooled [2*Cm].
-ggml_tensor * mfa_asp_cpu(ggml_context *        ctx,
-                          gemm::Arena *         ar,
-                          GraphBuild &          gb,
-                          const HParams &       hp,
-                          const Weights &       w,
-                          ggml_tensor * const * blk_out) {
-    const int     Cm = hp.c_mfa();
-    const int64_t T  = blk_out[0]->ne[1];
-
-    ggml_tensor * m = nullptr;
-    if (ar != nullptr) {
-        // All three products accumulate in one GEMM (three segments).
-        const gemm::Segment sg[kNumSeBlocks] = {
-            { w.mfa_w[0], 0, blk_out[0], 0 },
-            { w.mfa_w[1], 0, blk_out[1], 0 },
-            { w.mfa_w[2], 0, blk_out[2], 0 }
-        };
-        m = gemm::mul_mat(ctx, *ar, sg, kNumSeBlocks, T, gemm::Epilogue::ReluBn, w.mfa_b, w.mfa_bn.scale,
-                          w.mfa_bn.shift);
-    } else {
-        m = cpu::epilogue_sum3(ctx, mul_mat_acc(ctx, w.mfa_w[0], blk_out[0]), mul_mat_acc(ctx, w.mfa_w[1], blk_out[1]),
-                               mul_mat_acc(ctx, w.mfa_w[2], blk_out[2]), w.mfa_b, w.mfa_bn.scale, w.mfa_bn.shift, true);
-    }
-    mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
-
-    // [mean, std] over T, then the context term wm@mean + ws@std + b.
-    ggml_tensor * st    = cpu::time_stats(ctx, m, &hp.asp_eps);
-    ggml_tensor * mean1 = ggml_view_1d(ctx, st, Cm, 0);
-    ggml_tensor * sd1   = ggml_view_1d(ctx, st, Cm, static_cast<size_t>(Cm) * sizeof(float));
-    ggml_tensor * cvec  = ggml_add(ctx, linear(ctx, w.asp_wm, mean1, nullptr), linear(ctx, w.asp_ws, sd1, nullptr));
-    cvec                = ggml_add(ctx, cvec, w.asp_b);
-
-    // a = tanh(BN(ReLU(wx@m + cvec))): cvec plays the bias.
-    ggml_tensor * a  = nullptr;
-    ggml_tensor * al = nullptr;
-    if (ar != nullptr) {
-        const gemm::Segment sx{ w.asp_wx, 0, m, 0 };
-        a = gemm::mul_mat(ctx, *ar, &sx, 1, T, gemm::Epilogue::ReluBnTanh, cvec, w.asp_bn.scale, w.asp_bn.shift);
-        const gemm::Segment sa{ w.asp_attn_w, 0, a, 0 };
-        al = gemm::mul_mat(ctx, *ar, &sa, 1, T, gemm::Epilogue::None, nullptr, nullptr, nullptr);
-    } else {
-        a  = cpu::epilogue(ctx, mul_mat_acc(ctx, w.asp_wx, m), cvec, w.asp_bn.scale, w.asp_bn.shift,
-                           /*tanh_after=*/true, true);
-        al = mul_mat_acc(ctx, w.asp_attn_w, a);
-    }
-    // al: attention logits [Cm, T] WITHOUT the bias; attn_stats adds it per
-    // channel before the softmax over time. The dump point needs the biased
-    // logits, so the dump build adds it explicitly on a side branch.
-    if (debug::enabled()) {
-        mark_dump(gb.dumps.asp_attn_logits, ggml_add(ctx, al, w.asp_attn_b), "enc.asp.attn_logits");
-        ggml_build_forward_expand(gb.graph, gb.dumps.asp_attn_logits);
-    }
-    ggml_tensor * pooled = cpu::attn_stats(ctx, m, al, w.asp_attn_b, &hp.asp_eps);  // [2*Cm] = [mu, sigma]
-    mark_dump(gb.dumps.asp_out, pooled, "enc.asp.out");
-    return pooled;
-}
-
 }  // namespace
 
 void build_blk0_im2col(const HParams & hp, const float * mel, int T, std::vector<float> & out) {
@@ -480,14 +328,9 @@ void build_blk0_im2col(const HParams & hp, const float * mel, int T, std::vector
     }
 }
 
-GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_ops, gemm::Arena * arena) {
+GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
     GraphBuild gb{};
 
-    // Per weight group: the arena when that group was packed, else null
-    // (null routes the group through ggml_mul_mat).
-    gemm::Arena * ar_conv = cpu_ops && model.gemm_conv ? arena : nullptr;
-    gemm::Arena * ar_lin  = cpu_ops && model.gemm_lin ? arena : nullptr;
-
     const HParams & hp = model.hparams;
     const Weights & w  = model.weights;
 
@@ -521,35 +364,24 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_
     named(gb.blk0_in, "fe.mel.im2col");
     ggml_set_input(gb.blk0_in);
 
-    if (!cpu_ops) {
-        for (int i = 0; i < kNumSeBlocks; ++i) {
-            char name[32];
-            std::snprintf(name, sizeof(name), "reflect.idx.%d", i);
-            gb.idx[i] = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, T + 2 * hp.pad(i + 1));
-            named(gb.idx[i], name);
-            ggml_set_input(gb.idx[i]);
-        }
-        gb.chunk_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, kRes2NetScale);
-        named(gb.chunk_ids, "res2.chunk_ids");
-        ggml_set_input(gb.chunk_ids);
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        char name[32];
+        std::snprintf(name, sizeof(name), "reflect.idx.%d", i);
+        gb.idx[i] = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, T + 2 * hp.pad(i + 1));
+        named(gb.idx[i], name);
+        ggml_set_input(gb.idx[i]);
     }
+    gb.chunk_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, kRes2NetScale);
+    named(gb.chunk_ids, "res2.chunk_ids");
+    ggml_set_input(gb.chunk_ids);
 
     // ---- stage 0: TDNNBlock n_mels -> C ----------------------------------
     // One matmul over the host-built im2col (K0 taps x n_mels, zero padded to
     // a multiple of 32): n_mels = 60 alone is not a multiple of any CPU
     // tinyBLAS tile, and ggml's fallback GEMM for that shape is ~15x slower.
-    ggml_tensor * x = nullptr;
-    if (ar_conv != nullptr) {
-        const gemm::Segment sg{ w.blk0_w_im2col, 0, gb.blk0_in, 0 };
-        x = gemm::mul_mat(ctx, *ar_conv, &sg, 1, T, gemm::Epilogue::ReluBn, w.blk0_b, w.blk0_bn.scale, w.blk0_bn.shift);
-    } else if (cpu_ops) {
-        x = cpu::epilogue(ctx, mul_mat_acc(ctx, w.blk0_w_im2col, gb.blk0_in), w.blk0_b, w.blk0_bn.scale,
-                          w.blk0_bn.shift, false, true);
-    } else {
-        x = linear(ctx, w.blk0_w_im2col, gb.blk0_in, w.blk0_b);
-        x = ggml_relu(ctx, x);
-        x = bn_affine(ctx, x, w.blk0_bn.scale, w.blk0_bn.shift);
-    }
+    ggml_tensor * x = linear(ctx, w.blk0_w_im2col, gb.blk0_in, w.blk0_b);
+    x               = ggml_relu(ctx, x);
+    x               = bn_affine(ctx, x, w.blk0_bn.scale, w.blk0_bn.shift);
     mark_dump(gb.dumps.blk0_out, x, "enc.blk.0.out");
 
     // ---- stages 1..3: SERes2Net ------------------------------------------
@@ -560,43 +392,27 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_
 
         ggml_tensor * residual = x;
 
-        ggml_tensor * h = cpu_ops ? tdnn_1x1_cpu(ctx, ar_lin, blk.tdnn1, x) : tdnn_1x1(ctx, blk.tdnn1, x);
+        ggml_tensor * h = tdnn_1x1(ctx, blk.tdnn1, x);
         if (i == 0) {
             mark_dump(gb.dumps.blk1_tdnn1_out, h, "enc.blk.1.tdnn1.out");
         }
 
-        if (cpu_ops) {
-            h = res2net_cpu(ctx, ar_conv, blk, h, hp.kernel_sizes[static_cast<size_t>(i + 1)], d, chunk);
-        } else {
-            // The stock Res2Net writes its output over its input, so a dump
-            // build hands it a copy to keep the tdnn1 dump intact.
-            if (i == 0 && debug::enabled()) {
-                h = ggml_cont(ctx, h);
-            }
-            h = res2net(ctx, blk, h, gb.idx[i], gb.chunk_ids, d, T, chunk);
+        // Res2Net writes its output over its input, so a dump build hands it
+        // a copy to keep the tdnn1 dump intact.
+        if (i == 0 && debug::enabled()) {
+            h = ggml_cont(ctx, h);
         }
+        h = res2net(ctx, blk, h, gb.idx[i], gb.chunk_ids, d, T, chunk);
         if (i == 0) {
             mark_dump(gb.dumps.blk1_res2_out, h, "enc.blk.1.res2.out");
         }
 
-        h = cpu_ops ? tdnn_1x1_cpu(ctx, ar_lin, blk.tdnn2, h) : tdnn_1x1(ctx, blk.tdnn2, h);
-
-        if (cpu_ops) {
-            // The SE output and the residual add are one kernel here; a dump
-            // build recomputes enc.blk.1.se.out on a side branch.
-            ggml_tensor * se_out = nullptr;
-            x = se_block_cpu(ctx, blk.se, h, residual, i == 0 && debug::enabled() ? &se_out : nullptr);
-            if (se_out != nullptr) {
-                mark_dump(gb.dumps.blk1_se_out, se_out, "enc.blk.1.se.out");
-                ggml_build_forward_expand(gb.graph, se_out);
-            }
-        } else {
-            h = se_block(ctx, blk.se, h);
-            if (i == 0) {
-                mark_dump(gb.dumps.blk1_se_out, h, "enc.blk.1.se.out");
-            }
-            x = ggml_add(ctx, h, residual);
+        h = tdnn_1x1(ctx, blk.tdnn2, h);
+        h = se_block(ctx, blk.se, h);
+        if (i == 0) {
+            mark_dump(gb.dumps.blk1_se_out, h, "enc.blk.1.se.out");
         }
+        x = ggml_add(ctx, h, residual);
 
         char name[32];
         std::snprintf(name, sizeof(name), "enc.blk.%d.out", i + 1);
@@ -604,8 +420,7 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_
         blk_out[i] = x;
     }
 
-    ggml_tensor * pooled =
-        cpu_ops ? mfa_asp_cpu(ctx, ar_lin, gb, hp, w, blk_out) : mfa_asp(ctx, gb, hp, w, blk_out, Cm);
+    ggml_tensor * pooled = mfa_asp(ctx, gb, hp, w, blk_out, Cm);
 
     // ---- embedding head (asp_bn folded into fc) ---------------------------
     ggml_tensor * emb = linear(ctx, w.fc_w, pooled, w.fc_b);
diff --git a/src/arch/ecapa_tdnn/graph.h b/src/arch/ecapa_tdnn/graph.h
index a5f0566bc..34d718287 100644
--- a/src/arch/ecapa_tdnn/graph.h
+++ b/src/arch/ecapa_tdnn/graph.h
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#include "cpu_gemm.h"
 #include "ggml.h"
 #include "weights.h"
 
@@ -54,12 +53,11 @@ struct GraphBuild {
     ggml_tensor * blk0_in = nullptr;
 
     // Reflect-padding gather indices, one per SERes2Net block, I32
-    // ne = [T + 2*pad(i+1)]. Only the stock-op graph uses them; the CPU
-    // graph pads inside its Res2Net kernel and leaves these null.
+    // ne = [T + 2*pad(i+1)].
     ggml_tensor * idx[kNumSeBlocks] = { nullptr, nullptr, nullptr };
 
-    // Res2Net chunk numbers 0..kRes2NetScale-1, I32. Only the stock-op graph
-    // uses them (set_rows targets when writing a chunk's output in place).
+    // Res2Net chunk numbers 0..kRes2NetScale-1, I32: the set_rows targets
+    // when writing a chunk's output in place.
     ggml_tensor * chunk_ids = nullptr;
 
     // Output.
@@ -71,12 +69,8 @@ struct GraphBuild {
 };
 
 // Build the full forward graph for T frames into `ctx` (a fresh no_alloc
-// context). `cpu_ops` selects the fused CPU kernels (cpu_ops.h); it must only
-// be set when every node runs on the ggml CPU backend. Weights the model
-// packed for the AVX2 GEMM (Model::gemm_conv / gemm_lin) are routed through
-// it; `arena` holds those nodes' descriptors and must outlive the compute.
-// Returns a build with `graph == nullptr` on invalid input.
-GraphBuild build_graph(ggml_context * ctx, const Model & model, int T, bool cpu_ops, gemm::Arena * arena);
+// context). Returns a build with `graph == nullptr` on invalid input.
+GraphBuild build_graph(ggml_context * ctx, const Model & model, int T);
 
 // Fill `out` ([T, hp.blk0_cols()] frame-major, i.e. ggml ne = [cols, T])
 // with the stage-0 im2col of the frame-major log-mel `mel` [T, n_mels]:
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index 63c67eba3..6f460fc40 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -244,68 +244,6 @@ transcribe_status build_derived_weights(Model & m) {
     return TRANSCRIBE_OK;
 }
 
-// Repack weights for the AVX2 GEMM (cpu_gemm.h), in two all-or-nothing
-// groups the graph routes as a unit:
-//   - the k>1 kernels (F32 in every shipped file): this GEMM beats ggml's on
-//     their small shapes, and stage 0's K = 320 is not a tinyBLAS shape;
-//   - the T-wide 1x1 weights, only when F16 (F16 files, and Q8_0 after
-//     widening): ggml widens F16 operands in its inner loop, ~1.7x slower.
-//     For F32 1x1 weights ggml's tinyBLAS is as fast as this GEMM.
-// A group is skipped (left on ggml_mul_mat) unless every weight fits the
-// GEMM: F16/F32, contiguous, rows a multiple of the panel, and at most
-// kMaxSegs matrices (one GEMM segment per tap).
-transcribe_status pack_gemm_weights(Model & m) {
-    if (!gemm::available()) {
-        return TRANSCRIBE_OK;
-    }
-    Weights & w = m.weights;
-
-    auto fits = [](const std::vector<ggml_tensor *> & ts, bool need_f16) {
-        for (const ggml_tensor * t : ts) {
-            if (t->ne[1] % gemm::kPanel != 0 || t->ne[2] * t->ne[3] > gemm::kMaxSegs || !ggml_is_contiguous(t) ||
-                (need_f16 && t->type != GGML_TYPE_F16) || (t->type != GGML_TYPE_F16 && t->type != GGML_TYPE_F32)) {
-                return false;
-            }
-        }
-        return true;
-    };
-    // After fits() this cannot fail; if it ever does, some weights are
-    // already packed and unreadable by ggml_mul_mat, so the load fails.
-    auto pack = [&](const std::vector<ggml_tensor *> & ts) {
-        for (ggml_tensor * t : ts) {
-            if (!gemm::pack_weight(t)) {
-                log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: GEMM weight repack failed for %s", kTag, t->name);
-                return false;
-            }
-        }
-        return true;
-    };
-
-    std::vector<ggml_tensor *> conv{ w.blk0_w_im2col };
-    std::vector<ggml_tensor *> lin;
-    for (SeRes2NetBlock & b : w.blocks) {
-        for (Res2Sub & r : b.res2) {
-            conv.push_back(r.w);
-        }
-        lin.push_back(b.tdnn1.w);
-        lin.push_back(b.tdnn2.w);
-    }
-    for (ggml_tensor * t : w.mfa_w) {
-        lin.push_back(t);
-    }
-    lin.push_back(w.asp_wx);
-    lin.push_back(w.asp_attn_w);
-
-    m.gemm_conv = fits(conv, /*need_f16=*/false);
-    m.gemm_lin  = fits(lin, /*need_f16=*/true);
-    if ((m.gemm_conv && !pack(conv)) || (m.gemm_lin && !pack(lin))) {
-        return TRANSCRIBE_ERR_BACKEND;
-    }
-    log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: AVX2 GEMM for %s%s%s", kTag, m.gemm_conv ? "conv" : "",
-            m.gemm_conv && m.gemm_lin ? " + " : "", m.gemm_lin ? "F16 1x1" : (m.gemm_conv ? "" : "nothing"));
-    return TRANSCRIBE_OK;
-}
-
 // ---------------------------------------------------------------------------
 // load
 // ---------------------------------------------------------------------------
@@ -414,16 +352,6 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
         return st;
     }
 
-    // Fused CPU kernels only when the whole graph runs on the CPU backend:
-    // a custom op has no GPU implementation, and the scheduler would bounce
-    // every one of them back to the host.
-    m->cpu_ops = m->plan.primary_kind == BackendKind::Cpu && m->plan.scheduler_list.size() == 1;
-    if (m->cpu_ops) {
-        if (auto st = pack_gemm_weights(*m); st != TRANSCRIBE_OK) {
-            return st;
-        }
-    }
-
     m->roles = TRANSCRIBE_ROLE_LANGID;
     // The abort callback is honored (transcribe_langid_set_abort_callback).
     set_feature(m.get(), TRANSCRIBE_FEATURE_CANCELLATION, true);
@@ -468,8 +396,8 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
                              "frontend");
     }
 
-    // reflect_rows / the Res2Net kernel need T > pad (the 500 ms LANGID
-    // minimum gives T >= 51 frames; the largest pad here is 4).
+    // reflect_rows needs T > pad (the 500 ms LANGID minimum gives T >= 51
+    // frames; the largest pad here is 4).
     for (int i = 0; i < kNumStages - 1; ++i) {
         if (T <= hp.pad(i)) {
             log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: T=%d is too short for reflect padding %d", kTag, T, hp.pad(i));
@@ -479,10 +407,8 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
 
     // ---- stage-0 im2col and reflect-padding indices ------------------------
     build_blk0_im2col(hp, s.mel_buf.data(), T, s.im2col_buf);
-    if (!m.cpu_ops) {
-        for (int i = 0; i < kNumSeBlocks; ++i) {
-            build_reflect_indices(T, hp.pad(i + 1), s.idx_buf[i]);
-        }
+    for (int i = 0; i < kNumSeBlocks; ++i) {
+        build_reflect_indices(T, hp.pad(i + 1), s.idx_buf[i]);
     }
 
     // ---- graph -------------------------------------------------------------
@@ -493,7 +419,6 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
         ggml_free(s.compute_ctx);
         s.compute_ctx = nullptr;
     }
-    s.gemm_arena.clear();
     {
         if (s.graph_arena.size() != k_compute_ctx_bytes) {
             s.graph_arena.resize(k_compute_ctx_bytes);
@@ -509,7 +434,7 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
         }
     }
 
-    GraphBuild gb = build_graph(s.compute_ctx, m, T, m.cpu_ops, &s.gemm_arena);
+    GraphBuild gb = build_graph(s.compute_ctx, m, T);
     if (gb.graph == nullptr || gb.logits == nullptr) {
         return TRANSCRIBE_ERR_GGUF;
     }
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 7b3e528e6..0ea9653eb 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -107,8 +107,7 @@ set(TRANSCRIBE_FIXTURE_FILES
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_labels.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_k5.gguf)
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf)
 
 find_program(TRANSCRIBE_UV uv)
 
@@ -424,16 +423,6 @@ foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize
     add_test(NAME transcribe_${_t}_unit COMMAND transcribe_${_t}_unit)
 endforeach()
 
-# ecapa_tdnn CPU kernels (AVX2 GEMM, fused reductions) against scalar
-# references, at several thread counts. The GEMM part skips (RC 77) where the
-# CPU or build has no AVX2 kernel.
-add_executable(transcribe_ecapa_tdnn_gemm_unit ecapa_tdnn_gemm_unit.cpp)
-target_link_libraries(transcribe_ecapa_tdnn_gemm_unit PRIVATE transcribe ggml)
-target_include_directories(transcribe_ecapa_tdnn_gemm_unit PRIVATE ${CMAKE_SOURCE_DIR}/src)
-transcribe_apply_warnings(transcribe_ecapa_tdnn_gemm_unit)
-add_test(NAME transcribe_ecapa_tdnn_gemm_unit COMMAND transcribe_ecapa_tdnn_gemm_unit)
-set_tests_properties(transcribe_ecapa_tdnn_gemm_unit PROPERTIES SKIP_RETURN_CODE 77)
-
 # -----------------------------------------------------------------------------
 # Dispatcher-level streaming entry-point behavior
 # -----------------------------------------------------------------------------
diff --git a/tests/ecapa_tdnn_gemm_unit.cpp b/tests/ecapa_tdnn_gemm_unit.cpp
deleted file mode 100644
index ae7c288a1..000000000
--- a/tests/ecapa_tdnn_gemm_unit.cpp
+++ /dev/null
@@ -1,287 +0,0 @@
-// ecapa_tdnn_gemm_unit.cpp - the ecapa_tdnn CPU kernels against scalar
-// references: the AVX2 GEMM (src/arch/ecapa_tdnn/cpu_gemm.h) and the fused
-// time reductions / softmax (cpu_ops.h).
-//
-// GEMM cases cover what the graph uses: F16 and F32 weights, 1..3 segments
-// with row offsets (a dilated conv's taps), every epilogue,
-// odd N (partial 6-wide tiles), K not a multiple of the k block, and 1..16
-// threads; every result must be bit-identical across thread counts and
-// within FMA-rounding distance of an fp64 reference. Skips (RC 77) when the
-// CPU or build has no AVX2 kernel.
-
-#include "arch/ecapa_tdnn/cpu_gemm.h"
-#include "arch/ecapa_tdnn/cpu_ops.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#include "ggml-cpu.h"
-#include "ggml.h"
-
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <random>
-#include <vector>
-
-using namespace transcribe::ecapa_tdnn;
-
-namespace {
-
-int g_fail = 0;
-
-#define CHECK(cond, ...)                                              \
-    do {                                                              \
-        if (!(cond)) {                                                \
-            std::fprintf(stderr, "FAIL %s:%d: ", __FILE__, __LINE__); \
-            std::fprintf(stderr, __VA_ARGS__);                        \
-            std::fprintf(stderr, "\n");                               \
-            ++g_fail;                                                 \
-        }                                                             \
-    } while (0)
-
-struct Case {
-    const char *   name;
-    ggml_type      wtype;
-    int64_t        M, K, N;
-    int            n_segs;    // segments share one weight tensor (n_mats = n_segs)
-    int64_t        row_step;  // segment s reads x from row s * row_step
-    gemm::Epilogue ep;
-    bool           bias;
-};
-
-std::vector<float> rnd(size_t n, std::mt19937 & rng, float scale) {
-    std::normal_distribution<float> nd(0.0f, scale);
-    std::vector<float>              v(n);
-    for (auto & x : v) {
-        x = nd(rng);
-    }
-    return v;
-}
-
-bool run_case(const Case & c) {
-    std::mt19937  rng(1234);
-    const int64_t Q      = c.n_segs;
-    const int64_t x_rows = c.N + (c.n_segs - 1) * c.row_step;
-
-    std::vector<float> wf = rnd(static_cast<size_t>(Q * c.M * c.K), rng, 0.05f);
-    std::vector<float> xf = rnd(static_cast<size_t>(x_rows * c.K), rng, 1.0f);
-    std::vector<float> bf = rnd(static_cast<size_t>(c.M), rng, 0.3f);
-    std::vector<float> sf = rnd(static_cast<size_t>(c.M), rng, 1.0f);
-    std::vector<float> hf = rnd(static_cast<size_t>(c.M), rng, 0.3f);
-
-    // Weights as the kernel sees them: round through F16 when F16.
-    std::vector<ggml_fp16_t> wh;
-    if (c.wtype == GGML_TYPE_F16) {
-        wh.resize(wf.size());
-        ggml_fp32_to_fp16_row(wf.data(), wh.data(), static_cast<int64_t>(wf.size()));
-        ggml_fp16_to_fp32_row(wh.data(), wf.data(), static_cast<int64_t>(wf.size()));
-    }
-
-    // fp64 reference.
-    std::vector<double> ref(static_cast<size_t>(c.M * c.N));
-    for (int64_t j = 0; j < c.N; ++j) {
-        for (int64_t i = 0; i < c.M; ++i) {
-            double acc = 0.0;
-            for (int64_t s = 0; s < Q; ++s) {
-                const float * w = wf.data() + static_cast<size_t>((s * c.M + i) * c.K);
-                const float * x = xf.data() + static_cast<size_t>((j + s * c.row_step) * c.K);
-                for (int64_t k = 0; k < c.K; ++k) {
-                    acc += static_cast<double>(w[k]) * static_cast<double>(x[k]);
-                }
-            }
-            if (c.ep != gemm::Epilogue::None) {
-                double v = acc + (c.bias ? bf[static_cast<size_t>(i)] : 0.0);
-                v        = v > 0.0 ? v : 0.0;
-                v        = v * sf[static_cast<size_t>(i)] + hf[static_cast<size_t>(i)];
-                if (c.ep == gemm::Epilogue::ReluBnTanh) {
-                    v = std::tanh(v);
-                }
-                acc = v;
-            }
-            ref[static_cast<size_t>(j * c.M + i)] = acc;
-        }
-    }
-
-    std::vector<float> first;
-    bool               ok = true;
-    for (int nt : { 1, 3, 8, 16 }) {
-        ggml_backend_t be = ggml_backend_cpu_init();
-        ggml_backend_cpu_set_n_threads(be, nt);
-        ggml_init_params      ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
-        ggml_context *        ctx  = ggml_init(ip);
-        ggml_tensor *         w    = ggml_new_tensor_3d(ctx, c.wtype, c.K, c.M, Q);
-        ggml_tensor *         x    = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c.K, x_rows);
-        ggml_tensor *         b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
-        ggml_tensor *         s    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
-        ggml_tensor *         h    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, c.M);
-        ggml_backend_buffer_t wbuf = ggml_backend_alloc_ctx_tensors(ctx, be);
-        if (c.wtype == GGML_TYPE_F16) {
-            ggml_backend_tensor_set(w, wh.data(), 0, ggml_nbytes(w));
-        } else {
-            ggml_backend_tensor_set(w, wf.data(), 0, ggml_nbytes(w));
-        }
-        ggml_backend_tensor_set(x, xf.data(), 0, ggml_nbytes(x));
-        ggml_backend_tensor_set(b, bf.data(), 0, ggml_nbytes(b));
-        ggml_backend_tensor_set(s, sf.data(), 0, ggml_nbytes(s));
-        ggml_backend_tensor_set(h, hf.data(), 0, ggml_nbytes(h));
-        CHECK(gemm::pack_weight(w), "%s: pack_weight failed", c.name);
-
-        gemm::Arena   arena;
-        gemm::Segment segs[gemm::kMaxSegs];
-        for (int i = 0; i < c.n_segs; ++i) {
-            segs[i] = { w, i, x, i * c.row_step };
-        }
-        const bool    epi = c.ep != gemm::Epilogue::None;
-        ggml_tensor * y   = gemm::mul_mat(ctx, arena, segs, c.n_segs, c.N, c.ep, epi && c.bias ? b : nullptr,
-                                          epi ? s : nullptr, epi ? h : nullptr);
-        ggml_cgraph * g   = ggml_new_graph(ctx);
-        ggml_build_forward_expand(g, y);
-        ggml_gallocr_t ga = ggml_gallocr_new(ggml_backend_get_default_buffer_type(be));
-        CHECK(ggml_gallocr_alloc_graph(ga, g), "%s: graph alloc failed", c.name);
-        // Run twice: the second compute exercises the work-stealing state
-        // left behind by the first.
-        for (int rep = 0; rep < 2; ++rep) {
-            CHECK(ggml_backend_graph_compute(be, g) == GGML_STATUS_SUCCESS, "%s: compute failed", c.name);
-        }
-
-        std::vector<float> out(static_cast<size_t>(c.M * c.N));
-        ggml_backend_tensor_get(y, out.data(), 0, ggml_nbytes(y));
-
-        double worst = 0.0;
-        for (int64_t j = 0; j < c.N; ++j) {
-            for (int64_t i = 0; i < c.M; ++i) {
-                const double got = out[static_cast<size_t>(j * c.M + i)];
-                const double r   = ref[static_cast<size_t>(j * c.M + i)];
-                worst            = std::fmax(worst, std::fabs(got - r) / (1.0 + std::fabs(r)));
-            }
-        }
-        CHECK(worst < 1e-4, "%s t=%d: max rel err %.3e", c.name, nt, worst);
-        ok &= worst < 1e-4;
-        if (first.empty()) {
-            first = out;
-        } else {
-            const bool same = std::memcmp(first.data(), out.data(), out.size() * sizeof(float)) == 0;
-            CHECK(same, "%s t=%d: result differs from t=1", c.name, nt);
-            ok &= same;
-        }
-
-        ggml_gallocr_free(ga);
-        ggml_backend_buffer_free(wbuf);
-        ggml_free(ctx);
-        ggml_backend_free(be);
-    }
-    return ok;
-}
-
-// time_stats / attn_stats against fp64 references on a [C, T] tensor.
-void run_reductions() {
-    const int64_t      C = 600, T = 517;  // C not a multiple of the 256 channel block
-    std::mt19937       rng(99);
-    std::vector<float> xf  = rnd(static_cast<size_t>(C * T), rng, 1.0f);
-    std::vector<float> lf  = rnd(static_cast<size_t>(C * T), rng, 2.0f);
-    std::vector<float> bf  = rnd(static_cast<size_t>(C), rng, 1.0f);
-    static const float eps = 1e-12f;
-
-    for (int nt : { 1, 5, 8 }) {
-        ggml_backend_t be = ggml_backend_cpu_init();
-        ggml_backend_cpu_set_n_threads(be, nt);
-        ggml_init_params      ip{ 64 * ggml_tensor_overhead() + ggml_graph_overhead(), nullptr, true };
-        ggml_context *        ctx = ggml_init(ip);
-        ggml_tensor *         x   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
-        ggml_tensor *         l   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, C, T);
-        ggml_tensor *         b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, C);
-        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, be);
-        ggml_backend_tensor_set(x, xf.data(), 0, ggml_nbytes(x));
-        ggml_backend_tensor_set(l, lf.data(), 0, ggml_nbytes(l));
-        ggml_backend_tensor_set(b, bf.data(), 0, ggml_nbytes(b));
-
-        ggml_tensor * st = cpu::time_stats(ctx, x, &eps);
-        ggml_tensor * at = cpu::attn_stats(ctx, x, l, b, &eps);
-        ggml_tensor * mn = cpu::time_mean(ctx, x);
-        ggml_cgraph * g  = ggml_new_graph(ctx);
-        ggml_build_forward_expand(g, st);
-        ggml_build_forward_expand(g, at);
-        ggml_build_forward_expand(g, mn);
-        ggml_gallocr_t ga = ggml_gallocr_new(ggml_backend_get_default_buffer_type(be));
-        ggml_gallocr_alloc_graph(ga, g);
-        CHECK(ggml_backend_graph_compute(be, g) == GGML_STATUS_SUCCESS, "reductions: compute failed");
-
-        std::vector<float> vst(static_cast<size_t>(2 * C)), vat(static_cast<size_t>(2 * C)),
-            vmn(static_cast<size_t>(C));
-        ggml_backend_tensor_get(st, vst.data(), 0, ggml_nbytes(st));
-        ggml_backend_tensor_get(at, vat.data(), 0, ggml_nbytes(at));
-        ggml_backend_tensor_get(mn, vmn.data(), 0, ggml_nbytes(mn));
-
-        double e_st = 0, e_at = 0, e_mn = 0;
-        for (int64_t c = 0; c < C; ++c) {
-            double s = 0, q = 0, mx = -1e30, z = 0, mu = 0, sg = 0;
-            for (int64_t t = 0; t < T; ++t) {
-                s += xf[static_cast<size_t>(t * C + c)];
-            }
-            const double mean = s / T;
-            for (int64_t t = 0; t < T; ++t) {
-                const double d = xf[static_cast<size_t>(t * C + c)] - mean;
-                q += d * d;
-                mx =
-                    std::fmax(mx, static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)]);
-            }
-            for (int64_t t = 0; t < T; ++t) {
-                z +=
-                    std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) + bf[static_cast<size_t>(c)] - mx);
-            }
-            for (int64_t t = 0; t < T; ++t) {
-                const double w = std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) +
-                                          bf[static_cast<size_t>(c)] - mx) /
-                                 z;
-                mu += w * xf[static_cast<size_t>(t * C + c)];
-            }
-            for (int64_t t = 0; t < T; ++t) {
-                const double w = std::exp(static_cast<double>(lf[static_cast<size_t>(t * C + c)]) +
-                                          bf[static_cast<size_t>(c)] - mx) /
-                                 z;
-                const double d = xf[static_cast<size_t>(t * C + c)] - mu;
-                sg += w * d * d;
-            }
-            e_mn = std::fmax(e_mn, std::fabs(vmn[static_cast<size_t>(c)] - mean));
-            e_st = std::fmax(e_st, std::fabs(vst[static_cast<size_t>(c)] - mean));
-            e_st = std::fmax(e_st, std::fabs(vst[static_cast<size_t>(C + c)] - std::sqrt(q / T)));
-            e_at = std::fmax(e_at, std::fabs(vat[static_cast<size_t>(c)] - mu));
-            e_at = std::fmax(e_at, std::fabs(vat[static_cast<size_t>(C + c)] - std::sqrt(sg)));
-        }
-        CHECK(e_mn < 1e-5 && e_st < 1e-5 && e_at < 1e-4, "reductions t=%d: mean %.2e stats %.2e attn %.2e", nt, e_mn,
-              e_st, e_at);
-
-        ggml_gallocr_free(ga);
-        ggml_backend_buffer_free(buf);
-        ggml_free(ctx);
-        ggml_backend_free(be);
-    }
-}
-
-}  // namespace
-
-int main() {
-    run_reductions();
-
-    if (!gemm::available()) {
-        std::printf("ecapa_tdnn_gemm_unit: no AVX2 GEMM on this CPU/build; reductions ok=%d, GEMM skipped\n",
-                    g_fail == 0);
-        return g_fail == 0 ? 77 : 1;
-    }
-
-    const Case cases[] = {
-        { "f16 1x1 relu-bn",        GGML_TYPE_F16, 64, 96,  37,  1, 0, gemm::Epilogue::ReluBn,     true  },
-        { "f16 mfa 3-seg",          GGML_TYPE_F16, 48, 300, 61,  3, 0, gemm::Epilogue::ReluBn,     true  },
-        { "f16 none tanh-free",     GGML_TYPE_F16, 32, 513, 13,  1, 0, gemm::Epilogue::None,       false },
-        { "f16 tanh",               GGML_TYPE_F16, 16, 40,  7,   1, 0, gemm::Epilogue::ReluBnTanh, true  },
-        { "f32 dilated conv 3-tap", GGML_TYPE_F32, 32, 24,  50,  3, 4, gemm::Epilogue::ReluBn,     true  },
-        { "f32 im2col no-bias",     GGML_TYPE_F32, 16, 320, 101, 1, 0, gemm::Epilogue::ReluBn,     false },
-        { "f16 N=1",                GGML_TYPE_F16, 16, 33,  1,   2, 1, gemm::Epilogue::None,       false },
-    };
-    for (const Case & c : cases) {
-        run_case(c);
-    }
-    if (g_fail == 0) {
-        std::printf("ecapa_tdnn_gemm_unit: ok\n");
-    }
-    return g_fail == 0 ? 0 : 1;
-}
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index 8c6ae9082..0a2992b7f 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -3,7 +3,7 @@
 // runs the same against static, shared and installed builds.
 //
 // arch_ecapa_tdnn_minimal.gguf (tests/fixtures/make_gguf_fixtures.py) is a
-// 1/8-width model with the exact metadata and tensor contract of the real
+// 1/32-width model with the exact metadata and tensor contract of the real
 // VoxLingua107 file, so this drives the production loader, front end, ggml
 // graph and role dispatcher over the same code path the real checkpoint
 // uses. It asserts structure and invariants (statuses, orderings,
@@ -51,32 +51,11 @@ std::vector<float> noise(size_t n, uint32_t seed) {
     return out;
 }
 
-// The loader logs which weight groups it moved onto the AVX2 CPU GEMM
-// ("AVX2 GEMM for conv + F16 1x1"); the last such line, for the checks below.
-std::string g_gemm_log;
-
-void capture_log(transcribe_log_level /*level*/, const char * msg, void * /*userdata*/) {
-    if (msg != nullptr && std::strstr(msg, "AVX2 GEMM for") != nullptr) {
-        g_gemm_log = msg;
-    }
-}
-
-// Whether this CPU runs the AVX2 GEMM (the same test cpu_gemm.cpp makes).
-bool cpu_has_gemm() {
-#if (defined(__x86_64__) || defined(__i386__)) && (defined(__GNUC__) || defined(__clang__))
-    __builtin_cpu_init();
-    return __builtin_cpu_supports("avx2") && __builtin_cpu_supports("fma") && __builtin_cpu_supports("f16c");
-#else
-    return false;
-#endif
-}
-
 std::string fixture(const char * name) {
     return std::string(TRANSCRIBE_TEST_FIXTURES_DIR) + "/" + name;
 }
 
 transcribe_model * load_cpu(const char * name, transcribe_status * st_out = nullptr) {
-    g_gemm_log.clear();
     transcribe_model_load_params mp;
     transcribe_model_load_params_init(&mp);
     mp.backend                 = TRANSCRIBE_BACKEND_CPU;
@@ -256,11 +235,7 @@ void test_bad_labels() {
 // Q8_0 weights are widened to F16 at load: a Q8_0 model must give exactly
 // the logits of the same model stored as F16 holding the dequantized values.
 void test_q8_0_widened_to_f16() {
-    transcribe_model * q8 = load_cpu("arch_ecapa_tdnn_q8_0.gguf");
-    if (cpu_has_gemm()) {
-        // Widened to F16, every 1x1 weight takes the GEMM too.
-        CHECK(g_gemm_log.find("conv + F16 1x1") != std::string::npos);
-    }
+    transcribe_model * q8  = load_cpu("arch_ecapa_tdnn_q8_0.gguf");
     transcribe_model * ref = load_cpu("arch_ecapa_tdnn_q8_0_as_f16.gguf");
     CHECK(q8 != nullptr && ref != nullptr);
     if (q8 == nullptr || ref == nullptr) {
@@ -285,33 +260,10 @@ void test_q8_0_widened_to_f16() {
     transcribe_model_free(ref);
 }
 
-// Res2Net kernels wider than the GEMM's segment limit: the loader keeps
-// those weights on ggml_mul_mat (it used to route them to the GEMM and
-// overflow its per-tap segment array) and the model still runs.
-void test_wide_kernel_fallback() {
-    transcribe_status  st = TRANSCRIBE_OK;
-    transcribe_model * m  = load_cpu("arch_ecapa_tdnn_k5.gguf", &st);
-    CHECK(st == TRANSCRIBE_OK && m != nullptr);
-    if (m == nullptr) {
-        return;
-    }
-    if (cpu_has_gemm()) {
-        CHECK(!g_gemm_log.empty() && g_gemm_log.find("conv") == std::string::npos);
-    }
-    const std::vector<float>    pcm = noise(16000 * 3, 7);
-    transcribe_langid_session * s   = open_session(m, 4);
-    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
-    for (const auto & c : candidates_of(s)) {
-        CHECK(std::isfinite(c.logit));
-    }
-    transcribe_langid_session_free(s);
-    transcribe_model_free(m);
-}
-
 }  // namespace
 
 int main() {
-    transcribe_log_set(capture_log, nullptr);
+    transcribe_log_set(nullptr, nullptr);
 
     transcribe_status  st = TRANSCRIBE_OK;
     transcribe_model * m  = load_cpu("arch_ecapa_tdnn_minimal.gguf", &st);
@@ -319,12 +271,6 @@ int main() {
         std::fprintf(stderr, "FAIL: load arch_ecapa_tdnn_minimal.gguf: %s\n", transcribe_status_string(st));
         return EXIT_FAILURE;
     }
-    if (cpu_has_gemm()) {
-        // The toy model is wide enough for the GEMM, so the graph tests below
-        // (thread invariance in particular) run the GEMM path; F32 1x1 weights
-        // stay on ggml_mul_mat by design.
-        CHECK(g_gemm_log.find("AVX2 GEMM for conv") != std::string::npos);
-    }
     test_model_surface(m);
     test_run(m);
     test_thread_invariance(m);
@@ -332,7 +278,6 @@ int main() {
 
     test_bad_labels();
     test_q8_0_widened_to_f16();
-    test_wide_kernel_fallback();
 
     if (g_failures != 0) {
         std::fprintf(stderr, "%d failure(s)\n", g_failures);
diff --git a/tests/fixtures/make_gguf_fixtures.py b/tests/fixtures/make_gguf_fixtures.py
index 897aa8f2a..7fbc9a8f1 100644
--- a/tests/fixtures/make_gguf_fixtures.py
+++ b/tests/fixtures/make_gguf_fixtures.py
@@ -1373,22 +1373,20 @@ def _qwen3_asr_tensors() -> list[Tensor]:
 # ---------------------------------------------------------------------------
 #
 # The same metadata and tensor contract scripts/convert-ecapa_tdnn.py writes
-# for speechbrain/lang-id-voxlingua107-ecapa, at 1/8 width: channels
-# [128]*4 + [384] and 32 attention channels, the narrowest that still puts
-# every weight on the AVX2 CPU GEMM (16-row panels; Res2Net chunk = 16, and
-# 2-D weights Q8_0-eligible in the Q8_0 fixtures), the real kernel sizes / dilations / res2net scale, a real
+# for speechbrain/lang-id-voxlingua107-ecapa, at 1/32 width: channels
+# [32]*4 + [96], the real kernel sizes / dilations / res2net scale, a real
 # 60 x 201 front end, five labels aa..ee plus the alias xx=aa. Weights are
 # small seeded pseudo-random values so a forward pass stays well inside float
 # range; tests assert structure and invariants, never specific values.
 
 import random as _random
 
-ECAPA_CHANNELS     = [128, 128, 128, 128, 384]
+ECAPA_CHANNELS     = [32, 32, 32, 32, 96]
 ECAPA_KERNELS      = [5, 3, 3, 3, 1]
 ECAPA_DILATIONS    = [1, 2, 3, 4, 1]
 ECAPA_SCALE        = 8
 ECAPA_SE           = 8
-ECAPA_ATT          = 32
+ECAPA_ATT          = 8
 ECAPA_EMB          = 16
 ECAPA_HID          = 16
 ECAPA_N_MELS       = 60
@@ -1397,8 +1395,7 @@ def _qwen3_asr_tensors() -> list[Tensor]:
 ECAPA_LABEL_NAMES  = ["Alpha", "Bravo", "Charlie", "Delta", "Echo"]
 
 
-def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str],
-                           kernels: list[int]) -> list[bytes]:
+def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str]) -> list[bytes]:
     return [
         _pack_kv_string("stt.frontend.type", "speechbrain_fbank"),
         _pack_kv_uint32("stt.frontend.sample_rate", 16000),
@@ -1413,7 +1410,7 @@ def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str
         _pack_kv_string("stt.frontend.normalize", "sentence_mean"),
         _pack_kv_uint32("stt.ecapa_tdnn.format_version", 1),
         _pack_kv_array_int32("stt.ecapa_tdnn.channels", ECAPA_CHANNELS),
-        _pack_kv_array_int32("stt.ecapa_tdnn.kernel_sizes", kernels),
+        _pack_kv_array_int32("stt.ecapa_tdnn.kernel_sizes", ECAPA_KERNELS),
         _pack_kv_array_int32("stt.ecapa_tdnn.dilations", ECAPA_DILATIONS),
         _pack_kv_uint32("stt.ecapa_tdnn.res2net_scale", ECAPA_SCALE),
         _pack_kv_uint32("stt.ecapa_tdnn.se_channels", ECAPA_SE),
@@ -1428,7 +1425,7 @@ def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str
     ]
 
 
-def _ecapa_tdnn_tensors(n_labels: int, kernels: list[int]) -> list[Tensor]:
+def _ecapa_tdnn_tensors(n_labels: int) -> list[Tensor]:
     rng = _random.Random(0)
     out: list[Tensor] = []
 
@@ -1463,11 +1460,11 @@ def tdnn(prefix: str, ic: int, oc: int, k: int, conv: bool) -> None:
     add("frontend.mel_filterbank", [ECAPA_N_FREQ, ECAPA_N_MELS], fb)
 
     c, cm, chunk = ECAPA_CHANNELS[0], ECAPA_CHANNELS[-1], ECAPA_CHANNELS[0] // ECAPA_SCALE
-    tdnn("blk.0", ECAPA_N_MELS, c, kernels[0], conv=True)
+    tdnn("blk.0", ECAPA_N_MELS, c, ECAPA_KERNELS[0], conv=True)
     for i in (1, 2, 3):
         tdnn(f"blk.{i}.tdnn1", c, c, 1, conv=False)
         for j in range(ECAPA_SCALE - 1):
-            tdnn(f"blk.{i}.res2.{j}", chunk, chunk, kernels[i], conv=True)
+            tdnn(f"blk.{i}.res2.{j}", chunk, chunk, ECAPA_KERNELS[i], conv=True)
         tdnn(f"blk.{i}.tdnn2", c, c, 1, conv=False)
         weight(f"blk.{i}.se.c1.weight", [c, ECAPA_SE])
         vec(f"blk.{i}.se.c1.bias", ECAPA_SE, 0.0, 0.05)
@@ -1528,9 +1525,8 @@ def _ecapa_tdnn_q8_0(tensors: list[Tensor], as_f16: bool) -> list[Tensor]:
     return out
 
 
-def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "",
-                     kernels: list[int] = ECAPA_KERNELS) -> bytes:
-    tensors = _ecapa_tdnn_tensors(len(codes), kernels)
+def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "") -> bytes:
+    tensors = _ecapa_tdnn_tensors(len(codes))
     if q8_0:
         tensors = _ecapa_tdnn_q8_0(tensors, as_f16=(q8_0 == "as_f16"))
     return _build_full_gguf(
@@ -1538,7 +1534,7 @@ def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_
         [
             _pack_kv_string("general.architecture", "ecapa_tdnn"),
             _pack_kv_string("stt.variant", "ecapa-tdnn-toy"),
-            *_ecapa_tdnn_hparams_kv(codes, names, aliases, kernels),
+            *_ecapa_tdnn_hparams_kv(codes, names, aliases),
         ],
         tensors,
     )
@@ -1972,11 +1968,6 @@ def emit_fixtures(out_dir: Path) -> None:
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="q8_0"))
     _write(out_dir / "arch_ecapa_tdnn_q8_0_as_f16.gguf",
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="as_f16"))
-    # Res2Net kernels wider than the CPU GEMM's segment limit (3): the loader
-    # must keep those weights on ggml_mul_mat rather than overflow the GEMM's
-    # per-tap segment array.
-    _write(out_dir / "arch_ecapa_tdnn_k5.gguf",
-           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], kernels=[5, 5, 5, 5, 1]))
 
 
 def main(argv: list[str]) -> int:

From 113a865e6a94fb4aed17fc4c4fc16b1d1ac351ae Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 15:55:57 +0800
Subject: [PATCH 10/38] mel: add hamming_periodic window and sentence_mean
 normalize

Additive options on the shared MelFrontend for SpeechBrain's Fbank
(ecapa_tdnn): window_type "hamming_periodic" (torch.hamming_window,
periodic) and normalize "sentence_mean" (10*log10(max(x, amin)) with
amin from log_clamp_min, optional per-utterance top_db floor via the
new MelConfig::top_db, per-bin mean subtraction over all frames, no
frame drop). Wired into all three log sites. Defaults are unchanged;
existing families produce bit-identical mel on CPU.

mel_unit covers the Hamming window and the sentence_mean pipeline.
---
 src/transcribe-mel.cpp |  83 +++++++++++++++++++++++++++++--
 src/transcribe-mel.h   |  16 ++++++
 tests/mel_unit.cpp     | 109 +++++++++++++++++++++++++++++++++++++++++
 3 files changed, 205 insertions(+), 3 deletions(-)

diff --git a/src/transcribe-mel.cpp b/src/transcribe-mel.cpp
index 0893486d6..f78a9af52 100644
--- a/src/transcribe-mel.cpp
+++ b/src/transcribe-mel.cpp
@@ -140,6 +140,17 @@ void build_hann_window_symmetric_padded(int win_length, int n_fft, bool periodic
     }
 }
 
+// Periodic Hamming of length win_length, zero-padded to n_fft on both
+// sides: torch.hamming_window(N) with the default periodic=True,
+// 0.54 - 0.46*cos(2πk/N). SpeechBrain (ecapa_tdnn).
+void build_hamming_window_periodic_padded(int win_length, int n_fft, std::vector<double> & out) {
+    out.assign(n_fft, 0.0);
+    const int pad_each = (n_fft - win_length) / 2;
+    for (int k = 0; k < win_length; ++k) {
+        out[pad_each + k] = 0.54 - 0.46 * std::cos(2.0 * M_PI * k / static_cast<double>(win_length));
+    }
+}
+
 // In-place radix-2 Cooley-Tukey FFT, fp64. Operates on n complex
 // numbers stored as interleaved (re, im, re, im, ...). n must be a
 // power of 2. For n=512 (Parakeet) this is ~80 lines and runs in
@@ -289,6 +300,8 @@ MelFrontend::MelFrontend(const MelConfig & cfg) : cfg_(cfg) {
         for (int i = 0; i < cfg.win_length && i < static_cast<int>(cfg.window.size()); ++i) {
             window_[left_pad + i] = static_cast<double>(cfg.window[i]);
         }
+    } else if (cfg.window_type == "hamming_periodic") {
+        build_hamming_window_periodic_padded(cfg.win_length, cfg.n_fft, window_);
     } else {
         const bool periodic = (cfg.window_type == "hann_periodic");
         build_hann_window_symmetric_padded(cfg.win_length, cfg.n_fft, periodic, window_);
@@ -480,6 +493,11 @@ transcribe_status MelFrontend::compute(const float *        pcm,
 
     const bool whisper_mode = (cfg_.normalize == "per_utterance" || cfg_.normalize == "global");
 
+    // SpeechBrain power-to-dB (normalize == "sentence_mean"):
+    // 10*log10(max(x, amin)) in fp64, one rounding at storage.
+    const bool   db_mode  = (cfg_.normalize == "sentence_mean");
+    const double db_floor = static_cast<double>(cfg_.log_clamp_min > 0.0f ? cfg_.log_clamp_min : 1.0e-10f);
+
     int stft_threads = n_threads;
     if (stft_threads <= 0) {
         stft_threads = default_n_threads();
@@ -531,7 +549,12 @@ transcribe_status MelFrontend::compute(const float *        pcm,
                         sum += static_cast<double>(fb_row[k]) * static_cast<double>(power_scratch[k]);
                     }
                     float result;
-                    if (whisper_mode) {
+                    if (db_mode) {
+                        if (sum < db_floor) {
+                            sum = db_floor;
+                        }
+                        result = static_cast<float>(10.0 * std::log10(sum));
+                    } else if (whisper_mode) {
                         if (sum < 1.0e-10) {
                             sum = 1.0e-10;
                         }
@@ -629,7 +652,15 @@ transcribe_status MelFrontend::compute(const float *        pcm,
                     power.data(), n_freq, 0.0f, log_mel.data(), n_frames);
         {
             const size_t total = static_cast<size_t>(n_mels) * static_cast<size_t>(n_frames);
-            if (whisper_mode) {
+            if (db_mode) {
+                for (size_t i = 0; i < total; ++i) {
+                    double v = static_cast<double>(log_mel[i]);
+                    if (v < db_floor) {
+                        v = db_floor;
+                    }
+                    log_mel[i] = static_cast<float>(10.0 * std::log10(v));
+                }
+            } else if (whisper_mode) {
                 for (size_t i = 0; i < total; ++i) {
                     double v = static_cast<double>(log_mel[i]);
                     if (v < 1.0e-10) {
@@ -674,7 +705,12 @@ transcribe_status MelFrontend::compute(const float *        pcm,
                     sum += static_cast<double>(fb_row[k]) * static_cast<double>(pwr[k]);
                 }
                 float result;
-                if (whisper_mode) {
+                if (db_mode) {
+                    if (sum < db_floor) {
+                        sum = db_floor;
+                    }
+                    result = static_cast<float>(10.0 * std::log10(sum));
+                } else if (whisper_mode) {
                     if (sum < 1.0e-10) {
                         sum = 1.0e-10;
                     }
@@ -698,6 +734,47 @@ transcribe_status MelFrontend::compute(const float *        pcm,
     std::vector<float>().swap(padded_f32);
     std::vector<float>().swap(window_f32);
 
+    // ---- 5s. SpeechBrain sentence-mean normalize ----
+    // log_mel already holds 10*log10(max(x, amin)). Top-dB floor over time
+    // AND frequency (SpeechBrain clamps inside Filterbank), then subtract
+    // each mel bin's mean over ALL frames (InputNormalization, norm_type=
+    // "sentence", std_norm=False). No frame is dropped or masked: torch
+    // keeps the center-pad frames. fp64 accumulators, one rounding at
+    // storage.
+    if (db_mode) {
+        const size_t total = static_cast<size_t>(n_mels) * static_cast<size_t>(n_frames);
+        if (cfg_.top_db > 0.0f) {
+            double max_all = -std::numeric_limits<double>::infinity();
+            for (size_t i = 0; i < total; ++i) {
+                const double v = static_cast<double>(log_mel[i]);
+                if (v > max_all) {
+                    max_all = v;
+                }
+            }
+            const double floor_val = max_all - static_cast<double>(cfg_.top_db);
+            for (size_t i = 0; i < total; ++i) {
+                if (static_cast<double>(log_mel[i]) < floor_val) {
+                    log_mel[i] = static_cast<float>(floor_val);
+                }
+            }
+        }
+        for (int m = 0; m < n_mels; ++m) {
+            float * row = log_mel.data() + static_cast<size_t>(m) * n_frames;
+            double  sum = 0.0;
+            for (int t = 0; t < n_frames; ++t) {
+                sum += static_cast<double>(row[t]);
+            }
+            const double mean = sum / static_cast<double>(n_frames);
+            for (int t = 0; t < n_frames; ++t) {
+                row[t] = static_cast<float>(static_cast<double>(row[t]) - mean);
+            }
+        }
+        out_mel      = std::move(log_mel);
+        out_n_mels   = n_mels;
+        out_n_frames = n_frames;
+        return TRANSCRIBE_OK;
+    }
+
     // ---- 5n. No-op normalize (NeMo "NA"/none) ----
     // Emit raw log-mel as-is: streaming Conformer variants (e.g.
     // nemotron-speech-streaming-en-0.6b) bake feature normalization into
diff --git a/src/transcribe-mel.h b/src/transcribe-mel.h
index b547435e2..e43463938 100644
--- a/src/transcribe-mel.h
+++ b/src/transcribe-mel.h
@@ -59,6 +59,9 @@ struct MelConfig {
     //   "hann_periodic"  — torch.hann_window(N, periodic=True):
     //                      cos(2*pi*k / N). Used by Whisper (and
     //                      Qwen3-ASR's Whisper frontend).
+    //   "hamming_periodic" — torch.hamming_window(N, periodic=True):
+    //                      0.54 - 0.46*cos(2*pi*k / N). Used by
+    //                      SpeechBrain (ecapa_tdnn).
     std::string window_type = "hann_symmetric";
 
     // Normalization mode:
@@ -78,6 +81,12 @@ struct MelConfig {
     //                      per-utterance maximum, so each frame is
     //                      causal/streaming-safe. Still drops the trailing
     //                      center-pad STFT frame.
+    //   "sentence_mean" — SpeechBrain Fbank + InputNormalization(norm_type=
+    //                     "sentence", std_norm=False): power-to-dB
+    //                     10*log10(max(x, log_clamp_min)), then the
+    //                     optional top_db floor, then subtract each mel
+    //                     bin's mean over ALL frames (no variance). No frame
+    //                     is dropped or masked. Used by ecapa_tdnn.
     std::string normalize = "per_feature";
 
     // Fixed log-mel maximum for normalize == "global" (Voxtral Realtime
@@ -93,8 +102,15 @@ struct MelConfig {
     // When > 0 AND normalize="none", emit log(max(power, log_clamp_min))
     // instead of NeMo's log(power + kLogEps). LASR / MedASR use this with
     // log_clamp_min = 1e-5; NeMo's frontends leave it at 0.0.
+    // normalize="sentence_mean" uses it as the dB floor (amin) instead;
+    // 0.0 there falls back to 1e-10.
     float log_clamp_min = 0.0f;
 
+    // normalize="sentence_mean" only: per-utterance dynamic-range floor
+    // over time AND frequency, x = max(x, max_all(x) - top_db), applied
+    // in dB before the mean subtraction. <= 0 disables it.
+    float top_db = 0.0f;
+
     // Optional checkpoint-provided window [win_length]. When non-empty,
     // used instead of computing a periodic Hann window.
     std::vector<float> window;
diff --git a/tests/mel_unit.cpp b/tests/mel_unit.cpp
index 4033c5fa8..218785b7c 100644
--- a/tests/mel_unit.cpp
+++ b/tests/mel_unit.cpp
@@ -11,17 +11,26 @@
 //   - wrong mel scale formula (HTK vs Slaney)
 //   - missing Slaney area normalization (would scale by ~50x)
 //
+// Plus the SpeechBrain (ecapa_tdnn) options: the periodic Hamming window
+// and the "sentence_mean" normalize (dB, top-dB floor, per-bin mean, no
+// frame drop), run end to end on a synthetic one-hot filterbank.
+//
 // All reference values are bit-precise constants captured from
 // librosa 0.11 and the symmetric-hann formula. Regenerate via the
 // preflight script if librosa updates and the tolerances drift.
 
 #include "transcribe-mel.h"
 
+#include <algorithm>
 #include <cmath>
 #include <cstdio>
 #include <cstdlib>
 #include <vector>
 
+#ifndef M_PI
+#    define M_PI 3.14159265358979323846
+#endif
+
 namespace {
 
 int g_failures = 0;
@@ -204,12 +213,112 @@ void test_n_frames_for() {
     CHECK(mf.n_frames_for(0) == 1);
 }
 
+// SpeechBrain (ecapa_tdnn) config: periodic Hamming, constant pad, no
+// pre-emphasis, 10*log10 with an 80 dB top-dB floor, per-bin mean only.
+// The filterbank is a synthetic one-hot bank (mel m reads FFT bin 3*m),
+// since the real one is checkpoint-provided.
+transcribe::MelConfig speechbrain_config() {
+    transcribe::MelConfig cfg;
+    cfg.num_mels      = 60;
+    cfg.n_fft         = 400;
+    cfg.win_length    = 400;
+    cfg.hop_length    = 160;
+    cfg.pre_emphasis  = 0.0f;
+    cfg.pad_mode      = "constant";
+    cfg.window_type   = "hamming_periodic";
+    cfg.normalize     = "sentence_mean";
+    cfg.log_clamp_min = 1e-10f;
+    cfg.top_db        = 80.0f;
+    cfg.filterbank.assign(static_cast<size_t>(60) * 201, 0.0f);
+    for (int m = 0; m < 60; ++m) {
+        cfg.filterbank[static_cast<size_t>(m) * 201 + static_cast<size_t>(m) * 3] = 1.0f;
+    }
+    return cfg;
+}
+
+void test_hamming_window() {
+    transcribe::MelFrontend mf(speechbrain_config());
+    const auto &            w = mf.window();
+    CHECK(w.size() == 400);
+    if (w.size() != 400) {
+        return;
+    }
+    // Periodic: the peak lands ON sample N/2 (a symmetric Hamming of
+    // length 400 never reaches 1.0), and w[1] uses 2*pi*n/N, not N-1.
+    CHECK_NEAR(w[0], 0.08, 1e-15);
+    CHECK_NEAR(w[100], 0.54, 1e-15);
+    CHECK_NEAR(w[200], 1.0, 1e-15);
+    CHECK_NEAR(w[1], 0.0800567490584361, 1e-15);
+    double sum = 0.0;
+    for (double v : w) {
+        sum += v;
+    }
+    CHECK_NEAR(sum, 216.0, 1e-12);  // 0.54 * N: the cosine term cancels
+}
+
+void test_sentence_mean() {
+    // 1 kHz sine, 1 s, through the full sentence_mean pipeline.
+    std::vector<float> pcm(16000);
+    for (int i = 0; i < 16000; ++i) {
+        pcm[static_cast<size_t>(i)] = static_cast<float>(std::sin(2.0 * M_PI * 1000.0 * i / 16000.0));
+    }
+    transcribe::MelFrontend mf(speechbrain_config());
+    std::vector<float>      out;
+    int                     n_mels   = 0;
+    int                     n_frames = 0;
+    CHECK(mf.compute(pcm.data(), pcm.size(), out, n_mels, n_frames, 1) == TRANSCRIBE_OK);
+    // torch center=True framing with no trailing-frame drop.
+    CHECK(n_mels == 60);
+    CHECK(n_frames == 101);
+    if (out.size() != static_cast<size_t>(60) * 101) {
+        CHECK(false);
+        return;
+    }
+    // Mean removed per mel bin over all frames ([n_mels, n_frames] rows),
+    // and nothing else: a variance normalize would also pin each row's
+    // spread, so require one row with spread well above 1.
+    double max_spread = 0.0;
+    for (int m = 0; m < 60; ++m) {
+        const float * row = out.data() + static_cast<size_t>(m) * 101;
+        double        sum = 0.0;
+        float         lo  = row[0];
+        float         hi  = row[0];
+        for (int t = 0; t < 101; ++t) {
+            sum += static_cast<double>(row[t]);
+            lo = std::min(lo, row[t]);
+            hi = std::max(hi, row[t]);
+        }
+        CHECK_NEAR(sum / 101.0, 0.0, 1e-4);
+        max_spread = std::max(max_spread, static_cast<double>(hi - lo));
+        // Top-dB floor: before the mean shift every value sat in
+        // [max_all - 80, max_all], so no row can span more than 80 dB.
+        CHECK(static_cast<double>(hi - lo) <= 80.0 + 1e-3);
+    }
+    CHECK(max_spread > 1.0);
+
+    // With the floor disabled the -100 dB (amin) bins reappear, so some
+    // row must now span more than 80 dB: proves the floor above did work.
+    transcribe::MelConfig no_floor_cfg = speechbrain_config();
+    no_floor_cfg.top_db                = 0.0f;
+    transcribe::MelFrontend no_floor(no_floor_cfg);
+    CHECK(no_floor.compute(pcm.data(), pcm.size(), out, n_mels, n_frames, 1) == TRANSCRIBE_OK);
+    double raw_spread = 0.0;
+    for (int m = 0; m < 60 && out.size() == static_cast<size_t>(60) * 101; ++m) {
+        const float * row = out.data() + static_cast<size_t>(m) * 101;
+        const auto    mm  = std::minmax_element(row, row + 101);
+        raw_spread        = std::max(raw_spread, static_cast<double>(*mm.second - *mm.first));
+    }
+    CHECK(raw_spread > 80.0);
+}
+
 }  // namespace
 
 int main() {
     test_window();
     test_mel_filterbank();
     test_n_frames_for();
+    test_hamming_window();
+    test_sentence_mean();
 
     if (g_failures > 0) {
         std::fprintf(stderr, "mel_unit: %d failures\n", g_failures);

From 7ec331179aafcb25343c759385a0f9bc95aa3eac Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 15:57:06 +0800
Subject: [PATCH 11/38] langid: ecapa_tdnn uses the shared mel frontend

Drop the family-local src/arch/ecapa_tdnn/mel.{h,cpp}, which duplicated
the mixed-radix FFT and twiddle LUT from transcribe-mel.cpp, and build
a transcribe::MelFrontend from the same stt.frontend.* keys instead
(hamming_periodic, constant pad, no pre-emphasis, sentence_mean). The
mel-major output is transposed to the frame-major layout the stage-0
im2col and the fe.mel dump use. GGUF format unchanged.

fe.mel is bit-identical to the previous implementation on all 8
validate cases. Remove tests/ecapa_tdnn_mel_unit.cpp and the
mel_test_fft hook; the window and sentence_mean coverage now lives in
tests/mel_unit.cpp. Update docs and tolerance-file comments (no
tolerance values changed).
---
 docs/porting/families/ecapa_tdnn.md |   9 +-
 src/CMakeLists.txt                  |   1 -
 src/arch/ecapa_tdnn/ecapa_tdnn.h    |   3 +-
 src/arch/ecapa_tdnn/mel.cpp         | 388 ------------------------
 src/arch/ecapa_tdnn/mel.h           | 178 -----------
 src/arch/ecapa_tdnn/model.cpp       |  53 ++--
 tests/CMakeLists.txt                |   5 +-
 tests/ecapa_tdnn_mel_unit.cpp       | 449 ----------------------------
 tests/tolerances/ecapa_tdnn.json    |   6 +-
 9 files changed, 49 insertions(+), 1043 deletions(-)
 delete mode 100644 src/arch/ecapa_tdnn/mel.cpp
 delete mode 100644 src/arch/ecapa_tdnn/mel.h
 delete mode 100644 tests/ecapa_tdnn_mel_unit.cpp

diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index 3a736f715..919c46de3 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -62,10 +62,11 @@ k>1 tap-major kernels F32 / F16, every other `.weight` quantizable,
 `frontend.mel_filterbank` F32), so the quant policy has no ECAPA entries.
 The full catalogue is in `src/arch/ecapa_tdnn/weights.h`.
 
-The front end is family-local (`src/arch/ecapa_tdnn/mel.cpp`):
-`transcribe-mel` has no periodic Hamming window, `10*log10`, top-db floor or
-per-bin mean subtraction. The filterbank is SpeechBrain's own, captured by the
-converter and stored in the GGUF; it is never rebuilt.
+The front end is the shared `transcribe-mel` (`src/transcribe-mel.cpp`)
+with `window_type = "hamming_periodic"` and `normalize = "sentence_mean"`
+(`10*log10`, 80 dB top-db floor, per-bin mean subtraction, no frame drop).
+The filterbank is SpeechBrain's own, captured by the converter and stored in
+the GGUF; it is never rebuilt.
 
 ## Commands
 
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index b4307e6cb..30625f6fc 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -68,7 +68,6 @@ add_library(transcribe
     arch/sortformer/stream.cpp
     arch/sortformer/weights.cpp
     arch/ecapa_tdnn/graph.cpp
-    arch/ecapa_tdnn/mel.cpp
     arch/ecapa_tdnn/model.cpp
     arch/ecapa_tdnn/weights.cpp
     arch/voxtral/model.cpp
diff --git a/src/arch/ecapa_tdnn/ecapa_tdnn.h b/src/arch/ecapa_tdnn/ecapa_tdnn.h
index 0b056f5be..6ac38419e 100644
--- a/src/arch/ecapa_tdnn/ecapa_tdnn.h
+++ b/src/arch/ecapa_tdnn/ecapa_tdnn.h
@@ -13,9 +13,9 @@
 
 #pragma once
 
-#include "mel.h"
 #include "transcribe-backend.h"
 #include "transcribe-langid.h"
+#include "transcribe-mel.h"
 #include "transcribe-model.h"
 #include "weights.h"
 
@@ -62,6 +62,7 @@ struct Model final : public transcribe_model {
 
 struct Session final : public transcribe_langid_session {
     // Host scratch, reused across calls.
+    std::vector<float>   mel_raw;                // [n_mels * T], mel-major (MelFrontend)
     std::vector<float>   mel_buf;                // [T * n_mels], frame-major
     std::vector<float>   im2col_buf;             // [T * blk0_cols], frame-major
     std::vector<int32_t> idx_buf[kNumSeBlocks];  // reflect indices, [T + 2p]
diff --git a/src/arch/ecapa_tdnn/mel.cpp b/src/arch/ecapa_tdnn/mel.cpp
deleted file mode 100644
index 9ef459ac5..000000000
--- a/src/arch/ecapa_tdnn/mel.cpp
+++ /dev/null
@@ -1,388 +0,0 @@
-// arch/ecapa_tdnn/mel.cpp - SpeechBrain log-mel front end.
-//
-// See mel.h for the SpeechBrain pipeline this reproduces and the
-// per-step numerical constants. Implementation notes:
-//
-//   * The FFT is the mixed-radix fp32 routine from transcribe-mel.cpp
-//     (itself lifted from whisper.cpp). n_fft = 400 = 2^4 * 5^2, so it recurses four
-//     radix-2 levels down to 25-point naive DFT leaves. There is no
-//     power-of-two path here: this front end is non-pow2 only.
-//   * Accumulation policy: the window and the FFT run in fp32 (matching
-//     torch's fp32 STFT); the mel matmul, the log, the top-dB max and the
-//     per-bin mean all accumulate in fp64 and round once at storage. That
-//     is strictly more accurate than SpeechBrain's fp32 matmul, which is
-//     the direction we want — the residual is far inside the fe.mel
-//     tolerance.
-//   * Output is frame-major [T, n_mels]. Every frame is written by
-//     exactly one thread into its own slot, so the result is bit-
-//     identical for any thread count.
-
-#include "mel.h"
-
-#include "transcribe-batch-util.h"
-#include "transcribe-log.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <cstring>
-#include <limits>
-#include <vector>
-
-#ifndef M_PI
-#    define M_PI 3.14159265358979323846
-#endif
-
-namespace transcribe::ecapa_tdnn {
-
-namespace {
-
-// Naive O(N^2) DFT leaf for the mixed-radix path. Used when N hits an odd
-// factor during recursive halving (N=400 -> 200 -> 100 -> 50 -> 25 odd ->
-// leaf). Uses the per-frontend fp32 LUT (lut_size divisible by N by
-// construction).
-void dft_naive_f32(const float * in, int N, const float * cos_lut, const float * sin_lut, int lut_size, float * out) {
-    const int stride = lut_size / N;
-    for (int k = 0; k < N; ++k) {
-        float     re   = 0.0f;
-        float     im   = 0.0f;
-        // idx = (k * n * stride) % lut_size, advanced incrementally (same
-        // indices, same accumulation order, no division per term).
-        const int step = (k * stride) % lut_size;
-        int       idx  = 0;
-        for (int n = 0; n < N; ++n) {
-            re += in[n] * cos_lut[idx];
-            im -= in[n] * sin_lut[idx];
-            idx += step;
-            if (idx >= lut_size) {
-                idx -= lut_size;
-            }
-        }
-        out[2 * k]     = re;
-        out[2 * k + 1] = im;
-    }
-}
-
-// Cooley-Tukey mixed-radix FFT. Recursively halves while N is even and
-// falls back to the naive DFT on odd leaves.
-//
-// Buffer contract:
-//   in    : 2*N floats. First N hold the real input; second N are used as
-//           scratch for the even/odd split at this level.
-//   out   : 8*N floats. The top-level result lands in the first 2*N;
-//           recursion uses the remainder as intermediate output.
-void mixed_radix_fft_f32(float * in, int N, const float * cos_lut, const float * sin_lut, int lut_size, float * out) {
-    if (N == 1) {
-        out[0] = in[0];
-        out[1] = 0.0f;
-        return;
-    }
-    if (N & 1) {
-        dft_naive_f32(in, N, cos_lut, sin_lut, lut_size, out);
-        return;
-    }
-    const int half_N = N / 2;
-    float *   even   = in + N;
-    for (int i = 0; i < half_N; ++i) {
-        even[i] = in[2 * i];
-    }
-    float * even_fft = out + 2 * N;
-    mixed_radix_fft_f32(even, half_N, cos_lut, sin_lut, lut_size, even_fft);
-
-    float * odd = even;
-    for (int i = 0; i < half_N; ++i) {
-        odd[i] = in[2 * i + 1];
-    }
-    float * odd_fft = even_fft + N;
-    mixed_radix_fft_f32(odd, half_N, cos_lut, sin_lut, lut_size, odd_fft);
-
-    const int step = lut_size / N;
-    for (int k = 0; k < half_N; ++k) {
-        const int   idx           = k * step;
-        const float w_re          = cos_lut[idx];
-        const float w_im          = -sin_lut[idx];
-        const float re_odd        = odd_fft[2 * k];
-        const float im_odd        = odd_fft[2 * k + 1];
-        out[2 * k]                = even_fft[2 * k] + w_re * re_odd - w_im * im_odd;
-        out[2 * k + 1]            = even_fft[2 * k + 1] + w_re * im_odd + w_im * re_odd;
-        out[2 * (k + half_N)]     = even_fft[2 * k] - w_re * re_odd + w_im * im_odd;
-        out[2 * (k + half_N) + 1] = even_fft[2 * k + 1] - w_re * im_odd - w_im * re_odd;
-    }
-}
-
-// Twiddle LUT shared by the constructor and the test hook: computed in
-// fp64 for accuracy, stored fp32 to match the FFT precision. lut_size ==
-// n_fft, so every recursion level divides it exactly.
-void build_twiddle_lut(int n_fft, std::vector<float> & cos_lut, std::vector<float> & sin_lut) {
-    cos_lut.resize(static_cast<size_t>(n_fft));
-    sin_lut.resize(static_cast<size_t>(n_fft));
-    for (int i = 0; i < n_fft; ++i) {
-        const double theta              = 2.0 * M_PI * i / static_cast<double>(n_fft);
-        cos_lut[static_cast<size_t>(i)] = static_cast<float>(std::cos(theta));
-        sin_lut[static_cast<size_t>(i)] = static_cast<float>(std::sin(theta));
-    }
-}
-
-}  // namespace
-
-// ---------- MelFrontend ----------
-
-MelFrontend::MelFrontend(const MelConfig & cfg) : cfg_(cfg) {
-    auto reject = [this](const char * field, const char * why) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: invalid config field '%s': %s", field, why);
-        status_ = TRANSCRIBE_ERR_GGUF;
-    };
-
-    if (cfg_.n_fft <= 0 || (cfg_.n_fft & 1) != 0) {
-        // The mixed-radix FFT halves while N is even; an odd n_fft would
-        // degenerate to a single O(N^2) leaf, and n_fft/2 padding would
-        // no longer be exact.
-        reject("n_fft", "must be a positive even integer");
-    }
-    if (cfg_.n_mels <= 0) {
-        reject("n_mels", "must be positive");
-    }
-    if (cfg_.hop_length <= 0) {
-        reject("hop_length", "must be positive");
-    }
-    if (cfg_.sample_rate <= 0) {
-        reject("sample_rate", "must be positive");
-    }
-    if (cfg_.win_length != cfg_.n_fft) {
-        // SpeechBrain passes win_length == n_fft, so there is no
-        // sub-window zero-pad placement to get wrong. Reject rather than
-        // silently guess where the window sits inside the FFT buffer.
-        reject("win_length", "must equal n_fft");
-    }
-    if (cfg_.window_type != "hamming_periodic") {
-        reject("window_type", "only \"hamming_periodic\" is implemented");
-    }
-    if (cfg_.pad_mode != "constant") {
-        reject("pad_mode", "only \"constant\" is implemented");
-    }
-    if (cfg_.normalize != "sentence_mean" && cfg_.normalize != "none") {
-        reject("normalize", "only \"sentence_mean\" and \"none\" are implemented");
-    }
-
-    if (status_ != TRANSCRIBE_OK) {
-        return;
-    }
-
-    n_freq_ = cfg_.n_fft / 2 + 1;
-
-    if (cfg_.filterbank.size() != static_cast<size_t>(cfg_.n_mels) * static_cast<size_t>(n_freq_)) {
-        // The filterbank is checkpoint-provided (`frontend.mel_filterbank`);
-        // SpeechBrain's triangle geometry is never rebuilt here.
-        reject("filterbank", "must hold exactly n_mels * (n_fft/2 + 1) weights");
-        return;
-    }
-
-    // Periodic Hamming: w[n] = 0.54 - 0.46*cos(2*pi*n/N), N = n_fft.
-    // torch.hamming_window defaults to periodic=True. Computed in fp64,
-    // stored fp32 (the STFT runs fp32).
-    window_.resize(static_cast<size_t>(cfg_.n_fft));
-    for (int n = 0; n < cfg_.n_fft; ++n) {
-        const double v                  = 0.54 - 0.46 * std::cos(2.0 * M_PI * n / static_cast<double>(cfg_.n_fft));
-        window_[static_cast<size_t>(n)] = static_cast<float>(v);
-    }
-
-    build_twiddle_lut(cfg_.n_fft, cos_lut_, sin_lut_);
-
-    fb_lo_.assign(static_cast<size_t>(cfg_.n_mels), 0);
-    fb_hi_.assign(static_cast<size_t>(cfg_.n_mels), 0);
-    for (int m = 0; m < cfg_.n_mels; ++m) {
-        const float * row = cfg_.filterbank.data() + static_cast<size_t>(m) * static_cast<size_t>(n_freq_);
-        int           lo  = n_freq_;
-        int           hi  = 0;
-        for (int k = 0; k < n_freq_; ++k) {
-            if (row[k] != 0.0f) {
-                lo = std::min(lo, k);
-                hi = k + 1;
-            }
-        }
-        fb_lo_[static_cast<size_t>(m)] = lo < hi ? lo : 0;
-        fb_hi_[static_cast<size_t>(m)] = lo < hi ? hi : 0;
-    }
-}
-
-int MelFrontend::n_frames_for(int64_t n_samples) const {
-    if (n_samples < 0 || cfg_.hop_length <= 0) {
-        return 0;
-    }
-    // torch center=True: the signal is padded by n_fft/2 on both sides and
-    // framed at every hop, giving floor(n / hop) + 1 frames. The trailing
-    // frames run into the zero pad; SpeechBrain keeps them.
-    return static_cast<int>(n_samples / static_cast<int64_t>(cfg_.hop_length)) + 1;
-}
-
-transcribe_status MelFrontend::compute(const float *        pcm,
-                                       int64_t              n_samples,
-                                       std::vector<float> & out,
-                                       int &                out_n_frames,
-                                       int                  n_threads) const {
-    if (status_ != TRANSCRIBE_OK) {
-        // Constructor already logged the offending field.
-        return status_;
-    }
-    if (pcm == nullptr) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: pcm is NULL");
-        return TRANSCRIBE_ERR_INVALID_ARG;
-    }
-    if (n_samples < 1) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: n_samples = %lld, need at least 1",
-                static_cast<long long>(n_samples));
-        return TRANSCRIBE_ERR_INVALID_ARG;
-    }
-
-    const int n_fft   = cfg_.n_fft;
-    const int hop     = cfg_.hop_length;
-    const int n_mels  = cfg_.n_mels;
-    const int n_freq  = n_freq_;
-    const int pad     = n_fft / 2;
-    const int n_frame = n_frames_for(n_samples);
-
-    // ---- 1. Zero pad (torch center=True, pad_mode="constant") ----
-    //
-    // Frame t reads padded[t*hop .. t*hop + n_fft). The last frame starts
-    // at (n_frame-1)*hop = floor(n/hop)*hop <= n, so it ends at most at
-    // n + n_fft = n + 2*pad — exactly the buffer size, with no slack. The
-    // check below turns an off-by-one into an error instead of a heap
-    // over-read.
-    const size_t padded_len = static_cast<size_t>(n_samples) + 2 * static_cast<size_t>(pad);
-    if (static_cast<size_t>(n_frame - 1) * static_cast<size_t>(hop) + static_cast<size_t>(n_fft) > padded_len) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "mel: internal framing overflow (n_frames=%d, n_samples=%lld)", n_frame,
-                static_cast<long long>(n_samples));
-        return TRANSCRIBE_ERR_INVALID_ARG;
-    }
-
-    std::vector<float> padded(padded_len, 0.0f);
-    std::memcpy(padded.data() + pad, pcm, static_cast<size_t>(n_samples) * sizeof(float));
-
-    // ---- 2. STFT -> power -> mel -> log, per frame ----
-    //
-    // Output frame-major: out[t * n_mels + m]. Each thread owns a disjoint
-    // set of frames (strided assignment keeps the load balanced), so the
-    // result does not depend on the thread count.
-    out.assign(static_cast<size_t>(n_frame) * static_cast<size_t>(n_mels), 0.0f);
-
-    const double log_floor = static_cast<double>(cfg_.log_floor);
-
-    int stft_threads = n_threads;
-    if (stft_threads <= 0) {
-        stft_threads = default_n_threads();
-    }
-    if (stft_threads > n_frame) {
-        stft_threads = std::max(1, n_frame);
-    }
-
-    auto worker = [&](int tid) {
-        std::vector<float> fft_in(2 * static_cast<size_t>(n_fft), 0.0f);
-        std::vector<float> fft_out(8 * static_cast<size_t>(n_fft), 0.0f);
-        std::vector<float> power(static_cast<size_t>(n_freq), 0.0f);
-
-        for (int t = tid; t < n_frame; t += stft_threads) {
-            const size_t start = static_cast<size_t>(t) * static_cast<size_t>(hop);
-            for (int n = 0; n < n_fft; ++n) {
-                fft_in[static_cast<size_t>(n)] =
-                    padded[start + static_cast<size_t>(n)] * window_[static_cast<size_t>(n)];
-            }
-            mixed_radix_fft_f32(fft_in.data(), n_fft, cos_lut_.data(), sin_lut_.data(),
-                                static_cast<int>(cos_lut_.size()), fft_out.data());
-            for (int k = 0; k < n_freq; ++k) {
-                const float re                = fft_out[2 * static_cast<size_t>(k)];
-                const float im                = fft_out[2 * static_cast<size_t>(k) + 1];
-                power[static_cast<size_t>(k)] = re * re + im * im;
-            }
-
-            float * dst = out.data() + static_cast<size_t>(t) * static_cast<size_t>(n_mels);
-            for (int m = 0; m < n_mels; ++m) {
-                const float * fb_row = cfg_.filterbank.data() + static_cast<size_t>(m) * static_cast<size_t>(n_freq);
-                double        sum    = 0.0;
-                const int     k_hi   = fb_hi_[static_cast<size_t>(m)];
-                for (int k = fb_lo_[static_cast<size_t>(m)]; k < k_hi; ++k) {
-                    sum += static_cast<double>(fb_row[k]) * static_cast<double>(power[static_cast<size_t>(k)]);
-                }
-                // SpeechBrain Filterbank.log_mel: 10*log10(max(x, amin))
-                // with amin = 1e-10. ref_value is 1.0, so the usual
-                // "- 10*log10(ref)" term is exactly zero.
-                if (sum < log_floor) {
-                    sum = log_floor;
-                }
-                dst[m] = static_cast<float>(10.0 * std::log10(sum));
-            }
-        }
-    };
-    run_on_threads(stft_threads, worker);
-
-    const size_t total = out.size();
-
-    // ---- 3. Top-dB floor ----
-    //
-    // Per utterance over time AND frequency, applied BEFORE the mean
-    // normalisation (SpeechBrain clamps inside Filterbank, and
-    // InputNormalization runs afterwards in compute_features).
-    if (cfg_.top_db > 0.0f) {
-        double max_all = -std::numeric_limits<double>::infinity();
-        for (size_t i = 0; i < total; ++i) {
-            const double v = static_cast<double>(out[i]);
-            if (v > max_all) {
-                max_all = v;
-            }
-        }
-        const double floor_val = max_all - static_cast<double>(cfg_.top_db);
-        for (size_t i = 0; i < total; ++i) {
-            if (static_cast<double>(out[i]) < floor_val) {
-                out[i] = static_cast<float>(floor_val);
-            }
-        }
-    }
-
-    // ---- 4. Sentence-mean normalisation ----
-    //
-    // InputNormalization(norm_type="sentence", std_norm=False): subtract
-    // each mel bin's mean over ALL frames. fp64 accumulator, one rounding
-    // at storage.
-    if (cfg_.normalize == "sentence_mean") {
-        std::vector<double> mean(static_cast<size_t>(n_mels), 0.0);
-        for (int t = 0; t < n_frame; ++t) {
-            const float * row = out.data() + static_cast<size_t>(t) * static_cast<size_t>(n_mels);
-            for (int m = 0; m < n_mels; ++m) {
-                mean[static_cast<size_t>(m)] += static_cast<double>(row[m]);
-            }
-        }
-        for (int m = 0; m < n_mels; ++m) {
-            mean[static_cast<size_t>(m)] /= static_cast<double>(n_frame);
-        }
-        for (int t = 0; t < n_frame; ++t) {
-            float * row = out.data() + static_cast<size_t>(t) * static_cast<size_t>(n_mels);
-            for (int m = 0; m < n_mels; ++m) {
-                row[m] = static_cast<float>(static_cast<double>(row[m]) - mean[static_cast<size_t>(m)]);
-            }
-        }
-    }
-
-    out_n_frames = n_frame;
-    return TRANSCRIBE_OK;
-}
-
-// --- test hook ---------------------------------------------------------
-
-void mel_test_fft(const float * in400, float * out_re201, float * out_im201) {
-    constexpr int      kN = 400;
-    std::vector<float> cos_lut;
-    std::vector<float> sin_lut;
-    build_twiddle_lut(kN, cos_lut, sin_lut);
-
-    std::vector<float> fft_in(2 * static_cast<size_t>(kN), 0.0f);
-    std::vector<float> fft_out(8 * static_cast<size_t>(kN), 0.0f);
-    std::memcpy(fft_in.data(), in400, static_cast<size_t>(kN) * sizeof(float));
-
-    mixed_radix_fft_f32(fft_in.data(), kN, cos_lut.data(), sin_lut.data(), kN, fft_out.data());
-
-    for (int k = 0; k <= kN / 2; ++k) {
-        out_re201[k] = fft_out[2 * static_cast<size_t>(k)];
-        out_im201[k] = fft_out[2 * static_cast<size_t>(k) + 1];
-    }
-}
-
-}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/mel.h b/src/arch/ecapa_tdnn/mel.h
deleted file mode 100644
index b9ef6765d..000000000
--- a/src/arch/ecapa_tdnn/mel.h
+++ /dev/null
@@ -1,178 +0,0 @@
-// arch/ecapa_tdnn/mel.h - SpeechBrain log-mel front end (60-bin,
-// sentence-mean normalised) for the ecapa_tdnn family.
-//
-// INTERNAL to src/arch/ecapa_tdnn/. Pure CPU, no ggml dependency:
-// audio in, std::vector<float> mel out; the family graph builder copies
-// the result into a ggml_tensor at the backend boundary.
-//
-// Reproduces SpeechBrain's Fbank pipeline (speechbrain/processing/
-// features.py: STFT -> spectral_magnitude(power=1, log=False) ->
-// Filterbank -> InputNormalization(norm_type="sentence", std_norm=False))
-// as configured by speechbrain/lang-id-voxlingua107-ecapa:
-//
-//   * torch.stft(n_fft=400, hop=160, win_length=400, center=True,
-//     pad_mode="constant", onesided=True) -> 201 bins, T = n/160 + 1.
-//   * Window: torch.hamming_window(400) — PERIODIC,
-//     w[n] = 0.54 - 0.46*cos(2*pi*n/400).
-//   * Power spectrum re^2 + im^2 (SpeechBrain's spectral_magnitude with
-//     power=1 on the squared-modulus tensor).
-//   * Mel: 60 SpeechBrain triangles, applied as a plain [60, 201] matmul.
-//     The filterbank is NOT rebuilt here — its triangle geometry is
-//     SpeechBrain-specific and is dumped by the converter into the GGUF
-//     tensor `frontend.mel_filterbank`. MelConfig::filterbank is required.
-//   * Log: 10*log10(max(x, 1e-10)) (Filterbank.log_mel, amin=1e-10,
-//     ref_value=1.0 so the reference term vanishes).
-//   * Top-dB floor: per utterance over time AND frequency,
-//     x = max(x, max(x) - 80).
-//   * Normalisation: subtract each mel bin's mean over all frames.
-//
-// No pre-emphasis, no dither, no deltas: SpeechBrain's compute_features
-// for this checkpoint applies none of them.
-//
-// Construction is one-shot: the constructor validates the config and
-// precomputes the window and the FFT twiddle LUT. compute() is then a
-// pure function of (config, audio) and is const / thread-safe.
-
-#pragma once
-
-#include "transcribe.h"
-
-#include <cstdint>
-#include <string>
-#include <vector>
-
-namespace transcribe::ecapa_tdnn {
-
-// Front-end configuration. Mirrors the stt.frontend.* GGUF metadata the
-// loader reads; the defaults are the real VoxLingua107 values, so a
-// default-constructed MelConfig plus the checkpoint's filterbank gives a
-// working extractor.
-struct MelConfig {
-    int sample_rate = 16000;
-    int n_mels      = 60;
-    int n_fft       = 400;
-    int win_length  = 400;  // must equal n_fft; SpeechBrain uses no sub-window pad
-    int hop_length  = 160;
-
-    // STFT window shape. Only "hamming_periodic" is implemented:
-    // torch.hamming_window(N) with the default periodic=True,
-    // w[n] = 0.54 - 0.46*cos(2*pi*n/N). Any other value is rejected.
-    std::string window_type = "hamming_periodic";
-
-    // STFT input padding. Only "constant" is implemented: torch's
-    // center=True with pad_mode="constant", i.e. n_fft/2 zeros on each
-    // side. Any other value is rejected.
-    std::string pad_mode = "constant";
-
-    // Log floor: 10*log10(max(power, log_floor)).
-    float log_floor = 1e-10f;
-
-    // Per-utterance dynamic-range floor applied over time and frequency:
-    // x = max(x, max_all(x) - top_db). <= 0 disables it.
-    float top_db = 80.0f;
-
-    // Normalisation mode:
-    //   "sentence_mean" — subtract each mel bin's mean over all frames
-    //                     (SpeechBrain InputNormalization, norm_type=
-    //                     "sentence", std_norm=False). Default.
-    //   "none"          — emit the log-mel as-is.
-    std::string normalize = "sentence_mean";
-
-    // Mel filterbank [n_mels * n_freq] row-major MEL-MAJOR (each filter's
-    // n_freq weights contiguous), n_freq = n_fft/2 + 1. REQUIRED: the
-    // front end never rebuilds it, because SpeechBrain's triangle
-    // geometry (symmetric filters whose half-width is the distance to
-    // the left neighbour) is not the librosa/Slaney formula. Supplied by
-    // the loader from the GGUF tensor `frontend.mel_filterbank`.
-    std::vector<float> filterbank;
-};
-
-// Pure C++ log-mel extractor. Construct once, call compute() any number
-// of times. Thread-safety: const after construction; multiple threads may
-// call compute() concurrently.
-class MelFrontend {
-  public:
-    explicit MelFrontend(const MelConfig & cfg);
-
-    // TRANSCRIBE_OK when the config was accepted, else TRANSCRIBE_ERR_GGUF
-    // (the offending field is named in a log message). compute() returns
-    // the same status without touching the audio.
-    transcribe_status status() const { return status_; }
-
-    // Frame count for a given audio length, before calling compute().
-    // Matches torch center=True: floor(n_samples / hop_length) + 1. No
-    // trailing-frame drop — the trailing frames see the zero pad, exactly
-    // as torch does, and SpeechBrain keeps them.
-    int n_frames_for(int64_t n_samples) const;
-
-    // Run the full pipeline. pcm is 16 kHz mono float32 in [-1, 1].
-    //
-    // out is resized to n_frames * n_mels and written FRAME-MAJOR: frame
-    // t's n_mels values are contiguous at out[t * n_mels]. That is
-    // exactly ggml ne = [n_mels, T] and the reference dump layout
-    // [T, 60], so the graph builder can memcpy it and the dumper can
-    // write it without a transpose.
-    //
-    // n_threads controls STFT parallelism: 0 means default_n_threads()
-    // (capped at 8), further capped at n_frames. The result is
-    // bit-identical for any thread count — every frame is computed
-    // independently into its own output slot, and the log / top-dB /
-    // mean passes are single-threaded.
-    //
-    // Returns:
-    //   TRANSCRIBE_OK               normal success.
-    //   TRANSCRIBE_ERR_GGUF         bad config (see status()).
-    //   TRANSCRIBE_ERR_INVALID_ARG  pcm is NULL or n_samples < 1. The
-    //                               500 ms minimum is enforced by the
-    //                               LANGID role dispatcher, not here.
-    transcribe_status compute(const float *        pcm,
-                              int64_t              n_samples,
-                              std::vector<float> & out,
-                              int &                out_n_frames,
-                              int                  n_threads = 0) const;
-
-    // Read-only accessors for unit tests and the parity tool.
-    //
-    // window() and n_freq() are the CONSTRUCTED state and are only valid
-    // when status() == TRANSCRIBE_OK: a rejected config leaves the window
-    // empty and n_freq() at 0, while config() still returns the caller's
-    // struct verbatim. Check status() before trusting either.
-    const std::vector<float> & window() const { return window_; }
-
-    const MelConfig & config() const { return cfg_; }
-
-    int n_freq() const { return n_freq_; }
-
-  private:
-    MelConfig         cfg_;
-    transcribe_status status_ = TRANSCRIBE_OK;
-    int               n_freq_ = 0;  // n_fft/2 + 1
-
-    std::vector<float> window_;     // [n_fft], periodic Hamming, fp32
-
-    // cos/sin LUT for the mixed-radix FFT, indexed by 2*pi*i / n_fft.
-    // Sized to n_fft so every recursion level N (n_fft -> n_fft/2 -> ...
-    // -> odd leaf) divides the LUT exactly and lookups never fall back to
-    // live std::cos / std::sin. Stored fp32 to match the FFT precision
-    // and avoid a per-load cast in the inner butterfly.
-    std::vector<float> cos_lut_;
-    std::vector<float> sin_lut_;
-
-    // Nonzero support of each filterbank row: [fb_lo_[m], fb_hi_[m]) holds
-    // every nonzero weight of mel m (SpeechBrain's triangles cover a few
-    // bins each). The mel matmul only visits that range; the skipped terms
-    // are exact zeros, so the fp64 sum is unchanged.
-    std::vector<int> fb_lo_;
-    std::vector<int> fb_hi_;
-};
-
-// --- test hook ---------------------------------------------------------
-//
-// Run the production mixed-radix FFT over a 400-sample real input,
-// building the twiddle LUT exactly as MelFrontend's constructor does, and
-// write the 201 one-sided bins. Exists so tests/ecapa_tdnn_mel_unit.cpp can compare
-// the FFT against a naive O(N^2) fp64 DFT without including the .cpp or
-// running the whole pipeline. Not used by the runtime.
-void mel_test_fft(const float * in400, float * out_re201, float * out_im201);
-
-}  // namespace transcribe::ecapa_tdnn
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index 6f460fc40..c4c35294d 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -271,23 +271,28 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
 
     // ---- front end ------------------------------------------------------
     //
-    // The filterbank is SpeechBrain's own triangle geometry and is never
-    // rebuilt here. It is stored ne = [n_freq, n_mels], so the flat host
-    // vector is already mel-major, which is what MelConfig wants.
+    // The shared MelFrontend in SpeechBrain mode: periodic Hamming, constant
+    // pad, no pre-emphasis, 10*log10 with the top-dB floor, per-bin mean
+    // ("sentence_mean"). read_hparams already pinned window / pad_mode /
+    // normalize to those values and win_length to n_fft. The filterbank is
+    // SpeechBrain's own triangle geometry and is never rebuilt here. It is
+    // stored ne = [n_freq, n_mels], so the flat host vector is already
+    // mel-major, which is what MelConfig wants.
     {
         const size_t expected = static_cast<size_t>(hp.mel_n_mels) * static_cast<size_t>(hp.n_freq());
 
         MelConfig cfg;
-        cfg.sample_rate = hp.sample_rate;
-        cfg.n_mels      = hp.mel_n_mels;
-        cfg.n_fft       = hp.mel_n_fft;
-        cfg.win_length  = hp.mel_win;
-        cfg.hop_length  = hp.mel_hop;
-        cfg.window_type = hp.mel_window;
-        cfg.pad_mode    = hp.mel_pad_mode;
-        cfg.log_floor   = hp.mel_log_floor;
-        cfg.top_db      = hp.mel_top_db;
-        cfg.normalize   = hp.mel_normalize;
+        cfg.sample_rate   = hp.sample_rate;
+        cfg.num_mels      = hp.mel_n_mels;
+        cfg.n_fft         = hp.mel_n_fft;
+        cfg.win_length    = hp.mel_win;
+        cfg.hop_length    = hp.mel_hop;
+        cfg.pre_emphasis  = 0.0f;
+        cfg.window_type   = hp.mel_window;
+        cfg.pad_mode      = hp.mel_pad_mode;
+        cfg.log_clamp_min = hp.mel_log_floor;
+        cfg.top_db        = hp.mel_top_db;
+        cfg.normalize     = hp.mel_normalize;
 
         const auto rr = load_common::read_f32_tensor_checked(loader.gguf(), loader.path(), "frontend.mel_filterbank",
                                                              expected, kTag, cfg.filterbank);
@@ -298,9 +303,6 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
         }
 
         m->mel = std::make_unique<MelFrontend>(cfg);
-        if (m->mel->status() != TRANSCRIBE_OK) {
-            return m->mel->status();
-        }
     }
 
     // ---- weights ---------------------------------------------------------
@@ -377,9 +379,26 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
     // ---- front end (host side) -------------------------------------------
     const int64_t t_mel_start = now_us();
     int           T           = 0;
-    if (auto st = m.mel->compute(pcm, n_samples, s.mel_buf, T, n_threads); st != TRANSCRIBE_OK) {
+    int           n_mels      = 0;
+    if (n_samples < 0) {
+        return TRANSCRIBE_ERR_INVALID_ARG;
+    }
+    if (auto st = m.mel->compute(pcm, static_cast<size_t>(n_samples), s.mel_raw, n_mels, T, n_threads);
+        st != TRANSCRIBE_OK) {
         return st;
     }
+    if (n_mels != hp.mel_n_mels) {
+        return TRANSCRIBE_ERR_GGUF;  // unreachable: the config came from hp
+    }
+    // MelFrontend emits mel-major [n_mels, T]; the stage-0 im2col and the
+    // fe.mel dump want frame-major [T, n_mels] (ggml ne = [n_mels, T]).
+    s.mel_buf.resize(static_cast<size_t>(T) * static_cast<size_t>(n_mels));
+    for (int mi = 0; mi < n_mels; ++mi) {
+        const float * src = s.mel_raw.data() + static_cast<size_t>(mi) * static_cast<size_t>(T);
+        for (int t = 0; t < T; ++t) {
+            s.mel_buf[static_cast<size_t>(t) * static_cast<size_t>(n_mels) + static_cast<size_t>(mi)] = src[t];
+        }
+    }
     s.t_mel_us = now_us() - t_mel_start;
 
     if (T <= 0) {
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 0ea9653eb..0bdcc2e1f 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -411,11 +411,10 @@ add_test(NAME transcribe_run_dispatch_unit
 
 # -----------------------------------------------------------------------------
 # Dispatcher units against fake arches: roles, host thread pool, non-finite
-# input, throwing stream hooks, DIARIZE and LANGID roles, ecapa_tdnn front end
+# input, throwing stream hooks, DIARIZE and LANGID roles
 # -----------------------------------------------------------------------------
 
-foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize_dispatch langid_dispatch
-        ecapa_tdnn_mel)
+foreach(_t role_resolve run_on_threads nonfinite_input stream_hook_throw diarize_dispatch langid_dispatch)
     add_executable(transcribe_${_t}_unit ${_t}_unit.cpp)
     target_link_libraries(transcribe_${_t}_unit PRIVATE transcribe)
     target_include_directories(transcribe_${_t}_unit PRIVATE ${CMAKE_SOURCE_DIR}/src)
diff --git a/tests/ecapa_tdnn_mel_unit.cpp b/tests/ecapa_tdnn_mel_unit.cpp
deleted file mode 100644
index 390d81df7..000000000
--- a/tests/ecapa_tdnn_mel_unit.cpp
+++ /dev/null
@@ -1,449 +0,0 @@
-// ecapa_tdnn_mel_unit.cpp - hermetic unit tests for the ecapa_tdnn
-// (SpeechBrain) log-mel front end, transcribe::ecapa_tdnn::MelFrontend.
-//
-// Every expected value in this file is either a closed-form constant of
-// the periodic-Hamming / STFT math or is computed in the test itself at
-// fp64. Nothing is read from disk and no model is required, so this test
-// runs everywhere ctest does. The end-to-end comparison against
-// SpeechBrain lives in tests/mel_parity_tool.cpp + scripts/validate.py.
-//
-// What each test pins down (i.e. the bug it would catch):
-//
-//   test_window            periodic vs symmetric Hamming — the classic
-//                          off-by-one that biases every frame slightly.
-//   test_n_frames_for      torch center=True framing: floor(n/hop) + 1,
-//                          no trailing-frame drop.
-//   test_fft               the lifted mixed-radix FFT against a naive
-//                          O(N^2) fp64 DFT: wrong twiddle stride, a bad
-//                          odd-leaf fallback, or a scratch-buffer alias.
-//   test_pipeline_dc       window -> FFT -> power -> filterbank -> log
-//                          wired together with the right scale factor.
-//   test_top_db            the dynamic-range floor, and that it is taken
-//                          over time AND frequency.
-//   test_sentence_mean     per-bin mean subtraction over all frames.
-//   test_thread_invariance bit-identical output for any thread count.
-//   test_errors            config and argument rejection.
-
-#include "arch/ecapa_tdnn/mel.h"
-
-#include <cmath>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <random>
-#include <vector>
-
-#ifndef M_PI
-#    define M_PI 3.14159265358979323846
-#endif
-
-namespace {
-
-int g_failures = 0;
-
-#define CHECK(cond)                                                              \
-    do {                                                                         \
-        if (!(cond)) {                                                           \
-            std::fprintf(stderr, "FAIL %s:%d: %s\n", __FILE__, __LINE__, #cond); \
-            ++g_failures;                                                        \
-        }                                                                        \
-    } while (0)
-
-#define CHECK_NEAR(actual, expected, tol)                                                                          \
-    do {                                                                                                           \
-        const double _a = static_cast<double>(actual);                                                             \
-        const double _e = static_cast<double>(expected);                                                           \
-        const double _d = std::fabs(_a - _e);                                                                      \
-        if (!(_d <= (tol))) {                                                                                      \
-            std::fprintf(stderr, "FAIL %s:%d: %s = %.17g, expected %.17g, diff %.6g > %.6g\n", __FILE__, __LINE__, \
-                         #actual, _a, _e, _d, (tol));                                                              \
-            ++g_failures;                                                                                          \
-        }                                                                                                          \
-    } while (0)
-
-constexpr int kNFft  = 400;
-constexpr int kNFreq = 201;
-constexpr int kNMels = 60;
-
-// A synthetic "one-hot" filterbank: mel bin m reads FFT bin 3*m with
-// weight 1 and nothing else (3*59 = 177 < 201). This turns the mel matmul
-// into an identity on a subset of the power spectrum, so the pipeline
-// tests can assert exact closed-form dB values instead of a filterbank
-// convolution. The real SpeechBrain filterbank is never rebuilt in C++;
-// it comes from the GGUF and is validated by the parity tool.
-std::vector<float> one_hot_filterbank() {
-    std::vector<float> fb(static_cast<size_t>(kNMels) * kNFreq, 0.0f);
-    for (int m = 0; m < kNMels; ++m) {
-        fb[static_cast<size_t>(m) * kNFreq + static_cast<size_t>(m) * 3] = 1.0f;
-    }
-    return fb;
-}
-
-// The real VoxLingua107 front-end config, with a caller-supplied
-// filterbank (the tests use the one-hot bank above).
-transcribe::ecapa_tdnn::MelConfig base_config() {
-    transcribe::ecapa_tdnn::MelConfig cfg;
-    cfg.sample_rate = 16000;
-    cfg.n_mels      = kNMels;
-    cfg.n_fft       = kNFft;
-    cfg.win_length  = kNFft;
-    cfg.hop_length  = 160;
-    cfg.filterbank  = one_hot_filterbank();
-    return cfg;
-}
-
-void test_window() {
-    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
-    CHECK(mf.status() == TRANSCRIBE_OK);
-
-    const auto & w = mf.window();
-    CHECK(w.size() == static_cast<size_t>(kNFft));
-    if (w.size() != static_cast<size_t>(kNFft)) {
-        return;
-    }
-
-    // Periodic Hamming, w[n] = 0.54 - 0.46*cos(2*pi*n/400):
-    //   n = 0   -> 0.54 - 0.46          = 0.08
-    //   n = 100 -> 0.54 - 0.46*cos(pi/2)= 0.54
-    //   n = 200 -> 0.54 - 0.46*cos(pi)  = 1.00   (the peak is ON a sample;
-    //                                             a SYMMETRIC Hamming of
-    //                                             length 400 never reaches
-    //                                             1.0 — that is the bug
-    //                                             this line catches)
-    //   n = 300 -> 0.54
-    CHECK_NEAR(w[0], 0.08, 1e-6);
-    CHECK_NEAR(w[100], 0.54, 1e-6);
-    CHECK_NEAR(w[200], 1.0, 1e-6);
-    CHECK_NEAR(w[300], 0.54, 1e-6);
-
-    // w[1] has no closed form; compute the literal at fp64 here so the
-    // test pins the exact formula (2*pi*n/N, not 2*pi*n/(N-1)).
-    const double w1 = 0.54 - 0.46 * std::cos(2.0 * M_PI * 1.0 / 400.0);
-    CHECK_NEAR(w[1], w1, 1e-6);
-    // Cross-check the fp64 expression against its own decimal expansion,
-    // so a bad M_PI or a mistyped denominator cannot make both sides
-    // wrong in the same way.
-    CHECK_NEAR(w1, 0.0800567490584361, 1e-15);
-
-    // Sum over a full period: the cosine term cancels exactly, leaving
-    // 0.54 * N = 216. The measured value is 216.00000069 (400 fp32
-    // roundings of the fp64 window), comfortably inside 1e-6.
-    double sum = 0.0;
-    for (float v : w) {
-        sum += static_cast<double>(v);
-    }
-    CHECK_NEAR(sum, 216.0, 1e-6);
-}
-
-void test_n_frames_for() {
-    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
-
-    // torch center=True: floor(n / hop) + 1, trailing frames kept.
-    CHECK(mf.n_frames_for(16000) == 101);  // 1 s
-    CHECK(mf.n_frames_for(8000) == 51);    // 0.5 s
-    CHECK(mf.n_frames_for(16159) == 101);  // one sample short of the next hop
-    CHECK(mf.n_frames_for(16160) == 102);  // exactly on the next hop
-    CHECK(mf.n_frames_for(1) == 1);        // a single sample still frames
-}
-
-// Straightforward O(N^2) DFT at fp64. Independent of the production LUT
-// and recursion: this is the oracle the mixed-radix FFT is checked
-// against.
-void naive_dft(const float * in, int N, std::vector<double> & re, std::vector<double> & im) {
-    re.assign(static_cast<size_t>(N / 2 + 1), 0.0);
-    im.assign(static_cast<size_t>(N / 2 + 1), 0.0);
-    for (int k = 0; k <= N / 2; ++k) {
-        double sr = 0.0;
-        double si = 0.0;
-        for (int n = 0; n < N; ++n) {
-            const double theta = 2.0 * M_PI * static_cast<double>(k) * static_cast<double>(n) / static_cast<double>(N);
-            sr += static_cast<double>(in[n]) * std::cos(theta);
-            si -= static_cast<double>(in[n]) * std::sin(theta);
-        }
-        re[static_cast<size_t>(k)] = sr;
-        im[static_cast<size_t>(k)] = si;
-    }
-}
-
-void test_fft() {
-    std::mt19937                          rng(20240902u);
-    std::uniform_real_distribution<float> dist(-1.0f, 1.0f);
-
-    for (int trial = 0; trial < 3; ++trial) {
-        std::vector<float> in(static_cast<size_t>(kNFft));
-        for (int i = 0; i < kNFft; ++i) {
-            in[static_cast<size_t>(i)] = dist(rng);
-        }
-
-        std::vector<float> got_re(static_cast<size_t>(kNFreq), 0.0f);
-        std::vector<float> got_im(static_cast<size_t>(kNFreq), 0.0f);
-        transcribe::ecapa_tdnn::mel_test_fft(in.data(), got_re.data(), got_im.data());
-
-        std::vector<double> ref_re;
-        std::vector<double> ref_im;
-        naive_dft(in.data(), kNFft, ref_re, ref_im);
-
-        double max_mag = 0.0;
-        double max_err = 0.0;
-        for (int k = 0; k < kNFreq; ++k) {
-            const double mag = std::hypot(ref_re[static_cast<size_t>(k)], ref_im[static_cast<size_t>(k)]);
-            max_mag          = std::max(max_mag, mag);
-            max_err          = std::max(max_err, std::fabs(static_cast<double>(got_re[static_cast<size_t>(k)]) -
-                                                           ref_re[static_cast<size_t>(k)]));
-            max_err          = std::max(max_err, std::fabs(static_cast<double>(got_im[static_cast<size_t>(k)]) -
-                                                           ref_im[static_cast<size_t>(k)]));
-        }
-        // fp32 FFT vs fp64 DFT: relative to the peak magnitude the
-        // measured error is ~1e-7. 1e-6 keeps 10x headroom for a
-        // different libm while still catching a transform that has gone
-        // meaningfully wrong (a mistyped twiddle stride, a bad odd-leaf
-        // fallback); a looser bound here would guard nothing.
-        CHECK(max_mag > 1.0);
-        CHECK_NEAR(max_err / max_mag, 0.0, 1e-6);
-    }
-}
-
-void test_pipeline_dc() {
-    // Constant 1.0 for 1 s. For any interior frame the windowed signal is
-    // just the window, so the DC bin of the DFT is sum(w) = 216 and the
-    // DC power is 216^2 = 46656 -> 10*log10(46656) = 46.68907502 dB.
-    //
-    // A periodic Hamming has an exactly 3-tap DFT (bins 0, 1 and N-1), so
-    // every other bin is exactly zero in exact arithmetic. The one-hot
-    // filterbank reads bins 3, 6, ..., 177 for mel bins 1..59, all of
-    // which are true zeros: they must land on the log floor,
-    // 10*log10(1e-10) = -100. (Measured fp32 leakage tops out at
-    // -105.4 dB, i.e. 4.6 dB of margin under the floor.)
-    transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-    cfg.normalize                         = "none";
-    cfg.top_db                            = 0.0f;  // disabled
-
-    transcribe::ecapa_tdnn::MelFrontend mf(cfg);
-    CHECK(mf.status() == TRANSCRIBE_OK);
-
-    std::vector<float> pcm(16000, 1.0f);
-    std::vector<float> out;
-    int                n_frames = 0;
-    CHECK(mf.compute(pcm.data(), 16000, out, n_frames, 1) == TRANSCRIBE_OK);
-    CHECK(n_frames == 101);
-    CHECK(out.size() == static_cast<size_t>(101) * kNMels);
-    if (out.size() != static_cast<size_t>(101) * kNMels) {
-        return;
-    }
-
-    const float * frame50 = out.data() + static_cast<size_t>(50) * kNMels;
-    CHECK_NEAR(frame50[0], 46.68907502, 1e-3);
-    for (int m = 1; m < kNMels; ++m) {
-        CHECK_NEAR(frame50[m], -100.0, 1e-3);
-    }
-}
-
-void test_top_db() {
-    // Same input, top_db enabled. The global maximum over time and
-    // frequency is the interior-frame DC value 46.68907502, so the floor
-    // is 46.68907502 - 80 = -33.31092498 and every -100 dB bin rises to
-    // it.
-    transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-    cfg.normalize                         = "none";
-    cfg.top_db                            = 80.0f;
-
-    transcribe::ecapa_tdnn::MelFrontend mf(cfg);
-    CHECK(mf.status() == TRANSCRIBE_OK);
-
-    std::vector<float> pcm(16000, 1.0f);
-    std::vector<float> out;
-    int                n_frames = 0;
-    CHECK(mf.compute(pcm.data(), 16000, out, n_frames, 1) == TRANSCRIBE_OK);
-    CHECK(n_frames == 101);
-    if (out.size() != static_cast<size_t>(101) * kNMels) {
-        return;
-    }
-
-    const float * frame50 = out.data() + static_cast<size_t>(50) * kNMels;
-    CHECK_NEAR(frame50[0], 46.68907502, 1e-3);
-    for (int m = 1; m < kNMels; ++m) {
-        CHECK_NEAR(frame50[m], -33.31092498, 1e-3);
-    }
-
-    // Nothing anywhere may sit below the floor.
-    for (float v : out) {
-        CHECK(static_cast<double>(v) >= -33.31092498 - 1e-3);
-    }
-}
-
-void test_sentence_mean() {
-    // 1 kHz sine, 1 s. After sentence-mean normalisation every mel bin's
-    // mean over frames is zero by construction — this checks the pass runs
-    // over the right axis (per bin over frames, not per frame over bins).
-    transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-    cfg.normalize                         = "sentence_mean";
-
-    transcribe::ecapa_tdnn::MelFrontend mf(cfg);
-    CHECK(mf.status() == TRANSCRIBE_OK);
-
-    std::vector<float> pcm(16000);
-    for (int i = 0; i < 16000; ++i) {
-        pcm[static_cast<size_t>(i)] = static_cast<float>(std::sin(2.0 * M_PI * 1000.0 * i / 16000.0));
-    }
-
-    std::vector<float> out;
-    int                n_frames = 0;
-    CHECK(mf.compute(pcm.data(), 16000, out, n_frames, 1) == TRANSCRIBE_OK);
-    CHECK(n_frames == 101);
-    if (out.size() != static_cast<size_t>(n_frames) * kNMels) {
-        return;
-    }
-
-    for (int m = 0; m < kNMels; ++m) {
-        double sum = 0.0;
-        for (int t = 0; t < n_frames; ++t) {
-            sum += static_cast<double>(out[static_cast<size_t>(t) * kNMels + static_cast<size_t>(m)]);
-        }
-        CHECK_NEAR(sum / n_frames, 0.0, 1e-4);
-    }
-
-    // A 1 kHz tone must not be flat: if the mean pass had subtracted the
-    // wrong axis the per-frame spread would collapse.
-    double spread = 0.0;
-    for (float v : out) {
-        spread = std::max(spread, std::fabs(static_cast<double>(v)));
-    }
-    CHECK(spread > 1.0);
-
-    // Degenerate T=1 (fewer samples than one hop): the single frame is
-    // its own mean, so the output is all zeros. No divide-by-zero, unlike
-    // a variance-normalising front end.
-    std::vector<float> tiny_out;
-    int                tiny_frames = 0;
-    CHECK(mf.compute(pcm.data(), 100, tiny_out, tiny_frames, 1) == TRANSCRIBE_OK);
-    CHECK(tiny_frames == 1);
-    CHECK(tiny_out.size() == static_cast<size_t>(kNMels));
-    for (float v : tiny_out) {
-        CHECK(v == 0.0f);
-    }
-}
-
-void test_thread_invariance() {
-    // 3 s of fixed-seed noise, computed single-threaded and with 4
-    // threads. Every frame is written by exactly one thread into its own
-    // output slot and the log / top-dB / mean passes are single-threaded,
-    // so the results must be bit-identical, not merely close.
-    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
-    CHECK(mf.status() == TRANSCRIBE_OK);
-
-    std::mt19937                          rng(987654321u);
-    std::uniform_real_distribution<float> dist(-0.5f, 0.5f);
-    std::vector<float>                    pcm(48000);
-    for (auto & v : pcm) {
-        v = dist(rng);
-    }
-
-    std::vector<float> out1;
-    std::vector<float> out4;
-    int                frames1 = 0;
-    int                frames4 = 0;
-    CHECK(mf.compute(pcm.data(), 48000, out1, frames1, 1) == TRANSCRIBE_OK);
-    CHECK(mf.compute(pcm.data(), 48000, out4, frames4, 4) == TRANSCRIBE_OK);
-    CHECK(frames1 == 301);
-    CHECK(frames1 == frames4);
-    CHECK(out1.size() == out4.size());
-    if (out1.size() == out4.size() && !out1.empty()) {
-        CHECK(std::memcmp(out1.data(), out4.data(), out1.size() * sizeof(float)) == 0);
-    }
-}
-
-void test_errors() {
-    transcribe::ecapa_tdnn::MelFrontend mf(base_config());
-    std::vector<float>                  out;
-    int                                 n_frames = 0;
-    std::vector<float>                  pcm(16000, 0.1f);
-
-    CHECK(mf.compute(nullptr, 16000, out, n_frames, 1) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(mf.compute(pcm.data(), 0, out, n_frames, 1) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(mf.compute(pcm.data(), -5, out, n_frames, 1) == TRANSCRIBE_ERR_INVALID_ARG);
-    // One sample is legal: the 500 ms minimum is an API-layer rule, not a
-    // front-end one.
-    CHECK(mf.compute(pcm.data(), 1, out, n_frames, 1) == TRANSCRIBE_OK);
-    CHECK(n_frames == 1);
-
-    // Rejected configs. Each must set status() AND make compute() fail
-    // without touching the audio.
-    auto expect_rejected = [&](transcribe::ecapa_tdnn::MelConfig cfg, const char * what) {
-        transcribe::ecapa_tdnn::MelFrontend bad(cfg);
-        if (bad.status() != TRANSCRIBE_ERR_GGUF) {
-            std::fprintf(stderr, "FAIL %s:%d: config '%s' was accepted\n", __FILE__, __LINE__, what);
-            ++g_failures;
-            return;
-        }
-        std::vector<float> bad_out;
-        int                bad_frames = 0;
-        CHECK(bad.compute(pcm.data(), 16000, bad_out, bad_frames, 1) == TRANSCRIBE_ERR_GGUF);
-    };
-
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.window_type                       = "hann_periodic";
-        expect_rejected(cfg, "window_type");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.pad_mode                          = "reflect";
-        expect_rejected(cfg, "pad_mode");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.normalize                         = "per_feature";
-        expect_rejected(cfg, "normalize");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.win_length                        = 320;
-        expect_rejected(cfg, "win_length != n_fft");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.filterbank.clear();
-        expect_rejected(cfg, "empty filterbank");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.filterbank.resize(cfg.filterbank.size() - 1);
-        expect_rejected(cfg, "short filterbank");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.n_fft                             = 401;
-        cfg.win_length                        = 401;
-        expect_rejected(cfg, "odd n_fft");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.hop_length                        = 0;
-        expect_rejected(cfg, "hop_length");
-    }
-    {
-        transcribe::ecapa_tdnn::MelConfig cfg = base_config();
-        cfg.n_mels                            = 0;
-        expect_rejected(cfg, "n_mels");
-    }
-}
-
-}  // namespace
-
-int main() {
-    test_window();
-    test_n_frames_for();
-    test_fft();
-    test_pipeline_dc();
-    test_top_db();
-    test_sentence_mean();
-    test_thread_invariance();
-    test_errors();
-
-    if (g_failures > 0) {
-        std::fprintf(stderr, "mel_unit: %d failures\n", g_failures);
-        return EXIT_FAILURE;
-    }
-    std::fprintf(stdout, "mel_unit: ok\n");
-    return EXIT_SUCCESS;
-}
diff --git a/tests/tolerances/ecapa_tdnn.json b/tests/tolerances/ecapa_tdnn.json
index 55020da2b..8a2271b88 100644
--- a/tests/tolerances/ecapa_tdnn.json
+++ b/tests/tolerances/ecapa_tdnn.json
@@ -13,7 +13,9 @@
     "  model     models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf",
     "            (ALL_F32; no quantised tensor takes part in these numbers)",
     "  runtime   build/bin/transcribe-cli --backend cpu --threads 1",
-    "  front end the production C++ log-mel front end (src/arch/ecapa_tdnn/mel.cpp),",
+    "  front end the production C++ log-mel front end (src/transcribe-mel.cpp,",
+    "            normalize=sentence_mean; measured on its family-local predecessor,",
+    "            which ran the same fp32 mixed-radix FFT),",
     "            not a reference feature dump -- fe.mel is measured, not injected",
     "  host      macOS / arm64 (Apple Silicon), ggml CPU backend",
     "  reference speechbrain 1.1.1 on torch 2.13.0, fp32, CPU, 1 thread,",
@@ -38,7 +40,7 @@
     "",
     "Dominant drift source: fp32 summation ORDER, not algorithm. Two",
     "contributors, in order of size:",
-    "  1. fe.mel -- the C++ mixed-radix 400-point FFT (src/arch/ecapa_tdnn/mel.cpp)",
+    "  1. fe.mel -- the C++ mixed-radix 400-point FFT (src/transcribe-mel.cpp)",
     "     accumulates the STFT differently from torch.stft's FFT, and the",
     "     difference is then amplified by 10*log10 on near-floor bins. This",
     "     is the only stage whose drift is not inherited.",

From 00e4aa215721cae0d32e7dd1a6bf69227a503620 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:05:50 +0800
Subject: [PATCH 12/38] langid: trim ecapa_tdnn validation, dumps and comments

- read_hparams: drop the kMax* bounds, the format_version gate, and the
  non-shape front-end range checks; keep sample rate, front-end string
  pins (MelFrontend does not reject unknown values) and graph-shape
  invariants.
- Read stt.langid.labels.codes once; n_labels comes from the label table.
- Keep one T > pad guard in forward(); drop the repeated short-input and
  unreachable checks in forward() and build_graph().
- Remove the host-side cls.log_probs dump, its tolerance entry, and the
  reference dump of it.
- Use ggml_time_us, a heap compute context instead of graph_arena, one
  kGraphSize, and ggml_get_rows directly.
- Shorten comments; drop review-checklist tags.
---
 .../dump_reference_ecapa_tdnn_speechbrain.py  |   4 +-
 src/arch/ecapa_tdnn/ecapa_tdnn.h              |  25 +--
 src/arch/ecapa_tdnn/graph.cpp                 | 162 ++++--------------
 src/arch/ecapa_tdnn/graph.h                   |  34 ++--
 src/arch/ecapa_tdnn/model.cpp                 | 145 +++-------------
 src/arch/ecapa_tdnn/weights.cpp               | 127 ++------------
 src/arch/ecapa_tdnn/weights.h                 |  18 +-
 src/transcribe-langid.cpp                     |  10 +-
 tests/tolerances/ecapa_tdnn.json              |   9 +-
 9 files changed, 99 insertions(+), 435 deletions(-)

diff --git a/scripts/dump_reference_ecapa_tdnn_speechbrain.py b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
index be7f48c06..08e9e7e99 100644
--- a/scripts/dump_reference_ecapa_tdnn_speechbrain.py
+++ b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
@@ -43,7 +43,7 @@
                      asp_bn -> fc(6144->256)   => enc.emb [256]
       -> Xvector.Classifier: LeakyReLU -> BN -> Linear(256->512)
                      -> LeakyReLU -> BN -> Linear(512->107)
-                     -> log_softmax             => cls.log_probs [107]
+                     -> log_softmax
 
 Dump points (the contract with src/arch/ecapa_tdnn/graph.h, in that exact
 order and with those exact names). Every activation is written TIME-MAJOR `[T, C]`:
@@ -63,7 +63,6 @@
     enc.emb                 [256]       fc output
     cls.hidden              [512]       DNN.block_0.act output (pre-BN1)
     cls.logits_raw          [107]       the gate
-    cls.log_probs           [107]       informational
 
 Plus two run-invariant front-end tables, dumped so the converter and the
 C++ mel unit test can be checked against SpeechBrain directly:
@@ -504,7 +503,6 @@ def dump(name: str, array: np.ndarray, stage: str) -> None:
          vec(hooks["cls.logits_raw"].value, N_LABELS, name="cls.logits_raw"),
          "classifier")
     log_probs = vec(hooks["cls.log_probs"].value, N_LABELS, name="cls.log_probs")
-    dump("cls.log_probs", log_probs, "classifier")
 
     # The hooked softmax output must be the tensor classify_batch returned;
     # if it is not, the hook is on the wrong module.
diff --git a/src/arch/ecapa_tdnn/ecapa_tdnn.h b/src/arch/ecapa_tdnn/ecapa_tdnn.h
index 6ac38419e..f282ad5f0 100644
--- a/src/arch/ecapa_tdnn/ecapa_tdnn.h
+++ b/src/arch/ecapa_tdnn/ecapa_tdnn.h
@@ -1,15 +1,7 @@
-// arch/ecapa_tdnn/ecapa_tdnn.h - ECAPA-TDNN family model and session types.
+// arch/ecapa_tdnn/ecapa_tdnn.h - ECAPA-TDNN family model and session types
+// (SpeechBrain ECAPA-TDNN + VoxLingua107 classifier, LANGID role).
 //
-// INTERNAL to src/arch/ecapa_tdnn/. Defines the concrete classes deriving
-// from transcribe_model / transcribe_langid_session, plus the family's Arch
-// instance.
-//
-// The family is SpeechBrain's ECAPA-TDNN embedder with the VoxLingua107
-// classifier head on top, serving the LANGID role. One model owns the
-// weights, the GGUF metadata context, the backend buffer, the label table,
-// and a shared (const-after-construction) log-mel front end; one session
-// owns per-call host scratch plus the ggml scheduler inherited from
-// SessionCore.
+// INTERNAL to src/arch/ecapa_tdnn/.
 
 #pragma once
 
@@ -51,9 +43,8 @@ struct Model final : public transcribe_model {
     // Built at load from stt.langid.labels.*; immutable after.
     LangidLabels labels;
 
-    // Front end, built once at load from stt.frontend.* plus the
-    // `frontend.mel_filterbank` tensor. const after construction, so every
-    // session shares it.
+    // Built at load from stt.frontend.* and frontend.mel_filterbank; shared
+    // by every session.
     std::unique_ptr<MelFrontend> mel;
 
     Model() = default;
@@ -66,12 +57,6 @@ struct Session final : public transcribe_langid_session {
     std::vector<float>   mel_buf;                // [T * n_mels], frame-major
     std::vector<float>   im2col_buf;             // [T * blk0_cols], frame-major
     std::vector<int32_t> idx_buf[kNumSeBlocks];  // reflect indices, [T + 2p]
-
-    // Metadata arena for the per-call graph build, handed to ggml_init as
-    // mem_buffer so a per-call ggml_init does not malloc / free it on every
-    // run. Outlives compute_ctx's use: ggml_free never touches a borrowed
-    // mem_buffer.
-    std::vector<uint8_t> graph_arena;
 };
 
 extern const Arch arch;
diff --git a/src/arch/ecapa_tdnn/graph.cpp b/src/arch/ecapa_tdnn/graph.cpp
index 330f3c7ca..b306d7bb8 100644
--- a/src/arch/ecapa_tdnn/graph.cpp
+++ b/src/arch/ecapa_tdnn/graph.cpp
@@ -1,36 +1,18 @@
 // arch/ecapa_tdnn/graph.cpp - the ECAPA-TDNN forward graph.
 //
-// Mirrors speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN.forward followed by
-// Xvector.Classifier, with the four conversion-time rewrites already applied
-// (scripts/convert-ecapa_tdnn.py): BatchNorm as an affine scale/shift, the three
-// activation-free BatchNorms folded forward into the following linear map,
-// and the ASP / MFA input concatenations split into per-operand weight
-// blocks.
+// SpeechBrain ECAPA_TDNN.forward + Xvector.Classifier, after the converter's
+// rewrites: BatchNorm as scale/shift, activation-free BatchNorms folded into
+// the next linear map, and the MFA / ASP concats split into weight blocks.
 //
-// One graph of plain ggml ops serves every backend; reductions over time
-// run on a contiguous transposed copy [T, C] because ggml reduces over
-// ne[0]. Stage 0 is one im2col matmul (see build_blk0_im2col).
-//
-// Layout: every activation is ggml ne = [C, T].
-//
-// Helper contract (named / linear / bn_affine / reflect_rows / conv_taps):
-// an activation is ggml `ne = [C, T]`, CHANNEL-INNERMOST, frames are rows.
-// That makes
-//   - a 1x1 convolution a plain `ggml_mul_mat(w[IC, OC], x[IC, T])`,
-//   - a bias / BatchNorm vector `[C]` a free broadcast over T,
-//   - reflect padding a single `ggml_get_rows` over the frame axis,
-//   - a dilated k>1 convolution a sum of matmuls against row-slices of the
-//     padded activation, with no transposes (stage 0 alone runs as one
-//     matmul over a host-built im2col; see build_blk0_im2col).
-// Time-axis reductions (means, softmax, weighted sums) act on ggml's ne[0],
-// so the caller transposes to `[T, C]` first.
+// Activations are ne = [C, T]: a 1x1 conv is one mul_mat, reflect padding is
+// one get_rows over frames, and time reductions run on a [T, C] transpose.
 //
 //   mel [n_mels, T]
-//     -> blk.0        TDNNBlock  n_mels -> C, k=K0, reflect pad
-//     -> blk.1..3     SERes2Net  C -> C, dilation 2 / 3 / 4
-//     -> mfa          w1@x1 + w2@x2 + w3@x3 + b, ReLU, BN     [3C, T]
-//     -> asp          attentive statistics pooling            [6C]
-//     -> fc           6C -> emb (asp_bn folded in)            [emb]
+//     -> blk.0        TDNNBlock  n_mels -> C, k=K0 (host im2col + one matmul)
+//     -> blk.1..3     SERes2Net  C -> C
+//     -> mfa          1x1 over concat(blk.1..3)                [3C, T]
+//     -> asp          attentive statistics pooling             [6C]
+//     -> fc           6C -> emb                                [emb]
 //     -> classifier   LeakyReLU, emb -> hid, LeakyReLU, hid -> n_labels
 
 #include "graph.h"
@@ -38,7 +20,6 @@
 #include "ecapa_tdnn.h"
 #include "ggml.h"
 #include "transcribe-debug.h"
-#include "transcribe-log.h"
 
 #include <cfloat>
 #include <cstddef>
@@ -61,9 +42,6 @@ ggml_tensor * named(ggml_tensor * t, const char * name) {
 ggml_tensor * linear(ggml_context * ctx, ggml_tensor * w, ggml_tensor * x, ggml_tensor * b) {
     ggml_tensor * y = ggml_mul_mat(ctx, w, x);
     if (w->type == GGML_TYPE_F16) {
-        // F32 accumulation for F16 weights: the reductions here are 1024 and
-        // 3072 wide and the parity regime is measured with an F32
-        // accumulator, so never let a backend pick the F16 one.
         ggml_prec_set_acc(y, GGML_PREC_F32);
     }
     if (b != nullptr) {
@@ -79,28 +57,17 @@ ggml_tensor * bn_affine(ggml_context * ctx, ggml_tensor * x, ggml_tensor * scale
     return y;
 }
 
-// Reflect padding along the frame axis as one gather. idx is I32 of length
-// T + 2p holding [p, ..., 1, 0, 1, ..., T-1, T-2, ..., T-1-p] (torch "reflect",
-// edge excluded); the result ne = [C, T + 2p] is contiguous. Requires T > p.
-ggml_tensor * reflect_rows(ggml_context * ctx, ggml_tensor * x, ggml_tensor * idx) {
-    return ggml_get_rows(ctx, x, idx);
-}
-
-// Dilated 1-D conv with "same" padding as a sum over taps: xpad is the
-// reflect-padded ne = [IC, T + 2p] (p = dilation * (K - 1) / 2), w the
-// tap-major ne = [IC, OC, K]. y[t] = sum_k w_k . xpad[t + k*dilation], which
-// is torch's Conv1d(padding="same", padding_mode="reflect"). ne = [OC, T].
+// Dilated 1-D conv as a sum over taps: xpad is the reflect-padded
+// ne = [IC, T + 2p], w the tap-major ne = [IC, OC, K].
+// y[t] = sum_k w_k . xpad[t + k*dilation], ne = [OC, T].
 ggml_tensor * conv_taps(ggml_context * ctx, ggml_tensor * xpad, ggml_tensor * w, ggml_tensor * b, int dilation, int T) {
     const int64_t IC = w->ne[0];
     const int64_t K  = w->ne[2];
 
     ggml_tensor * acc = nullptr;
     for (int64_t k = 0; k < K; ++k) {
-        // Tap k's operand: rows [k*d, k*d + T) of the padded activation. The
-        // row stride is xpad->nb[1] and the offset is a whole number of rows,
-        // so the view is contiguous and usable as a matmul src1 everywhere.
+        // Rows [k*d, k*d + T) of xpad, and tap k's [IC, OC] slice of w.
         ggml_tensor * v  = ggml_view_2d(ctx, xpad, IC, T, xpad->nb[1], static_cast<size_t>(k * dilation) * xpad->nb[1]);
-        // Tap k's kernel: the contiguous [IC, OC] slice at ne[2] == k.
         ggml_tensor * wk = ggml_view_2d(ctx, w, IC, w->ne[1], w->nb[1], static_cast<size_t>(k) * w->nb[2]);
 
         ggml_tensor * y = ggml_mul_mat(ctx, wk, v);
@@ -116,20 +83,13 @@ ggml_tensor * conv_taps(ggml_context * ctx, ggml_tensor * xpad, ggml_tensor * w,
     return acc;
 }
 
-// Nodes reserved in the cgraph. The real graph is 571 nodes for the published
-// configuration and is independent of T.
-constexpr size_t kGraphSize = 2048;
-
-// Name a tensor, mark it as a graph output so the scheduler cannot reuse its
-// buffer before the post-compute dump pass reads it (a no-op unless
-// TRANSCRIBE_DUMP_DIR is set), and stash the pointer.
+// Name a tensor, mark it for dumping (no-op unless TRANSCRIBE_DUMP_DIR is
+// set), and stash the pointer.
 void mark_dump(ggml_tensor *& slot, ggml_tensor * t, const char * name) {
     named(t, name);
     debug::mark_tensor_for_dump(t);
     if (t->view_src != nullptr) {
-        // In-place ops (Res2Net's set_rows) return a view; the allocator
-        // frees the view's backing tensor, so that is what has to be kept
-        // alive.
+        // In-place ops return a view; keep its backing tensor alive too.
         debug::mark_tensor_for_dump(t->view_src);
     }
     slot = t;
@@ -162,23 +122,10 @@ ggml_tensor * mean_over_time(ggml_context * ctx, ggml_tensor * x) {
     return ggml_reshape_1d(ctx, ggml_mean(ctx, xT), x->ne[0]);
 }
 
-// SpeechBrain Res2NetBlock with scale S: split the C channels into S chunks
-// of C/S, pass the first through unchanged, and run each remaining chunk
-// through its own dilated conv after adding the previous chunk's output.
-//
-//   y0 = x0
-//   y1 = B0(x1)
-//   yi = B(i-1)(xi + y(i-1))    i = 2 .. S-1
-//   out = concat(y0 .. y(S-1))
-//
-// The output is assembled in h itself: chunk 0 already holds y0, and each yi
-// is written over chunk i once it has been read, so there is no concat
-// chain. The write is a set_rows into h viewed as [C/S, S, T] (row i of each
-// frame is chunk i; chunk_ids holds 0..S-1), which touches only that chunk;
-// ggml_set would rewrite all of h on some backends. Chunk i is read through
-// the previous write's result, which orders the read after it. The strided
-// chunk views feed get_rows / add directly; both accept a row stride, so no
-// ggml_cont either.
+// SpeechBrain Res2NetBlock with scale S over chunks x0..x(S-1) of C/S:
+//   y0 = x0,  y1 = B0(x1),  yi = B(i-1)(xi + y(i-1)),  out = concat(y0..y(S-1))
+// Each yi is written over chunk i of h in place with set_rows (h viewed as
+// [C/S, S, T]), so there is no concat.
 ggml_tensor * res2net(ggml_context *         ctx,
                       const SeRes2NetBlock & blk,
                       ggml_tensor *          h,
@@ -196,7 +143,7 @@ ggml_tensor * res2net(ggml_context *         ctx,
         ggml_tensor *   ci = ggml_view_2d(ctx, out, chunk, T, out->nb[2], static_cast<size_t>(i) * chunk_bytes);
         ggml_tensor *   in = (i == 1) ? ci : ggml_add(ctx, ci, prev);
         const Res2Sub & s  = blk.res2[i - 1];
-        ggml_tensor *   yi = tdnn_conv(ctx, reflect_rows(ctx, in, idx), s.w, s.b, s.bn, dilation, T);
+        ggml_tensor *   yi = tdnn_conv(ctx, ggml_get_rows(ctx, in, idx), s.w, s.b, s.bn, dilation, T);
 
         out  = ggml_set_rows(ctx, out, ggml_reshape_3d(ctx, yi, chunk, 1, T),
                              ggml_view_1d(ctx, chunk_ids, 1, static_cast<size_t>(i) * sizeof(int32_t)));
@@ -225,11 +172,8 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     const int64_t T = blk_out[0]->ne[1];
 
     // ---- MFA: 1x1 over concat(blk1, blk2, blk3), split into three blocks --
-    // All three products are expanded before the adds, and the chain starts
-    // from the last block's product, so both adds wait on it and sit next to
-    // each other where Vulkan fuses them into one pass. (Starting from block
-    // 1's product lets the Vulkan graph reorderer hoist that add into block
-    // 3, which splits the pair.)
+    // Expand all three products first and start the sum from part[2] so the
+    // two adds stay adjacent (Vulkan fuses them).
     ggml_tensor * part[kNumSeBlocks] = {};
     for (int i = 0; i < kNumSeBlocks; ++i) {
         part[i] = linear(ctx, w.mfa_w[i], blk_out[i], nullptr);
@@ -242,14 +186,7 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     mark_dump(gb.dumps.mfa_out, m, "enc.mfa.out");
 
     // ---- attentive statistics pooling ------------------------------------
-    //
-    // mean / std are the utterance statistics over T with the uniform
-    // (unpadded) weighting SpeechBrain uses when wav_lens is all-ones; the
-    // variance is the biased 1/T one, and the clamp before the sqrt is
-    // SpeechBrain's `.clamp(eps)` and must not be dropped.
-    //
-    // Every statistic works on d = m - mean, so the attention-weighted ones
-    // below reuse d and d^2 instead of making their own full-size passes.
+    // Uniform mean / biased std over T, std clamped at eps (SpeechBrain).
     ggml_tensor * mT   = ggml_cont(ctx, ggml_transpose(ctx, m));  // [T, Cm]
     ggml_tensor * mean = ggml_mean(ctx, mT);                      // [1, Cm]
     ggml_tensor * d    = ggml_sub(ctx, mT, mean);                 // [T, Cm]
@@ -260,9 +197,7 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     ggml_tensor * mean1 = ggml_reshape_1d(ctx, mean, Cm);
     ggml_tensor * sd1   = ggml_reshape_1d(ctx, sd, Cm);
 
-    // The context term wm@mean + ws@std + b is a [att] vector broadcast over
-    // T, so the 3*Cm -> att convolution costs one Cm x T matmul instead of
-    // three.
+    // wm@mean + ws@std + b is constant over T: one [att] vector, broadcast.
     ggml_tensor * cvec = ggml_add(ctx, linear(ctx, w.asp_wm, mean1, nullptr), linear(ctx, w.asp_ws, sd1, nullptr));
     cvec               = ggml_add(ctx, cvec, w.asp_b);
 
@@ -271,11 +206,7 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     a               = bn_affine(ctx, a, w.asp_bn.scale, w.asp_bn.shift);
     a               = ggml_tanh(ctx, a);
 
-    // Attention logits WITHOUT the per-channel bias: the softmax runs over T
-    // within each channel, so a per-channel constant cancels out of it. The
-    // dump point needs the biased logits, so the dump build adds the bias on
-    // a side branch, marked BEFORE any transpose: ggml ne = [Cm, T] lands on
-    // disk as [T, Cm], the orientation the reference dumps.
+    // The per-channel bias cancels in the softmax over T; only the dump adds it.
     ggml_tensor * al = linear(ctx, w.asp_attn_w, a, nullptr);  // [Cm, T]
     if (debug::enabled()) {
         mark_dump(gb.dumps.asp_attn_logits, ggml_add(ctx, al, w.asp_attn_b), "enc.asp.attn_logits");
@@ -285,11 +216,8 @@ ggml_tensor * mfa_asp(ggml_context *        ctx,
     ggml_tensor * alT  = ggml_cont(ctx, ggml_transpose(ctx, al));  // [T, Cm]
     ggml_tensor * attn = ggml_soft_max(ctx, alT);                  // over ne[0] = T
 
-    // Weighted moments about the uniform mean, each a per-channel dot product
-    // over T (a batched mat-vec, one batch per channel; no product tensor):
-    //   e1 = sum_t attn d,  e2 = sum_t attn d^2
-    //   mu = mean + e1,     sigma^2 = sum_t attn (m - mu)^2 = e2 - e1^2
-    // (sum_t attn = 1).
+    // Weighted moments about the uniform mean (sum_t attn = 1):
+    //   e1 = sum_t attn d,  e2 = sum_t attn d^2,  mu = mean + e1,  sigma^2 = e2 - e1^2
     ggml_tensor * attn3 = ggml_reshape_3d(ctx, attn, T, 1, Cm);
     ggml_tensor * e1    = ggml_reshape_2d(ctx, ggml_mul_mat(ctx, attn3, ggml_reshape_3d(ctx, d, T, 1, Cm)), 1, Cm);
     ggml_tensor * e2    = ggml_reshape_2d(ctx, ggml_mul_mat(ctx, attn3, ggml_reshape_3d(ctx, d2, T, 1, Cm)), 1, Cm);
@@ -334,30 +262,10 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
     const HParams & hp = model.hparams;
     const Weights & w  = model.weights;
 
-    if (ctx == nullptr || T <= 0) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "ecapa_tdnn graph: invalid arg (ctx=%p, T=%d)", static_cast<void *>(ctx),
-                T);
-        return gb;
-    }
-
     const int Cm    = hp.c_mfa();
     const int chunk = hp.c_chunk();
 
-    // reflect_rows needs T > p so the mirrored indices stay in range. The
-    // 500 ms LANGID minimum gives T >= 51 frames; the largest p here is 4.
-    for (int i = 0; i < kNumSeBlocks; ++i) {
-        if (T <= hp.pad(i + 1)) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "ecapa_tdnn graph: T=%d is too short for reflect padding %d", T,
-                    hp.pad(i + 1));
-            return gb;
-        }
-    }
-
     gb.graph = ggml_new_graph_custom(ctx, kGraphSize, /*grads=*/false);
-    if (gb.graph == nullptr) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "ecapa_tdnn graph: ggml_new_graph_custom failed");
-        return gb;
-    }
 
     // ---- inputs ----------------------------------------------------------
     gb.blk0_in = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.blk0_cols(), T);
@@ -376,9 +284,8 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
     ggml_set_input(gb.chunk_ids);
 
     // ---- stage 0: TDNNBlock n_mels -> C ----------------------------------
-    // One matmul over the host-built im2col (K0 taps x n_mels, zero padded to
-    // a multiple of 32): n_mels = 60 alone is not a multiple of any CPU
-    // tinyBLAS tile, and ggml's fallback GEMM for that shape is ~15x slower.
+    // One matmul over the host im2col, inner dim padded to a multiple of 32
+    // for the CPU tinyBLAS tiles.
     ggml_tensor * x = linear(ctx, w.blk0_w_im2col, gb.blk0_in, w.blk0_b);
     x               = ggml_relu(ctx, x);
     x               = bn_affine(ctx, x, w.blk0_bn.scale, w.blk0_bn.shift);
@@ -397,8 +304,7 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
             mark_dump(gb.dumps.blk1_tdnn1_out, h, "enc.blk.1.tdnn1.out");
         }
 
-        // Res2Net writes its output over its input, so a dump build hands it
-        // a copy to keep the tdnn1 dump intact.
+        // Res2Net writes in place; copy so the tdnn1 dump survives.
         if (i == 0 && debug::enabled()) {
             h = ggml_cont(ctx, h);
         }
@@ -427,9 +333,7 @@ GraphBuild build_graph(ggml_context * ctx, const Model & model, int T) {
     mark_dump(gb.dumps.emb, emb, "enc.emb");
 
     // ---- classifier -------------------------------------------------------
-    // LeakyReLU comes FIRST, before the BatchNorm that is folded into the
-    // following linear map; getting that order wrong is invisible until the
-    // logits are compared.
+    // LeakyReLU precedes each (folded) BatchNorm.
     ggml_tensor * e = ggml_leaky_relu(ctx, emb, hp.leaky_slope, /*inplace=*/false);
     ggml_tensor * hcls =
         ggml_leaky_relu(ctx, linear(ctx, w.cls_l1_w, e, w.cls_l1_b), hp.leaky_slope, /*inplace=*/false);
diff --git a/src/arch/ecapa_tdnn/graph.h b/src/arch/ecapa_tdnn/graph.h
index 34d718287..e7626c1db 100644
--- a/src/arch/ecapa_tdnn/graph.h
+++ b/src/arch/ecapa_tdnn/graph.h
@@ -1,17 +1,13 @@
 // arch/ecapa_tdnn/graph.h - ECAPA-TDNN forward graph builder.
 //
-// INTERNAL to src/arch/ecapa_tdnn/. The LANGID ops table's run hook builds
-// and computes this graph once per call.
-//
-// Every activation is ggml `ne = [C, T]` (channel-innermost); see the
-// helper notes at the top of graph.cpp. Topology is independent of T, so the
-// scheduler's allocator settles after the first call.
+// INTERNAL to src/arch/ecapa_tdnn/. Activations are ggml ne = [C, T].
 
 #pragma once
 
 #include "ggml.h"
 #include "weights.h"
 
+#include <cstddef>
 #include <vector>
 
 struct ggml_context;
@@ -22,12 +18,12 @@ namespace transcribe::ecapa_tdnn {
 
 struct Model;
 
-// Named intermediates for the numerical-parity harness. Each is a borrowed
-// pointer into the compute context and is already marked as a graph output
-// when TRANSCRIBE_DUMP_DIR is set (otherwise the scheduler may reuse its
-// buffer). The dump names are the contract with the reference dumper
-// (scripts/dump_reference_ecapa_tdnn_speechbrain.py); each tensor's ggml `ne`
-// is reversed on disk, so `ne = [C, T]` lands as the reference's `[T, C]`.
+// Graph node capacity (the built graph is ~571 nodes, independent of T).
+constexpr size_t kGraphSize = 2048;
+
+// Named intermediates for the parity dumps (marked as graph outputs when
+// TRANSCRIBE_DUMP_DIR is set). Names match
+// scripts/dump_reference_ecapa_tdnn_speechbrain.py.
 struct Dumps {
     ggml_tensor * blk0_out              = nullptr;  // enc.blk.0.out          [C, T]
     ggml_tensor * blk1_tdnn1_out        = nullptr;  // enc.blk.1.tdnn1.out    [C, T]
@@ -36,9 +32,6 @@ struct Dumps {
     ggml_tensor * blk_out[kNumSeBlocks] = { nullptr, nullptr, nullptr };
     // enc.blk.{1,2,3}.out    [C, T]
     ggml_tensor * mfa_out               = nullptr;  // enc.mfa.out            [Cm, T]
-    // asp.attn's output BEFORE the transpose: ggml ne = [Cm, T] lands on disk
-    // as [T, Cm], which is the orientation the reference dumps. Marking the
-    // transposed copy instead would be a silent SHAPE mismatch.
     ggml_tensor * asp_attn_logits       = nullptr;  // enc.asp.attn_logits    [Cm, T]
     ggml_tensor * asp_out               = nullptr;  // enc.asp.out            [2*Cm]
     ggml_tensor * emb                   = nullptr;  // enc.emb                [emb]
@@ -47,17 +40,14 @@ struct Dumps {
 };
 
 struct GraphBuild {
-    // Stage-0 input: the host-built im2col of the reflect-padded log-mel,
-    // ne = [blk0_cols, T] f32. Row t holds mel_pad[t + k*d0] for k = 0..K0-1
-    // back to back, then zeros up to blk0_cols (build_blk0_im2col).
+    // Stage-0 input: im2col of the log-mel, ne = [blk0_cols, T] (build_blk0_im2col).
     ggml_tensor * blk0_in = nullptr;
 
     // Reflect-padding gather indices, one per SERes2Net block, I32
     // ne = [T + 2*pad(i+1)].
     ggml_tensor * idx[kNumSeBlocks] = { nullptr, nullptr, nullptr };
 
-    // Res2Net chunk numbers 0..kRes2NetScale-1, I32: the set_rows targets
-    // when writing a chunk's output in place.
+    // 0..kRes2NetScale-1, I32: set_rows targets for the Res2Net chunks.
     ggml_tensor * chunk_ids = nullptr;
 
     // Output.
@@ -68,8 +58,8 @@ struct GraphBuild {
     ggml_cgraph * graph = nullptr;
 };
 
-// Build the full forward graph for T frames into `ctx` (a fresh no_alloc
-// context). Returns a build with `graph == nullptr` on invalid input.
+// Build the forward graph for T frames into a fresh no_alloc `ctx`.
+// Requires T > hp.pad(i) for every stage.
 GraphBuild build_graph(ggml_context * ctx, const Model & model, int T);
 
 // Fill `out` ([T, hp.blk0_cols()] frame-major, i.e. ggml ne = [cols, T])
diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index c4c35294d..fa039f2da 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -1,11 +1,5 @@
-// arch/ecapa_tdnn/model.cpp - ECAPA-TDNN family handler (LANGID role).
-//
-// load() reads the hyperparameters and label table, builds the tensor
-// catalogue, uploads the weights to the chosen backend, and constructs the
-// shared log-mel front end from the GGUF's own filterbank. The LANGID run
-// hook drives the front end host-side, builds and computes the graph in
-// graph.cpp, and hands one logit per label back to the role dispatcher
-// (transcribe-langid.cpp), which owns the softmax / ranking.
+// arch/ecapa_tdnn/model.cpp - ECAPA-TDNN family handler (LANGID role):
+// load, forward pass, and the LANGID ops table.
 
 #include "ecapa_tdnn.h"
 #include "ggml-alloc.h"
@@ -24,9 +18,6 @@
 #include "transcribe-path.h"
 #include "weights.h"
 
-#include <algorithm>
-#include <chrono>
-#include <cmath>
 #include <cstddef>
 #include <cstdint>
 #include <cstring>
@@ -42,25 +33,11 @@ namespace {
 
 constexpr const char * kTag = "ecapa_tdnn";
 
-// stt.variant is optional in the GGUF; this is the family's only shipped
-// checkpoint and the default when the key is absent.
+// Default when stt.variant is absent.
 constexpr const char k_default_variant[] = "lang-id-voxlingua107-ecapa";
 
-// Nodes reserved in the scheduler. Must be >= the graph's node count; see
-// graph.cpp's kGraphSize. The built graph is 571 nodes and is independent of
-// T, so this is ~3.5x headroom.
-constexpr size_t k_sched_graph_size = 2048;
-
-// Metadata arena for one per-call graph build. The published configuration
-// uses 0.36 MiB of it (the graph is independent of T); the rest is headroom
-// so a future graph change cannot silently truncate.
 constexpr size_t k_compute_ctx_bytes = 4u * 1024u * 1024u;
 
-int64_t now_us() {
-    using clock = std::chrono::steady_clock;
-    return std::chrono::duration_cast<std::chrono::microseconds>(clock::now().time_since_epoch()).count();
-}
-
 // Frees the gguf_context on every exit path of load().
 struct GgufGuard {
     gguf_context * ctx = nullptr;
@@ -77,10 +54,8 @@ struct GgufGuard {
     GgufGuard & operator=(const GgufGuard &) = delete;
 };
 
-// Reflect-padding gather indices for a [C, T] activation:
-//   [p, p-1, ..., 1, 0, 1, ..., T-1, T-2, ..., T-1-p]
-// i.e. torch's "reflect" mode, which mirrors WITHOUT repeating the edge
-// frame. Requires T > p, which the 500 ms audio minimum guarantees.
+// Reflect-padding gather indices (torch "reflect", edge not repeated):
+//   [p, p-1, ..., 1, 0, 1, ..., T-1, T-2, ..., T-1-p]. Requires T > p.
 void build_reflect_indices(int T, int p, std::vector<int32_t> & out) {
     out.resize(static_cast<size_t>(T) + 2 * static_cast<size_t>(p));
     for (int j = 0; j < p; ++j) {
@@ -94,8 +69,7 @@ void build_reflect_indices(int T, int p, std::vector<int32_t> & out) {
     }
 }
 
-// Read stt.langid.labels.* into a validated label table (H6). Aliases are
-// optional; a present-but-mistyped key is a converter bug.
+// Read stt.langid.labels.* into a label table. Aliases are optional.
 transcribe_status read_labels(const gguf_context * gguf, LangidLabels & out) {
     std::vector<std::string> codes;
     std::vector<std::string> names;
@@ -249,10 +223,8 @@ transcribe_status build_derived_weights(Model & m) {
 // ---------------------------------------------------------------------------
 
 transcribe_status load(Loader & loader, const transcribe_model_load_params * params, transcribe_model ** out_model) {
-    const int64_t t_load_start = now_us();
+    const int64_t t_load_start = ggml_time_us();
 
-    // Owned by RAII until the very end, so a failure (or a throw) anywhere
-    // below, label construction included, frees everything built so far (H4).
     auto m     = std::make_unique<Model>();
     m->arch    = &arch;
     m->variant = loader.variant().empty() ? k_default_variant : loader.variant();
@@ -265,19 +237,11 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
     if (auto st = read_labels(loader.gguf(), m->labels); st != TRANSCRIBE_OK) {
         return st;
     }
-    if (static_cast<int32_t>(m->labels.codes.size()) != hp.n_labels) {
-        return TRANSCRIBE_ERR_GGUF;  // unreachable: both read the same KV
-    }
+    m->hparams.n_labels = static_cast<int32_t>(m->labels.codes.size());
 
     // ---- front end ------------------------------------------------------
-    //
-    // The shared MelFrontend in SpeechBrain mode: periodic Hamming, constant
-    // pad, no pre-emphasis, 10*log10 with the top-dB floor, per-bin mean
-    // ("sentence_mean"). read_hparams already pinned window / pad_mode /
-    // normalize to those values and win_length to n_fft. The filterbank is
-    // SpeechBrain's own triangle geometry and is never rebuilt here. It is
-    // stored ne = [n_freq, n_mels], so the flat host vector is already
-    // mel-major, which is what MelConfig wants.
+    // The filterbank is ne = [n_freq, n_mels], i.e. mel-major as MelConfig
+    // expects.
     {
         const size_t expected = static_cast<size_t>(hp.mel_n_mels) * static_cast<size_t>(hp.n_freq());
 
@@ -306,9 +270,6 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
     }
 
     // ---- weights ---------------------------------------------------------
-    //
-    // Reopen the file with no_alloc so ctx_meta holds tensor structs whose
-    // data pointers are filled in by the backend allocation below.
     GgufGuard guard;
     {
         gguf_init_params init_params{};
@@ -355,9 +316,8 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
     }
 
     m->roles = TRANSCRIBE_ROLE_LANGID;
-    // The abort callback is honored (transcribe_langid_set_abort_callback).
     set_feature(m.get(), TRANSCRIBE_FEATURE_CANCELLATION, true);
-    m->t_load_us = now_us() - t_load_start;
+    m->t_load_us = ggml_time_us() - t_load_start;
     *out_model   = m.release();
     return TRANSCRIBE_OK;
 }
@@ -365,11 +325,8 @@ transcribe_status load(Loader & loader, const transcribe_model_load_params * par
 // ---------------------------------------------------------------------------
 // forward pass
 // ---------------------------------------------------------------------------
-//
-// On success the returned GraphBuild's tensors are live: they belong to
-// s.compute_ctx, which stays alive until the dispatcher releases the scratch,
-// and their data belongs to the scheduler's allocation for the graph that was
-// just computed. The caller reads outputs with ggml_backend_tensor_get.
+
+// On success gb_out's tensors stay valid until the session scratch is released.
 transcribe_status forward(Session & s, const Model & m, const float * pcm, int n_samples, GraphBuild & gb_out) {
     debug::init();
 
@@ -377,21 +334,14 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
     const int       n_threads = s.n_threads > 0 ? s.n_threads : default_n_threads();
 
     // ---- front end (host side) -------------------------------------------
-    const int64_t t_mel_start = now_us();
+    const int64_t t_mel_start = ggml_time_us();
     int           T           = 0;
     int           n_mels      = 0;
-    if (n_samples < 0) {
-        return TRANSCRIBE_ERR_INVALID_ARG;
-    }
     if (auto st = m.mel->compute(pcm, static_cast<size_t>(n_samples), s.mel_raw, n_mels, T, n_threads);
         st != TRANSCRIBE_OK) {
         return st;
     }
-    if (n_mels != hp.mel_n_mels) {
-        return TRANSCRIBE_ERR_GGUF;  // unreachable: the config came from hp
-    }
-    // MelFrontend emits mel-major [n_mels, T]; the stage-0 im2col and the
-    // fe.mel dump want frame-major [T, n_mels] (ggml ne = [n_mels, T]).
+    // MelFrontend emits mel-major [n_mels, T]; transpose to frame-major.
     s.mel_buf.resize(static_cast<size_t>(T) * static_cast<size_t>(n_mels));
     for (int mi = 0; mi < n_mels; ++mi) {
         const float * src = s.mel_raw.data() + static_cast<size_t>(mi) * static_cast<size_t>(T);
@@ -399,12 +349,8 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
             s.mel_buf[static_cast<size_t>(t) * static_cast<size_t>(n_mels) + static_cast<size_t>(mi)] = src[t];
         }
     }
-    s.t_mel_us = now_us() - t_mel_start;
+    s.t_mel_us = ggml_time_us() - t_mel_start;
 
-    if (T <= 0) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: front end produced no frames for %d samples", kTag, n_samples);
-        return TRANSCRIBE_ERR_INPUT_TOO_SHORT;
-    }
     if (s.poll_abort()) {
         return TRANSCRIBE_ERR_ABORTED;
     }
@@ -415,8 +361,7 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
                              "frontend");
     }
 
-    // reflect_rows needs T > pad (the 500 ms LANGID minimum gives T >= 51
-    // frames; the largest pad here is 4).
+    // Reflect padding needs T > pad.
     for (int i = 0; i < kNumStages - 1; ++i) {
         if (T <= hp.pad(i)) {
             log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: T=%d is too short for reflect padding %d", kTag, T, hp.pad(i));
@@ -431,20 +376,14 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
     }
 
     // ---- graph -------------------------------------------------------------
-    //
-    // A fresh no_alloc metadata context per call: the previous build's tensor
-    // structs are dead the moment the scheduler is reset.
     if (s.compute_ctx != nullptr) {
         ggml_free(s.compute_ctx);
         s.compute_ctx = nullptr;
     }
     {
-        if (s.graph_arena.size() != k_compute_ctx_bytes) {
-            s.graph_arena.resize(k_compute_ctx_bytes);
-        }
         ggml_init_params init_params{};
-        init_params.mem_size   = s.graph_arena.size();
-        init_params.mem_buffer = s.graph_arena.data();
+        init_params.mem_size   = k_compute_ctx_bytes;
+        init_params.mem_buffer = nullptr;
         init_params.no_alloc   = true;
         s.compute_ctx          = ggml_init(init_params);
         if (s.compute_ctx == nullptr) {
@@ -454,16 +393,11 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
     }
 
     GraphBuild gb = build_graph(s.compute_ctx, m, T);
-    if (gb.graph == nullptr || gb.logits == nullptr) {
-        return TRANSCRIBE_ERR_GGUF;
-    }
 
     if (s.sched == nullptr) {
-        // Cast away const for the scheduler list only: the scheduler borrows
-        // the model's backends and never mutates the model.
         auto & backends = const_cast<std::vector<ggml_backend_t> &>(m.plan.scheduler_list);
-        s.sched         = ggml_backend_sched_new(backends.data(), nullptr, static_cast<int>(backends.size()),
-                                                 k_sched_graph_size, /*parallel=*/false, /*op_offload=*/true);
+        s.sched = ggml_backend_sched_new(backends.data(), nullptr, static_cast<int>(backends.size()), kGraphSize,
+                                         /*parallel=*/false, /*op_offload=*/true);
         if (s.sched == nullptr) {
             log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: scheduler allocation failed", kTag);
             return TRANSCRIBE_ERR_OOM;
@@ -478,14 +412,11 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
     }
 
     // ---- inputs -------------------------------------------------------------
-    // Frame-major [T, cols], byte-identical to ggml ne = [cols, T].
     ggml_backend_tensor_set(gb.blk0_in, s.im2col_buf.data(), 0, s.im2col_buf.size() * sizeof(float));
     for (int i = 0; i < kNumSeBlocks; ++i) {
-        if (gb.idx[i] != nullptr) {
-            ggml_backend_tensor_set(gb.idx[i], s.idx_buf[i].data(), 0, s.idx_buf[i].size() * sizeof(int32_t));
-        }
+        ggml_backend_tensor_set(gb.idx[i], s.idx_buf[i].data(), 0, s.idx_buf[i].size() * sizeof(int32_t));
     }
-    if (gb.chunk_ids != nullptr) {
+    {
         int32_t ids[kRes2NetScale];
         for (int i = 0; i < kRes2NetScale; ++i) {
             ids[i] = i;
@@ -494,12 +425,12 @@ transcribe_status forward(Session & s, const Model & m, const float * pcm, int n
     }
 
     // ---- compute -------------------------------------------------------------
-    const int64_t t_enc_start = now_us();
+    const int64_t t_enc_start = ggml_time_us();
     if (const ggml_status gs = ggml_backend_sched_graph_compute(s.sched, gb.graph); gs != GGML_STATUS_SUCCESS) {
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s run: graph compute failed (%d)", kTag, static_cast<int>(gs));
         return TRANSCRIBE_ERR_BACKEND;
     }
-    s.t_encode_us = now_us() - t_enc_start;
+    s.t_encode_us = ggml_time_us() - t_enc_start;
 
     gb_out = gb;
     return TRANSCRIBE_OK;
@@ -532,30 +463,6 @@ void dump_stages(const GraphBuild & gb) {
     try_dump("cls.logits_raw", gb.dumps.cls_logits, "classifier");
 }
 
-// SpeechBrain's classifier ends in log_softmax; the role needs raw logits, so
-// the log-probabilities are recomputed here purely for the validation dump.
-// Stable form: z - (max + log(sum(exp(z - max)))).
-void dump_log_probs(const std::vector<float> & logits) {
-    if (!debug::enabled() || logits.empty()) {
-        return;
-    }
-
-    const float max_logit = *std::max_element(logits.begin(), logits.end());
-    double      sum       = 0.0;
-    for (const float v : logits) {
-        sum += std::exp(static_cast<double>(v - max_logit));
-    }
-    const double log_z = static_cast<double>(max_logit) + std::log(sum);
-
-    std::vector<float> lp(logits.size());
-    for (size_t i = 0; i < logits.size(); ++i) {
-        lp[i] = static_cast<float>(static_cast<double>(logits[i]) - log_z);
-    }
-
-    const long long shape[1] = { static_cast<long long>(lp.size()) };
-    debug::dump_host_f32("cls.log_probs", lp.data(), static_cast<long long>(lp.size()), shape, 1, "classifier");
-}
-
 // ---------------------------------------------------------------------------
 // LANGID ops
 // ---------------------------------------------------------------------------
@@ -587,7 +494,6 @@ transcribe_status langid_run(transcribe_langid_session * session,
     ggml_backend_tensor_get(gb.logits, logits.data(), 0, logits.size() * sizeof(float));
 
     dump_stages(gb);
-    dump_log_probs(logits);
     return TRANSCRIBE_OK;
 }
 
@@ -600,7 +506,6 @@ const LangidOps k_langid_ops = { langid_labels, langid_new_session, langid_run }
 // ---------------------------------------------------------------------------
 
 Model::~Model() {
-    mel.reset();
     if (ctx_meta != nullptr) {
         ggml_free(ctx_meta);
     }
diff --git a/src/arch/ecapa_tdnn/weights.cpp b/src/arch/ecapa_tdnn/weights.cpp
index b45681633..111e75147 100644
--- a/src/arch/ecapa_tdnn/weights.cpp
+++ b/src/arch/ecapa_tdnn/weights.cpp
@@ -1,10 +1,4 @@
 // arch/ecapa_tdnn/weights.cpp - read_hparams + build_weights.
-//
-// read_hparams is deliberately strict: every stt.ecapa_tdnn.* shape the graph
-// builder assumes is checked here, so a converter change that the graph
-// cannot express fails at load with a named key instead of producing silently
-// wrong logits. Every dimension is also bounded, so the products the loader
-// and graph derive from them (tensor sizes, frame counts) cannot overflow.
 
 #include "weights.h"
 
@@ -14,31 +8,15 @@
 #include "transcribe-meta.h"
 #include "transcribe-weights-util.h"
 
-#include <cmath>
 #include <cstdio>
-#include <initializer_list>
 #include <string>
 
 namespace transcribe::ecapa_tdnn {
 
 namespace {
 
-// Upper bounds on metadata-derived dimensions. Far above any real ECAPA
-// (C = 1024, n_fft = 400, 107 labels) and small enough that every product
-// the loader and graph form fits comfortably in int64 / size_t.
-constexpr int32_t kMaxChannels = 1 << 16;
-constexpr int32_t kMaxKernel   = 63;
-constexpr int32_t kMaxDilation = 64;
-constexpr int32_t kMaxNfft     = 1 << 14;
-constexpr int32_t kMaxMels     = 1024;
-constexpr int32_t kMaxLabels   = 1 << 16;
-
 constexpr const char * kTag = "ecapa_tdnn";
 
-// GGUF format revision this loader implements. Bumped only when the tensor /
-// KV contract changes incompatibly.
-constexpr int32_t kFormatVersion = 1;
-
 // Required int32 array of exactly `n` entries.
 transcribe_status read_required_i32_array_kv(const gguf_context *   gguf,
                                              const char *           key,
@@ -55,15 +33,6 @@ transcribe_status read_required_i32_array_kv(const gguf_context *   gguf,
     return TRANSCRIBE_OK;
 }
 
-transcribe_status require_string_value(const std::string & got, const char * want, const char * key) {
-    if (got != want) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: unsupported %s \"%s\" (only \"%s\" is implemented)", kTag, key,
-                got.c_str(), want);
-        return TRANSCRIBE_ERR_GGUF;
-    }
-    return TRANSCRIBE_OK;
-}
-
 }  // namespace
 
 transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
@@ -79,15 +48,6 @@ transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
         }                                     \
     } while (0)
 
-    // ---- format gate ---------------------------------------------------
-    REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.format_version", kTag, hp.format_version));
-    if (hp.format_version != kFormatVersion) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.ecapa_tdnn.format_version is %d, this build implements %d", kTag,
-                hp.format_version, kFormatVersion);
-        return TRANSCRIBE_ERR_GGUF;
-    }
-
-    // ---- front end -----------------------------------------------------
     REQ(read_required_u32_kv(gguf, "stt.frontend.sample_rate", kTag, hp.sample_rate));
     REQ(read_required_u32_kv(gguf, "stt.frontend.n_fft", kTag, hp.mel_n_fft));
     REQ(read_required_u32_kv(gguf, "stt.frontend.hop_length", kTag, hp.mel_hop));
@@ -99,11 +59,6 @@ transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
     REQ(read_required_f32_kv(gguf, "stt.frontend.top_db", kTag, hp.mel_top_db));
     REQ(read_required_string_kv(gguf, "stt.frontend.normalize", kTag, hp.mel_normalize));
 
-    REQ(require_string_value(hp.mel_window, "hamming_periodic", "stt.frontend.window"));
-    REQ(require_string_value(hp.mel_pad_mode, "constant", "stt.frontend.pad_mode"));
-    REQ(require_string_value(hp.mel_normalize, "sentence_mean", "stt.frontend.normalize"));
-
-    // ---- embedding network ---------------------------------------------
     REQ(read_required_i32_array_kv(gguf, "stt.ecapa_tdnn.channels", kNumStages, hp.channels));
     REQ(read_required_i32_array_kv(gguf, "stt.ecapa_tdnn.kernel_sizes", kNumStages, hp.kernel_sizes));
     REQ(read_required_i32_array_kv(gguf, "stt.ecapa_tdnn.dilations", kNumStages, hp.dilations));
@@ -112,109 +67,53 @@ transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
     REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.attention_channels", kTag, hp.attention_channels));
     REQ(read_required_f32_kv(gguf, "stt.ecapa_tdnn.asp_eps", kTag, hp.asp_eps));
     REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.embedding_dim", kTag, hp.embedding_dim));
-
-    // ---- classifier ------------------------------------------------------
     REQ(read_required_u32_kv(gguf, "stt.ecapa_tdnn.classifier_hidden", kTag, hp.classifier_hidden));
     REQ(read_required_f32_kv(gguf, "stt.ecapa_tdnn.classifier_leaky_slope", kTag, hp.leaky_slope));
 
-    // ---- label count -----------------------------------------------------
-    // The label table itself is built (and validated) by load(); only the
-    // length matters here, for the classifier's output width.
-    {
-        std::vector<std::string> codes;
-        if (read_string_array_kv(gguf, "stt.langid.labels.codes", codes) != KvResult::Ok || codes.empty() ||
-            codes.size() > static_cast<size_t>(kMaxLabels)) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.langid.labels.codes missing, malformed, empty, or too long",
-                    kTag);
-            return TRANSCRIBE_ERR_GGUF;
-        }
-        hp.n_labels = static_cast<int32_t>(codes.size());
-    }
-
 #undef REQ
 
-    // ---- value-domain invariants ----------------------------------------
     if (hp.sample_rate != 16000) {
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.frontend.sample_rate is %d; only 16000 is supported", kTag,
                 hp.sample_rate);
         return TRANSCRIBE_ERR_GGUF;
     }
-    if (hp.mel_n_fft <= 1 || hp.mel_n_fft > kMaxNfft || hp.mel_hop <= 0 || hp.mel_hop > hp.mel_n_fft ||
-        hp.mel_win <= 0 || hp.mel_n_mels <= 0 || hp.mel_n_mels > kMaxMels) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: front-end dimensions out of range", kTag);
-        return TRANSCRIBE_ERR_GGUF;
-    }
-    if (hp.mel_win != hp.mel_n_fft) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.frontend.win_length (%d) must equal stt.frontend.n_fft (%d)", kTag,
-                hp.mel_win, hp.mel_n_fft);
-        return TRANSCRIBE_ERR_GGUF;
-    }
-    if (!(hp.mel_log_floor > 0.0f) || !(hp.mel_top_db >= 0.0f) || !std::isfinite(hp.mel_top_db)) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.frontend.log_clamp_min must be > 0 and top_db >= 0", kTag);
+    // MelFrontend falls back silently on unknown values.
+    if (hp.mel_window != "hamming_periodic" || hp.mel_pad_mode != "constant" || hp.mel_normalize != "sentence_mean") {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: unsupported front end (window=%s pad_mode=%s normalize=%s)", kTag,
+                hp.mel_window.c_str(), hp.mel_pad_mode.c_str(), hp.mel_normalize.c_str());
         return TRANSCRIBE_ERR_GGUF;
     }
 
     for (int i = 0; i < kNumStages; ++i) {
-        const int32_t c = hp.channels[static_cast<size_t>(i)];
         const int32_t k = hp.kernel_sizes[static_cast<size_t>(i)];
         const int32_t d = hp.dilations[static_cast<size_t>(i)];
-        if (c <= 0 || c > kMaxChannels || k <= 0 || k > kMaxKernel || d <= 0 || d > kMaxDilation) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stage %d channels/kernel/dilation out of range (%d/%d/%d)", kTag,
-                    i, c, k, d);
-            return TRANSCRIBE_ERR_GGUF;
-        }
-        if (k % 2 == 0) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
-                    "%s: stage %d kernel size %d is even; \"same\" reflect padding needs odd", kTag, i, k);
+        if (k <= 0 || k % 2 == 0 || d <= 0) {
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stage %d needs an odd kernel and positive dilation (k=%d d=%d)",
+                    kTag, i, k, d);
             return TRANSCRIBE_ERR_GGUF;
         }
     }
-
-    // The MFA stage is a 1x1 convolution over the concatenated block outputs.
     if (hp.kernel_sizes[kNumStages - 1] != 1 || hp.dilations[kNumStages - 1] != 1) {
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: MFA stage must be k=1 d=1, got k=%d d=%d", kTag,
                 hp.kernel_sizes[kNumStages - 1], hp.dilations[kNumStages - 1]);
         return TRANSCRIBE_ERR_GGUF;
     }
-
-    // The three SERes2Net blocks have no shortcut projection, so every
-    // embedding-block width must be identical.
+    // SERes2Net blocks have no shortcut projection: every block width matches.
     for (int i = 1; i < kNumStages - 1; ++i) {
         if (hp.channels[static_cast<size_t>(i)] != hp.channels[0]) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
-                    "%s: channels[%d] = %d differs from channels[0] = %d; the SERes2Net "
-                    "residual requires equal widths",
-                    kTag, i, hp.channels[static_cast<size_t>(i)], hp.channels[0]);
+            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: channels[%d] = %d differs from channels[0] = %d", kTag, i,
+                    hp.channels[static_cast<size_t>(i)], hp.channels[0]);
             return TRANSCRIBE_ERR_GGUF;
         }
     }
-
     if (hp.channels[kNumStages - 1] != (kNumSeBlocks * hp.channels[0])) {
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: MFA channels %d must equal %d x block channels %d", kTag,
                 hp.channels[kNumStages - 1], kNumSeBlocks, hp.channels[0]);
         return TRANSCRIBE_ERR_GGUF;
     }
-
-    if (hp.res2net_scale != kRes2NetScale) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: res2net_scale %d is not supported (only %d is implemented)", kTag,
-                hp.res2net_scale, kRes2NetScale);
-        return TRANSCRIBE_ERR_GGUF;
-    }
-    if (hp.channels[0] % hp.res2net_scale != 0) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: channels[0] = %d is not divisible by res2net_scale = %d", kTag,
-                hp.channels[0], hp.res2net_scale);
-        return TRANSCRIBE_ERR_GGUF;
-    }
-
-    for (const int32_t v : { hp.se_channels, hp.attention_channels, hp.embedding_dim, hp.classifier_hidden }) {
-        if (v <= 0 || v > kMaxChannels) {
-            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: se/attention/embedding/hidden dimensions out of range", kTag);
-            return TRANSCRIBE_ERR_GGUF;
-        }
-    }
-    if (!(hp.asp_eps > 0.0f) || !std::isfinite(hp.leaky_slope)) {
-        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: stt.ecapa_tdnn.asp_eps must be positive (got %g), leaky slope finite",
-                kTag, static_cast<double>(hp.asp_eps));
+    if (hp.res2net_scale != kRes2NetScale || hp.channels[0] <= 0 || hp.channels[0] % hp.res2net_scale != 0) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: res2net_scale must be %d and divide channels[0] (got %d, %d)", kTag,
+                kRes2NetScale, hp.res2net_scale, hp.channels[0]);
         return TRANSCRIBE_ERR_GGUF;
     }
 
diff --git a/src/arch/ecapa_tdnn/weights.h b/src/arch/ecapa_tdnn/weights.h
index 1a29fd08a..3d1a141ee 100644
--- a/src/arch/ecapa_tdnn/weights.h
+++ b/src/arch/ecapa_tdnn/weights.h
@@ -67,20 +67,16 @@ constexpr int kNumStages = 5;
 // SERes2Net blocks, i.e. stages 1..3.
 constexpr int kNumSeBlocks = 3;
 
-// Res2Net scale this implementation supports. The per-chunk channel count is
-// derived from HParams (channels[0] / res2net_scale); only the split count
-// itself is pinned, because it fixes the weight-slot array size below.
+// Res2Net scale this implementation supports (fixes the weight-slot count).
 constexpr int kRes2NetScale = 8;
 
 // Sub-convolutions inside one Res2Net block: the first chunk is passed
 // through unchanged, so there are scale - 1 of them.
 constexpr int kRes2NetSubs = kRes2NetScale - 1;
 
-// Every stt.* KV the family reads, plus the label count (the length of
-// stt.langid.labels.codes, which sizes the classifier output).
+// Every stt.* KV the family reads, plus the label count.
 struct HParams {
-    int32_t format_version = 0;
-    int32_t sample_rate    = 0;
+    int32_t sample_rate = 0;
 
     // Front end (stt.frontend.*).
     int32_t     mel_n_fft  = 0;
@@ -107,7 +103,7 @@ struct HParams {
     int32_t classifier_hidden = 0;
     float   leaky_slope       = 0.0f;
 
-    // Length of stt.langid.labels.codes.
+    // Set by load() from the label table.
     int32_t n_labels = 0;
 
     // ---- derived ------------------------------------------------------
@@ -215,10 +211,8 @@ struct Weights {
     ggml_tensor * cls_out_b = nullptr;  // [n_labels]
 };
 
-// Read every stt.* KV the family needs and validate the value domain.
-// Anything outside the shapes this implementation can build is rejected with
-// TRANSCRIBE_ERR_GGUF and a log line naming the offending key. Sizes derived
-// from these values are bounded here, so later arithmetic cannot overflow.
+// Read every stt.* KV the family needs and reject shapes the graph cannot
+// build. Does not set n_labels.
 transcribe_status read_hparams(const gguf_context * gguf, HParams & hp);
 
 // Bind every tensor in the catalogue above to a borrowed pointer in
diff --git a/src/transcribe-langid.cpp b/src/transcribe-langid.cpp
index 4801fb837..de8d4e8e3 100644
--- a/src/transcribe-langid.cpp
+++ b/src/transcribe-langid.cpp
@@ -51,8 +51,8 @@ int32_t label_index(const LangidLabels & labels, const char * code) {
     return it != labels.index.end() ? it->second : -1;
 }
 
-// Resolve params->allowed into a per-label mask. Rejects every malformed
-// form (H2): only NULL with n_allowed == 0 means "all".
+// Resolve params->allowed into a per-label mask. Only NULL with
+// n_allowed == 0 means "all".
 transcribe_status build_allowed_mask(const LangidLabels &             labels,
                                      const transcribe_langid_params * params,
                                      std::vector<uint8_t> &           mask,
@@ -130,9 +130,6 @@ transcribe_status transcribe::build_langid_labels(std::vector<std::string>
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: label table: %zu codes vs %zu names", tag, codes.size(), names.size());
         return TRANSCRIBE_ERR_GGUF;
     }
-    if (codes.size() > static_cast<size_t>(INT32_MAX)) {
-        return fail("too many labels", std::to_string(codes.size()));
-    }
     LangidLabels staged;
     for (size_t i = 0; i < codes.size(); ++i) {
         if (codes[i].empty()) {
@@ -228,7 +225,6 @@ static transcribe_status langid_session_init_impl(transcribe_model *
     if (params->n_threads < 0) {
         return TRANSCRIBE_ERR_INVALID_ARG;
     }
-    // H1: a window below the minimum could never score anything.
     const int32_t max_audio_ms =
         params->max_audio_ms == 0 ? transcribe::k_langid_default_audio_ms : params->max_audio_ms;
     if (max_audio_ms < transcribe::k_langid_min_audio_ms) {
@@ -267,7 +263,7 @@ static transcribe_status langid_run_impl(transcribe_langid_session *      sessio
     if (const auto st = build_allowed_mask(labels, params, mask, n_allowed); st != TRANSCRIBE_OK) {
         return st;
     }
-    // Score the last max_audio_ms; the minimum applies to what is scored (H1).
+    // Score the last max_audio_ms; the minimum applies to what is scored.
     const int64_t max_samples = static_cast<int64_t>(session->max_audio_ms) * k_samples_per_ms;
     const int64_t n_used      = std::min<int64_t>(n_samples, max_samples);
     if (n_used < static_cast<int64_t>(transcribe::k_langid_min_audio_ms) * k_samples_per_ms) {
diff --git a/tests/tolerances/ecapa_tdnn.json b/tests/tolerances/ecapa_tdnn.json
index 8a2271b88..235baf22f 100644
--- a/tests/tolerances/ecapa_tdnn.json
+++ b/tests/tolerances/ecapa_tdnn.json
@@ -60,10 +60,7 @@
     "Both are max_abs on wide [T, C] activations; a re-measure on different",
     "CPU SIMD width may need a fresh Stage-4 pass rather than a hand edit.",
     "",
-    "cls.logits_raw is the gate; cls.log_probs is informational (it is",
-    "recomputed host-side from the logits, so it carries the logits' drift",
-    "plus one log-sum-exp). SpeechBrain's log_softmax is finite everywhere",
-    "here (no masked positions), so no inf-tolerance escape hatch is needed.",
+    "cls.logits_raw is the gate.",
     "",
     "fe.window [400] and fe.filters [201, 60] are dumped by the reference but",
     "deliberately absent from this file: the C++ runtime recomputes the window",
@@ -125,9 +122,5 @@
   "cls.logits_raw": {
     "max_abs": 0.001809,
     "mean_abs": 0.0001014
-  },
-  "cls.log_probs": {
-    "max_abs": 0.003533,
-    "mean_abs": 0.0002712
   }
 }

From 106ac484053467bcabc53254c55ff24845c65b86 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:12:45 +0800
Subject: [PATCH 13/38] langid: trim ecapa_tdnn and langid tests

Keep one case per behavior in langid_dispatch_unit and drop the
dispatcher checks ecapa_tdnn_smoke duplicated, along with the
bad_labels fixture (the unit covers duplicate codes) and the unread
format_version fixture key. api_smoke keeps only LANGID ABI size/align
checks. cli_langid_smoke loses its error-path and real-model checks.
ecapa_tdnn_real_smoke uses one GPU bound (0.1) instead of reading
general.file_type, and cuts fleurs-en to 800 ms in-test, replacing
samples/short-800ms.wav. The fixture block returns to its original
place in tests/CMakeLists.txt, and the ecapa smoke is registered with
the other family smokes.
---
 samples/README.md                    |   1 -
 samples/short-800ms.wav              | Bin 25644 -> 0 bytes
 tests/CMakeLists.txt                 | 135 +++++++-------
 tests/api_smoke.c                    |  37 +---
 tests/cli_langid_smoke.cmake         |  22 +--
 tests/ecapa_tdnn_real_smoke.cpp      |  46 ++---
 tests/ecapa_tdnn_smoke.cpp           |  64 +------
 tests/fixtures/make_gguf_fixtures.py |   7 +-
 tests/langid_dispatch_unit.cpp       | 260 ++++-----------------------
 9 files changed, 130 insertions(+), 442 deletions(-)
 delete mode 100644 samples/short-800ms.wav

diff --git a/samples/README.md b/samples/README.md
index ca60cd5a4..14f25d91c 100644
--- a/samples/README.md
+++ b/samples/README.md
@@ -64,7 +64,6 @@ split, licensed **CC-BY-4.0**.
 | `fleurs-zh.wav` | 10.38 s | `cmn_hans_cn` | 1906 |
 | `fleurs-ru.wav` | 7.92 s | `ru_ru` | 1917 |
 | `fleurs-id.wav` | 8.58 s | `id_id` | 1909 |
-| `short-800ms.wav` | 0.80 s | `en_us` | 1904 (first 800 ms) |
 
 ## Everything else
 
diff --git a/samples/short-800ms.wav b/samples/short-800ms.wav
deleted file mode 100644
index 734ca670adcea5dd65e66c35155eb83b08a29d7e..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 0bdcc2e1f..234a34b02 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -83,74 +83,6 @@ add_test(
         -DTRANSCRIBE_SOURCE_DIR=${PROJECT_SOURCE_DIR}
         -P ${CMAKE_CURRENT_SOURCE_DIR}/check_extension_umbrella.cmake)
 
-# -----------------------------------------------------------------------------
-# Synthetic GGUF fixtures (for the loader smoke test)
-# -----------------------------------------------------------------------------
-
-set(TRANSCRIBE_FIXTURE_GENERATOR
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/make_gguf_fixtures.py)
-
-set(TRANSCRIBE_FIXTURE_FILES
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_parakeet.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_sensevoice.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_funasr_nano.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_unknown.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/corrupt_magic.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_v3.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_cache_aware.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_buffered.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_cohere_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_granite5_ctc_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_qwen3_asr_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_labels.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf)
-
-find_program(TRANSCRIBE_UV uv)
-
-if(TRANSCRIBE_UV)
-    # add_custom_command(OUTPUT ...) makes the .gguf files first-class
-    # build outputs. The build graph treats the generator script as a
-    # source dependency, so the fixtures are regenerated whenever the
-    # script changes — and a failing uv invocation produces a hard
-    # build error rather than a configure-time warning.
-    add_custom_command(
-        OUTPUT  ${TRANSCRIBE_FIXTURE_FILES}
-        COMMAND ${TRANSCRIBE_UV} run --quiet ${TRANSCRIBE_FIXTURE_GENERATOR}
-        DEPENDS ${TRANSCRIBE_FIXTURE_GENERATOR}
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/fixtures
-        COMMENT "Generating synthetic GGUF fixtures"
-        VERBATIM)
-
-    # Aggregate target. The loader test target depends on this; building
-    # `fixtures` directly is the supported way to hand-regenerate.
-    add_custom_target(fixtures
-        DEPENDS ${TRANSCRIBE_FIXTURE_FILES})
-else()
-    message(STATUS
-        "uv not found on PATH; transcribe_loader_smoke will not be "
-        "registered. Install uv (https://docs.astral.sh/uv/) and "
-        "reconfigure to enable.")
-endif()
-
-# -----------------------------------------------------------------------------
-# ecapa_tdnn (LANGID role) fixture smoke: public API only, so it is registered
-# above the shared-library cut and runs against static and shared builds
-# -----------------------------------------------------------------------------
-
-if(TRANSCRIBE_UV)
-    add_executable(transcribe_ecapa_tdnn_smoke ecapa_tdnn_smoke.cpp)
-    target_link_libraries(transcribe_ecapa_tdnn_smoke PRIVATE transcribe)
-    target_compile_definitions(transcribe_ecapa_tdnn_smoke PRIVATE
-        "TRANSCRIBE_TEST_FIXTURES_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/fixtures\"")
-    transcribe_apply_warnings(transcribe_ecapa_tdnn_smoke)
-    add_dependencies(transcribe_ecapa_tdnn_smoke fixtures)
-    add_test(NAME transcribe_ecapa_tdnn_smoke COMMAND transcribe_ecapa_tdnn_smoke)
-endif()
-
 # -----------------------------------------------------------------------------
 # Shared-library build: only the public-ABI smoke is meaningful
 # -----------------------------------------------------------------------------
@@ -562,6 +494,57 @@ transcribe_apply_warnings(transcribe_threadpool_oversubscription)
 add_test(NAME transcribe_threadpool_oversubscription
     COMMAND transcribe_threadpool_oversubscription)
 
+# -----------------------------------------------------------------------------
+# Synthetic GGUF fixtures (for the loader smoke test)
+# -----------------------------------------------------------------------------
+
+set(TRANSCRIBE_FIXTURE_GENERATOR
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/make_gguf_fixtures.py)
+
+set(TRANSCRIBE_FIXTURE_FILES
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_parakeet.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_sensevoice.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_funasr_nano.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_unknown.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/corrupt_magic.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_v3.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_cache_aware.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/tokenizer_minimal_streaming_buffered.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_cohere_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_granite5_ctc_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_qwen3_asr_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf)
+
+find_program(TRANSCRIBE_UV uv)
+
+if(TRANSCRIBE_UV)
+    # add_custom_command(OUTPUT ...) makes the .gguf files first-class
+    # build outputs. The build graph treats the generator script as a
+    # source dependency, so the fixtures are regenerated whenever the
+    # script changes — and a failing uv invocation produces a hard
+    # build error rather than a configure-time warning.
+    add_custom_command(
+        OUTPUT  ${TRANSCRIBE_FIXTURE_FILES}
+        COMMAND ${TRANSCRIBE_UV} run --quiet ${TRANSCRIBE_FIXTURE_GENERATOR}
+        DEPENDS ${TRANSCRIBE_FIXTURE_GENERATOR}
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/fixtures
+        COMMENT "Generating synthetic GGUF fixtures"
+        VERBATIM)
+
+    # Aggregate target. The loader test target depends on this; building
+    # `fixtures` directly is the supported way to hand-regenerate.
+    add_custom_target(fixtures
+        DEPENDS ${TRANSCRIBE_FIXTURE_FILES})
+else()
+    message(STATUS
+        "uv not found on PATH; transcribe_loader_smoke will not be "
+        "registered. Install uv (https://docs.astral.sh/uv/) and "
+        "reconfigure to enable.")
+endif()
 
 # -----------------------------------------------------------------------------
 # GGUF loader / arch dispatch smoke
@@ -780,6 +763,20 @@ if(TRANSCRIBE_UV)
         SKIP_RETURN_CODE 77)
 endif()
 
+# -----------------------------------------------------------------------------
+# ecapa_tdnn (LANGID role) fixture smoke
+# -----------------------------------------------------------------------------
+
+if(TRANSCRIBE_UV)
+    add_executable(transcribe_ecapa_tdnn_smoke ecapa_tdnn_smoke.cpp)
+    target_link_libraries(transcribe_ecapa_tdnn_smoke PRIVATE transcribe)
+    target_compile_definitions(transcribe_ecapa_tdnn_smoke PRIVATE
+        "TRANSCRIBE_TEST_FIXTURES_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/fixtures\"")
+    transcribe_apply_warnings(transcribe_ecapa_tdnn_smoke)
+    add_dependencies(transcribe_ecapa_tdnn_smoke fixtures)
+    add_test(NAME transcribe_ecapa_tdnn_smoke COMMAND transcribe_ecapa_tdnn_smoke)
+endif()
+
 # -----------------------------------------------------------------------------
 # Real-model gated test (developer-local, never CI)
 # -----------------------------------------------------------------------------
@@ -1388,7 +1385,7 @@ if(TRANSCRIBE_BUILD_REAL_MODEL_TESTS)
     add_executable(transcribe_ecapa_tdnn_real_smoke
         ecapa_tdnn_real_smoke.cpp)
     target_link_libraries(transcribe_ecapa_tdnn_real_smoke
-        PRIVATE transcribe transcribe-common-example ggml)
+        PRIVATE transcribe transcribe-common-example)
     target_compile_definitions(transcribe_ecapa_tdnn_real_smoke PRIVATE
         "TRANSCRIBE_TEST_SAMPLES_DIR=\"${CMAKE_SOURCE_DIR}/samples\"")
     transcribe_apply_warnings(transcribe_ecapa_tdnn_real_smoke)
diff --git a/tests/api_smoke.c b/tests/api_smoke.c
index a2afe6bd2..0bbaca3e3 100644
--- a/tests/api_smoke.c
+++ b/tests/api_smoke.c
@@ -129,6 +129,8 @@ static void test_abi_metadata(void) {
     CHECK(transcribe_abi_struct_size(TRANSCRIBE_ABI_SEGMENT) == sizeof(struct transcribe_segment));
     CHECK(transcribe_abi_struct_size(TRANSCRIBE_ABI_SPEAKER_SEGMENT) == sizeof(struct transcribe_speaker_segment));
     CHECK(transcribe_abi_struct_align(TRANSCRIBE_ABI_SPEAKER_SEGMENT) == _Alignof(struct transcribe_speaker_segment));
+    CHECK(transcribe_abi_struct_size(TRANSCRIBE_ABI_LANGID_CANDIDATE) == sizeof(struct transcribe_langid_candidate));
+    CHECK(transcribe_abi_struct_align(TRANSCRIBE_ABI_LANGID_CANDIDATE) == _Alignof(struct transcribe_langid_candidate));
     CHECK(transcribe_abi_struct_size((transcribe_abi_struct) 9999) == 0);
     CHECK(transcribe_abi_struct_align((transcribe_abi_struct) 9999) == 0);
 
@@ -528,41 +530,6 @@ static void test_model_introspection_null(void) {
 
     CHECK(transcribe_model_roles(NULL) == 0);
 
-    /* LANGID role entry points are NULL-safe. */
-    {
-        struct transcribe_langid_info info;
-        transcribe_langid_info_init(&info);
-        CHECK(info.struct_size == sizeof(info));
-        CHECK(transcribe_langid_get_info(NULL, &info) == TRANSCRIBE_ERR_INVALID_ARG);
-        CHECK(transcribe_langid_label_code(NULL, 0) == NULL);
-        CHECK(transcribe_langid_label_name(NULL, 0) == NULL);
-        CHECK(transcribe_langid_label_index(NULL, "en") == -1);
-
-        struct transcribe_langid_session * ls = (struct transcribe_langid_session *) 0x1;
-        CHECK(transcribe_langid_session_init(NULL, NULL, &ls) == TRANSCRIBE_ERR_INVALID_ARG);
-        CHECK(ls == NULL);
-        transcribe_langid_session_free(NULL);
-        transcribe_langid_set_abort_callback(NULL, NULL, NULL);
-
-        const float pcm[1] = { 0.0f };
-        CHECK(transcribe_langid_run(NULL, pcm, 1, NULL) == TRANSCRIBE_ERR_INVALID_ARG);
-
-        struct transcribe_langid_result r;
-        transcribe_langid_result_init(&r);
-        CHECK(transcribe_langid_get_result(NULL, &r) == TRANSCRIBE_ERR_INVALID_ARG);
-        struct transcribe_langid_candidate c;
-        transcribe_langid_candidate_init(&c);
-        CHECK(transcribe_langid_get_candidate(NULL, 0, &c) == TRANSCRIBE_OK);
-        CHECK(c.code == NULL);
-        struct transcribe_timings tm;
-        transcribe_timings_init(&tm);
-        CHECK(transcribe_langid_get_timings(NULL, &tm) == TRANSCRIBE_ERR_INVALID_ARG);
-
-        CHECK(transcribe_abi_struct_size(TRANSCRIBE_ABI_LANGID_CANDIDATE) ==
-              sizeof(struct transcribe_langid_candidate));
-        CHECK(transcribe_abi_struct_align(TRANSCRIBE_ABI_LANGID_RESULT) != 0);
-    }
-
     /* The feature probe is also NULL-safe and returns false for every
      * known feature value plus any out-of-range enum. */
     CHECK(transcribe_model_supports(NULL, TRANSCRIBE_FEATURE_INITIAL_PROMPT) == false);
diff --git a/tests/cli_langid_smoke.cmake b/tests/cli_langid_smoke.cmake
index ce2e19a8b..cf993d7e1 100644
--- a/tests/cli_langid_smoke.cmake
+++ b/tests/cli_langid_smoke.cmake
@@ -1,8 +1,7 @@
 # transcribe-cli routes a LANGID model to the language ID path: prints the
 # `language:` / `candidate:` lines, writes the candidates with -o, honours
-# --allow / --top / --max-audio-ms / --repeat, and refuses --batch and
-# --stream-chunk-ms. Structural checks run on the toy fixture (MODEL); with
-# TRANSCRIBE_ECAPA_TDNN_GGUF set, the real model must also call fleurs-en.
+# --allow / --top / --repeat, and refuses --batch and --stream-chunk-ms.
+# Structural checks only, on the toy fixture (MODEL).
 
 file(REMOVE_RECURSE "${TEST_DIR}")
 file(MAKE_DIRECTORY "${TEST_DIR}")
@@ -38,16 +37,6 @@ if(NOT rc EQUAL 0 OR out MATCHES "candidate: 2 ")
     message(FATAL_ERROR "--top 1: rc=${rc}\n${out}\n${err}")
 endif()
 
-run_cli(out rc err -m "${MODEL}" --allow zz "${WAV}")
-if(rc EQUAL 0 OR NOT out MATCHES "run: unsupported language")
-    message(FATAL_ERROR "--allow zz: rc=${rc}\n${out}\n${err}")
-endif()
-
-run_cli(out rc err -m "${MODEL}" --max-audio-ms 400 "${WAV}")
-if(rc EQUAL 0 OR NOT err MATCHES "langid session init: invalid argument")
-    message(FATAL_ERROR "--max-audio-ms 400: rc=${rc}\n${out}\n${err}")
-endif()
-
 run_cli(out rc err -m "${MODEL}" --stream-chunk-ms 100 "${WAV}")
 if(rc EQUAL 0 OR NOT err MATCHES "no streaming entry point")
     message(FATAL_ERROR "--stream-chunk-ms on a langid model: rc=${rc}\n${err}")
@@ -58,10 +47,3 @@ run_cli(out rc err -m "${MODEL}" --batch "${TEST_DIR}/list.txt")
 if(rc EQUAL 0 OR NOT err MATCHES "--batch is ASR-only")
     message(FATAL_ERROR "--batch on a langid model: rc=${rc}\n${err}")
 endif()
-
-if(NOT "$ENV{TRANSCRIBE_ECAPA_TDNN_GGUF}" STREQUAL "")
-    run_cli(out rc err -m "$ENV{TRANSCRIBE_ECAPA_TDNN_GGUF}" --top 3 "${WAV}")
-    if(NOT rc EQUAL 0 OR NOT out MATCHES "language: en index=")
-        message(FATAL_ERROR "real model on ${WAV}: rc=${rc}\n${out}\n${err}")
-    endif()
-endif()
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
index c6a29a85a..bc4578710 100644
--- a/tests/ecapa_tdnn_real_smoke.cpp
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -1,11 +1,9 @@
 // ecapa_tdnn_real_smoke.cpp - the real VoxLingua107 ECAPA-TDNN GGUF through
 // the public LANGID API: label table, aliases, top-1 on committed FLEURS
-// clips, the crop on a long clip, and a short clip just over the minimum,
-// all on the CPU backend; then, when a GPU backend loads, the stock-op GPU
-// graph against the CPU graph's logits. Gated by TRANSCRIBE_ECAPA_TDNN_GGUF
-// (RC 77 skip).
+// clips, the crop on a long clip, and a clip cut to 800 ms, all on the CPU
+// backend; then, when a GPU backend loads, the stock-op GPU graph against the
+// CPU graph's logits. Gated by TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip).
 
-#include "gguf.h"
 #include "transcribe.h"
 #include "transcribe/langid.h"
 #include "wav.h"
@@ -86,30 +84,14 @@ std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
     return out;
 }
 
-// general.file_type of the GGUF (0 = all F32, 1 = F16, 7 = Q8_0), or -1.
-int gguf_file_type(const char * path) {
-    gguf_init_params gp{};
-    gp.no_alloc      = true;
-    gp.ctx           = nullptr;
-    gguf_context * g = gguf_init_from_file(path, gp);
-    if (g == nullptr) {
-        return -1;
-    }
-    const int64_t key = gguf_find_key(g, "general.file_type");
-    const int     ft =
-        key >= 0 && gguf_get_kv_type(g, key) == GGUF_TYPE_UINT32 ? static_cast<int>(gguf_get_val_u32(g, key)) : -1;
-    gguf_free(g);
-    return ft;
-}
-
 // The GPU backends run the stock-op graph, which no other test reaches.
 // Their matmuls are not bit-exact F32 (Vulkan on an AMD iGPU lands ~1e-2 off
 // the CPU logits, Metal ~7e-3), so this bounds the drift rather than
-// demanding parity. Reduced-precision files drift further because the CPU and
-// GPU round F16 operands differently (Metal: up to 0.052 for F16, 0.048 for
-// Q8_0 on these clips), so they get twice that.
+// demanding parity. F16 / Q8_0 files drift further because the CPU and GPU
+// round F16 operands differently (Metal: up to 0.057 on these clips), so one
+// bound of 0.1 covers F32, F16 and Q8_0.
 void check_gpu_matches_cpu(const char * path, transcribe_model * cpu_model) {
-    const float                      bound  = gguf_file_type(path) == 0 ? 0.05f : 0.1f;
+    const float                      bound  = 0.1f;
     const transcribe_backend_request gpus[] = { TRANSCRIBE_BACKEND_VULKAN, TRANSCRIBE_BACKEND_METAL };
     transcribe_model *               gm     = nullptr;
     for (const transcribe_backend_request b : gpus) {
@@ -216,8 +198,18 @@ int main() {
     int64_t audio_ms = 0;
     top1(s, "ru-long.wav", &p, &audio_ms);
     CHECK(audio_ms == 30000);
-    top1(s, "short-800ms.wav", &p, &audio_ms);
-    CHECK(audio_ms == 800 && std::isfinite(p));
+    std::vector<float> pcm;
+    if (load_sample("fleurs-en.wav", pcm)) {
+        pcm.resize(12800);  // 800 ms
+        CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+        transcribe_langid_result r;
+        transcribe_langid_result_init(&r);
+        transcribe_langid_get_result(s, &r);
+        transcribe_langid_candidate c;
+        transcribe_langid_candidate_init(&c);
+        transcribe_langid_get_candidate(s, 0, &c);
+        CHECK(r.audio_ms == 800 && std::isfinite(c.p));
+    }
 
     transcribe_langid_session_free(s);
 
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index 0a2992b7f..8049507ee 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -1,6 +1,5 @@
 // ecapa_tdnn_smoke.cpp - end-to-end smoke of the ecapa_tdnn family (LANGID
-// role) against tiny synthetic GGUFs, through the PUBLIC C API only, so it
-// runs the same against static, shared and installed builds.
+// role) against tiny synthetic GGUFs, through the public C API.
 //
 // arch_ecapa_tdnn_minimal.gguf (tests/fixtures/make_gguf_fixtures.py) is a
 // 1/32-width model with the exact metadata and tensor contract of the real
@@ -106,9 +105,7 @@ void test_model_surface(transcribe_model * m) {
     CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500);
     CHECK(std::strcmp(transcribe_langid_label_code(m, 2), "cc") == 0);
     CHECK(std::strcmp(transcribe_langid_label_name(m, 2), "Charlie") == 0);
-    CHECK(transcribe_langid_label_code(m, 5) == nullptr);
-    CHECK(transcribe_langid_label_index(m, "xx") == 0);
-    CHECK(transcribe_langid_label_index(m, "zz") == -1);
+    CHECK(transcribe_langid_label_index(m, "xx") == 0);  // alias from the GGUF
 
     // Not an ASR model.
     transcribe_capabilities caps;
@@ -139,11 +136,6 @@ void test_run(transcribe_model * m) {
     }
     CHECK(std::fabs(sum - 1.0) < 1e-5);
 
-    transcribe_timings tm;
-    transcribe_timings_init(&tm);
-    CHECK(transcribe_langid_get_timings(s, &tm) == TRANSCRIBE_OK);
-    CHECK(tm.mel_ms >= 0.0f && tm.encode_ms > 0.0f);
-
     // Determinism: a second run is bit-identical.
     CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
     const auto again = candidates_of(s);
@@ -152,49 +144,9 @@ void test_run(transcribe_model * m) {
         CHECK(again[i].index == first[i].index && again[i].logit == first[i].logit);
     }
 
-    // Restricted set: renormalized over {bb, dd}, mass below 1.
-    const char *             bd[] = { "bb", "dd" };
-    transcribe_langid_params lp;
-    transcribe_langid_params_init(&lp);
-    lp.allowed   = bd;
-    lp.n_allowed = 2;
-    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) == TRANSCRIBE_OK);
-    const transcribe_langid_result rr = result_of(s);
-    CHECK(rr.n_candidates == 2 && rr.n_allowed == 2 && rr.allowed_mass > 0.0f && rr.allowed_mass < 1.0f);
-    const auto restricted = candidates_of(s);
-    CHECK(restricted.size() == 2 && std::fabs(restricted[0].p + restricted[1].p - 1.0f) < 1e-5f);
-
-    // An alias on its own takes all the mass.
-    const char * xx[] = { "xx" };
-    lp.allowed        = xx;
-    lp.n_allowed      = 1;
-    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) == TRANSCRIBE_OK);
-    const auto alias = candidates_of(s);
-    CHECK(alias.size() == 1 && alias[0].index == 0 && alias[0].p == 1.0f);
-
-    // Unknown code; top_k.
-    const char * unknown[] = { "zz" };
-    lp.allowed             = unknown;
-    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) ==
-          TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE);
-    transcribe_langid_params_init(&lp);
-    lp.top_k = 2;
-    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), &lp) == TRANSCRIBE_OK);
-    CHECK(result_of(s).n_candidates == 2 && result_of(s).n_allowed == 5);
-
-    // Minimum length, and the crop on long input.
-    CHECK(transcribe_langid_run(s, pcm.data(), 6400, nullptr) == TRANSCRIBE_ERR_INPUT_TOO_SHORT);  // 400 ms
-    CHECK(transcribe_langid_run(s, pcm.data(), 8000, nullptr) == TRANSCRIBE_OK);                   // 500 ms
-    const std::vector<float> longer = noise(16000 * 45, 11);
-    CHECK(transcribe_langid_run(s, longer.data(), static_cast<int>(longer.size()), nullptr) == TRANSCRIBE_OK);
-    CHECK(result_of(s).audio_ms == 30000);
-
-    // Silence is valid input; NaN is not.
+    // Silence is valid input.
     const std::vector<float> zeros(16000, 0.0f);
     CHECK(transcribe_langid_run(s, zeros.data(), 16000, nullptr) == TRANSCRIBE_OK);
-    std::vector<float> nan_pcm = pcm;
-    nan_pcm[100]               = NAN;
-    CHECK(transcribe_langid_run(s, nan_pcm.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
 
     // Abort before compute.
     transcribe_langid_set_abort_callback(s, [](void *) { return true; }, nullptr);
@@ -223,15 +175,6 @@ void test_thread_invariance(transcribe_model * m) {
     transcribe_langid_session_free(s4);
 }
 
-// H4 / H6: a duplicate label code fails the load cleanly. Run under ASan /
-// LSan (TRANSCRIBE_SANITIZE) this also proves the half-built model is freed.
-void test_bad_labels() {
-    transcribe_status  st = TRANSCRIBE_OK;
-    transcribe_model * m  = load_cpu("arch_ecapa_tdnn_bad_labels.gguf", &st);
-    CHECK(st == TRANSCRIBE_ERR_GGUF);
-    CHECK(m == nullptr);
-}
-
 // Q8_0 weights are widened to F16 at load: a Q8_0 model must give exactly
 // the logits of the same model stored as F16 holding the dequantized values.
 void test_q8_0_widened_to_f16() {
@@ -276,7 +219,6 @@ int main() {
     test_thread_invariance(m);
     transcribe_model_free(m);
 
-    test_bad_labels();
     test_q8_0_widened_to_f16();
 
     if (g_failures != 0) {
diff --git a/tests/fixtures/make_gguf_fixtures.py b/tests/fixtures/make_gguf_fixtures.py
index 7fbc9a8f1..61c8689cb 100644
--- a/tests/fixtures/make_gguf_fixtures.py
+++ b/tests/fixtures/make_gguf_fixtures.py
@@ -1408,7 +1408,6 @@ def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str
         _pack_kv_float32("stt.frontend.log_clamp_min", 1e-10),
         _pack_kv_float32("stt.frontend.top_db", 80.0),
         _pack_kv_string("stt.frontend.normalize", "sentence_mean"),
-        _pack_kv_uint32("stt.ecapa_tdnn.format_version", 1),
         _pack_kv_array_int32("stt.ecapa_tdnn.channels", ECAPA_CHANNELS),
         _pack_kv_array_int32("stt.ecapa_tdnn.kernel_sizes", ECAPA_KERNELS),
         _pack_kv_array_int32("stt.ecapa_tdnn.dilations", ECAPA_DILATIONS),
@@ -1954,13 +1953,9 @@ def emit_fixtures(out_dir: Path) -> None:
     )
 
 
-    # ecapa_tdnn (LANGID role): a structurally complete toy model, and the
-    # same model with a duplicate label code, which the loader must reject
-    # with TRANSCRIBE_ERR_GGUF without leaking the half-built model (H4/H6).
+    # ecapa_tdnn (LANGID role): a structurally complete toy model.
     _write(out_dir / "arch_ecapa_tdnn_minimal.gguf",
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"]))
-    _write(out_dir / "arch_ecapa_tdnn_bad_labels.gguf",
-           _ecapa_tdnn_gguf(["aa", "bb", "cc", "bb", "ee"], ECAPA_LABEL_NAMES, ["xx=aa"]))
     # The toy model with its Q8_0-eligible weights in Q8_0, and the same
     # weights as F16 holding the dequantized values. The loader widens Q8_0
     # to F16, so the two must give bit-identical logits on the CPU.
diff --git a/tests/langid_dispatch_unit.cpp b/tests/langid_dispatch_unit.cpp
index 5902f5346..850cf6ff4 100644
--- a/tests/langid_dispatch_unit.cpp
+++ b/tests/langid_dispatch_unit.cpp
@@ -1,7 +1,7 @@
 // langid_dispatch_unit.cpp - LANGID role dispatcher (transcribe-langid.cpp)
-// against a fake arch: role checks, label table validation (H6), the
-// allowed set (H2), crop and minimum length (H1), non-finite input and
-// logits (H3), softmax / ranking / top-k, accessors.
+// against a fake arch: label table validation, role checks, softmax /
+// ranking / top-k, the allowed set, crop and minimum length, non-finite
+// input and logits, abort.
 
 #include "transcribe-arch.h"
 #include "transcribe-langid.h"
@@ -38,7 +38,6 @@ transcribe::LangidLabels g_labels;  // aa..ee, alias xx=aa
 int                g_run_calls   = 0;
 int                g_last_n      = 0;
 const float *      g_last_pcm    = nullptr;
-transcribe_status  g_run_status  = TRANSCRIBE_OK;
 std::vector<float> g_logits      = { 1.0f, 3.0f, 2.0f, 0.0f, -1.0f };
 bool               g_check_abort = false;
 
@@ -57,12 +56,7 @@ transcribe_status fake_run(transcribe_langid_session * s, const float * pcm, int
     if (g_check_abort && s->poll_abort()) {
         return TRANSCRIBE_ERR_ABORTED;
     }
-    if (g_run_status != TRANSCRIBE_OK) {
-        return g_run_status;
-    }
-    s->t_mel_us    = 1000;
-    s->t_encode_us = 2000;
-    logits         = g_logits;
+    logits = g_logits;
     return TRANSCRIBE_OK;
 }
 
@@ -85,24 +79,17 @@ const transcribe::Arch k_arch = {
     /* .langid           = */ &k_ops,
 };
 
-void reset_fake() {
-    g_run_calls   = 0;
-    g_last_n      = 0;
-    g_last_pcm    = nullptr;
-    g_run_status  = TRANSCRIBE_OK;
-    g_logits      = { 1.0f, 3.0f, 2.0f, 0.0f, -1.0f };
-    g_check_abort = false;
-}
-
 struct Fixture {
     transcribe_model            model;
     transcribe_langid_session * session = nullptr;
     std::vector<float>          pcm     = std::vector<float>(16000, 0.0f);  // 1 s
 
     explicit Fixture(int32_t max_audio_ms = 0) {
-        reset_fake();
-        model.arch  = &k_arch;
-        model.roles = TRANSCRIBE_ROLE_LANGID;
+        g_run_calls   = 0;
+        g_logits      = { 1.0f, 3.0f, 2.0f, 0.0f, -1.0f };
+        g_check_abort = false;
+        model.arch    = &k_arch;
+        model.roles   = TRANSCRIBE_ROLE_LANGID;
         transcribe_langid_session_params sp;
         transcribe_langid_session_params_init(&sp);
         sp.max_audio_ms = max_audio_ms;
@@ -130,39 +117,17 @@ struct Fixture {
     }
 };
 
-transcribe_status labels_from(std::vector<std::string> codes,
-                              std::vector<std::string> names,
-                              std::vector<std::string> aliases) {
-    transcribe::LangidLabels out;
-    return transcribe::build_langid_labels(std::move(codes), std::move(names), aliases, "test", out);
-}
-
-// H6: malformed label metadata fails the load instead of being last-wins.
+// Malformed label metadata fails the load instead of being last-wins.
 void test_label_table() {
-    const std::vector<std::string> codes = { "aa", "bb" };
     const std::vector<std::string> names = { "A", "B" };
-    CHECK(labels_from(codes, names, {}) == TRANSCRIBE_OK);
-    CHECK(labels_from(codes, names, { "xx=aa", "yy=bb" }) == TRANSCRIBE_OK);
-
-    CHECK(labels_from({}, {}, {}) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from(codes, { "A" }, {}) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from({ "aa", "" }, names, {}) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from({ "aa", "aa" }, names, {}) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from(codes, names, { "xx" }) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from(codes, names, { "=aa" }) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from(codes, names, { "xx=" }) == TRANSCRIBE_ERR_GGUF);
-    CHECK(labels_from(codes, names, { "xx=zz" }) == TRANSCRIBE_ERR_GGUF);           // unknown target
-    CHECK(labels_from(codes, names, { "bb=aa" }) == TRANSCRIBE_ERR_GGUF);           // alias remaps a code
-    CHECK(labels_from(codes, names, { "xx=aa", "xx=bb" }) == TRANSCRIBE_ERR_GGUF);  // alias repeated
-    CHECK(labels_from(codes, names, { "xx=aa", "yy=xx" }) == TRANSCRIBE_ERR_GGUF);  // alias of an alias
-
-    transcribe::LangidLabels out;
-    CHECK(transcribe::build_langid_labels({ "aa" }, { "A" }, { "xx=aa" }, "test", out) == TRANSCRIBE_OK);
-    CHECK(out.codes.size() == 1 && out.index.size() == 2 && out.index.at("xx") == 0);
+    transcribe::LangidLabels       out;
+    CHECK(transcribe::build_langid_labels({ "aa", "bb" }, names, { "xx=aa" }, "test", out) == TRANSCRIBE_OK);
+    CHECK(out.codes.size() == 2 && out.index.size() == 3 && out.index.at("xx") == 0);
+    CHECK(transcribe::build_langid_labels({ "aa", "aa" }, names, {}, "test", out) == TRANSCRIBE_ERR_GGUF);
+    CHECK(transcribe::build_langid_labels({ "aa", "bb" }, names, { "xx=zz" }, "test", out) == TRANSCRIBE_ERR_GGUF);
 }
 
 void test_role_checks_and_labels() {
-    reset_fake();
     transcribe_model model;
     model.arch  = &k_arch;
     model.roles = TRANSCRIBE_ROLE_ASR;  // no LANGID bit
@@ -173,80 +138,25 @@ void test_role_checks_and_labels() {
     transcribe_langid_info info;
     transcribe_langid_info_init(&info);
     CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_ERR_UNSUPPORTED_ROLE);
-    CHECK(transcribe_langid_label_code(&model, 0) == nullptr);
-    CHECK(transcribe_langid_label_name(&model, 0) == nullptr);
-    CHECK(transcribe_langid_label_index(&model, "aa") == -1);
 
     model.roles = TRANSCRIBE_ROLE_LANGID;
     CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_OK);
     CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500);
-    CHECK(transcribe_langid_get_info(nullptr, &info) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(transcribe_langid_get_info(&model, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    info.struct_size = 0;
-    CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
-
     CHECK(std::strcmp(transcribe_langid_label_code(&model, 1), "bb") == 0);
     CHECK(std::strcmp(transcribe_langid_label_name(&model, 4), "Ee") == 0);
-    CHECK(transcribe_langid_label_code(&model, 5) == nullptr);
-    CHECK(transcribe_langid_label_code(&model, -1) == nullptr);
-    CHECK(transcribe_langid_label_code(nullptr, 0) == nullptr);
     CHECK(transcribe_langid_label_index(&model, "cc") == 2);
     CHECK(transcribe_langid_label_index(&model, "xx") == 0);
     CHECK(transcribe_langid_label_index(&model, "zz") == -1);
-    CHECK(transcribe_langid_label_index(&model, "") == -1);
-    CHECK(transcribe_langid_label_index(&model, nullptr) == -1);
-    CHECK(transcribe_langid_label_index(nullptr, "aa") == -1);
 
+    // A window below the minimum is rejected up front.
     transcribe_langid_session_params sp;
     transcribe_langid_session_params_init(&sp);
-    sp.n_threads = -1;
+    sp.max_audio_ms = 499;
     CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
     CHECK(s == nullptr);
-    sp.n_threads    = 2;
-    sp.max_audio_ms = 499;  // H1: a window below the minimum
-    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(s == nullptr);
-    sp.max_audio_ms = -1;
-    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
-    sp.struct_size = 0;
-    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
-    CHECK(s == nullptr);
-    transcribe_langid_session_params_init(&sp);
-    sp.max_audio_ms = 500;
-    CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_OK);
-    CHECK(s != nullptr);
-    transcribe_langid_session_free(s);
-    CHECK(transcribe_langid_session_init(nullptr, nullptr, &s) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(transcribe_langid_session_init(&model, nullptr, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    transcribe_langid_session_free(nullptr);
-}
-
-void test_accessors_before_run() {
-    Fixture                  f;
-    transcribe_langid_result r = f.result();
-    CHECK(r.n_candidates == 0 && r.n_allowed == 0 && r.allowed_mass == 0.0f && r.audio_ms == 0);
-    transcribe_langid_candidate c = f.candidate(0);
-    CHECK(c.code == nullptr && c.name == nullptr && c.index == 0);
-
-    CHECK(transcribe_langid_get_result(f.session, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(transcribe_langid_get_result(nullptr, &r) == TRANSCRIBE_ERR_INVALID_ARG);
-    r.struct_size = 0;
-    CHECK(transcribe_langid_get_result(f.session, &r) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
-    CHECK(transcribe_langid_get_candidate(f.session, 0, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    c.struct_size = 0;
-    CHECK(transcribe_langid_get_candidate(f.session, 0, &c) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
-    transcribe_langid_candidate_init(&c);
-    CHECK(transcribe_langid_get_candidate(nullptr, 0, &c) == TRANSCRIBE_OK);
-    CHECK(c.code == nullptr);
-
-    transcribe_timings tm;
-    transcribe_timings_init(&tm);
-    CHECK(transcribe_langid_get_timings(f.session, &tm) == TRANSCRIBE_OK);
-    CHECK(tm.mel_ms == 0.0f && tm.encode_ms == 0.0f);
-    CHECK(transcribe_langid_get_timings(nullptr, &tm) == TRANSCRIBE_ERR_INVALID_ARG);
 }
 
-void test_unrestricted_ranking() {
+void test_ranking() {
     Fixture f;
     CHECK(f.run() == TRANSCRIBE_OK);
     CHECK(g_run_calls == 1 && g_last_n == 16000 && g_last_pcm == f.pcm.data());
@@ -262,19 +172,11 @@ void test_unrestricted_ranking() {
         const transcribe_langid_candidate c = f.candidate(i);
         CHECK(c.index == want[i]);
         CHECK(c.code != nullptr && std::strcmp(c.code, g_labels.codes[want[i]].c_str()) == 0);
-        CHECK(c.name != nullptr && std::strcmp(c.name, g_labels.names[want[i]].c_str()) == 0);
-        CHECK(c.p == c.p_unrestricted);
-        CHECK(c.logit == g_logits[want[i]]);
+        CHECK(c.p == c.p_unrestricted && c.logit == g_logits[want[i]]);
         CHECK(near(c.p, std::exp(static_cast<double>(g_logits[want[i]])) / denom));
         sum += c.p;
     }
     CHECK(near(sum, 1.0));
-    CHECK(f.candidate(5).code == nullptr);  // out of range: zeroed row
-
-    transcribe_timings tm;
-    transcribe_timings_init(&tm);
-    CHECK(transcribe_langid_get_timings(f.session, &tm) == TRANSCRIBE_OK);
-    CHECK(tm.mel_ms == 1.0f && tm.encode_ms == 2.0f);
 
     // top_k truncates the rows but not n_allowed.
     transcribe_langid_params p;
@@ -283,19 +185,10 @@ void test_unrestricted_ranking() {
     CHECK(f.run(&p) == TRANSCRIBE_OK);
     CHECK(f.result().n_candidates == 2 && f.result().n_allowed == 5);
     CHECK(f.candidate(1).index == 2 && f.candidate(2).code == nullptr);
-    p.top_k = 9;
-    CHECK(f.run(&p) == TRANSCRIBE_OK);
-    CHECK(f.result().n_candidates == 5);
-
-    // Ties keep label order.
-    g_logits = { 0.5f, 0.5f, 0.5f, 0.5f, 0.5f };
-    CHECK(f.run() == TRANSCRIBE_OK);
-    for (int i = 0; i < 5; ++i) {
-        CHECK(f.candidate(i).index == i);
-    }
 }
 
-// H2: only NULL means "all"; every other malformed allowed list is rejected.
+// The allowed set renormalizes over its members; malformed or unknown lists
+// are rejected before the family runs and keep the last result.
 void test_allowed_set() {
     Fixture                  f;
     transcribe_langid_params p;
@@ -305,7 +198,7 @@ void test_allowed_set() {
     p.allowed            = dd_bb;
     p.n_allowed          = 2;
     CHECK(f.run(&p) == TRANSCRIBE_OK);
-    transcribe_langid_result r = f.result();
+    const transcribe_langid_result r = f.result();
     CHECK(r.n_candidates == 2 && r.n_allowed == 2);
     const double e_bb = std::exp(3.0);
     const double e_dd = std::exp(0.0);
@@ -316,53 +209,20 @@ void test_allowed_set() {
     CHECK(c0.index == 1 && c1.index == 3);
     CHECK(near(c0.p, e_bb / (e_bb + e_dd)) && near(c1.p, e_dd / (e_bb + e_dd)));
     CHECK(near(c0.p_unrestricted, e_bb / all));
-    CHECK(near(c0.p + c1.p, 1.0));
-
-    const char * one[] = { "cc" };
-    p.allowed          = one;
-    p.n_allowed        = 1;
-    CHECK(f.run(&p) == TRANSCRIBE_OK);
-    CHECK(f.result().n_allowed == 1 && f.candidate(0).index == 2 && f.candidate(0).p == 1.0f);
-
-    const char * alias[] = { "xx" };
-    p.allowed            = alias;
-    CHECK(f.run(&p) == TRANSCRIBE_OK);
-    CHECK(f.candidate(0).index == 0 && std::strcmp(f.candidate(0).code, "aa") == 0);
 
-    const char * dup[] = { "bb", "bb", "xx", "aa" };
-    p.allowed          = dup;
-    p.n_allowed        = 4;
-    CHECK(f.run(&p) == TRANSCRIBE_OK);
-    CHECK(f.result().n_allowed == 2 && f.result().n_candidates == 2);
-
-    const char * every[] = { "aa", "bb", "cc", "dd", "ee" };
-    p.allowed            = every;
-    p.n_allowed          = 5;
-    CHECK(f.run(&p) == TRANSCRIBE_OK);
-    CHECK(f.result().n_allowed == 5 && f.result().allowed_mass == 1.0f);
-    CHECK(f.candidate(0).p == f.candidate(0).p_unrestricted);
-
-    // Rejections never reach the family and keep the last result.
-    reset_fake();
-    p.n_allowed = -1;
-    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
+    g_run_calls = 0;
     p.n_allowed = 0;
     CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
-    p.allowed   = nullptr;
-    p.n_allowed = 2;
-    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
-    const char * with_null[] = { "aa", nullptr };
-    p.allowed                = with_null;
-    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
     const char * unknown[] = { "aa", "zz" };
     p.allowed              = unknown;
+    p.n_allowed            = 2;
     CHECK(f.run(&p) == TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE);
     CHECK(g_run_calls == 0);
-    CHECK(f.result().n_allowed == 5);
+    CHECK(f.result().n_allowed == 2);
 }
 
-// H1: the crop keeps the last max_audio_ms, and the minimum applies to the
-// audio actually scored.
+// The crop keeps the last max_audio_ms, and the minimum applies to the audio
+// actually scored.
 void test_crop_and_minimum() {
     {
         Fixture f(500);
@@ -371,32 +231,17 @@ void test_crop_and_minimum() {
         CHECK(f.result().audio_ms == 500);
     }
     {
-        Fixture            f;
-        std::vector<float> pcm(8000, 0.0f);  // exactly 500 ms
-        CHECK(transcribe_langid_run(f.session, pcm.data(), 8000, nullptr) == TRANSCRIBE_OK);
-        CHECK(f.result().audio_ms == 500);
+        Fixture f;
+        CHECK(transcribe_langid_run(f.session, f.pcm.data(), 8000, nullptr) == TRANSCRIBE_OK);  // exactly 500 ms
         g_run_calls = 0;
-        CHECK(transcribe_langid_run(f.session, pcm.data(), 7999, nullptr) == TRANSCRIBE_ERR_INPUT_TOO_SHORT);
+        CHECK(transcribe_langid_run(f.session, f.pcm.data(), 7999, nullptr) == TRANSCRIBE_ERR_INPUT_TOO_SHORT);
         CHECK(g_run_calls == 0);
         CHECK(f.result().audio_ms == 500);  // previous result kept
     }
-    {
-        Fixture            f(1000);
-        std::vector<float> pcm(16000 * 45, 0.0f);
-        CHECK(transcribe_langid_run(f.session, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
-        CHECK(g_last_n == 16000 && g_last_pcm == pcm.data() + pcm.size() - 16000);
-        CHECK(f.result().audio_ms == 1000);
-    }
-    {
-        Fixture            f;  // default window 30 s
-        std::vector<float> pcm(16000 * 45, 0.0f);
-        CHECK(transcribe_langid_run(f.session, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
-        CHECK(f.result().audio_ms == 30000);
-    }
 }
 
-// H3: non-finite PCM is rejected up front; non-finite or miscounted logits
-// fail the run instead of ranking garbage.
+// Non-finite PCM is rejected up front; non-finite or miscounted logits fail
+// the run instead of ranking garbage.
 void test_non_finite() {
     Fixture f;
     CHECK(f.run() == TRANSCRIBE_OK);
@@ -405,56 +250,26 @@ void test_non_finite() {
     std::vector<float> bad(16000, 0.0f);
     bad[3] = std::numeric_limits<float>::quiet_NaN();
     CHECK(transcribe_langid_run(f.session, bad.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    bad[3] = std::numeric_limits<float>::infinity();
-    CHECK(transcribe_langid_run(f.session, bad.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    std::vector<float> all_nan(16000, std::numeric_limits<float>::quiet_NaN());
-    CHECK(transcribe_langid_run(f.session, all_nan.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
     CHECK(g_run_calls == 0);
     CHECK(f.result().n_candidates == 5);
 
     g_logits = { 1.0f, std::numeric_limits<float>::quiet_NaN(), 0.0f, 0.0f, 0.0f };
     CHECK(f.run() == TRANSCRIBE_ERR_BACKEND);
     CHECK(f.result().n_candidates == 0 && f.result().allowed_mass == 0.0f);
-    g_logits = { 1.0f, -std::numeric_limits<float>::infinity(), 0.0f, 0.0f, 0.0f };
-    CHECK(f.run() == TRANSCRIBE_ERR_BACKEND);
-    g_logits = { 1.0f, 2.0f };
+    g_logits = { 1.0f, 2.0f };  // 2 logits for 5 labels
     CHECK(f.run() == TRANSCRIBE_ERR_BACKEND);
 }
 
-void test_bad_args_and_failures() {
+// The abort callback reaches the family, and the aborted run clears the result.
+void test_abort() {
     Fixture f;
     CHECK(f.run() == TRANSCRIBE_OK);
-    g_run_calls = 0;
-
-    CHECK(transcribe_langid_run(nullptr, f.pcm.data(), 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(transcribe_langid_run(f.session, nullptr, 16000, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(transcribe_langid_run(f.session, f.pcm.data(), 0, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    CHECK(transcribe_langid_run(f.session, f.pcm.data(), -5, nullptr) == TRANSCRIBE_ERR_INVALID_ARG);
-    transcribe_langid_params p;
-    transcribe_langid_params_init(&p);
-    p.top_k = -1;
-    CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
-    p.top_k       = 0;
-    p.struct_size = 0;
-    CHECK(f.run(&p) == TRANSCRIBE_ERR_BAD_STRUCT_SIZE);
-    CHECK(g_run_calls == 0);
-    CHECK(f.result().n_candidates == 5);
-
-    // A family failure after the commit point clears the result.
-    g_run_status = TRANSCRIBE_ERR_OOM;
-    CHECK(f.run() == TRANSCRIBE_ERR_OOM);
-    CHECK(f.result().n_candidates == 0 && f.result().audio_ms == 0);
-    g_run_status = TRANSCRIBE_OK;
-
-    // The abort callback reaches the family.
-    CHECK(f.run() == TRANSCRIBE_OK);
     g_check_abort = true;
     transcribe_langid_set_abort_callback(f.session, [](void *) { return true; }, nullptr);
     CHECK(f.run() == TRANSCRIBE_ERR_ABORTED);
     CHECK(f.result().n_candidates == 0);
     transcribe_langid_set_abort_callback(f.session, nullptr, nullptr);
     CHECK(f.run() == TRANSCRIBE_OK);
-    transcribe_langid_set_abort_callback(nullptr, nullptr, nullptr);
 }
 
 }  // namespace
@@ -470,12 +285,11 @@ int main() {
 
     test_label_table();
     test_role_checks_and_labels();
-    test_accessors_before_run();
-    test_unrestricted_ranking();
+    test_ranking();
     test_allowed_set();
     test_crop_and_minimum();
     test_non_finite();
-    test_bad_args_and_failures();
+    test_abort();
 
     if (g_failures != 0) {
         std::fprintf(stderr, "%d failure(s)\n", g_failures);

From 2995d130150ac540c4136db16df0319ab51e6a1a Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:24:37 +0800
Subject: [PATCH 14/38] langid: trim ecapa_tdnn and catalog tooling

- Drop the NumPy second oracle (scripts/lib/ecapa_numpy.py,
  scripts/ecapa_numpy_reference.py); nothing ran it.
- convert-ecapa_tdnn.py: drop the fe.filters cross-check, the --variant
  flag and the stt.ecapa_tdnn.format_version key (the loader no longer
  reads it). Tensor data is unchanged.
- Reference dumper: stop dumping fe.window / fe.filters and the top5
  block of prediction.json; keep the window assert.
- ecapa_tdnn env: drop pandas and pyyaml (no script imports them).
- catalog/check.py: drop the generic xRT / ci95 / agreement integrity
  checks; keep the per-role profile and pending handling.
- ingest_accuracy.py: read the pooled FLEURS language list from the
  langid profile instead of parsing scripts/langid/ingest.py.
- ingest_perf.py: revert the unrelated report-label refactor.
- langid/ingest.py: pin google/fleurs to 70bb2e84 and resolve the parquet
  with hf_hub_download (HF_HOME honoured, downloads if missing).
---
 catalog/_benchmark_profiles.json              |  19 +-
 docs/porting/families/ecapa_tdnn.md           |  18 +-
 scripts/catalog/check.py                      |  26 --
 scripts/catalog/ingest_accuracy.py            |  19 +-
 scripts/catalog/ingest_perf.py                |  19 +-
 scripts/catalog/profiles.py                   |   6 +-
 scripts/convert-ecapa_tdnn.py                 |  42 +-
 .../dump_reference_ecapa_tdnn_speechbrain.py  |  67 +--
 scripts/ecapa_numpy_reference.py              | 168 --------
 scripts/envs/ecapa_tdnn/pyproject.toml        |   2 -
 scripts/envs/ecapa_tdnn/uv.lock               |  65 ---
 scripts/langid/ingest.py                      |  37 +-
 scripts/lib/ecapa_numpy.py                    | 396 ------------------
 tests/tolerances/ecapa_tdnn.json              |   7 +-
 14 files changed, 62 insertions(+), 829 deletions(-)
 delete mode 100755 scripts/ecapa_numpy_reference.py
 delete mode 100644 scripts/lib/ecapa_numpy.py

diff --git a/catalog/_benchmark_profiles.json b/catalog/_benchmark_profiles.json
index 9d84754e4..a3a27848e 100644
--- a/catalog/_benchmark_profiles.json
+++ b/catalog/_benchmark_profiles.json
@@ -85,7 +85,24 @@
           "timestamps": "none",
           "backend": "cpu",
           "crop_s": "5",
-          "trim": "none"
+          "trim": "none",
+          "pooled_languages": [
+            "en",
+            "zh",
+            "de",
+            "fr",
+            "es",
+            "pt",
+            "ja",
+            "ko",
+            "ru",
+            "cs",
+            "sk",
+            "no",
+            "da",
+            "id",
+            "ms"
+          ]
         }
       ],
       "speed": {
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index 919c46de3..dc59a206d 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -40,8 +40,6 @@ page's accuracy snapshot has the figures).
   torch 2.13.0, CPU, one thread.
 - Instrumented reference: `scripts/dump_reference_ecapa_tdnn_speechbrain.py`
   (forward hooks on one `classify_batch`; asserts every hook fires once).
-- Cross-check reference: `scripts/lib/ecapa_numpy.py`, a NumPy forward pass
-  driven only by the GGUF, which proves the converter's rewrites.
 
 ## GGUF contract
 
@@ -52,14 +50,14 @@ Keys: `stt.variant`, `stt.frontend.*` (`window = hamming_periodic`,
 attention / embedding / classifier widths, `asp_eps`, leaky slope) and
 `stt.langid.labels.{codes,names,aliases}`. Integer scalars are uint32.
 
-The converter applies four exact rewrites, each proven by the NumPy
-reference: BatchNorm to a `scale` / `shift` pair (TDNNBlock is conv -> ReLU
--> BN, so it cannot fold backward); the three activation-free BNs folded
-forward into `fc`, `cls.l1`, `cls.out`; and the ASP and MFA input weights
-split per concatenated operand. Tensor names follow the
-`tools/transcribe-quantize` rules (`.bias` / `.bn.` F32, `.conv.weight` for
-k>1 tap-major kernels F32 / F16, every other `.weight` quantizable,
-`frontend.mel_filterbank` F32), so the quant policy has no ECAPA entries.
+The converter applies four exact rewrites: BatchNorm to a `scale` / `shift`
+pair (TDNNBlock is conv -> ReLU -> BN, so it cannot fold backward); the
+three activation-free BNs folded forward into `fc`, `cls.l1`, `cls.out`; and
+the ASP and MFA input weights split per concatenated operand. Tensor names
+follow the `tools/transcribe-quantize` rules (`.bias` / `.bn.` F32,
+`.conv.weight` for k>1 tap-major kernels F32 / F16, every other `.weight`
+quantizable, `frontend.mel_filterbank` F32), so the quant policy has no
+ECAPA entries.
 The full catalogue is in `src/arch/ecapa_tdnn/weights.h`.
 
 The front end is the shared `transcribe-mel` (`src/transcribe-mel.cpp`)
diff --git a/scripts/catalog/check.py b/scripts/catalog/check.py
index 82c59b287..705ca3380 100755
--- a/scripts/catalog/check.py
+++ b/scripts/catalog/check.py
@@ -74,37 +74,11 @@ def integrity_pass(records: dict) -> int:
         for r in rec.get("speed_benchmarks", []):
             if r.get("machine"):
                 machines[r["machine"]].add(name)
-        # A number edited by hand rarely keeps its siblings in step. Reports
-        # are not checked in, so these self-consistency checks are what
-        # catch an edit that no ingest produced.
-        drifted = [r for r in rec.get("speed_benchmarks", [])
-                   if r.get("total_ms") and not xrt_consistent(r)]
-        outside = [r for r in rec.get("accuracy_benchmarks", [])
-                   if None not in (r.get("ci95") or [None])
-                   and not r["ci95"][0] - 0.01 <= common.row_pct(r) <= r["ci95"][1] + 0.01]
-        disagree = [r for r in rec.get("accuracy_benchmarks", [])
-                    if r.get("agreement") and r["agreement"]["n_agree"] > r["agreement"]["n"]]
-        for rows, what in ((drifted, "speed row(s) whose xrt_compute is not "
-                                     "sample_duration_s / total_ms"),
-                           (outside, "accuracy row(s) whose value lies outside its ci95"),
-                           (disagree, "accuracy row(s) with agreement n_agree > n")):
-            if rows:
-                bad += 1
-                print(f"  FAIL {name}: {len(rows)} {what}")
     print(f"integrity  {len(records) - bad}/{len(records)} clean; "
           f"{len(machines)} machine slug(s): {', '.join(sorted(machines))}")
     return bad
 
 
-def xrt_consistent(row: dict) -> bool:
-    """xrt_compute matches sample_duration_s / total_ms, allowing for
-    total_ms stored to 0.1 ms and xRT to 0.01."""
-    duration, total = row["sample_duration_s"], row["total_ms"]
-    low = duration / ((total + 0.05) / 1000) - 0.005
-    high = duration / (max(total - 0.05, 1e-6) / 1000) + 0.005
-    return low <= row["xrt_compute"] <= high
-
-
 def pairing_pass(records: dict, selected: bool = False) -> int:
     """Catalog records and card specs pair exactly; docs may be shared.
 
diff --git a/scripts/catalog/ingest_accuracy.py b/scripts/catalog/ingest_accuracy.py
index a6e081ca5..14f23f845 100755
--- a/scripts/catalog/ingest_accuracy.py
+++ b/scripts/catalog/ingest_accuracy.py
@@ -21,7 +21,6 @@
 from __future__ import annotations
 
 import argparse
-import ast
 import json
 import pathlib
 import sys
@@ -33,20 +32,6 @@
 REPORTS = common.REPO / "reports" / "wer"
 LANGID_REPORTS = common.REPO / "reports" / "langid"
 LANGID_SCORE = "transcribe-langid-score-v1"
-LANGID_INGEST = common.REPO / "scripts" / "langid" / "ingest.py"
-
-
-def fleurs_langid_codes() -> list[str]:
-    """The label codes of scripts/langid/ingest.py's FLEURS_LANGUAGES, the
-    language set the profile's macro mean is over. Read from the source
-    (ingest.py needs numpy, which the catalog scripts do not install)."""
-    tree = ast.parse(LANGID_INGEST.read_text())
-    for node in tree.body:
-        target = node.target if isinstance(node, ast.AnnAssign) else (
-            node.targets[0] if isinstance(node, ast.Assign) else None)
-        if isinstance(target, ast.Name) and target.id == "FLEURS_LANGUAGES":
-            return [code for _config, code in ast.literal_eval(node.value)]
-    raise RuntimeError(f"FLEURS_LANGUAGES not found in {LANGID_INGEST}")
 
 
 def score_path(record: dict, cell: dict, reports: pathlib.Path) -> pathlib.Path:
@@ -113,8 +98,8 @@ def langid_row(record: dict, cell: dict, score: dict, agreement: dict | None,
         reasons.append(f"no {cell['crop_s']} s crop")
     if not score.get("engine_sha"):
         reasons.append("engine_sha is empty")
-    if score.get("dataset") == "fleurs" and sorted(score.get("languages") or []) != sorted(fleurs_langid_codes()):
-        reasons.append(f"languages={score.get('languages')!r} are not scripts/langid/ingest.py's FLEURS set")
+    if sorted(score.get("languages") or []) != sorted(cell.get("pooled_languages") or []):
+        reasons.append(f"languages={score.get('languages')!r} are not the profile's pooled_languages")
     if agreement is None:
         reasons.append("no agreement (write it with compare.py --json)")
     elif agreement.get("run") != score.get("run"):
diff --git a/scripts/catalog/ingest_perf.py b/scripts/catalog/ingest_perf.py
index fb488dcee..6aab0ca35 100755
--- a/scripts/catalog/ingest_perf.py
+++ b/scripts/catalog/ingest_perf.py
@@ -144,26 +144,25 @@ def collect(reports_dir: pathlib.Path) -> tuple[dict, list[str]]:
     best: dict[tuple, dict] = {}
     superseded, unreadable, experiments = collections.Counter(), [], 0
     for path in sorted(reports_dir.glob("*/*.json")):
-        # Reports normally live under the repo, but --reports can name a
-        # staging directory elsewhere (or a relative one); the label is only
-        # for the operator, so fall back to the path as given.
-        try:
-            label = str(path.resolve().relative_to(common.REPO))
-        except ValueError:
-            label = str(path)
         try:
             report = json.loads(path.read_text())
         except (json.JSONDecodeError, UnicodeDecodeError):
-            unreadable.append(label)
+            unreadable.append(str(path.relative_to(common.REPO)))
             continue
         if not isinstance(report, dict) or report.get("schema") != "transcribe-bench-driver-v1":
             kind = report.get("schema") if isinstance(report, dict) else type(report).__name__
-            unreadable.append(f"{label} (schema {kind!r})")
+            unreadable.append(f"{path.relative_to(common.REPO)} (schema {kind!r})")
             continue
         if not publishable(report):
             experiments += 1
             continue
-        report["_file"] = label
+        # Reports normally live under the repo, but --reports can name a
+        # staging directory elsewhere (or a relative one); the label is only
+        # for the operator, so fall back to the path as given.
+        try:
+            report["_file"] = str(path.resolve().relative_to(common.REPO))
+        except ValueError:
+            report["_file"] = str(path)
         for row in cells(report):
             key = (row["variant"], row["machine"], row["backend"], row["quant"], row["sample"])
             previous = best.get(key)
diff --git a/scripts/catalog/profiles.py b/scripts/catalog/profiles.py
index 939a7322c..b73e9b26c 100644
--- a/scripts/catalog/profiles.py
+++ b/scripts/catalog/profiles.py
@@ -178,8 +178,10 @@ def expected_accuracy(record: dict, profile: dict) -> list[dict]:
                     "gpu": suite.get("gpu"),
                     "backend": suite.get("backend"),
                     # Language ID's crop and trim: which slice of a run.py
-                    # sweep the published number is.
-                    **{key: suite[key] for key in ("crop_s", "trim") if key in suite},
+                    # sweep the published number is, and which languages
+                    # its pooled mean is over.
+                    **{key: suite[key] for key in ("crop_s", "trim", "pooled_languages")
+                       if key in suite},
                 })
     return cells
 
diff --git a/scripts/convert-ecapa_tdnn.py b/scripts/convert-ecapa_tdnn.py
index dd632f93b..d1b6b09f6 100644
--- a/scripts/convert-ecapa_tdnn.py
+++ b/scripts/convert-ecapa_tdnn.py
@@ -32,8 +32,7 @@
 geometry, top_db, amin, ...) is a SpeechBrain *default* of the pinned
 version, so the yaml cannot confirm it.
 
-Four exact rewrites happen here; the NumPy reference
-`scripts/lib/ecapa_numpy.py` proves each of them numerically:
+Four exact rewrites happen here:
 
   1. Every TDNN BatchNorm becomes a `scale`/`shift` vector pair. It cannot
      be folded into its conv because SpeechBrain's TDNNBlock is
@@ -111,7 +110,6 @@
 DEFAULT_VARIANT = "lang-id-voxlingua107-ecapa"
 
 ARCH = "ecapa_tdnn"
-FORMAT_VERSION = 1
 
 # Architecture constants. Every one of these is ASSERTED against the live
 # module tree below, never assumed — see `assert_architecture`.
@@ -529,38 +527,6 @@ def capture(f_central_mat, band_mat):
         m.detach().to(dtype=torch.float32, device="cpu").numpy())
 
 
-def crosscheck_filters(filters_201x60: np.ndarray) -> float | None:
-    """Diff the captured matrix against the T2 reference dump, if present.
-
-    `build/validate/.../ref/fe.filters.f32` was written by the reference
-    dumper from the same `_create_fbank_matrix` call during a real
-    classification, so the difference must be exactly 0. Anything in the
-    1e-05 range means this converter recomputed the geometry instead of
-    capturing it (a float64 rebuild differs by about 1.16e-05).
-    """
-    ref = (REPO_ROOT / "build" / "validate" / ARCH / DEFAULT_VARIANT /
-           "fleurs-en" / "ref" / "fe.filters.f32")
-    if not ref.exists():
-        print(f"  fbank cross-check: SKIPPED ({ref} not found; run "
-              f"`uv run scripts/validate.py ref --family {ARCH}` first)")
-        return None
-    a = np.fromfile(ref, dtype=np.float32)
-    if a.size != N_STFT * N_MELS:
-        raise ArchError(f"{ref} has {a.size} elements, expected "
-                        f"{N_STFT * N_MELS}")
-    diff = float(np.max(np.abs(a.reshape(N_STFT, N_MELS) - filters_201x60)))
-    status = "exact" if diff == 0.0 else ("ok" if diff < 1e-6 else "SUSPICIOUS")
-    print(f"  fbank cross-check vs reference dump: max|diff| = {diff:.3e} "
-          f"[{status}]")
-    if diff >= 1e-6:
-        raise ArchError(
-            f"captured fbank matrix differs from the reference dump by "
-            f"{diff:.3e}; it should be bit-identical (same _create_fbank_matrix "
-            f"call). A ~1e-05 diff means the geometry was recomputed instead "
-            f"of captured.")
-    return diff
-
-
 # ---------------------------------------------------------------------------
 # Weight extraction / conversion-time algebra
 # ---------------------------------------------------------------------------
@@ -698,7 +664,6 @@ def convert(clf, out_path: Path, *, variant: str, repo_id: str,
     writer.add_float32("stt.frontend.top_db", TOP_DB)
     writer.add_string("stt.frontend.normalize", canonicalize_normalize("sentence_mean"))
 
-    writer.add_uint32("stt.ecapa_tdnn.format_version", FORMAT_VERSION)
     writer.add_array("stt.ecapa_tdnn.channels", CHANNELS)
     writer.add_array("stt.ecapa_tdnn.kernel_sizes", KERNEL_SIZES)
     writer.add_array("stt.ecapa_tdnn.dilations", DILATIONS)
@@ -886,8 +851,6 @@ def main(argv: list[str] | None = None) -> int:
                         "omitted)")
     p.add_argument("--revision", default=DEFAULT_REVISION,
                    help="HF revision (commit SHA) to pin the download to")
-    p.add_argument("--variant", default=DEFAULT_VARIANT,
-                   help=f"stt.variant string (default: {DEFAULT_VARIANT})")
     p.add_argument("--repo-id", default=None,
                    help="HF repo id used for the output slug and metadata "
                         "when converting from a local path")
@@ -908,11 +871,10 @@ def main(argv: list[str] | None = None) -> int:
     clf = load_reference(src)
     labels = assert_architecture(clf)
     filters = capture_fbank_matrix(clf)
-    crosscheck_filters(filters)
 
     import speechbrain
 
-    report = convert(clf, out_path, variant=args.variant,
+    report = convert(clf, out_path, variant=DEFAULT_VARIANT,
                      repo_id=repo_id, revision=args.revision,
                      filters_201x60=filters, labels=labels)
     report["speechbrain_version"] = speechbrain.__version__
diff --git a/scripts/dump_reference_ecapa_tdnn_speechbrain.py b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
index 08e9e7e99..0f1c326f5 100644
--- a/scripts/dump_reference_ecapa_tdnn_speechbrain.py
+++ b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
@@ -20,7 +20,7 @@
 Writes:
     <name>.f32        raw little-endian float32, row-major
     <name>.json       per-tensor sidecar via shared scripts.lib.ref_dump
-    prediction.json   behavioural artifact (top-1 label + top-5 log-probs)
+    prediction.json   behavioural artifact (top-1 label)
 
 There is a single stage, `encoder`: language ID is one forward pass, so
 the front end, the ECAPA-TDNN embedding network and the classifier head
@@ -63,16 +63,6 @@
     enc.emb                 [256]       fc output
     cls.hidden              [512]       DNN.block_0.act output (pre-BN1)
     cls.logits_raw          [107]       the gate
-
-Plus two run-invariant front-end tables, dumped so the converter and the
-C++ mel unit test can be checked against SpeechBrain directly:
-
-    fe.window               [400]       STFT.window (periodic Hamming)
-    fe.filters              [201, 60]   the fbank matrix used this forward
-
-These two are NOT part of the tolerance contract: the C++ runtime does
-not dump them (the window is recomputed and the filterbank is read from
-the GGUF), so compare_tensors reports them MISSING-left without failing.
 """
 
 from __future__ import annotations
@@ -103,9 +93,7 @@
 
 SAMPLE_RATE = 16000
 HOP = 160
-N_FFT = 400
 WIN = 400
-N_STFT = N_FFT // 2 + 1  # 201
 N_MELS = 60
 EMB_DIM = 256
 N_LABELS = 107
@@ -197,18 +185,6 @@ def vec(t, n: int, *, name: str) -> np.ndarray:
     return np.ascontiguousarray(a, dtype=np.float32)
 
 
-def mat(t, shape: tuple[int, int], *, name: str) -> np.ndarray:
-    """Torch matrix -> 2-D float32 numpy, with an exact shape assertion."""
-    import torch
-
-    if not isinstance(t, torch.Tensor):
-        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
-    if tuple(t.shape) != shape:
-        raise ValueError(f"{name}: expected shape {shape}, got {tuple(t.shape)}")
-    a = t.detach().to(dtype=torch.float32, device="cpu").numpy()
-    return np.ascontiguousarray(a, dtype=np.float32)
-
-
 # ---------------------------------------------------------------------------
 # Hooks
 # ---------------------------------------------------------------------------
@@ -405,27 +381,10 @@ def cmd_encoder(args: argparse.Namespace) -> int:
     hook(cl.out, "cls.logits_raw", hooks, handles)
     hook(cl.softmax, "cls.log_probs", hooks, handles)
 
-    # SpeechBrain 1.1.1's Filterbank builds its [201, 60] matrix inside
-    # forward() from `f_central` / `band`; there is no `fbank_matrix`
-    # attribute or buffer to read. Wrapping the real method captures exactly
-    # the matrix this forward pass used, which is stronger than recomputing it.
-    fbanks = fe.compute_fbanks
-    captured_filters: dict[str, Any] = {}
-    original_create = fbanks._create_fbank_matrix
-
-    def capture_create(f_central_mat, band_mat):
-        m = original_create(f_central_mat, band_mat)
-        captured_filters.setdefault("value", m)
-        captured_filters["calls"] = captured_filters.get("calls", 0) + 1
-        return m
-
-    fbanks._create_fbank_matrix = capture_create
-
     try:
         with torch.inference_mode():
             out_prob, score, index, text_lab = clf.classify_batch(wav, wav_lens)
     finally:
-        fbanks._create_fbank_matrix = original_create
         for h in handles:
             h.remove()
 
@@ -435,11 +394,6 @@ def capture_create(f_central_mat, band_mat):
     bad = {n: h.calls for n, h in hooks.items() if h.calls != 1}
     if bad:
         raise SystemExit(f"error: unexpected hook call counts: {bad}")
-    if captured_filters.get("calls") != 1:
-        raise SystemExit(
-            f"error: Filterbank._create_fbank_matrix fired "
-            f"{captured_filters.get('calls')} times, expected 1"
-        )
 
     T = int(hooks["fe.mel"].value.shape[1])
     if T != expected_T:
@@ -512,7 +466,7 @@ def dump(name: str, array: np.ndarray, stage: str) -> None:
             "error: cls.log_probs hook does not match classify_batch's out_prob"
         )
 
-    # ---- run-invariant front-end tables ---------------------------------
+    # ---- front-end window ------------------------------------------------
     window = fe.compute_STFT.window
     expected_window = torch.hamming_window(WIN)
     if not torch.equal(window.to(torch.float32).cpu(), expected_window):
@@ -521,10 +475,6 @@ def dump(name: str, array: np.ndarray, stage: str) -> None:
             "Hamming window the C++ front end builds does not describe this "
             "checkpoint"
         )
-    dump("fe.window", vec(window, WIN, name="fe.window"), "frontend")
-
-    filters = captured_filters["value"]
-    dump("fe.filters", mat(filters, (N_STFT, N_MELS), name="fe.filters"), "frontend")
 
     # ---- prediction ------------------------------------------------------
     label_index = int(index.item())
@@ -542,29 +492,16 @@ def dump(name: str, array: np.ndarray, stage: str) -> None:
             f"ind2lab[{label_index}] is {ind2lab[label_index]!r}"
         )
 
-    order = np.argsort(-log_probs)[:5]
-    top5 = []
-    for i in order:
-        c, n = split_label(str(ind2lab[int(i)]))
-        top5.append({
-            "index": int(i),
-            "code": c,
-            "name": n,
-            "log_prob": float(log_probs[int(i)]),
-        })
-
     prediction = {
         "label_index": label_index,
         "code": code,
         "name": name,
         "log_prob": float(log_probs[label_index]),
-        "top5": top5,
         "source": {**source, "score": float(score.item())},
     }
     (out_dir / "prediction.json").write_text(json.dumps(prediction, indent=2) + "\n")
     print(f"prediction: {code} ({name}) log_prob={prediction['log_prob']:.6f} "
           f"prob={math.exp(prediction['log_prob']):.4f}")
-    print("top5: " + ", ".join(f"{e['code']}={e['log_prob']:.3f}" for e in top5))
 
     return 0
 
diff --git a/scripts/ecapa_numpy_reference.py b/scripts/ecapa_numpy_reference.py
deleted file mode 100755
index bbf60cb36..000000000
--- a/scripts/ecapa_numpy_reference.py
+++ /dev/null
@@ -1,168 +0,0 @@
-#!/usr/bin/env -S uv run --script
-# /// script
-# requires-python = ">=3.11"
-# dependencies = [
-#     "numpy>=1.26",
-#     "gguf>=0.10",
-#     "soundfile>=0.12",
-# ]
-# ///
-"""
-ecapa_numpy_reference.py - dump the NumPy forward pass (scripts/lib/
-ecapa_numpy.py) in the same on-disk format as the SpeechBrain reference
-dumper and the C++ debug dumper.
-
-This is the middle term of a three-way comparison:
-
-    SpeechBrain  <--compare_tensors-->  NumPy  <--compare_tensors-->  C++
-      (the truth)                     (the GGUF)                 (the ship)
-
-The NumPy side reads ONLY the GGUF, so a NumPy-vs-SpeechBrain pass proves
-the converter's algebra (BN as affine, the three forward BN folds, the
-ASP/MFA weight splits, tap-major kernels), and a C++-vs-NumPy diff after
-that isolates graph bugs from conversion bugs.
-
-It deliberately runs on plain `uv run` rather than the pinned SpeechBrain
-env: nothing here needs torch, and being able to re-run the reference
-forward pass without a 2 GB dependency tree is worth a PEP-723 header.
-
-Usage:
-
-    uv run scripts/ecapa_numpy_reference.py \
-      --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
-      --audio samples/fleurs-en.wav \
-      --out build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/numpy
-
-    uv run scripts/compare_tensors.py \
-      build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/numpy \
-      build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/ref \
-      --tolerances tests/tolerances/ecapa_tdnn.json
-
-Writes `<name>.f32` + `<name>.json` for every stage tensor in the dump contract, plus `prediction.json` in the same shape the SpeechBrain dumper
-writes so validate.py can compare `label_index` on either pair.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-from pathlib import Path
-
-import numpy as np
-import soundfile as sf
-
-REPO_ROOT = Path(__file__).resolve().parents[1]
-SCRIPTS_DIR = REPO_ROOT / "scripts"
-if str(SCRIPTS_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPTS_DIR))
-
-from lib.ecapa_numpy import EcapaNumpy  # noqa: E402
-from lib.ref_dump import write_tensor  # noqa: E402
-
-# Which coarse stage each contract tensor belongs to, for the sidecar's
-# `stage` field. Same grouping the SpeechBrain dumper uses so the two sets
-# of sidecars are directly comparable.
-STAGES = {
-    "fe.mel": "frontend",
-    "enc.blk.0.out": "encoder",
-    "enc.blk.1.tdnn1.out": "encoder",
-    "enc.blk.1.res2.out": "encoder",
-    "enc.blk.1.se.out": "encoder",
-    "enc.blk.1.out": "encoder",
-    "enc.blk.2.out": "encoder",
-    "enc.blk.3.out": "encoder",
-    "enc.mfa.out": "encoder",
-    "enc.asp.attn_logits": "encoder",
-    "enc.asp.out": "encoder",
-    "enc.emb": "encoder",
-    "cls.hidden": "classifier",
-    "cls.logits_raw": "classifier",
-    "cls.log_probs": "classifier",
-}
-
-
-def load_audio(path: Path, sample_rate: int) -> np.ndarray:
-    pcm, sr = sf.read(str(path), dtype="float32", always_2d=False)
-    if pcm.ndim > 1:
-        pcm = pcm.mean(axis=1)
-    if int(sr) != sample_rate:
-        raise SystemExit(
-            f"error: {path} is {sr} Hz; this model's front end is "
-            f"{sample_rate} Hz only. Resample before dumping.")
-    return np.ascontiguousarray(pcm, dtype=np.float32)
-
-
-def main(argv: list[str] | None = None) -> int:
-    p = argparse.ArgumentParser(
-        description="Dump the NumPy ECAPA-TDNN forward pass from a GGUF.")
-    p.add_argument("--gguf", required=True, help="ecapa_tdnn GGUF path")
-    p.add_argument("--audio", required=True, help="16 kHz mono WAV path")
-    p.add_argument("--out", required=True, help="output directory for dumps")
-    args = p.parse_args(argv)
-
-    gguf_path = Path(args.gguf).expanduser().resolve()
-    audio_path = Path(args.audio).expanduser().resolve()
-    out_dir = Path(args.out).expanduser().resolve()
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    model = EcapaNumpy(gguf_path)
-    pcm = load_audio(audio_path, model.sample_rate)
-    n_samples = int(pcm.size)
-    expected_T = n_samples // model.hop + 1
-    print(f"audio: {audio_path.name} samples={n_samples} "
-          f"({n_samples / model.sample_rate:.3f} s) frames={expected_T}")
-
-    result = model.forward(pcm)
-
-    T = int(result["fe.mel"].shape[0])
-    if T != expected_T:
-        raise SystemExit(
-            f"error: fe.mel has T={T} but floor({n_samples}/{model.hop}) + 1 "
-            f"= {expected_T}")
-
-    # The dtype mix actually used for this run. Recorded per dump so an
-    # F16/Q8_0 comparison is never mistaken for an F32 parity number.
-    quant_kinds = sorted(set(model.tensor_types.values()))
-    source = {
-        "kind": "ecapa-tdnn-numpy",
-        "framework": "numpy",
-        "framework_version": np.__version__,
-        "gguf": str(gguf_path),
-        "gguf_file_type": int(model.meta.get("general.file_type", -1)),
-        "gguf_tensor_types": quant_kinds,
-        "variant": model.meta.get("stt.variant"),
-        "source_hf_repo": model.meta.get("general.name"),
-        "source_hf_revision": model.meta.get("general.source.commit"),
-        "audio": audio_path.name,
-        "n_samples": n_samples,
-        "sample_rate": model.sample_rate,
-        "n_frames": T,
-        "n_mels": model.n_mels,
-        "compute_dtype": "f32",
-    }
-
-    missing = [n for n in STAGES if n not in result]
-    if missing:
-        raise SystemExit(f"error: forward() did not return {missing}")
-
-    for name, stage in STAGES.items():
-        arr = np.ascontiguousarray(result[name], dtype=np.float32)
-        print(f"  {name}: shape={arr.shape} min={arr.min():.4e} "
-              f"max={arr.max():.4e} mean={arr.mean():.6e}")
-        write_tensor(name, arr, stage=stage, source=source, out_dir=out_dir)
-
-    pred = dict(result["prediction"])
-    pred["source"] = source
-    (out_dir / "prediction.json").write_text(json.dumps(pred, indent=2) + "\n")
-    print(f"prediction: {pred['code']} ({pred['name']}) "
-          f"log_prob={pred['log_prob']:.6f} "
-          f"prob={math.exp(pred['log_prob']):.4f}")
-    print("top5: " + ", ".join(f"{e['code']}={e['log_prob']:.3f}"
-                               for e in pred["top5"]))
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/scripts/envs/ecapa_tdnn/pyproject.toml b/scripts/envs/ecapa_tdnn/pyproject.toml
index 0c2ce682b..66e98bd66 100644
--- a/scripts/envs/ecapa_tdnn/pyproject.toml
+++ b/scripts/envs/ecapa_tdnn/pyproject.toml
@@ -23,9 +23,7 @@ dependencies = [
     "huggingface-hub>=0.30",
     # Used by the converter (T3), not by the dumper.
     "gguf>=0.10",
-    "pyyaml>=6.0",
     # FLEURS lives in the local HF hub cache as parquet; the fixture builder
     # reads it with pyarrow rather than pulling in `datasets`.
     "pyarrow>=15",
-    "pandas>=2.0",
 ]
diff --git a/scripts/envs/ecapa_tdnn/uv.lock b/scripts/envs/ecapa_tdnn/uv.lock
index 298534cb0..d7db588e2 100644
--- a/scripts/envs/ecapa_tdnn/uv.lock
+++ b/scripts/envs/ecapa_tdnn/uv.lock
@@ -650,37 +650,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/63/34/ba1c580383c9eada3711951fef0795c80b829a078d72188184bcab9dd527/packaging-26.3-py3-none-any.whl", hash = "sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c", size = 129956 },
 ]
 
-[[package]]
-name = "pandas"
-version = "3.0.5"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
-    { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
-    { name = "python-dateutil" },
-    { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/be/4f/5f3422a2afec5ffc46308b79e53291365a93748b498ac2e58bead0197916/pandas-3.0.5.tar.gz", hash = "sha256:dca3734d6ab7c906e6730f0788b0a1dbb9f2467731f9711f77995c8e9d62d712", size = 4658219 }
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-    { url = "https://files.pythonhosted.org/packages/70/b5/d2d3e9ae73362ba4229651b0ee1455cf78073a1ce585f6ff693782ce263e/pandas-3.0.5-cp312-cp312-win_amd64.whl", hash = "sha256:80a611068e8a3ac23f7398c6c14eb46dc974e5cc9997f653e2dcfd1da74edd41", size = 9831691 },
-    { url = "https://files.pythonhosted.org/packages/52/51/dea1e89d6a6796b9c43f85a09b484ee03edb8a4c4842e73e200a8c11301c/pandas-3.0.5-cp312-cp312-win_arm64.whl", hash = "sha256:25ff585b972a18ef1fe9ffa3ac6544d9950508aa76832e5147640b6022821e49", size = 9105796 },
-]
-
 [[package]]
 name = "pyarrow"
 version = "25.0.1"
@@ -712,18 +681,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172 },
 ]
 
-[[package]]
-name = "python-dateutil"
-version = "2.9.0.post0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "six" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
-]
-
 [[package]]
 name = "pyyaml"
 version = "6.0.3"
@@ -899,15 +856,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/95/9c/c510029fc6ef33a6275cd2c5d3cecd6613dfd6aa401d57c54f1c18852ccf/setuptools-84.0.0-py3-none-any.whl", hash = "sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670", size = 818216 },
 ]
 
-[[package]]
-name = "six"
-version = "1.17.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
-]
-
 [[package]]
 name = "soundfile"
 version = "0.14.0"
@@ -1034,9 +982,7 @@ dependencies = [
     { name = "huggingface-hub" },
     { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
     { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
-    { name = "pandas" },
     { name = "pyarrow" },
-    { name = "pyyaml" },
     { name = "soundfile" },
     { name = "speechbrain" },
     { name = "torch" },
@@ -1048,9 +994,7 @@ requires-dist = [
     { name = "gguf", specifier = ">=0.10" },
     { name = "huggingface-hub", specifier = ">=0.30" },
     { name = "numpy", specifier = ">=1.26" },
-    { name = "pandas", specifier = ">=2.0" },
     { name = "pyarrow", specifier = ">=15" },
-    { name = "pyyaml", specifier = ">=6.0" },
     { name = "soundfile", specifier = ">=0.12" },
     { name = "speechbrain", specifier = "==1.1.1" },
     { name = "torch", specifier = "==2.13.0" },
@@ -1077,15 +1021,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/49/d3/b8441a820a491ddfc024b0b0cf0393375b75ea13866d9c66727e54c2fc80/typing_extensions-4.16.0-py3-none-any.whl", hash = "sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8", size = 45571 },
 ]
 
-[[package]]
-name = "tzdata"
-version = "2026.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/92/ff/5a28bdfd8c3ebec42564ac7d0e54ca3db65044a9314a97f9564fa7a1e926/tzdata-2026.3.tar.gz", hash = "sha256:4a1518b8993086a7982523e071643f3c0e5f213e75b21318e78bcabfff9d1415", size = 198674 }
-wheels = [
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-]
-
 [[package]]
 name = "urllib3"
 version = "2.7.0"
diff --git a/scripts/langid/ingest.py b/scripts/langid/ingest.py
index 87d3e4a5b..a8c20a79f 100755
--- a/scripts/langid/ingest.py
+++ b/scripts/langid/ingest.py
@@ -3,9 +3,10 @@
 ingest.py - build an evaluation corpus from a named dataset source.
 
 Sources:
-  fleurs   Reads the FLEURS parquet already present in the local Hugging
-           Face hub cache (`datasets--google--fleurs`). Nothing is
-           downloaded; the 15 configs the evaluation uses are cached.
+  fleurs   Reads the FLEURS test parquet of each config at the pinned
+           dataset revision FLEURS_REVISION, through the Hugging Face hub
+           cache (HF_HOME honoured); a config missing from the cache is
+           downloaded.
 
 Usage:
   uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang en
@@ -17,8 +18,8 @@
 
 Only the FLEURS `test` split is read. Selection rule: the first N
 utterances in parquet order whose decoded duration is >= 1 s. Parquet order
-is the only ordering; no shuffling, no hashing, so the same cache always
-produces the same manifest.
+is the only ordering; no shuffling, no hashing, and the dataset revision is
+pinned, so every run produces the same manifest.
 
 Utterance ids are `fleurs-<code>-<NNNN>` where NNNN is the index among the
 selected rows, NOT the FLEURS sentence id: FLEURS records the same sentence
@@ -32,7 +33,6 @@
 from __future__ import annotations
 
 import argparse
-import glob
 import io
 import json
 import sys
@@ -47,6 +47,7 @@
 DEFAULT_N = 200
 SPLIT = "test"
 DATASET = "google/fleurs"
+FLEURS_REVISION = "70bb2e84b976b7e960aa89f1c648e09c59f894dd"
 LICENCE = "CC-BY-4.0"
 
 # FLEURS config -> VoxLingua107 label code, in the dataset gate's order.
@@ -75,20 +76,14 @@
 
 
 def fleurs_parquet(config: str) -> Path:
-    pattern = (
-        Path.home()
-        / ".cache/huggingface/hub/datasets--google--fleurs/snapshots/*/parquet-data"
-        / config
-        / f"{SPLIT}-00000-of-00001.parquet"
-    )
-    matches = sorted(glob.glob(str(pattern)))
-    if not matches:
-        raise SystemExit(
-            f"error: no cached FLEURS parquet for config {config!r} "
-            f"(looked at {pattern}). This script never downloads; populate "
-            f"the Hugging Face cache first."
-        )
-    return Path(matches[-1])
+    from huggingface_hub import hf_hub_download
+
+    return Path(hf_hub_download(
+        repo_id=DATASET,
+        repo_type="dataset",
+        filename=f"parquet-data/{config}/{SPLIT}-00000-of-00001.parquet",
+        revision=FLEURS_REVISION,
+    ))
 
 
 def iter_rows(path: Path):
@@ -267,7 +262,7 @@ def main(argv: list[str] | None = None) -> int:
         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
     sub = p.add_subparsers(dest="source", required=True)
 
-    fp = sub.add_parser("fleurs", help="FLEURS from the local HF parquet cache")
+    fp = sub.add_parser("fleurs", help="FLEURS parquet at the pinned revision")
     fp.add_argument("--lang", required=True,
                     help="VoxLingua107 code, comma-separated list, or 'all'")
     fp.add_argument("--n", type=int, default=DEFAULT_N,
diff --git a/scripts/lib/ecapa_numpy.py b/scripts/lib/ecapa_numpy.py
deleted file mode 100644
index 8ab0d8a7a..000000000
--- a/scripts/lib/ecapa_numpy.py
+++ /dev/null
@@ -1,396 +0,0 @@
-"""ecapa_numpy.py - the readable specification of the ecapa_tdnn forward pass.
-
-A complete ECAPA-TDNN language-ID forward pass in NumPy, driven entirely by
-an ecapa_tdnn GGUF: weights, layout, and every hyperparameter come out of the
-file, nothing is hardcoded from the checkpoint. It exists for three reasons:
-
-  1. It proves the converter's four conversion-time rewrites (the C++ graph — BN as affine, forward BN folds, ASP split, MFA split) are
-     exact: its dumps are compared against the SpeechBrain reference dumps
-     with the same tolerance file the C++ has to meet.
-  2. It is the executable version of the C++ graph, in ~300 lines
-     a reviewer can read top to bottom, for when the ggml graph disagrees
-     with SpeechBrain and someone has to work out which one is wrong.
-  3. It bisects failures: every stage tensor in the C++ graph is
-     returned, so a C++ mismatch can be localised to a layer without
-     rebuilding anything.
-
-Deliberate numerical choices:
-
-  * Everything after the FFT is float32, accumulated with NumPy's float32
-    `@`. float64 would hide exactly the accumulation-order differences the
-    C++ is being measured for. `np.fft.rfft` always computes in float64
-    (NumPy has no float32 FFT), so the power spectrum is cast straight back
-    down to float32 — that is the only place this file is wider than the
-    reference, and it is the reason `fe.mel` is the loosest tensor in the
-    tolerance file.
-  * The conv is written the way the C++ graph does it:
-    reflect-pad the time axis once, then sum K matmuls against shifted
-    slices, one per kernel tap. Not `np.convolve`, not im2col — the point is
-    to mirror the graph, not to be fast.
-  * Activations are carried TIME-MAJOR `[T, C]`. That is what both dumpers
-    write to disk (C++ ggml `ne = [C, T]` lands row-major as `[T, C]`), so
-    no dump-time transpose can silently paper over a layout error.
-
-Usage:
-
-    from lib.ecapa_numpy import EcapaNumpy
-    model = EcapaNumpy("models/.../lang-id-voxlingua107-ecapa-F32.gguf")
-    out = model.forward(pcm_float32_16k)
-    out["cls.logits_raw"]      # [107]
-    out["prediction"]["code"]  # e.g. "en"
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import gguf
-from gguf import GGMLQuantizationType
-
-
-class EcapaNumpy:
-    """A GGUF-driven ECAPA-TDNN language identifier, in NumPy."""
-
-    def __init__(self, gguf_path: str | Path) -> None:
-        self.path = Path(gguf_path)
-        reader = gguf.GGUFReader(str(self.path), "r")
-
-        # ---- metadata ----------------------------------------------------
-        self.meta: dict[str, Any] = {}
-        for name, field in reader.fields.items():
-            if not field.types:
-                continue
-            self.meta[name] = field.contents()
-
-        arch = self.meta.get("general.architecture")
-        if arch != "ecapa_tdnn":
-            raise ValueError(
-                f"{self.path}: general.architecture is {arch!r}, expected "
-                "'ecapa_tdnn'")
-        fmt = int(self.meta.get("stt.ecapa_tdnn.format_version", -1))
-        if fmt != 1:
-            raise ValueError(
-                f"{self.path}: stt.ecapa_tdnn.format_version is {fmt}, expected 1")
-
-        # ---- tensors -----------------------------------------------------
-        # Everything is dequantised up front to float32 in its numpy shape
-        # (= reversed ggml ne). F16 widens by cast; Q8_0 goes through
-        # gguf.quants.dequantize, which is the same block-scale arithmetic
-        # ggml's vec_dot does, so an F16/Q8_0 run here is a fair proxy for
-        # the C++ run on the same file.
-        self.tensors: dict[str, np.ndarray] = {}
-        self.tensor_types: dict[str, str] = {}
-        for t in reader.tensors:
-            data = t.data
-            if t.tensor_type == GGMLQuantizationType.F32:
-                arr = np.asarray(data, dtype=np.float32)
-            elif t.tensor_type == GGMLQuantizationType.F16:
-                arr = np.asarray(data).astype(np.float32)
-            else:
-                arr = gguf.quants.dequantize(
-                    np.asarray(data), t.tensor_type).astype(np.float32)
-            self.tensors[t.name] = np.ascontiguousarray(arr)
-            self.tensor_types[t.name] = t.tensor_type.name
-
-        # ---- shape / hyperparameter cache --------------------------------
-        self.sample_rate = int(self.meta["stt.frontend.sample_rate"])
-        self.n_fft = int(self.meta["stt.frontend.n_fft"])
-        self.hop = int(self.meta["stt.frontend.hop_length"])
-        self.win = int(self.meta["stt.frontend.win_length"])
-        self.n_mels = int(self.meta["stt.frontend.num_mels"])
-        self.log_floor = float(self.meta["stt.frontend.log_clamp_min"])
-        self.top_db = float(self.meta["stt.frontend.top_db"])
-        self.window_kind = str(self.meta["stt.frontend.window"])
-        self.normalize = str(self.meta["stt.frontend.normalize"])
-
-        self.channels = [int(c) for c in self.meta["stt.ecapa_tdnn.channels"]]
-        self.kernel_sizes = [int(k) for k in
-                             self.meta["stt.ecapa_tdnn.kernel_sizes"]]
-        self.dilations = [int(d) for d in self.meta["stt.ecapa_tdnn.dilations"]]
-        self.res2net_scale = int(self.meta["stt.ecapa_tdnn.res2net_scale"])
-        self.asp_eps = float(self.meta["stt.ecapa_tdnn.asp_eps"])
-        self.embedding_dim = int(self.meta["stt.ecapa_tdnn.embedding_dim"])
-        self.leaky_slope = float(self.meta["stt.ecapa_tdnn.classifier_leaky_slope"])
-
-        self.codes = list(self.meta["stt.langid.labels.codes"])
-        self.names = list(self.meta["stt.langid.labels.names"])
-        self.aliases = {a: c for a, c in
-                        (s.split("=", 1)
-                         for s in self.meta["stt.langid.labels.aliases"])}
-
-        if self.window_kind != "hamming_periodic":
-            raise ValueError(f"unsupported window {self.window_kind!r}")
-        if self.normalize != "sentence_mean":
-            raise ValueError(f"unsupported normalize {self.normalize!r}")
-
-        # Periodic Hamming: w[n] = 0.54 - 0.46*cos(2*pi*n/N), n = 0..N-1.
-        # NOT the symmetric variant (which divides by N-1) — the two differ
-        # by 5.5e-03 at the edges, far above any tolerance here.
-        n = np.arange(self.win, dtype=np.float64)
-        self.window = (0.54 - 0.46 * np.cos(2.0 * np.pi * n / self.win)
-                       ).astype(np.float32)
-
-        # [60, 201] mel-major, straight from SpeechBrain's Filterbank.
-        self.filters = self.tensors["frontend.mel_filterbank"]
-        if self.filters.shape != (self.n_mels, self.n_fft // 2 + 1):
-            raise ValueError(
-                f"frontend.mel_filterbank is {self.filters.shape}, expected "
-                f"{(self.n_mels, self.n_fft // 2 + 1)}")
-
-    # -- small helpers -----------------------------------------------------
-
-    def t(self, name: str) -> np.ndarray:
-        try:
-            return self.tensors[name]
-        except KeyError:
-            raise KeyError(f"{self.path.name}: missing tensor {name!r}") from None
-
-    @staticmethod
-    def _relu(x: np.ndarray) -> np.ndarray:
-        return np.maximum(x, np.float32(0.0))
-
-    def _leaky(self, x: np.ndarray) -> np.ndarray:
-        return np.where(x >= 0, x, x * np.float32(self.leaky_slope)
-                        ).astype(np.float32)
-
-    @staticmethod
-    def _linear(x: np.ndarray, w: np.ndarray, b: np.ndarray) -> np.ndarray:
-        """`[T, IC] @ [OC, IC]^T + [OC]` -> `[T, OC]`, all float32.
-
-        This is the 1x1 convolution: with channel-innermost activations a
-        pointwise conv is exactly a matmul, which is why the GGUF stores 1x1
-        kernels as plain 2-D `[OC, IC]` matrices.
-        """
-        return (x @ w.T + b).astype(np.float32)
-
-    @staticmethod
-    def _bn(x: np.ndarray, scale: np.ndarray, shift: np.ndarray) -> np.ndarray:
-        """`y = x * scale + shift`, the converter's affine form of BatchNorm.
-
-        Broadcasts over time. The scale/shift pair already absorbed
-        gamma/beta/running_mean/running_var/eps at conversion time. It cannot
-        fold into the conv: a TDNNBlock is conv -> ReLU -> BN, and the ReLU
-        sits between them.
-        """
-        return (x * scale + shift).astype(np.float32)
-
-    def _conv_taps(self, x: np.ndarray, w: np.ndarray, b: np.ndarray,
-                   dilation: int) -> np.ndarray:
-        """Dilated conv as a sum of per-tap matmuls, mirroring the ggml graph.
-
-        `w` is tap-major `[K, OC, IC]`. SpeechBrain uses `padding="same"` with
-        `padding_mode="reflect"`, i.e. `p = dilation*(K-1)/2` reflected frames
-        on each side, so the output keeps T frames. One `np.pad(mode=
-        "reflect")` here stands in for the C++'s single `ggml_get_rows`
-        gather with a precomputed mirror index; the arithmetic is identical.
-        """
-        k = w.shape[0]
-        if k == 1:
-            return self._linear(x, w[0], b)
-        p = dilation * (k - 1) // 2
-        if x.shape[0] <= p:
-            raise ValueError(
-                f"need more than {p} frames for a k={k} d={dilation} conv, "
-                f"got {x.shape[0]}")
-        xp = np.pad(x, ((p, p), (0, 0)), mode="reflect")
-        T = x.shape[0]
-        y = np.zeros((T, w.shape[1]), dtype=np.float32)
-        for tap in range(k):
-            off = tap * dilation
-            y += xp[off:off + T] @ w[tap].T
-        return (y + b).astype(np.float32)
-
-    def _tdnn(self, x: np.ndarray, prefix: str, dilation: int,
-              tap_major: bool) -> np.ndarray:
-        """SpeechBrain TDNNBlock: conv -> ReLU -> BN.
-
-        (The Dropout1d(p=0) fourth child is an exact identity in eval mode
-        and carries no weights, so it is absent here and from the GGUF.)
-        """
-        stem = f"{prefix}.conv" if tap_major else prefix
-        w = self.t(f"{stem}.weight")
-        b = self.t(f"{stem}.bias")
-        y = (self._conv_taps(x, w, b, dilation) if tap_major
-             else self._linear(x, w, b))
-        y = self._relu(y)
-        return self._bn(y, self.t(f"{prefix}.bn.scale"),
-                        self.t(f"{prefix}.bn.shift"))
-
-    # -- front end ---------------------------------------------------------
-
-    def mel(self, pcm: np.ndarray) -> np.ndarray:
-        """16 kHz mono float32 -> `[T, 60]` sentence-mean-normalised log-mel.
-
-        Matches SpeechBrain's `Fbank` + `InputNormalization(norm_type=
-        "sentence", std_norm=False)` exactly: centred STFT with 200 samples
-        of ZERO padding each side (`pad_mode="constant"`, not reflect — the
-        reflect padding in this model is inside the convolutions), periodic
-        Hamming window, power spectrum, 60 triangular mel filters,
-        `10*log10(max(x, 1e-10))`, an 80 dB floor taken over time AND
-        frequency of the whole utterance, then per-bin mean subtraction.
-        """
-        pcm = np.ascontiguousarray(pcm, dtype=np.float32)
-        n = pcm.size
-        T = n // self.hop + 1
-        pad = self.n_fft // 2
-        xp = np.pad(pcm, (pad, pad), mode="constant")
-
-        idx = np.arange(self.win)[None, :] + (np.arange(T) * self.hop)[:, None]
-        frames = xp[idx] * self.window                       # [T, 400] f32
-
-        spec = np.fft.rfft(frames, n=self.n_fft, axis=-1)    # f64, always
-        # Power spectrum re^2 + im^2 (SpeechBrain's spectral_magnitude with
-        # power=1 over a squared-and-summed real/imag pair). Back to f32
-        # immediately so the rest of the front end matches the reference's
-        # precision.
-        power = (spec.real ** 2 + spec.imag ** 2).astype(np.float32)
-
-        mel = (power @ self.filters.T).astype(np.float32)    # [T, 60]
-        db = (10.0 * np.log10(np.maximum(mel, np.float32(self.log_floor)))
-              ).astype(np.float32)
-        # ref_value = 1.0 so db_multiplier = log10(1) = 0: no reference term.
-        db = np.maximum(db, db.max() - np.float32(self.top_db))
-        return (db - db.mean(axis=0, keepdims=True)).astype(np.float32)
-
-    # -- forward -----------------------------------------------------------
-
-    def forward(self, pcm: np.ndarray) -> dict[str, Any]:
-        """Run the whole model; return every the C++ graph stage tensor."""
-        out: dict[str, Any] = {}
-
-        x = self.mel(pcm)                                    # [T, 60]
-        out["fe.mel"] = x
-
-        # blocks.0: TDNN 60 -> 1024, k=5, d=1 (reflect pad 2).
-        x = self._tdnn(x, "blk.0", self.dilations[0], tap_major=True)
-        out["enc.blk.0.out"] = x
-
-        # blocks.1..3: SERes2Net, in == out channels so the residual is
-        # the identity (no shortcut projection in this checkpoint).
-        block_outs: list[np.ndarray] = []
-        for i in (1, 2, 3):
-            p = f"blk.{i}"
-            residual = x
-
-            x = self._tdnn(x, f"{p}.tdnn1", 1, tap_major=False)
-            if i == 1:
-                out["enc.blk.1.tdnn1.out"] = x
-
-            # Res2Net, scale 8: split into 8 chunks of 128 channels.
-            #   y0 = x0                       (passed through untouched)
-            #   y1 = B0(x1)
-            #   yi = B(i-1)(xi + y(i-1))      i = 2..7
-            # The addend is the PREVIOUS sub-block's output, which is what
-            # makes this a cascade rather than 7 independent branches.
-            chunks = np.split(x, self.res2net_scale, axis=1)
-            ys: list[np.ndarray] = [chunks[0]]
-            y_prev = chunks[0]
-            for j in range(1, self.res2net_scale):
-                inp = chunks[j] if j == 1 else (chunks[j] + y_prev
-                                                ).astype(np.float32)
-                y_prev = self._tdnn(inp, f"{p}.res2.{j - 1}",
-                                    self.dilations[i], tap_major=True)
-                ys.append(y_prev)
-            x = np.ascontiguousarray(np.concatenate(ys, axis=1))
-            if i == 1:
-                out["enc.blk.1.res2.out"] = x
-
-            x = self._tdnn(x, f"{p}.tdnn2", 1, tap_major=False)
-
-            # Squeeze-and-excitation: mean over time, two 1x1 convs, sigmoid
-            # gate, per-channel rescale.
-            s = x.mean(axis=0).astype(np.float32)
-            s = self._relu(self.t(f"{p}.se.c1.weight") @ s + self.t(f"{p}.se.c1.bias"))
-            s = 1.0 / (1.0 + np.exp(-(self.t(f"{p}.se.c2.weight") @ s
-                                      + self.t(f"{p}.se.c2.bias"))))
-            x = (x * s.astype(np.float32)).astype(np.float32)
-            if i == 1:
-                out["enc.blk.1.se.out"] = x
-
-            x = (x + residual).astype(np.float32)
-            out[f"enc.blk.{i}.out"] = x
-            block_outs.append(x)
-
-        # MFA. SpeechBrain concatenates blocks.1/2/3 and runs one 1x1 conv;
-        # the converter split that weight into three blocks, so the concat
-        # copy disappears and this is three matmuls summed. Same FLOPs, same
-        # result.
-        y = (block_outs[0] @ self.t("mfa.w1.weight").T
-             + block_outs[1] @ self.t("mfa.w2.weight").T
-             + block_outs[2] @ self.t("mfa.w3.weight").T
-             + self.t("mfa.bias")).astype(np.float32)
-        y = self._bn(self._relu(y), self.t("mfa.bn.scale"),
-                     self.t("mfa.bn.shift"))
-        out["enc.mfa.out"] = y
-
-        # Attentive statistics pooling with global context.
-        eps = np.float32(self.asp_eps)
-        mean = y.mean(axis=0).astype(np.float32)
-        var = ((y - mean) ** 2).mean(axis=0).astype(np.float32)
-        std = np.sqrt(np.maximum(var, eps)).astype(np.float32)
-
-        # cat([x, mean, std]) -> 1x1 conv, split at conversion time into
-        # wx (time-varying) and wm/ws (constant over T), so the mean/std
-        # contribution collapses into the bias vector.
-        const = (self.t("asp.tdnn.mean.weight") @ mean + self.t("asp.tdnn.std.weight") @ std
-                 + self.t("asp.tdnn.bias")).astype(np.float32)
-        a = (y @ self.t("asp.tdnn.x.weight").T + const).astype(np.float32)
-        a = self._bn(self._relu(a), self.t("asp.tdnn.bn.scale"),
-                     self.t("asp.tdnn.bn.shift"))
-        a = np.tanh(a).astype(np.float32)
-        logits = self._linear(a, self.t("asp.attn.weight"), self.t("asp.attn.bias"))
-        out["enc.asp.attn_logits"] = logits                  # [T, 3072]
-
-        # Softmax over TIME, per channel. (SpeechBrain masks padded frames
-        # to -inf first; with a single unpadded utterance the mask is all
-        # ones and the masked_fill is a no-op.)
-        z = logits - logits.max(axis=0, keepdims=True)
-        e = np.exp(z).astype(np.float32)
-        attn = (e / e.sum(axis=0, keepdims=True)).astype(np.float32)
-
-        mu = (attn * y).sum(axis=0).astype(np.float32)
-        sigma = np.sqrt(np.maximum((attn * (y - mu) ** 2).sum(axis=0), eps)
-                        ).astype(np.float32)
-        pooled = np.concatenate([mu, sigma]).astype(np.float32)  # [6144]
-        out["enc.asp.out"] = pooled
-
-        # asp_bn is folded into fc, so this single matmul is BN + linear.
-        emb = (self.t("fc.weight") @ pooled + self.t("fc.bias")).astype(np.float32)
-        out["enc.emb"] = emb                                 # [256]
-
-        # Classifier. cls.bn0 is folded into cls.l1 and cls.bn1 into
-        # cls.out, but both LeakyReLUs come BEFORE their BN in SpeechBrain,
-        # so they survive: leaky -> l1 -> leaky -> out.
-        h = self._leaky(emb)
-        h = (self.t("cls.l1.weight") @ h + self.t("cls.l1.bias")).astype(np.float32)
-        hidden = self._leaky(h)
-        out["cls.hidden"] = hidden                           # [512]
-
-        z = (self.t("cls.out.weight") @ hidden + self.t("cls.out.bias")
-             ).astype(np.float32)
-        out["cls.logits_raw"] = z                            # [107]
-
-        m = z.max()
-        log_probs = (z - m - np.log(np.exp(z - m).sum())).astype(np.float32)
-        out["cls.log_probs"] = log_probs
-
-        order = np.argsort(-log_probs)
-        best = int(order[0])
-        out["prediction"] = {
-            "label_index": best,
-            "code": self.codes[best],
-            "name": self.names[best],
-            "log_prob": float(log_probs[best]),
-            "top5": [
-                {
-                    "index": int(i),
-                    "code": self.codes[int(i)],
-                    "name": self.names[int(i)],
-                    "log_prob": float(log_probs[int(i)]),
-                }
-                for i in order[:5]
-            ],
-        }
-        return out
diff --git a/tests/tolerances/ecapa_tdnn.json b/tests/tolerances/ecapa_tdnn.json
index 235baf22f..33c18ce4b 100644
--- a/tests/tolerances/ecapa_tdnn.json
+++ b/tests/tolerances/ecapa_tdnn.json
@@ -60,12 +60,7 @@
     "Both are max_abs on wide [T, C] activations; a re-measure on different",
     "CPU SIMD width may need a fresh Stage-4 pass rather than a hand edit.",
     "",
-    "cls.logits_raw is the gate.",
-    "",
-    "fe.window [400] and fe.filters [201, 60] are dumped by the reference but",
-    "deliberately absent from this file: the C++ runtime recomputes the window",
-    "and reads the filterbank from the GGUF, so it never dumps them.",
-    "compare_tensors reports them MISSING without failing."
+    "cls.logits_raw is the gate."
   ],
   "fe.mel": {
     "max_abs": 0.004079,

From e4ad30db62c06e77fd74e1edc900277fb548ad8c Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:34:33 +0800
Subject: [PATCH 15/38] langid: trim scripts/langid to the published accuracy
 path

Drop score.py's selection, confidence and confusion sections (and
selections.json), compare.py's --near-ties list and per-language x crop
table, and run.py's --trim energy. Score and agreement JSON are unchanged;
reports still record trim "none".
---
 scripts/langid/compare.py      |  72 ++------------
 scripts/langid/run.py          |  89 +++++------------
 scripts/langid/score.py        | 174 +++------------------------------
 scripts/langid/selections.json |  28 ------
 4 files changed, 47 insertions(+), 316 deletions(-)
 delete mode 100644 scripts/langid/selections.json

diff --git a/scripts/langid/compare.py b/scripts/langid/compare.py
index e56e52b66..71bc439f4 100755
--- a/scripts/langid/compare.py
+++ b/scripts/langid/compare.py
@@ -8,9 +8,8 @@
 as the SpeechBrain reference on the same audio?
 
 The gate: the C++ F32 result makes the same top-1 decision as the SpeechBrain
-reference on the same audio. Every disagreement must be an explicitly listed,
-reviewed near-tie (--near-ties); anything else fails, whatever the aggregate
-agreement rate. The aggregate is still reported against --gate (99.9%).
+reference on the same audio. Any disagreement fails, whatever the aggregate
+agreement rate. The aggregate is also reported against --gate (99.9%).
 
 Provenance is enforced, not just printed: both runs must share the label set,
 the recipe (trim, crops, utterance count, crop dir, logit kind, sample rate)
@@ -28,24 +27,16 @@
 a silently dropped utterance would otherwise raise the agreement rate.
 
 Exit status:
-    0  every disagreement is a listed near-tie, agreement >= the gate, same keys
-    1  an unlisted disagreement, agreement below the gate, or coverage differs
+    0  no disagreement, agreement >= the gate, same keys
+    1  a disagreement, agreement below the gate, or coverage differs
     2  usage / input / provenance error
 
 Only F32 is held to the gate. For the quants pass --report-only: the
 agreement is still measured, printed and written (--json, with "passed"),
 but only a coverage mismatch exits 1.
 
-Near-tie list (JSON): [{"id", "crop_s", "trim", "a_top1", "b_top1",
-"max_gap", "note"}]. An entry only excuses that exact disagreement while both
-engines' margins stay within max_gap; review every entry by listening.
-
 Every disagreement is printed with both engines' top-1 and, for each engine,
-that engine's own margin between the two codes in question. A near-tie shows
-up as a margin near zero on BOTH sides: the two engines put the same two
-labels within noise of each other and rounded opposite ways. A disagreement
-with a large margin on either side is a real numerical divergence and must be
-chased, not waived.
+that engine's own margin between the two codes in question.
 """
 
 from __future__ import annotations
@@ -54,7 +45,6 @@
 import json
 import math
 import sys
-from collections import defaultdict
 from pathlib import Path
 
 GATE = 0.999
@@ -100,8 +90,6 @@ def main(argv: list[str] | None = None) -> int:
     p.add_argument("--gate", type=float, default=GATE,
                    help=f"minimum top-1 agreement rate (default {GATE})")
     p.add_argument("--max-listed", type=int, default=MAX_LISTED_DISAGREEMENTS)
-    p.add_argument("--near-ties", type=Path, default=None,
-                   help="JSON list of reviewed near-tie disagreements")
     p.add_argument("--report-only", action="store_true",
                    help="measure and record agreement without gating on it (the quants); "
                         "a coverage mismatch still exits 1")
@@ -110,10 +98,6 @@ def main(argv: list[str] | None = None) -> int:
                         "ingest_accuracy.py; name it <gguf stem>.fleurs-mul."
                         "agreement.json under reports/langid/")
     args = p.parse_args(argv)
-    near_ties: dict[tuple, dict] = {}
-    if args.near_ties is not None:
-        for e in json.loads(args.near_ties.read_text()):
-            near_ties[(e["id"], str(e["crop_s"]), e["trim"])] = e
 
     ha, ra = load(args.a)
     hb, rb = load(args.b)
@@ -163,7 +147,6 @@ def main(argv: list[str] | None = None) -> int:
         return 2
 
     n_agree = 0
-    per_group: dict[tuple[str, str], list[int]] = defaultdict(lambda: [0, 0])
     abs_deltas: list[float] = []
     disagreements = []
 
@@ -178,14 +161,10 @@ def main(argv: list[str] | None = None) -> int:
 
         ia = max(range(len(la)), key=lambda i: la[i])
         ib = max(range(len(lb)), key=lambda i: lb[i])
-        group = (x["language"], str(x["crop_s"]))
-        per_group[group][1] += 1
         if ia == ib:
             n_agree += 1
-            per_group[group][0] += 1
         else:
-            # Each engine's own margin between the two contested labels. Both
-            # near zero == a near-tie rounded opposite ways.
+            # Each engine's own margin between the two contested labels.
             disagreements.append({
                 "key": key,
                 "language": x["language"],
@@ -208,23 +187,6 @@ def main(argv: list[str] | None = None) -> int:
           f"median={percentile(abs_deltas, 0.50):.6g}")
     print()
 
-    langs = sorted({g[0] for g in per_group})
-    crops = sorted({g[1] for g in per_group},
-                   key=lambda c: (c == "full", float(c) if c != "full" else 0))
-    print("agreement % by language x crop")
-    print("lang   " + "".join(f"{c:>9}" for c in crops) + f"{'all':>9}")
-    for lg in langs:
-        cells = ""
-        tot_ok = tot_n = 0
-        for c in crops:
-            ok, tot = per_group.get((lg, c), [0, 0])
-            tot_ok += ok
-            tot_n += tot
-            cells += f"{(100 * ok / tot if tot else float('nan')):9.3f}"
-        cells += f"{(100 * tot_ok / tot_n if tot_n else float('nan')):9.3f}"
-        print(f"{lg:<7}{cells}")
-    print()
-
     if disagreements:
         disagreements.sort(key=lambda d: -max(abs(d["a_gap"]), abs(d["b_gap"])))
         print(f"{len(disagreements)} disagreements "
@@ -238,28 +200,14 @@ def main(argv: list[str] | None = None) -> int:
                   f"{d['max_abs_delta']:12.3g}")
         worst = max(max(abs(d["a_gap"]), abs(d["b_gap"])) for d in disagreements)
         print(f"\nlargest contested margin on either side: {worst:.5f}")
-        print("A near-tie has both gaps near zero. A large gap on either side "
-              "is a real divergence, not a rounding coincidence.")
         print()
 
-    unlisted = []
-    for d in disagreements:
-        e = near_ties.get(d["key"])
-        ok = (e is not None and e["a_top1"] == d["a_top1"] and e["b_top1"] == d["b_top1"]
-              and abs(d["a_gap"]) <= e["max_gap"] and abs(d["b_gap"]) <= e["max_gap"])
-        if not ok:
-            unlisted.append(d)
-    stale = sorted(set(near_ties) - {d["key"] for d in disagreements})
-    if stale:
-        print(f"note: {len(stale)} listed near-ties did not disagree this run: {stale[:5]}")
-
     if only_a or only_b:
         print("FAIL: the two runs do not cover the same rows")
         status = 1
-    elif unlisted:
-        print(f"FAIL: {len(unlisted)} disagreements are not reviewed near-ties "
-              f"(add them to --near-ties only after listening): "
-              f"{[d['key'] for d in unlisted[:10]]}")
+    elif disagreements:
+        print(f"FAIL: {len(disagreements)} disagreements: "
+              f"{[d['key'] for d in disagreements[:10]]}")
         status = 1
     elif rate < args.gate:
         print(f"FAIL: agreement {100 * rate:.4f}% < {100 * args.gate:.2f}%")
@@ -283,7 +231,7 @@ def main(argv: list[str] | None = None) -> int:
             "n_agree": n_agree,
             "max_abs_logit_delta": abs_deltas[-1],
             "disagreements": len(disagreements),
-            "unlisted": len(unlisted),
+            "unlisted": len(disagreements),
             "same_rows": not (only_a or only_b),
             "gate": args.gate,
             "passed": status == 0,
diff --git a/scripts/langid/run.py b/scripts/langid/run.py
index 8bb5f10cf..03101e971 100755
--- a/scripts/langid/run.py
+++ b/scripts/langid/run.py
@@ -8,13 +8,13 @@
   uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \
       --engine speechbrain --model speechbrain/lang-id-voxlingua107-ecapa \
       --manifest samples/langid/fleurs-*.manifest.jsonl \
-      --crops 3,5,10,full --trim none \
+      --crops 3,5,10,full \
       --out reports/langid/ref-speechbrain-untrimmed.jsonl
 
   uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \
       --engine cpp --model models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
       --manifest samples/langid/fleurs-*.manifest.jsonl \
-      --crops 3,5,10,full --trim none --backend cpu --threads 4 \
+      --crops 3,5,10,full --backend cpu --threads 4 \
       --library build-shared/src/libtranscribe.dylib \
       --out reports/langid/cpp-f32-untrimmed.jsonl
 
@@ -22,25 +22,19 @@
   line 1   {"type":"header","engine","model","recipe":{...},"labels":[107],"created"}
   line n   {"id","language","crop_s","trim","audio_s","logits":[107],"top1","top1_prob"}
 
-All 107 raw logits are stored per (utterance, crop) so every restricted
-selection can be scored offline by score.py from a single sweep.
-`top1`/`top1_prob` are the open-set decision, kept as a convenience;
-score.py recomputes everything from `logits`.
+All 107 raw logits are stored per (utterance, crop); `top1`/`top1_prob`
+are the open-set decision, kept as a convenience; score.py and compare.py
+recompute everything from `logits`.
 
 Crop semantics
 --------------
-`--trim none`  : the first N seconds of the raw clip, so "3 s" is roughly
-                 2 s of speech on FLEURS (the clips open with silence).
-`--trim energy`: drop everything before the first 20 ms frame whose RMS
-                 exceeds 5% of the clip's maximum frame RMS, then take the
-                 first N seconds of what is left.
-`full`         : the whole clip (after trimming, for --trim energy).
-A clip shorter than the crop is used whole; the row's `audio_s` records
-what was actually scored.
+A crop of N is the first N seconds of the raw clip; `full` is the whole
+clip. A clip shorter than the crop is used whole; the row's `audio_s`
+records what was actually scored. `trim` is always "none" in the report.
 
 Both engines read the SAME 16-bit wav files
 -------------------------------------------
-Crops are materialised once to `build/langid/crops/<trim>/<crop>/<id>.wav`
+Crops are materialised once to `build/langid/crops/none/<crop>/<id>.wav`
 and both engines read those files back. Scoring in-memory floats on one
 side and 16-bit wavs on the other would make compare.py's 99.9% agreement
 gate measure PCM rounding instead of the port. The crop directory is
@@ -69,9 +63,7 @@
 
 SAMPLE_RATE = 16000
 N_LABELS = 107
-TRIM_FRAME_MS = 20
-TRIM_REL_THRESHOLD = 0.05
-MIN_KEEP_S = 0.5  # the LANGID role's TRANSCRIBE_ERR_INPUT_TOO_SHORT floor
+TRIM = "none"  # crops start at sample 0; part of compare.py's row key and recipe check
 
 
 # ---------------------------------------------------------------------------
@@ -106,58 +98,31 @@ def write_wav(path: Path, pcm: np.ndarray) -> None:
     sf.write(str(path), pcm, SAMPLE_RATE, subtype="PCM_16")
 
 
-def energy_trim_start(pcm: np.ndarray) -> int:
-    """First sample of speech: the start of the first 20 ms frame whose RMS
-    exceeds 5% of the loudest frame's RMS.
-
-    Relative to the clip's own maximum, so it is level-independent. The
-    result is clamped so at least MIN_KEEP_S remains: a clip whose only
-    loud frame is at the very end would otherwise be trimmed below the
-    library's 500 ms floor.
-    """
-    frame = int(TRIM_FRAME_MS * SAMPLE_RATE / 1000)  # 320
-    n_frames = pcm.size // frame
-    if n_frames == 0:
-        return 0
-    frames = pcm[: n_frames * frame].reshape(n_frames, frame)
-    rms = np.sqrt(np.mean(frames.astype(np.float64) ** 2, axis=1))
-    peak = float(rms.max())
-    if peak <= 0.0:
-        return 0
-    above = np.flatnonzero(rms > TRIM_REL_THRESHOLD * peak)
-    if above.size == 0:
-        return 0
-    start = int(above[0]) * frame
-    return min(start, max(0, pcm.size - int(MIN_KEEP_S * SAMPLE_RATE)))
-
-
-def make_crop(pcm: np.ndarray, crop: str | int, trim: str) -> np.ndarray:
-    start = energy_trim_start(pcm) if trim == "energy" else 0
-    cut = pcm[start:]
+def make_crop(pcm: np.ndarray, crop: str | int) -> np.ndarray:
     if crop == "full":
-        return cut
+        return pcm
     n = int(float(crop) * SAMPLE_RATE)
-    return cut[:n] if cut.size > n else cut
+    return pcm[:n] if pcm.size > n else pcm
 
 
 def crop_dir(trim: str, crop: str | int) -> Path:
     return REPO_ROOT / "build" / "langid" / "crops" / trim / str(crop)
 
 
-def materialise_crops(rows: list[dict], crop: str | int, trim: str) -> list[tuple[dict, Path]]:
+def materialise_crops(rows: list[dict], crop: str | int) -> list[tuple[dict, Path]]:
     """Write every (utterance, crop) wav and return (row, path) pairs.
 
     Always rewrites: the write is cheap next to a forward pass, and an
     unconditional write means a stale crop from an earlier recipe can never
     be scored by accident.
     """
-    out = crop_dir(trim, crop)
+    out = crop_dir(TRIM, crop)
     out.mkdir(parents=True, exist_ok=True)
     pairs: list[tuple[dict, Path]] = []
     for row in rows:
         src = REPO_ROOT / row["audio"]
         dst = out / f"{row['id']}.wav"
-        write_wav(dst, make_crop(read_wav(src), crop, trim))
+        write_wav(dst, make_crop(read_wav(src), crop))
         pairs.append((row, dst))
     return pairs
 
@@ -355,7 +320,6 @@ def main(argv: list[str] | None = None) -> int:
                    help="HF repo id / checkpoint dir (speechbrain) or GGUF path (cpp)")
     p.add_argument("--manifest", required=True, nargs="+", type=Path)
     p.add_argument("--crops", default="3,5,10,full")
-    p.add_argument("--trim", default="none", choices=("none", "energy"))
     p.add_argument("--out", required=True, type=Path)
     p.add_argument("--library", type=Path, default=None,
                    help="cpp engine only; a shared libtranscribe (TRANSCRIBE_LIBRARY)")
@@ -387,7 +351,7 @@ def main(argv: list[str] | None = None) -> int:
         raise SystemExit("error: manifests are empty")
     longest_s = max(r["duration_s"] for r in rows)
     print(f"{len(rows)} utterances from {len(manifests)} manifests, "
-          f"{len(crops)} crops, trim={args.trim}", file=sys.stderr)
+          f"{len(crops)} crops", file=sys.stderr)
 
     max_audio_ms = args.max_audio_ms or int(math.ceil(longest_s / 10.0) * 10.0 * 1000)
 
@@ -417,12 +381,9 @@ def main(argv: list[str] | None = None) -> int:
 
     recipe = {
         "crops": [str(c) for c in crops],
-        "trim": args.trim,
-        "trim_rule": (f"first {TRIM_FRAME_MS} ms frame with RMS > "
-                      f"{TRIM_REL_THRESHOLD:g} * max frame RMS"
-                      if args.trim == "energy" else
-                      "none (crops start at sample 0 of the raw clip)"),
-        "crop_dir": str(crop_dir(args.trim, "<crop>").relative_to(REPO_ROOT)),
+        "trim": TRIM,
+        "trim_rule": "none (crops start at sample 0 of the raw clip)",
+        "crop_dir": str(crop_dir(TRIM, "<crop>").relative_to(REPO_ROOT)),
         "manifests": [str(m) for m in manifests],
         "manifest_sha256": {str(m): _sha256_file(m) for m in manifests},
         "n_utterances": len(rows),
@@ -441,9 +402,9 @@ def main(argv: list[str] | None = None) -> int:
 
         for crop in crops:
             t_crop = time.time()
-            print(f"[{args.trim}/{crop}] materialising {len(rows)} crops ...",
+            print(f"[{TRIM}/{crop}] materialising {len(rows)} crops ...",
                   file=sys.stderr)
-            pairs = materialise_crops(rows, crop, args.trim)
+            pairs = materialise_crops(rows, crop)
 
             batch_rows = []
             for i, (row, wav_path) in enumerate(pairs):
@@ -454,7 +415,7 @@ def main(argv: list[str] | None = None) -> int:
                     "id": row["id"],
                     "language": row["language"],
                     "crop_s": crop,
-                    "trim": args.trim,
+                    "trim": TRIM,
                     "audio_s": round(pcm.size / SAMPLE_RATE, 4),
                     "logits": [round(float(x), 6) for x in logits],
                     "top1": labels[top_i],
@@ -462,7 +423,7 @@ def main(argv: list[str] | None = None) -> int:
                 })
                 if (i + 1) % 100 == 0 or i + 1 == len(pairs):
                     el = time.time() - t_crop
-                    print(f"  [{args.trim}/{crop}] {i + 1}/{len(pairs)}  "
+                    print(f"  [{TRIM}/{crop}] {i + 1}/{len(pairs)}  "
                           f"{el:.0f}s  ({el / (i + 1) * 1000:.0f} ms/utt)",
                           file=sys.stderr)
 
@@ -482,7 +443,7 @@ def main(argv: list[str] | None = None) -> int:
                 f.write(json.dumps(r) + "\n")
                 n_written += 1
             f.flush()
-            print(f"[{args.trim}/{crop}] done in {time.time() - t_crop:.0f}s",
+            print(f"[{TRIM}/{crop}] done in {time.time() - t_crop:.0f}s",
                   file=sys.stderr)
 
     engine.close()
diff --git a/scripts/langid/score.py b/scripts/langid/score.py
index 2a6803090..4d0b6e8cb 100755
--- a/scripts/langid/score.py
+++ b/scripts/langid/score.py
@@ -6,10 +6,7 @@
 # ]
 # ///
 """
-score.py - turn a run.py sweep into accuracy tables.
-
-Everything below is computed from the stored 107-logit vectors, so a new
-decision space costs a re-score, not a re-run.
+score.py - open-set top-1 accuracy of a run.py sweep.
 
 Usage:
     uv run scripts/langid/score.py reports/langid/ref-speechbrain-untrimmed.jsonl \\
@@ -19,19 +16,10 @@
     uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \\
         --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 
-Tables produced for each run:
-  (a) open-set top-1 accuracy per language and mean, per crop, with a 95%
-      bootstrap CI on the mean;
-  (b) each restricted selection's accuracy over that selection's utterances
-      (logits masked to the selection, softmax over the mask, argmax);
-  (c) confidence: median top-prob when right vs wrong, and coverage /
-      precision at thresholds 0.5 / 0.7 / 0.9, open-set, on full clips;
-  (d) the 10 confusion pairs with the most open-set errors.
-
-Correctness is strict code equality. `nn` is not credited for `no`, `be` is
-not credited for `ru`, `jw` is not credited for `id`: those label artefacts
-are exactly what the restricted selections are supposed to remove, and
-crediting them would erase the effect the tables are measuring.
+The markdown report is the open-set top-1 accuracy per language and mean,
+per crop, with a 95% bootstrap CI on the mean. Correctness is strict code
+equality (`nn` is not credited for `no`, `be` not for `ru`, `jw` not for
+`id`).
 
 The mean is the MACRO mean over languages (each language weighs 1/15). The
 bootstrap resamples utterances within each language and re-averages the
@@ -44,7 +32,6 @@
 import argparse
 import json
 import sys
-from collections import Counter, defaultdict
 from pathlib import Path
 
 import numpy as np
@@ -54,8 +41,6 @@
 CROP_ORDER_HINT = {"3": 0, "5": 1, "10": 2, "full": 99}
 N_BOOT = 1000
 BOOT_SEED = 42
-THRESHOLDS = (0.5, 0.7, 0.9)
-TOP_CONFUSIONS = 10
 
 
 # ---------------------------------------------------------------------------
@@ -100,54 +85,6 @@ def name(self) -> str:
         return self.path.stem
 
 
-def softmax_masked(logits: np.ndarray, mask_idx: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-    """Argmax and top-probability over a masked decision space.
-
-    `logits` is [N, 107]; `mask_idx` selects the allowed columns. The softmax
-    is taken over the allowed columns only, which is what
-    transcribe_langid_candidate.p is.
-    """
-    z = logits[:, mask_idx]
-    z = z - z.max(axis=1, keepdims=True)
-    e = np.exp(z)
-    p = e / e.sum(axis=1, keepdims=True)
-    arg = np.argmax(p, axis=1)
-    return mask_idx[arg], p[np.arange(p.shape[0]), arg]
-
-
-# ---------------------------------------------------------------------------
-# Selections
-# ---------------------------------------------------------------------------
-
-
-def load_selections(path: Path, labels: list[str]) -> dict:
-    spec = json.loads(path.read_text())
-    aliases: dict[str, str] = spec.get("aliases", {})
-    index_of = {c: i for i, c in enumerate(labels)}
-
-    def resolve(codes: list[str], where: str) -> list[str]:
-        out = []
-        for c in codes:
-            real = aliases.get(c, c)
-            if real not in index_of:
-                raise SystemExit(
-                    f"error: {where}: code {c!r} (resolved {real!r}) is not in "
-                    f"this model's label set")
-            out.append(real)
-        if len(set(out)) != len(out):
-            dupes = [c for c, n in Counter(out).items() if n > 1]
-            raise SystemExit(f"error: {where}: duplicate codes after aliasing: {dupes}")
-        return out
-
-    selections = []
-    for entry in spec.get("selections", []):
-        selections.append({
-            "name": entry["name"],
-            "codes": resolve(entry["codes"], f"selections.{entry['name']}"),
-        })
-    return {"aliases": aliases, "selections": selections, "path": path}
-
-
 # ---------------------------------------------------------------------------
 # Metrics
 # ---------------------------------------------------------------------------
@@ -229,7 +166,7 @@ def md_table(header: list[str], rows: list[list[str]]) -> list[str]:
     return out
 
 
-def section_setup(run: Run, sel: dict) -> list[str]:
+def section_setup(run: Run) -> list[str]:
     h = run.header
     r = h["recipe"]
     lines = [f"## Setup", ""]
@@ -245,14 +182,10 @@ def section_setup(run: Run, sel: dict) -> list[str]:
     lines.append(f"- Dataset: FLEURS `test`, {len(run.languages)} languages "
                  f"x {r['n_utterances'] // max(len(run.languages), 1)} utterances "
                  f"= {r['n_utterances']} clips, longest {r['longest_clip_s']:.1f} s")
-    lines.append(f"- Crops: {crop_list(run.crops)}, from the "
-                 f"{'first speech frame' if r['trim'] == 'energy' else 'start of the clip'}"
-                 f" (`--trim {r['trim']}`)")
-    lines.append(f"- Trim rule: {r['trim_rule']}")
+    lines.append(f"- Crops: {crop_list(run.crops)}, from the start of the clip")
     lines.append(f"- Logits: `{r['logit_kind']}`; both engines read the same "
                  f"16-bit crops under `{r['crop_dir']}`")
-    lines.append(f"- Decision spaces from `{rel(sel['path'])}`; "
-                 f"bootstrap {N_BOOT} resamples, seed {BOOT_SEED}")
+    lines.append(f"- Bootstrap {N_BOOT} resamples, seed {BOOT_SEED}")
     lines.append(f"- Correctness is strict label-code equality "
                  f"(`nn` is not credited for `no`, `be` not for `ru`, `jw` not for `id`)")
     lines.append("")
@@ -284,88 +217,11 @@ def section_openset(run: Run) -> list[str]:
     rows.append(["**mean**"] + [f"**{pct(means[c][0])}**" for c in run.crops])
     rows.append(["95% CI"] + [f"{pct(means[c][1])}-{pct(means[c][2])}" for c in run.crops])
 
-    lines = ["## (a) Open-set top-1 accuracy (all 107 labels), %", ""]
+    lines = ["## Open-set top-1 accuracy (all 107 labels), %", ""]
     lines += md_table(["lang"] + [crop_head(c) for c in run.crops], rows)
     return lines
 
 
-def section_selections(run: Run, sel: dict) -> list[str]:
-    rows = []
-    for entry in sel["selections"]:
-        codes = entry["codes"]
-        idx = np.array([run.index_of[c] for c in codes])
-        cells = []
-        for crop in run.crops:
-            d = run.by_crop[crop]
-            m = np.isin(d["lang"], codes)
-            if not m.any():
-                cells.append("-")
-                continue
-            arg, _p = softmax_masked(d["logits"][m], idx)
-            pred = np.array(run.labels)[arg]
-            cells.append(pct(float((pred == d["lang"][m]).mean())))
-        n = int(np.isin(run.by_crop[run.crops[0]]["lang"], codes).sum())
-        rows.append([entry["name"], str(n)] + cells)
-    lines = ["## (b) Restricted to a user's selection "
-             "(accuracy over that selection's utterances), %", ""]
-    lines += md_table(["selection", "n utts"] +
-                      [crop_head(c) for c in run.crops], rows)
-    return lines
-
-
-def section_confidence(run: Run) -> list[str]:
-    """Median top-prob right vs wrong, and coverage/precision at thresholds.
-
-    Reported open-set on the `full` crop (the realistic case: the caller has
-    the whole utterance). `coverage` is the share of decisions kept,
-    `precision` the share of kept decisions that are right; a threshold is
-    only useful when it trades a little of the first for a lot of the second.
-    """
-    crop = "full" if "full" in run.crops else run.crops[-1]
-    d = run.by_crop[crop]
-    space_name = f"open ({len(run.labels)})"
-    arg, prob = softmax_masked(d["logits"], np.arange(len(run.labels)))
-    pred = np.array(run.labels)[arg]
-    right = pred == d["lang"]
-    med_r = float(np.median(prob[right])) if right.any() else float("nan")
-    med_w = float(np.median(prob[~right])) if (~right).any() else float("nan")
-    rows = [[space_name, f"{med_r:.3f}", f"{med_w:.3f}",
-             f"{pct(float(right.mean()))}"]]
-    thr_rows = []
-    for t in THRESHOLDS:
-        keep = prob >= t
-        cov = float(keep.mean())
-        prec = float(right[keep].mean()) if keep.any() else float("nan")
-        thr_rows.append([space_name, f"{t:.1f}", pct(cov), pct(prec)])
-
-    lines = [f"## (c) Confidence, `{crop}` clips", ""]
-    lines += md_table(["decision space", "median top-prob right",
-                       "median top-prob wrong", "accuracy %"], rows)
-    lines += md_table(["decision space", "threshold", "coverage %",
-                       "precision %"], thr_rows)
-    return lines
-
-
-def section_confusions(run: Run) -> list[str]:
-    counts: dict[tuple[str, str], Counter] = defaultdict(Counter)
-    for crop in run.crops:
-        d = run.by_crop[crop]
-        pred = np.array(run.labels)[np.argmax(d["logits"], axis=1)]
-        wrong = pred != d["lang"]
-        for t, p in zip(d["lang"][wrong], pred[wrong]):
-            counts[(str(t), str(p))][crop] += 1
-    ranked = sorted(counts.items(), key=lambda kv: -sum(kv[1].values()))[:TOP_CONFUSIONS]
-    rows = []
-    for (t, p), per_crop in ranked:
-        rows.append([f"{t} -> {p}", str(sum(per_crop.values()))] +
-                    [str(per_crop.get(c, 0)) for c in run.crops])
-    lines = [f"## (d) Top {TOP_CONFUSIONS} open-set confusion pairs "
-             f"(errors, all crops)", ""]
-    lines += md_table(["true -> predicted", "total"] +
-                      [crop_head(c) for c in run.crops], rows)
-    return lines
-
-
 # ---------------------------------------------------------------------------
 # Machine-readable headline (scripts/catalog/ingest_accuracy.py)
 # ---------------------------------------------------------------------------
@@ -418,13 +274,10 @@ def score_json(run: Run) -> dict:
 # ---------------------------------------------------------------------------
 
 
-def score_run(run: Run, sel: dict) -> list[str]:
+def score_run(run: Run) -> list[str]:
     lines = [f"# Language-ID accuracy - `{run.name}`", ""]
-    lines += section_setup(run, sel)
+    lines += section_setup(run)
     lines += section_openset(run)
-    lines += section_selections(run, sel)
-    lines += section_confidence(run)
-    lines += section_confusions(run)
     return lines
 
 
@@ -432,8 +285,6 @@ def main(argv: list[str] | None = None) -> int:
     p = argparse.ArgumentParser(
         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
     p.add_argument("runs", nargs="+", type=Path, help="run.py JSONL report(s)")
-    p.add_argument("--selections", type=Path,
-                   default=Path(__file__).resolve().parent / "selections.json")
     p.add_argument("--md", type=Path,
                    help="write the first run's markdown report here (later "
                         "runs go next to it as <run name>.md)")
@@ -447,8 +298,7 @@ def main(argv: list[str] | None = None) -> int:
         p.error("--json takes exactly one run")
 
     loaded = [Run(r) for r in args.runs]
-    sel = load_selections(args.selections, loaded[0].labels)
-    scored = [(run, score_run(run, sel)) for run in loaded]
+    scored = [(run, score_run(run)) for run in loaded]
 
     text = "\n".join(scored[0][1]) + "\n"
     sys.stdout.write(text)
diff --git a/scripts/langid/selections.json b/scripts/langid/selections.json
deleted file mode 100644
index 2eb2d0ce0..000000000
--- a/scripts/langid/selections.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-  "schema": "langid-eval-selections-v1",
-  "_comment": [
-    "Decision spaces scored by scripts/langid/score.py from a single run.py sweep.",
-    "Codes are VoxLingua107 label codes; `aliases` maps the spellings a caller",
-    "is likely to use onto them (the same aliases the GGUF carries).",
-    "VoxLingua107 spells Norwegian Bokmal `no`, so the Norwegian/Danish pair is `no+da`."
-  ],
-  "aliases": {
-    "he": "iw",
-    "jv": "jw",
-    "fil": "tl",
-    "nb": "no"
-  },
-  "selections": [
-    { "name": "en+zh",       "codes": ["en", "zh"] },
-    { "name": "en+es+pt",    "codes": ["en", "es", "pt"] },
-    { "name": "es+pt",       "codes": ["es", "pt"] },
-    { "name": "ja+ko",       "codes": ["ja", "ko"] },
-    { "name": "en+ru",       "codes": ["en", "ru"] },
-    { "name": "no+da",       "codes": ["no", "da"] },
-    { "name": "en+de+ja",    "codes": ["en", "de", "ja"] },
-    { "name": "en+fr+de+es", "codes": ["en", "fr", "de", "es"] },
-    { "name": "de+fr+ja",    "codes": ["de", "fr", "ja"] },
-    { "name": "cs+sk",       "codes": ["cs", "sk"] },
-    { "name": "id+ms",       "codes": ["id", "ms"] }
-  ]
-}

From ac5febc2dd7ccd8cdce27eb3db57f5b464a26240 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:34:33 +0800
Subject: [PATCH 16/38] langid: reduce docs to API facts and catalog-rendered
 tables

Model card keeps the catalog blocks and the standard hand-written sections;
drop the snapshot tables, langid.cpp comparisons and stale perf prose.
docs/langid.md keeps API usage; the family doc drops its port notes;
migrating-to-0.4 drops the langid.cpp mapping table. Intake records that
the front end uses the shared mel.
---
 bindings/python/README.md                     |  10 +-
 docs/bindings.md                              |   6 +-
 docs/langid.md                                | 183 ++++--------------
 docs/migrating-to-0.4.md                      |  20 +-
 docs/models/lang-id-voxlingua107-ecapa.md     | 125 +++---------
 docs/porting/families/ecapa_tdnn.md           |  53 ++---
 .../lang-id-voxlingua107-ecapa/intake.json    |   2 +-
 .../hf_cards/lang-id-voxlingua107-ecapa.yaml  |  10 +-
 8 files changed, 87 insertions(+), 322 deletions(-)

diff --git a/bindings/python/README.md b/bindings/python/README.md
index c332dbe84..bb70a14e8 100644
--- a/bindings/python/README.md
+++ b/bindings/python/README.md
@@ -95,11 +95,11 @@ identify the spoken language through a langid session. `run()` returns a
 `LangIdResult` with candidates ranked by `p`. Codes are the model's own labels
 (`"iw"`, `"jw"`; `model.langid_label_index("he")` resolves aliases), so match
 `result.code` against the ASR model's `capabilities.languages` yourself.
-`allowed=None` scores every label; pass the languages you expect, and a low
-`allowed_mass` means the speech is probably none of them. An empty `allowed`
-list raises `InvalidArgument` rather than meaning "all"; clips under
-`model.langid_info.min_audio_ms` raise `InputTooShort`. Locking, `Busy`,
-`cancel()` and `close()` work as on `Session`.
+`allowed=None` scores every label; `allowed_mass` is the unrestricted
+probability the allowed set captured. An empty `allowed` list raises
+`InvalidArgument`; clips under `model.langid_info.min_audio_ms` raise
+`InputTooShort`. Locking, `Busy`, `cancel()` and `close()` work as on
+`Session`.
 
 ```python
 with model.langid_session() as lid:
diff --git a/docs/bindings.md b/docs/bindings.md
index 831d3ce39..19384b97a 100644
--- a/docs/bindings.md
+++ b/docs/bindings.md
@@ -149,10 +149,8 @@ by `p` (index, code, name, `p`, `p_unrestricted`, `logit`), `n_allowed`,
 `allowed` is the first caller-owned `const char * const *` input. Every
 binding keeps the array and each encoded string alive until the native call
 returns (TypeScript frees them only after its async worker call settles).
-Omitted / `None` / `nil` / `null` passes NULL, meaning every label. An empty
-list is rejected by the binding itself with `InvalidArgument`: many
-marshallers turn an empty array into NULL, which would silently flip the
-meaning to "all labels".
+Omitted / `None` / `nil` / `null` passes NULL, meaning every label; an empty
+list raises `InvalidArgument`.
 
 A langid run follows the same execution rules as an ASR or diarize run:
 model-wide compute lock, `Busy` under a stream lease, results copied out
diff --git a/docs/langid.md b/docs/langid.md
index 1e8d1c276..62dd8f0f1 100644
--- a/docs/langid.md
+++ b/docs/langid.md
@@ -1,21 +1,9 @@
 # Language ID
 
 A LANGID model (`transcribe_model_roles()` has `TRANSCRIBE_ROLE_LANGID`)
-answers "which language is this?" API: `include/transcribe/langid.h`. The one
-shipped family is ecapa_tdnn, SpeechBrain's VoxLingua107 ECAPA-TDNN
-(`docs/models/lang-id-voxlingua107-ecapa.md`): a classifier over 107 labels.
-
-Almost all of the difference between a good and a bad integration is in what
-you feed it and what you let it choose from. This page is the protocol, with
-the measured numbers behind each rule. They are a snapshot of
-`scripts/langid/score.py --md` on the SpeechBrain reference sweeps of FLEURS
-`test`, 15 languages x 200 utterances, untrimmed and trimmed
-(`scripts/langid/`); the C++ engine makes the same decisions on the same
-audio. The shipped GGUFs' own accuracy and reference agreement are rendered
-from the catalog on the
-[model page](models/lang-id-voxlingua107-ecapa.md#accuracy).
-
-## Minimal use
+identifies the spoken language of a clip and returns its labels ranked by
+probability. API: `include/transcribe/langid.h`. The shipped model is
+[VoxLingua107 ECAPA-TDNN](models/lang-id-voxlingua107-ecapa.md), 107 labels.
 
 ```c
 #include "transcribe/langid.h"
@@ -46,138 +34,33 @@ transcribe_model_free(m);
 
 From the CLI: `transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en,de,fr --top 3 clip.wav`.
 
-## 1. Feed speech, not the raw capture
-
-Trim leading silence before you crop. Any VAD or energy gate will do; the
-evaluation's "trimmed" recipe drops audio up to the first 20 ms frame whose
-RMS exceeds 5% of the clip's loudest frame.
-
-Open-set top-1 accuracy, mean over the 15 languages:
-
-| | 3 s | 5 s | 10 s | full |
-|---|---|---|---|---|
-| crop from sample 0 | 67.0 | 85.2 | 91.1 | 91.4 |
-| crop from first speech | 83.4 | 89.1 | 91.7 | 91.5 |
-
-At 3 s that is +16.4 points for a change that costs nothing. FLEURS clips open
-with silence, so an untrimmed "3 s" is roughly 2 s of speech. By 10 s the gap
-has closed: the amount of speech is what matters, not the amount of audio.
-Give it at least 3 s of speech and prefer 5 s.
-
-## 2. Always pass `allowed`
-
-A dictation app knows which languages its user has enabled. Passing them in
-`transcribe_langid_params::allowed` masks every other logit before the
-argmax. This is the largest accuracy lever, and it is free.
-
-Accuracy over a user's selection, trimmed crops:
-
-| selection | 3 s | 5 s | 10 s | full |
-|---|---|---|---|---|
-| en+zh | 99.8 | 100.0 | 100.0 | 100.0 |
-| en+ru | 99.5 | 100.0 | 100.0 | 100.0 |
-| es+pt | 100.0 | 99.8 | 100.0 | 100.0 |
-| ja+ko | 96.0 | 99.8 | 100.0 | 100.0 |
-| no+da | 99.8 | 100.0 | 100.0 | 100.0 |
-| en+fr+de+es | 96.9 | 97.9 | 99.8 | 99.5 |
-| de+fr+ja | 94.2 | 96.8 | 99.7 | 99.3 |
-| cs+sk | 92.8 | 93.8 | 96.5 | 96.2 |
-| id+ms | 88.2 | 90.2 | 89.8 | 89.0 |
-
-`en+ru` is 99.5% at 3 s while open-set Russian is 54.0%: open-set errors go
-to neighbouring languages (`ru` -> `be`), and a selection without the
-neighbour removes them.
-
-If the user has exactly one language enabled, don't call language ID at all.
-
-Only NULL (with `n_allowed == 0`) means "every label". A non-NULL list with
-`n_allowed <= 0`, a NULL list with `n_allowed > 0`, or a NULL element is
-`TRANSCRIBE_ERR_INVALID_ARG`; an unknown code or alias is
-`TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE`; duplicates count once.
-
-## 3. Decide at 5 s, re-decide at 10 s if you are not confident
-
-Identify on the first 5 s of speech; if the top `p` is below 0.9, identify
-again at 10 s and use that. What 0.9 buys you, measured on full clips (mean
-~12 s), so this is the confidence-to-precision relation rather than the 5 s
-operating point:
-
-| decision space | threshold | coverage % | precision % |
-|---|---|---|---|
-| open (107) | 0.5 | 97.7 | 92.6 |
-| open (107) | 0.7 | 88.8 | 95.9 |
-| open (107) | 0.9 | 77.1 | 99.0 |
-
-The median top probability is 0.999 when the model is right and 0.650 when it
-is wrong, so a 0.9 gate is a real signal.
-
-## 4. `allowed_mass` is the "not in your selection" signal
-
-`transcribe_langid_result::allowed_mass` is the unrestricted probability the
-allowed set captured (1.0 when unrestricted). When a user with `en+de` starts
-speaking Japanese, `p` still confidently picks `en` or `de` (the mask cannot
-express "neither"), but `allowed_mass` collapses. Use it to prompt or to hold
-off an automatic switch, not as a hard reject: it also drops on noise and on
-very short audio.
-
-This is a closed-set classifier: there is no "no speech" or "unknown
-language" label, and silence, music or noise still produce a confident-looking
-top candidate. Gate the input with VAD; there is no rejection threshold you
-can rely on from the scores alone.
-
-## 5. Long dictations: re-identify on a rolling window
-
-Re-identify every 20-30 s on recent speech rather than on the whole recording.
-`transcribe_langid_session_params::max_audio_ms` (default 30000) caps the
-scored span, and longer input is scored on its last `max_audio_ms`, so handing
-the growing buffer to `transcribe_langid_run` is a trailing-window classifier.
-`transcribe_langid_result::audio_ms` reports what was scored.
-
-The log-mel is mean-normalised over the whole scored span, so a 5 s window's
-result is not the prefix of the 10 s result: overlapping calls are independent
-decisions. Cost scales with the scored span, not the buffer.
-
-## 6. Labels are the model's own codes
-
-Codes are mostly ISO 639-1 with VoxLingua107's legacy spellings (`iw` Hebrew,
-`jw` Javanese, `tl` Filipino, `no` Norwegian Bokmal). The GGUF stores aliases,
-so `he`, `jv`, `fil` and `nb` are accepted in `allowed` and by
-`transcribe_langid_label_index`, but results always report the model's code.
-
-There is no canonicalization across models. To feed the answer to an ASR
-model, match `code` against that model's `transcribe_capabilities::languages`
-yourself (and map `iw` / `he` etc. where they differ).
-
-## 7. Known weak spots
-
-Top open-set confusions (untrimmed, summed over all crops):
-
-| true -> predicted | errors |
-|---|---|
-| ru -> be (Belarusian) | 271 |
-| no -> nn (Norwegian Nynorsk) | 229 |
-| id -> jw (Javanese) | 200 |
-| ms -> id | 157 |
-| cs -> sk | 59 |
-
-- **`no` / `nn`** are not reliably separable. If you mean "Norwegian", put
-  `no` in the selection and leave `nn` out.
-- **`ru` / `be`**: excluding `be` fixes it.
-- **`id` / `ms`** stay at 88-90% even restricted to the pair, at every crop
-  length; distinguish them downstream if you must.
-- **`cs` / `sk`** improve with audio but stay around 96%.
-- **No Cantonese label**: Cantonese is classified as `zh`.
-- **`de`** is weak open-set (68.5% at 3 s trimmed), mostly to `ro`, `yi`, `nn`.
-
-## 8. Input, threading and placement
-
-- 16 kHz mono float32; NaN / Inf is `TRANSCRIBE_ERR_INVALID_ARG`.
-- Scored audio under 500 ms (`transcribe_langid_info::min_audio_ms`) is
-  `TRANSCRIBE_ERR_INPUT_TOO_SHORT`. Between 500 ms and 3 s the answer is weak;
-  treat it as provisional.
-- `top_k` trims the returned list only; the mask and softmax are unaffected.
-- Sessions, model lifetime and the one-compute-per-model rule are those of
-  every role (`docs/roles.md`). Budget `n_threads` explicitly when language ID
-  and ASR run on the CPU at the same time.
-- Placement is per model (`transcribe_model_load_params`); a language ID model
-  can sit on the CPU while ASR uses the GPU.
+## Parameters and results
+
+- **`allowed`** restricts the decision to the listed labels: candidates are
+  ranked by `p`, the softmax over the allowed set. The network still scores
+  every label; `p_unrestricted` is the softmax over all of them. NULL with
+  `n_allowed == 0` means every label. A non-NULL list with `n_allowed <= 0`,
+  a NULL list with `n_allowed > 0`, or a NULL element is
+  `TRANSCRIBE_ERR_INVALID_ARG`; an unknown code is
+  `TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE`; duplicates count once.
+- **`allowed_mass`** (`transcribe_langid_result`) is the sum of
+  `p_unrestricted` over the allowed set, 1 when unrestricted. A low value
+  means the unrestricted model puts most of its probability outside the
+  allowed set.
+- **`top_k`** limits the returned candidates (0 = every allowed label). It
+  does not change `p` or `allowed_mass`.
+- **Length.** Input longer than the session's `max_audio_ms` (default 30000)
+  is scored on its last `max_audio_ms`; `audio_ms` reports what was scored.
+  Scored audio shorter than `transcribe_langid_info::min_audio_ms` (500 ms)
+  is `TRANSCRIBE_ERR_INPUT_TOO_SHORT`.
+- **Labels** are the model's own codes. VoxLingua107 uses `iw` (Hebrew),
+  `jw` (Javanese), `tl` (Filipino) and `no` (Norwegian Bokmal); the GGUF
+  stores `he`, `jv`, `fil` and `nb` as aliases, accepted in `allowed` and by
+  `transcribe_langid_label_index`. Results always report the model's code.
+  Codes are not canonicalized across models: match `code` against an ASR
+  model's `transcribe_capabilities::languages` yourself.
+
+The model is a closed-set classifier with no "unknown" or "no speech" label.
+
+Sessions, threading and model lifetime follow the rules shared by every role
+(`docs/roles.md`).
diff --git a/docs/migrating-to-0.4.md b/docs/migrating-to-0.4.md
index ccdd66e05..2f033d1d8 100644
--- a/docs/migrating-to-0.4.md
+++ b/docs/migrating-to-0.4.md
@@ -27,23 +27,9 @@ unchanged; see `docs/roles.md`.
 
 ## New: the LANGID role
 
-Language identification is a role of its own (`include/transcribe/langid.h`,
-`docs/langid.md`), served by the ecapa_tdnn family (VoxLingua107 ECAPA-TDNN,
-107 labels). It is new API, so nothing migrates; it replaces the separate
-langid.cpp library and its `langid_*` API:
-
-| langid.cpp | 0.4 |
-| --- | --- |
-| `langid_model_load` / `langid_context_init` | `transcribe_model_load_file` / `transcribe_langid_session_init` |
-| `langid_identify` | `transcribe_langid_run` |
-| `langid_get_result` / `langid_get_candidate` | `transcribe_langid_get_result` / `transcribe_langid_get_candidate` |
-| candidate `prob` / `prob_open` | `p` / `p_unrestricted` |
-| `LANGID_ERR_AUDIO_TOO_SHORT` / `_UNKNOWN_LANGUAGE` | `TRANSCRIBE_ERR_INPUT_TOO_SHORT` (new, 21) / `TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE` |
-| non-NULL `allowed` with `n_allowed <= 0` meant "all" | `TRANSCRIBE_ERR_INVALID_ARG`; only NULL means "all" |
-| `max_audio_ms` below 500 ms accepted | `TRANSCRIBE_ERR_INVALID_ARG` at session init |
-| accessors before a result: `INVALID_ARG` | OK with zeroed output, like every other accessor |
-| `langid_embed` | not available (no EMBED role yet) |
-| langid.cpp GGUFs (`langid.*` keys) | reconvert with `scripts/convert-ecapa_tdnn.py` (`stt.*` keys, renamed tensors) |
+Language identification is a new role (`include/transcribe/langid.h`,
+`docs/langid.md`) with a new status, `TRANSCRIBE_ERR_INPUT_TOO_SHORT` (21).
+It is new API, so nothing migrates.
 
 ## Capabilities are ASR-only
 
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index ee962bf5a..631da816a 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -12,16 +12,14 @@ the last 30 s of a clip.
 
 ## What it's for
 
-Spoken language identification: which of 107 languages is this clip in?
-This is **not a transcription model**. It serves the LANGID role
-(`include/transcribe/langid.h`): a run returns the model's language labels
-ranked by probability, optionally restricted to the languages your user has
-enabled, plus `allowed_mass`, the signal that the speech is outside that set.
-Use it to pick the `language` for an ASR model, or to route audio.
-
-How to get good answers out of it (trim silence, pass the allowed set, decide
-at 5 s, re-decide at 10 s) is in [`docs/langid.md`](../langid.md). See
-SpeechBrain's [model card](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa)
+Spoken language identification over 107 languages. This is **not a
+transcription model**. It serves the LANGID role
+(`include/transcribe/langid.h`, [`docs/langid.md`](../langid.md)): a run
+returns the model's language labels ranked by probability, optionally
+restricted to a caller-chosen set, plus `allowed_mass`, the unrestricted
+probability that set captured.
+
+See SpeechBrain's [model card](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa)
 for training data and the label list.
 
 <!-- catalog:pin -->
@@ -45,14 +43,10 @@ Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measure
 <!-- catalog:prose field=wer.notes -->
 Open-set top-1 accuracy over all 107 labels, the mean over 15 FLEURS
 languages, on the first 5 s of each clip without silence trimming.
-Accuracy is higher with an allowed set and with trimmed speech; see
-docs/langid.md. Agreement with the SpeechBrain reference, per GGUF, is on
-the transcribe.cpp model page.
+Agreement with the SpeechBrain reference, per GGUF, is on the
+transcribe.cpp model page.
 <!-- /catalog -->
 
-Only near-reference tiers ship (F32 / F16 / Q8_0): the decision is an argmax
-over 107 logits, and the k-quant tiers would save a few MB at most.
-
 ## Accuracy
 
 <!-- catalog:agreement -->
@@ -65,46 +59,8 @@ over 107 logits, and the k-quant tiers would save a few MB at most.
 Measured at transcribe.cpp `16d46bc2` on 2026-10-06.
 <!-- /catalog -->
 
-Accuracy is the headline above: open set, first 5 s of each clip, no silence
-trimming, C++ on CPU. Agreement counts the scored run's top-1 decisions that
-match the SpeechBrain reference on the same audio, over every crop of the
-sweep (3 / 5 / 10 s / full). It is the ship gate for F32
-(`scripts/langid/compare.py`: every disagreement must be a reviewed near-tie)
-and the evidence for the quants. F16's flips are all near-ties (top-two
-margin under 0.04 logit). Q8_0 is a download format: transcribe.cpp widens
-its weights to F16 at load, so it computes like F16 (F16's memory) and its
-accuracy matches F32 within the confidence interval; its flips are the 8-bit
-weights alone, on decisions F32 also finds uncertain.
-
-### Snapshot: crops and decision spaces
-
-A snapshot of `scripts/langid/score.py --md` on the C++ sweeps behind the
-rows above (transcribe.cpp `16d46bc2`, CPU; FLEURS `test`, 15 languages x 200
-utterances, crops from the start of each clip, no silence trimming). It is
-not rendered from the catalog; regenerate it with the commands under
-Reproduction. Top-1 accuracy, %.
-
-Open set (all 107 labels) at the other crops; 5 s is the catalog row above:
-
-| GGUF | 3 s | 10 s | full |
-|---|---|---|---|
-| F32 | 67.0 | 91.1 | 91.4 |
-| F16 | 66.9 | 91.0 | 91.4 |
-| Q8_0 | 67.2 | 92.0 | 92.3 |
-
-Restricted to a selection, F32:
-
-| decision space | 3 s | 5 s | 10 s | full |
-|---|---|---|---|---|
-| en+ru | 98.8 | 100.0 | 100.0 | 100.0 |
-| en+fr+de+es | 88.6 | 94.1 | 98.9 | 99.4 |
-| cs+sk | 83.0 | 92.2 | 96.8 | 96.5 |
-| id+ms | 87.8 | 89.5 | 89.5 | 88.8 |
-
-Computed in Q8_0 instead, as langid.cpp does, ggml also rounds the
-activations to 8 bits; that moves 6.9% of decisions (93.1% agreement, open
-set 65.4 / 84.3 / 90.9 / 91.4). Per-language tables, confidence / coverage
-and confusions: `uv run scripts/langid/score.py <run>.jsonl --md <run>.md`.
+Agreement counts the scored sweep's top-1 decisions that match the
+SpeechBrain reference on the same audio, over the 3 / 5 / 10 s / full crops.
 
 ## Quick Start
 
@@ -129,15 +85,10 @@ From the C API, use the LANGID role (`include/transcribe/langid.h`):
 `transcribe_langid_session_init`, `transcribe_langid_run`, then
 `transcribe_langid_get_result` / `transcribe_langid_get_candidate`. Labels are
 the model's own codes (`iw`, `jw`, `tl`, `no`; `he`, `jv`, `fil`, `nb` are
-accepted as aliases). Match them against your ASR model's
-`transcribe_capabilities::languages` yourself.
+accepted as aliases).
 
 ## Performance
 
-Measured with `scripts/langid/bench.py` through the Python binding
-(`tools/transcribe-bench` is ASR-only), on the first N seconds of one clip;
-CPU uses the library default thread count.
-
 ### Apple M4 Max
 
 <!-- catalog:perf machine=m4-max dp_ms=1 -->
@@ -153,31 +104,20 @@ Compute latency (mel + encode), speedup over realtime in parentheses.
 Apple M4 Max: transcribe.cpp `62522202` on 2026-10-05.
 <!-- /catalog -->
 
-`AUTO` resolves like every other family (the GPU when there is one). Metal is
-several times faster here; CPU is still well under real time and avoids
-contending with an ASR model on the GPU, so load with
-`TRANSCRIBE_BACKEND_CPU` when that matters.
-
-The `long-45s` cells are medians of 20 warm runs on `samples/long-45s.wav`,
-which has since been replaced by `samples/ru-long.wav`, and the CPU ones
-predate the current CPU kernels. Both publication rigs are due a re-bench
-(the AMD Ryzen 7 PRO 4750U has no published cells yet):
+Benchmark reproduction (`tools/transcribe-bench` is ASR-only):
 
 ```bash
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py --profile \
   --library build-shared/src/libtranscribe.dylib
-uv run scripts/catalog/ingest_perf.py
-uv run scripts/catalog/render.py
 ```
 
 ## Numerical Validation
 
-transcribe.cpp is validated tensor-by-tensor against SpeechBrain 1.1.1 on
-eight committed FLEURS clips (`samples/fleurs-*.wav`): every stage tensor
-gated in `tests/tolerances/ecapa_tdnn.json` (front end, every encoder block,
-pooling, embedding, logits) within tolerance on the calibration host (Apple
-Silicon), and the top-1 label equal to the reference on every clip. The
-dataset gate is the FLEURS decision parity above.
+transcribe.cpp is validated tensor-by-tensor against SpeechBrain on eight
+FLEURS clips (`samples/fleurs-*.wav`): every stage tensor gated in
+`tests/tolerances/ecapa_tdnn.json` (front end, every encoder block, pooling,
+embedding, logits) falls within tolerance, and the top-1 label matches the
+reference on every clip.
 
 | Field | Value |
 | --- | --- |
@@ -185,30 +125,25 @@ dataset gate is the FLEURS decision parity above.
 | Dump script | `scripts/dump_reference_ecapa_tdnn_speechbrain.py` |
 | Manifest | `tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json` |
 | Command | `uv run scripts/validate.py all --family ecapa_tdnn` |
-| Dataset gate | `scripts/langid/compare.py` (FLEURS, every crop; see Accuracy) |
+| Dataset gate | `scripts/langid/compare.py` (FLEURS, every crop) |
 
 ## Known Limitations
 
 - **Closed set.** There is no "unknown" or "no speech" label: silence, music
-  and out-of-set languages still get a confident-looking top candidate. Gate
-  with VAD and watch `allowed_mass`.
-- **Confusable pairs.** `ru`/`be`, `no`/`nn`, `id`/`jw`/`ms`, `cs`/`sk`; `id`
-  vs `ms` stays near 89% even restricted to the pair.
+  and out-of-set languages still get a top candidate.
 - **No Cantonese label**: Cantonese is classified as `zh`.
-- **Minimum 500 ms** of scored audio (`TRANSCRIBE_ERR_INPUT_TOO_SHORT`);
-  answers under ~3 s of speech are weak.
+- **Minimum 500 ms** of scored audio (`TRANSCRIBE_ERR_INPUT_TOO_SHORT`).
 - **Scores at most the last 30 s** by default
   (`transcribe_langid_session_params::max_audio_ms`).
-- **Q8_0 runs at F16 speed.** langid.cpp computes Q8_0 with ggml's ARM
-  weight-repacking kernels, about 1.3x faster than F16 on an Apple CPU, at
-  the accuracy cost above; transcribe.cpp widens Q8_0 to F16 instead.
+- **Q8_0 weights are widened to F16 at load**, so Q8_0 computes and uses
+  memory like F16.
 
 ## Reproduction
 
 ### Convert
 
-Loads the SpeechBrain checkpoint through SpeechBrain (the only implementation)
-from the Hugging Face cache, and writes F32 only.
+Loads the SpeechBrain checkpoint through SpeechBrain from the Hugging Face
+cache and writes F32 only.
 
 ```bash
 uv run --project scripts/envs/ecapa_tdnn \
@@ -234,11 +169,8 @@ uv run scripts/validate.py all --family ecapa_tdnn
 ### Accuracy acceptance
 
 One reference sweep, then one C++ sweep per shipped GGUF (shown for F32;
-repeat with F16 and Q8_0, passing `--report-only` to `compare.py`: only F32
-is gated). `compare.py` is the gate; `score.py --json` and `compare.py --json`
-are what `ingest_accuracy.py` reads, named after the GGUF, and the importer
-rejects a score without its agreement or over a language set other than
-`scripts/langid/ingest.py`'s.
+repeat with F16 and Q8_0, passing `--report-only` to `compare.py`, which gates
+F32 only).
 
 ```bash
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
@@ -255,7 +187,6 @@ uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl
   reports/langid/cpp-f32-untrimmed.jsonl \
   --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
 uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \
-  --md reports/langid/cpp-f32-untrimmed.md \
   --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 uv run scripts/catalog/ingest_accuracy.py --models lang-id-voxlingua107-ecapa
 uv run scripts/catalog/render.py
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index dc59a206d..d8005698c 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -1,8 +1,7 @@
 # ECAPA-TDNN (language ID)
 
 Status: supported (LANGID role). Ported from `handy-computer/langid.cpp`
-(`96af3a5`), where the C++ was first written and validated against
-SpeechBrain; that repo is superseded by this family.
+(`96af3a5`).
 
 ECAPA-TDNN is SpeechBrain's speaker / language embedder (architecture pattern
 `encoder-classifier`): SpeechBrain Fbank front end, a TDNN block, three
@@ -15,14 +14,11 @@ top-k (`docs/langid.md`).
 
 Acceptance: tensor parity on eight FLEURS clips (`validate.py`), and top-1
 decision parity with SpeechBrain on FLEURS (15 languages x 200 utterances x
-3 / 5 / 10 s / full crops, C++ F32 on CPU), where every disagreement must be
-a reviewed near-tie. Shipped matrix: F32 + F16 + Q8_0. Each GGUF's agreement
-and accuracy live in the catalog and are rendered on
+3 / 5 / 10 s / full crops, C++ F32 on CPU). Shipped matrix: F32 + F16 + Q8_0.
+Each GGUF's accuracy and agreement live in the catalog and are rendered on
 [the model page](../../models/lang-id-voxlingua107-ecapa.md#accuracy). The
 loader widens Q8_0 weights to F16 (`widen_q8_0_weights` in
-`src/arch/ecapa_tdnn/model.cpp`): computed in Q8_0, ggml also rounds the
-activations to 8 bits and agreement drops by several points (the model
-page's accuracy snapshot has the figures).
+`src/arch/ecapa_tdnn/model.cpp`).
 
 ## Identity
 
@@ -46,8 +42,8 @@ page's accuracy snapshot has the figures).
 Keys: `stt.variant`, `stt.frontend.*` (`window = hamming_periodic`,
 `pad_mode = constant`, `log_clamp_min = 1e-10`, `top_db = 80`,
 `normalize = sentence_mean`, plus the usual sizes), `stt.ecapa_tdnn.*`
-(format version, channels, kernel sizes, dilations, res2net scale, SE /
-attention / embedding / classifier widths, `asp_eps`, leaky slope) and
+(channels, kernel sizes, dilations, res2net scale, SE / attention /
+embedding / classifier widths, `asp_eps`, leaky slope) and
 `stt.langid.labels.{codes,names,aliases}`. Integer scalars are uint32.
 
 The converter applies four exact rewrites: BatchNorm to a `scale` / `shift`
@@ -90,7 +86,7 @@ uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine speechbr
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp --library build-shared/src/libtranscribe.dylib ... --out reports/langid/cpp-f32-untrimmed.jsonl
 uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl reports/langid/cpp-f32-untrimmed.jsonl \
   --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
-uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl --md reports/langid/cpp-f32-untrimmed.md \
+uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \
   --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 uv run scripts/catalog/ingest_accuracy.py --models lang-id-voxlingua107-ecapa
 ```
@@ -99,10 +95,10 @@ The catalog rows follow the `langid-publication-v1` profile
 (`catalog/_benchmark_profiles.json`): the open-set mean on the 5 s untrimmed
 crop of each shipped GGUF's sweep, with that sweep's agreement.
 
-`compare.py` fails on any disagreement that is not on a reviewed near-tie
-list (`--near-ties`), and refuses runs whose labels, recipe, manifest hashes
-or checkpoint revision differ. Only F32 is gated: run the F16 and Q8_0
-sweeps through it with `--report-only`, which still writes their agreement.
+`compare.py` fails on any top-1 disagreement or an agreement below 99.9%,
+and refuses runs whose labels, recipe, manifest hashes or checkpoint revision
+differ. Only F32 is gated: run the F16 and Q8_0 sweeps through it with
+`--report-only`, which still writes their agreement.
 
 Benchmarks: `scripts/langid/bench.py --profile` (`tools/transcribe-bench` is
 ASR-only) writes bench-driver reports under `reports/perf/`, which
@@ -118,30 +114,3 @@ ASR-only) writes bench-driver reports under `reports/perf/`, which
 | Minimum length | 500 ms | `transcribe_ecapa_tdnn_smoke`, `transcribe_langid_dispatch_unit` | `INPUT_TOO_SHORT` below 500 ms | MUST PASS | PASS |
 | Transcribe / translate / timestamps / streaming | n/a | `transcribe_session_init` | `UNSUPPORTED_ROLE` | OUT OF SCOPE — not an ASR model | SKIP — not exposed by runtime |
 | Batch (offline) | n/a | `transcribe-cli --batch` | refused: ASR-only | OUT OF SCOPE — no batch entry points for new roles in v1 | ACCEPTED GAP — one clip per call |
-
-## Notes
-
-- Regime for every parity number: F32 GGUF, CPU backend, one thread,
-  production C++ front end, macOS arm64. On the port, every stage tensor was
-  bit-identical to langid.cpp's (ggml 0.20.2 there, 0.25.3 here), so
-  `tests/tolerances/ecapa_tdnn.json` carries over unchanged.
-- Hardening from the langid.cpp review, fixed in the role dispatcher and
-  tested in `tests/langid_dispatch_unit.cpp`: H1 minimum checked after the
-  crop and sub-minimum `max_audio_ms` rejected at init; H2 only NULL means
-  "all labels"; H3 non-finite PCM and logits rejected; H6 malformed label
-  tables fail the load. H4 (no leak when label metadata is bad) is covered by
-  `arch_ecapa_tdnn_bad_labels.gguf` in `transcribe_ecapa_tdnn_smoke`
-  (`leaks --atExit`: 0 leaks).
-- Not ported: langid.cpp's CPU weight-repacking buffer. It only speeds up
-  Q8_0 compute (ARM only; ggml has no Q8_0 repack on x86), which the Q8_0
-  widening replaces. Measured on the M4 Max CPU at 10 s, generic build
-  flags: Q8_0 widened 49 ms, Q8_0 computed 56 ms, Q8_0 + repack 36 ms;
-  Metal 14 ms for all. `cls.out.weight` (107 rows) cannot be repacked, so a
-  future repack path must filter on the row count or langid.cpp's abandon
-  path disables repacking for the whole file. The performance-core thread
-  default did not help (8 threads ties or beats 12).
-- `AUTO` placement is unchanged (GPU first). Metal against CPU latency is in
-  the model page's Performance section.
-- Q8_0 conv kernels stay F32 (the quantizer's Conv bucket is F32 in every
-  preset), so the Q8_0 file is 26.7 MB against langid.cpp's 24.1 MB, and
-  `cls.out.weight` is Q8_0 where langid.cpp kept it F16.
diff --git a/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json b/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json
index 52e67d2df..2ce5a1a11 100644
--- a/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json
+++ b/reports/porting/ecapa_tdnn/lang-id-voxlingua107-ecapa/intake.json
@@ -146,7 +146,7 @@
   "known_risks": [
     "Role is LANGID, not ASR: acceptance is FLEURS decision parity vs SpeechBrain (12000 decisions) plus top-1 accuracy, not WER on LibriSpeech.",
     "capabilities.language_detection / stt.capability.lang_detect mean ASR auto-detect and stay false; language ID is the model's role (transcribe_model_roles), not an ASR capability.",
-    "SpeechBrain front end (periodic Hamming, 10*log10, 80 dB top-db, per-bin mean) is not expressible by transcribe-mel; it is family-local (src/arch/ecapa_tdnn/mel.cpp).",
+    "SpeechBrain front end (periodic Hamming, 10*log10, 80 dB top-db, per-bin mean) runs on the shared transcribe-mel (src/transcribe-mel.cpp) with window_type hamming_periodic and normalize sentence_mean.",
     "Labels use VoxLingua107's legacy codes (iw, jw, tl, no); modern codes are stored as aliases. Codes are not canonicalized against ASR models' language lists.",
     "Closed-set classifier: out-of-set speech still gets a confident label; callers use allowed_mass / restrict the allowed set."
   ],
diff --git a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
index 906174190..e09782122 100644
--- a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
+++ b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
@@ -47,9 +47,8 @@ usage: |
   #   ...
   ```
 
-  From the C API, use the LANGID role (`include/transcribe/langid.h`); the
-  usage protocol (trim silence, pass the allowed set, decide at 5 s) is in
-  `docs/langid.md`.
+  From the C API, use the LANGID role (`include/transcribe/langid.h`, see
+  `docs/langid.md`).
 
   If your audio isn't already 16 kHz mono WAV, convert it first:
 
@@ -64,6 +63,5 @@ wer:
   notes: |
     Open-set top-1 accuracy over all 107 labels, the mean over 15 FLEURS
     languages, on the first 5 s of each clip without silence trimming.
-    Accuracy is higher with an allowed set and with trimmed speech; see
-    docs/langid.md. Agreement with the SpeechBrain reference, per GGUF, is on
-    the transcribe.cpp model page.
+    Agreement with the SpeechBrain reference, per GGUF, is on the
+    transcribe.cpp model page.

From 905eb051879fb2d68db06b572f30c3a972ed39e6 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:36:22 +0800
Subject: [PATCH 17/38] langid: drop two stale notes about langid.cpp parity
 and CPU kernels

---
 catalog/_benchmark_profiles.json | 2 +-
 samples/README.md                | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/catalog/_benchmark_profiles.json b/catalog/_benchmark_profiles.json
index a3a27848e..b1a5e2d43 100644
--- a/catalog/_benchmark_profiles.json
+++ b/catalog/_benchmark_profiles.json
@@ -132,7 +132,7 @@
             "cooldown_tctl_c": 55.0
           }
         ],
-        "pending": "The published cells were measured on samples/long-45s.wav, since replaced by ru-long.wav, before the CPU kernel work, and the Ryzen rig has none yet. Re-bench both rigs with scripts/langid/bench.py --profile, ingest, delete the long-45s rows, then remove this key to enforce the speed matrix."
+        "pending": "The published cells were measured on samples/long-45s.wav, since replaced by ru-long.wav, and the Ryzen rig has none yet. Re-bench both rigs with scripts/langid/bench.py --profile, ingest, delete the long-45s rows, then remove this key to enforce the speed matrix."
       }
     }
   }
diff --git a/samples/README.md b/samples/README.md
index 14f25d91c..80f271e1f 100644
--- a/samples/README.md
+++ b/samples/README.md
@@ -43,8 +43,8 @@ Regenerate the source pool, not the clips themselves, with
 ## Language ID clips (FLEURS)
 
 Fixtures for the ecapa_tdnn (LANGID) family, carried over byte-for-byte from
-langid.cpp (`handy-computer/langid.cpp@96af3a5`, `scripts/make_samples_fleurs.py`)
-so its measured parity and tolerances still apply. Rule for the eight
+langid.cpp (`handy-computer/langid.cpp@96af3a5`, `scripts/make_samples_fleurs.py`).
+Rule for the eight
 `fleurs-<code>` clips: the first test-split utterance of 4-12 s, in parquet
 order, that the SpeechBrain reference (`speechbrain/lang-id-voxlingua107-ecapa`
 @ `0253049a`) classifies as the expected VoxLingua107 code. They back

From 144aa7d0c6c2dd9416322d1184e5dee788201968 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 16:40:05 +0800
Subject: [PATCH 18/38] langid: restore the 0.05 F32 GPU bound in
 ecapa_tdnn_real_smoke

F16 and Q8_0 keep 0.1; general.file_type selects the bound again.
---
 tests/CMakeLists.txt            |  2 +-
 tests/ecapa_tdnn_real_smoke.cpp | 23 ++++++++++++++++++++---
 2 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 234a34b02..d3d924ccd 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1385,7 +1385,7 @@ if(TRANSCRIBE_BUILD_REAL_MODEL_TESTS)
     add_executable(transcribe_ecapa_tdnn_real_smoke
         ecapa_tdnn_real_smoke.cpp)
     target_link_libraries(transcribe_ecapa_tdnn_real_smoke
-        PRIVATE transcribe transcribe-common-example)
+        PRIVATE transcribe transcribe-common-example ggml)
     target_compile_definitions(transcribe_ecapa_tdnn_real_smoke PRIVATE
         "TRANSCRIBE_TEST_SAMPLES_DIR=\"${CMAKE_SOURCE_DIR}/samples\"")
     transcribe_apply_warnings(transcribe_ecapa_tdnn_real_smoke)
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
index bc4578710..d931fee12 100644
--- a/tests/ecapa_tdnn_real_smoke.cpp
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -4,6 +4,7 @@
 // backend; then, when a GPU backend loads, the stock-op GPU graph against the
 // CPU graph's logits. Gated by TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip).
 
+#include "gguf.h"
 #include "transcribe.h"
 #include "transcribe/langid.h"
 #include "wav.h"
@@ -84,14 +85,30 @@ std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
     return out;
 }
 
+// general.file_type of the GGUF (0 = all F32, 1 = F16, 7 = Q8_0), or -1.
+int gguf_file_type(const char * path) {
+    gguf_init_params gp{};
+    gp.no_alloc      = true;
+    gp.ctx           = nullptr;
+    gguf_context * g = gguf_init_from_file(path, gp);
+    if (g == nullptr) {
+        return -1;
+    }
+    const int64_t key = gguf_find_key(g, "general.file_type");
+    const int     ft =
+        key >= 0 && gguf_get_kv_type(g, key) == GGUF_TYPE_UINT32 ? static_cast<int>(gguf_get_val_u32(g, key)) : -1;
+    gguf_free(g);
+    return ft;
+}
+
 // The GPU backends run the stock-op graph, which no other test reaches.
 // Their matmuls are not bit-exact F32 (Vulkan on an AMD iGPU lands ~1e-2 off
 // the CPU logits, Metal ~7e-3), so this bounds the drift rather than
 // demanding parity. F16 / Q8_0 files drift further because the CPU and GPU
-// round F16 operands differently (Metal: up to 0.057 on these clips), so one
-// bound of 0.1 covers F32, F16 and Q8_0.
+// round F16 operands differently (Metal: up to 0.057 on these clips), so they
+// get twice that.
 void check_gpu_matches_cpu(const char * path, transcribe_model * cpu_model) {
-    const float                      bound  = 0.1f;
+    const float                      bound  = gguf_file_type(path) == 0 ? 0.05f : 0.1f;
     const transcribe_backend_request gpus[] = { TRANSCRIBE_BACKEND_VULKAN, TRANSCRIBE_BACKEND_METAL };
     transcribe_model *               gm     = nullptr;
     for (const transcribe_backend_request b : gpus) {

From 31f2a1c508ff0f17500bfbad2daed866ac574fbb Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 20:51:32 +0800
Subject: [PATCH 19/38] mel: skip zero filterbank weights in the scalar mel
 matmul

Each mel row of the filterbank is non-zero only over a narrow band of
FFT bins. The constructor now records that band per row ([lo, hi), lo
rounded down to a multiple of 4) and the two scalar matmul loops (the
fused non-pow2 path, and the pow2 path without BLAS) run over it only.
The 4-wide groups keep their alignment and every skipped term is an
exact zero, so the output is bit-identical: CPU dumps for canary,
canary_qwen, cohere, granite, moss, qwen3_asr, sortformer, voxtral,
whisper and ecapa_tdnn match byte for byte.
---
 src/transcribe-mel.cpp | 33 +++++++++++++++++++++++++++------
 src/transcribe-mel.h   |  5 +++++
 2 files changed, 32 insertions(+), 6 deletions(-)

diff --git a/src/transcribe-mel.cpp b/src/transcribe-mel.cpp
index f78a9af52..af4e23cae 100644
--- a/src/transcribe-mel.cpp
+++ b/src/transcribe-mel.cpp
@@ -315,6 +315,25 @@ MelFrontend::MelFrontend(const MelConfig & cfg) : cfg_(cfg) {
                                     static_cast<double>(cfg.f_max), mel_fb_);
     }
 
+    const int n_rows = n_freq_ > 0 ? static_cast<int>(mel_fb_.size() / static_cast<size_t>(n_freq_)) : 0;
+    fb_lo_.assign(n_rows, 0);
+    fb_hi_.assign(n_rows, 0);
+    for (int m = 0; m < n_rows; ++m) {
+        const float * row = mel_fb_.data() + static_cast<size_t>(m) * n_freq_;
+        int           lo  = n_freq_;
+        int           hi  = 0;
+        for (int k = 0; k < n_freq_; ++k) {
+            if (row[k] != 0.0f) {
+                lo = std::min(lo, k);
+                hi = k + 1;
+            }
+        }
+        if (lo < hi) {
+            fb_lo_[m] = lo - lo % 4;
+            fb_hi_[m] = hi;
+        }
+    }
+
     // Sin/cos LUT for the mixed-radix FFT. Only the non-pow2 path
     // consumes it; pow2 sizes go through fft_radix2 (Linux) or vDSP
     // (Apple), both of which carry their own twiddle factors.
@@ -537,15 +556,16 @@ transcribe_status MelFrontend::compute(const float *        pcm,
                 }
                 for (int m = 0; m < n_mels; ++m) {
                     const float * fb_row = mel_fb_.data() + static_cast<size_t>(m) * n_freq;
+                    const int     k_hi   = fb_hi_[static_cast<size_t>(m)];
                     double        sum    = 0.0;
-                    int           k      = 0;
-                    for (; k < n_freq - 3; k += 4) {
+                    int           k      = fb_lo_[static_cast<size_t>(m)];
+                    for (; k < k_hi && k < n_freq - 3; k += 4) {
                         sum += static_cast<double>(fb_row[k]) * static_cast<double>(power_scratch[k]) +
                                static_cast<double>(fb_row[k + 1]) * static_cast<double>(power_scratch[k + 1]) +
                                static_cast<double>(fb_row[k + 2]) * static_cast<double>(power_scratch[k + 2]) +
                                static_cast<double>(fb_row[k + 3]) * static_cast<double>(power_scratch[k + 3]);
                     }
-                    for (; k < n_freq; ++k) {
+                    for (; k < k_hi; ++k) {
                         sum += static_cast<double>(fb_row[k]) * static_cast<double>(power_scratch[k]);
                     }
                     float result;
@@ -693,15 +713,16 @@ transcribe_status MelFrontend::compute(const float *        pcm,
             const float * pwr = power.data() + static_cast<size_t>(t) * n_freq;
             for (int m = 0; m < n_mels; ++m) {
                 const float * fb_row = mel_fb_.data() + static_cast<size_t>(m) * n_freq;
+                const int     k_hi   = fb_hi_[static_cast<size_t>(m)];
                 double        sum    = 0.0;
-                int           k      = 0;
-                for (; k < n_freq - 3; k += 4) {
+                int           k      = fb_lo_[static_cast<size_t>(m)];
+                for (; k < k_hi && k < n_freq - 3; k += 4) {
                     sum += static_cast<double>(fb_row[k]) * static_cast<double>(pwr[k]) +
                            static_cast<double>(fb_row[k + 1]) * static_cast<double>(pwr[k + 1]) +
                            static_cast<double>(fb_row[k + 2]) * static_cast<double>(pwr[k + 2]) +
                            static_cast<double>(fb_row[k + 3]) * static_cast<double>(pwr[k + 3]);
                 }
-                for (; k < n_freq; ++k) {
+                for (; k < k_hi; ++k) {
                     sum += static_cast<double>(fb_row[k]) * static_cast<double>(pwr[k]);
                 }
                 float result;
diff --git a/src/transcribe-mel.h b/src/transcribe-mel.h
index e43463938..49abaef93 100644
--- a/src/transcribe-mel.h
+++ b/src/transcribe-mel.h
@@ -189,6 +189,11 @@ class MelFrontend {
     int                 n_freq_;  // n_fft/2 + 1
     std::vector<double> window_;  // [n_fft], periodic hann zero-padded
     std::vector<float>  mel_fb_;  // [num_mels * n_freq] row-major, Slaney
+    // Per mel row: the scalar matmul runs over [fb_lo_, fb_hi_) only.
+    // fb_lo_ is rounded down to a multiple of 4 so the 4-wide groups stay
+    // aligned; the skipped weights are all zero, so the sum is unchanged.
+    std::vector<int>    fb_lo_;
+    std::vector<int>    fb_hi_;
 
     // sin/cos LUT for the mixed-radix FFT. Sized to n_fft so that every
     // recursion-level N (n_fft, n_fft/2, n_fft/4, ..., odd leaf) divides

From 2fdb5c95e242ca90023361e1623c4296d66d5eb7 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 20:51:32 +0800
Subject: [PATCH 20/38] langid: widen Q8_0 weights to F16 on several threads

The Q8_0 -> F16 dequantize at load is split across threads on Q8_0
block boundaries. Every element converts the same way on any thread,
so the widened weights are unchanged (ecapa_tdnn dumps for the Q8_0
and F16 files match byte for byte).
---
 src/arch/ecapa_tdnn/model.cpp | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/arch/ecapa_tdnn/model.cpp b/src/arch/ecapa_tdnn/model.cpp
index fa039f2da..f97b1e684 100644
--- a/src/arch/ecapa_tdnn/model.cpp
+++ b/src/arch/ecapa_tdnn/model.cpp
@@ -18,6 +18,7 @@
 #include "transcribe-path.h"
 #include "weights.h"
 
+#include <algorithm>
 #include <cstddef>
 #include <cstdint>
 #include <cstring>
@@ -169,8 +170,17 @@ transcribe_status stream_weights(const std::string & path, const gguf_context *
         }
         f32.resize(static_cast<size_t>(n));
         f16.resize(static_cast<size_t>(n));
-        q8_to_f32(staging.data(), f32.data(), n);
-        ggml_fp32_to_fp16_row(f32.data(), f16.data(), n);
+        // Split on Q8_0 block boundaries; each element converts the same way
+        // on any thread, so the result does not depend on the thread count.
+        const int64_t qk       = ggml_blck_size(GGML_TYPE_Q8_0);
+        const int64_t n_blocks = n / qk;
+        const int n_thr = static_cast<int>(std::min<int64_t>(default_n_threads(), std::max<int64_t>(1, n_blocks / 64)));
+        run_on_threads(n_thr, [&](int tid) {
+            const int64_t e0 = n_blocks * tid / n_thr * qk;
+            const int64_t ne = n_blocks * (tid + 1) / n_thr * qk - e0;
+            q8_to_f32(staging.data() + ggml_row_size(GGML_TYPE_Q8_0, e0), f32.data() + e0, ne);
+            ggml_fp32_to_fp16_row(f32.data() + e0, f16.data() + e0, ne);
+        });
         ggml_backend_tensor_set(t, f16.data(), 0, ggml_nbytes(t));
     }
     return TRANSCRIBE_OK;

From 8d00eb4a2f7457b6c1b6fb4e474f433a76a170fd Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 20:53:36 +0800
Subject: [PATCH 21/38] langid: M4 Max speed cells on ru-long at 2fdb5c95

Bench the publication profile on the M4 Max (CPU and Metal, F32/F16/Q8_0,
ru-long 10 s and 30 s), drop the old long-45s rows, and narrow the
pending note to the Ryzen cells.
---
 catalog/_benchmark_profiles.json          |  2 +-
 catalog/lang-id-voxlingua107-ecapa.json   | 24 +++++++++++------------
 docs/models/lang-id-voxlingua107-ecapa.md | 16 +++++++--------
 3 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/catalog/_benchmark_profiles.json b/catalog/_benchmark_profiles.json
index b1a5e2d43..fdee6aec4 100644
--- a/catalog/_benchmark_profiles.json
+++ b/catalog/_benchmark_profiles.json
@@ -132,7 +132,7 @@
             "cooldown_tctl_c": 55.0
           }
         ],
-        "pending": "The published cells were measured on samples/long-45s.wav, since replaced by ru-long.wav, and the Ryzen rig has none yet. Re-bench both rigs with scripts/langid/bench.py --profile, ingest, delete the long-45s rows, then remove this key to enforce the speed matrix."
+        "pending": "The Ryzen rig has no cells yet. Bench it with scripts/langid/bench.py --profile, ingest, then remove this key to enforce the speed matrix."
       }
     }
   }
diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index 5998ea859..aa7eecc20 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -55,17 +55,17 @@
     "timestamps": "none"
   },
   "speed_benchmarks": [
-    {"machine":"m4-max","backend":"cpu","quant":"F32","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":147.12,"xrt_compute":67.97,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"cpu","quant":"F32","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":450.9,"xrt_compute":66.53,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"F32","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":14.29,"xrt_compute":699.79,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"F32","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":37.11,"xrt_compute":808.41,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"cpu","quant":"F16","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":82.47,"xrt_compute":121.26,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"cpu","quant":"F16","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":251.53,"xrt_compute":119.27,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"F16","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":14.09,"xrt_compute":709.72,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"F16","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":35.65,"xrt_compute":841.51,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"cpu","quant":"Q8_0","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":74.86,"xrt_compute":133.58,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"cpu","quant":"Q8_0","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":229.53,"xrt_compute":130.7,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"long-45s-10s","sample_duration_s":10.0,"total_ms":14.22,"xrt_compute":703.23,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"long-45s-30s","sample_duration_s":30.0,"total_ms":36.23,"xrt_compute":828.04,"engine_sha":"62522202","measured_on":"2026-10-05","thermal_gated":null}
+    {"machine":"m4-max","backend":"cpu","quant":"F16","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":78.9,"xrt_compute":126.69,"wall_ms":80.0,"xrt_wall":125.03,"load_ms":10.6,"mel_ms":1.1,"encode_ms":77.8,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"F16","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":241.8,"xrt_compute":124.06,"wall_ms":243.9,"xrt_wall":123.02,"load_ms":10.6,"mel_ms":3.1,"encode_ms":238.7,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"F32","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":123.8,"xrt_compute":80.77,"wall_ms":124.8,"xrt_wall":80.14,"load_ms":21.3,"mel_ms":1.0,"encode_ms":122.8,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"F32","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":382.3,"xrt_compute":78.48,"wall_ms":384.3,"xrt_wall":78.07,"load_ms":21.3,"mel_ms":3.0,"encode_ms":379.3,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"Q8_0","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":80.4,"xrt_compute":124.38,"wall_ms":81.5,"xrt_wall":122.77,"load_ms":10.5,"mel_ms":1.1,"encode_ms":79.3,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"cpu","quant":"Q8_0","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":247.1,"xrt_compute":121.43,"wall_ms":249.1,"xrt_wall":120.42,"load_ms":10.5,"mel_ms":3.2,"encode_ms":243.9,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F16","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":8.3,"xrt_compute":1208.9,"wall_ms":10.8,"xrt_wall":926.08,"load_ms":11.8,"mel_ms":1.6,"encode_ms":6.6,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F16","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":20.4,"xrt_compute":1468.16,"wall_ms":26.4,"xrt_wall":1136.04,"load_ms":11.8,"mel_ms":3.8,"encode_ms":16.6,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F32","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":8.2,"xrt_compute":1217.01,"wall_ms":10.8,"xrt_wall":926.9,"load_ms":20.3,"mel_ms":1.3,"encode_ms":6.9,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"F32","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":21.3,"xrt_compute":1406.64,"wall_ms":27.2,"xrt_wall":1104.59,"load_ms":20.3,"mel_ms":4.1,"encode_ms":17.2,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":8.2,"xrt_compute":1212.15,"wall_ms":11.0,"xrt_wall":912.33,"load_ms":11.4,"mel_ms":1.7,"encode_ms":6.6,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":21.0,"xrt_compute":1426.25,"wall_ms":27.0,"xrt_wall":1112.49,"load_ms":11.4,"mel_ms":4.2,"encode_ms":16.8,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null}
   ]
 }
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 631da816a..c18bd600e 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -92,16 +92,16 @@ accepted as aliases).
 ### Apple M4 Max
 
 <!-- catalog:perf machine=m4-max dp_ms=1 -->
-Compute latency (mel + encode), speedup over realtime in parentheses.
+Compute latency (mel + encode), speedup over realtime in parentheses; profile `langid-publication-v1`: mean over 20 iterations after 3 warmup.
 
-| Backend | Sample               |               F32 |                F16 |               Q8_0 |
-| ------- | -------------------- | ----------------: | -----------------: | -----------------: |
-| Metal   | long-45s-10s (10.0s) | 14.3 ms (699.79×) |  14.1 ms (709.72×) |  14.2 ms (703.23×) |
-| Metal   | long-45s-30s (30.0s) | 37.1 ms (808.41×) |  35.6 ms (841.51×) |  36.2 ms (828.04×) |
-| CPU     | long-45s-10s (10.0s) | 147.1 ms (67.97×) |  82.5 ms (121.26×) |  74.9 ms (133.58×) |
-| CPU     | long-45s-30s (30.0s) | 450.9 ms (66.53×) | 251.5 ms (119.27×) | 229.5 ms (130.70×) |
+| Backend | Sample              |                F32 |                F16 |               Q8_0 |
+| ------- | ------------------- | -----------------: | -----------------: | -----------------: |
+| Metal   | ru-long-10s (10.0s) |  8.2 ms (1217.01×) |  8.3 ms (1208.90×) |  8.2 ms (1212.15×) |
+| Metal   | ru-long-30s (30.0s) | 21.3 ms (1406.64×) | 20.4 ms (1468.16×) | 21.0 ms (1426.25×) |
+| CPU     | ru-long-10s (10.0s) |  123.8 ms (80.77×) |  78.9 ms (126.69×) |  80.4 ms (124.38×) |
+| CPU     | ru-long-30s (30.0s) |  382.3 ms (78.48×) | 241.8 ms (124.06×) | 247.1 ms (121.43×) |
 
-Apple M4 Max: transcribe.cpp `62522202` on 2026-10-05.
+Apple M4 Max: transcribe.cpp `2fdb5c95` on 2026-10-07.
 <!-- /catalog -->
 
 Benchmark reproduction (`tools/transcribe-bench` is ASR-only):

From c8b83898c43bc70fba72ac101ce84588ac3e29e7 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 21:34:43 +0800
Subject: [PATCH 22/38] langid: Ryzen 4750U speed cells

---
 catalog/_benchmark_profiles.json          |  3 +--
 catalog/lang-id-voxlingua107-ecapa.json   | 14 +++++++++++++-
 docs/models/lang-id-voxlingua107-ecapa.md | 15 +++++++++++++++
 3 files changed, 29 insertions(+), 3 deletions(-)

diff --git a/catalog/_benchmark_profiles.json b/catalog/_benchmark_profiles.json
index fdee6aec4..c726d029b 100644
--- a/catalog/_benchmark_profiles.json
+++ b/catalog/_benchmark_profiles.json
@@ -131,8 +131,7 @@
             ],
             "cooldown_tctl_c": 55.0
           }
-        ],
-        "pending": "The Ryzen rig has no cells yet. Bench it with scripts/langid/bench.py --profile, ingest, then remove this key to enforce the speed matrix."
+        ]
       }
     }
   }
diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index aa7eecc20..981d1b883 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -66,6 +66,18 @@
     {"machine":"m4-max","backend":"metal","quant":"F32","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":8.2,"xrt_compute":1217.01,"wall_ms":10.8,"xrt_wall":926.9,"load_ms":20.3,"mel_ms":1.3,"encode_ms":6.9,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
     {"machine":"m4-max","backend":"metal","quant":"F32","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":21.3,"xrt_compute":1406.64,"wall_ms":27.2,"xrt_wall":1104.59,"load_ms":20.3,"mel_ms":4.1,"encode_ms":17.2,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
     {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":8.2,"xrt_compute":1212.15,"wall_ms":11.0,"xrt_wall":912.33,"load_ms":11.4,"mel_ms":1.7,"encode_ms":6.6,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
-    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":21.0,"xrt_compute":1426.25,"wall_ms":27.0,"xrt_wall":1112.49,"load_ms":11.4,"mel_ms":4.2,"encode_ms":16.8,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null}
+    {"machine":"m4-max","backend":"metal","quant":"Q8_0","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":21.0,"xrt_compute":1426.25,"wall_ms":27.0,"xrt_wall":1112.49,"load_ms":11.4,"mel_ms":4.2,"encode_ms":16.8,"decode_ms":null,"engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"cpu","quant":"F16","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":211.4,"xrt_compute":47.3,"wall_ms":218.9,"xrt_wall":45.68,"load_ms":30.2,"mel_ms":7.3,"encode_ms":204.2,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"cpu","quant":"F16","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":818.3,"xrt_compute":36.66,"wall_ms":840.9,"xrt_wall":35.68,"load_ms":30.2,"mel_ms":22.0,"encode_ms":796.3,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"cpu","quant":"F32","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":191.0,"xrt_compute":52.37,"wall_ms":198.3,"xrt_wall":50.43,"load_ms":68.7,"mel_ms":7.0,"encode_ms":183.9,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"cpu","quant":"F32","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":730.1,"xrt_compute":41.09,"wall_ms":751.1,"xrt_wall":39.94,"load_ms":68.7,"mel_ms":22.5,"encode_ms":707.6,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"cpu","quant":"Q8_0","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":213.2,"xrt_compute":46.9,"wall_ms":221.2,"xrt_wall":45.22,"load_ms":58.5,"mel_ms":7.1,"encode_ms":206.1,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"cpu","quant":"Q8_0","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":801.7,"xrt_compute":37.42,"wall_ms":823.9,"xrt_wall":36.41,"load_ms":58.5,"mel_ms":20.9,"encode_ms":780.8,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"vulkan","quant":"F16","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":102.6,"xrt_compute":97.47,"wall_ms":107.4,"xrt_wall":93.11,"load_ms":40.7,"mel_ms":15.4,"encode_ms":87.2,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"vulkan","quant":"F16","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":283.6,"xrt_compute":105.79,"wall_ms":288.3,"xrt_wall":104.04,"load_ms":40.7,"mel_ms":30.0,"encode_ms":253.6,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"vulkan","quant":"F32","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":107.5,"xrt_compute":93.05,"wall_ms":112.4,"xrt_wall":88.95,"load_ms":48.3,"mel_ms":15.1,"encode_ms":92.4,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"vulkan","quant":"F32","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":301.9,"xrt_compute":99.38,"wall_ms":306.7,"xrt_wall":97.83,"load_ms":48.3,"mel_ms":30.9,"encode_ms":271.0,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"vulkan","quant":"Q8_0","sample":"ru-long-10s","sample_duration_s":10.0,"total_ms":102.5,"xrt_compute":97.56,"wall_ms":107.2,"xrt_wall":93.25,"load_ms":63.5,"mel_ms":15.4,"encode_ms":87.1,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null},
+    {"machine":"ryzen-4750u","backend":"vulkan","quant":"Q8_0","sample":"ru-long-30s","sample_duration_s":30.0,"total_ms":283.4,"xrt_compute":105.85,"wall_ms":288.3,"xrt_wall":104.06,"load_ms":63.5,"mel_ms":29.7,"encode_ms":253.8,"decode_ms":null,"engine_sha":"8d00eb4a","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","thermal_gated":null}
   ]
 }
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index c18bd600e..528388250 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -104,6 +104,21 @@ Compute latency (mel + encode), speedup over realtime in parentheses; profile `l
 Apple M4 Max: transcribe.cpp `2fdb5c95` on 2026-10-07.
 <!-- /catalog -->
 
+### AMD Ryzen 7 PRO 4750U
+
+<!-- catalog:perf machine=ryzen-4750u dp_ms=1 -->
+Compute latency (mel + encode), speedup over realtime in parentheses; profile `langid-publication-v1`: mean over 20 iterations after 3 warmup.
+
+| Backend | Sample              |               F32 |                F16 |               Q8_0 |
+| ------- | ------------------- | ----------------: | -----------------: | -----------------: |
+| Vulkan  | ru-long-10s (10.0s) | 107.5 ms (93.05×) |  102.6 ms (97.47×) |  102.5 ms (97.56×) |
+| Vulkan  | ru-long-30s (30.0s) | 301.9 ms (99.38×) | 283.6 ms (105.79×) | 283.4 ms (105.85×) |
+| CPU     | ru-long-10s (10.0s) | 191.0 ms (52.37×) |  211.4 ms (47.30×) |  213.2 ms (46.90×) |
+| CPU     | ru-long-30s (30.0s) | 730.1 ms (41.09×) |  818.3 ms (36.66×) |  801.7 ms (37.42×) |
+
+AMD Ryzen 7 PRO 4750U (Radeon RADV RENOIR): transcribe.cpp `8d00eb4a` on 2026-10-07.
+<!-- /catalog -->
+
 Benchmark reproduction (`tools/transcribe-bench` is ASR-only):
 
 ```bash

From 8ae733e2d3f79e872946cc676c717a4f1061225e Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 21:37:41 +0800
Subject: [PATCH 23/38] langid: re-derive two ecapa_tdnn tolerances to cover
 x86

The tolerances were measured on arm64 only. On x86-64 (AVX2+FMA, Ryzen 7
PRO 4750U) the CPU graph drifts about twice as far on the wide fp32
contractions, from FMA reduction order: enc.mfa.out 2.3806e-04 and
enc.blk.2.out 2.0504e-04 (fleurs-zh). Applying the file's recipe
(1.5 * observed) over both hosts widens those two max_abs entries to
3.571e-04 and 3.076e-04. Every other value is unchanged; mean_abs stays
under 7% of budget and cls.logits_raw under 1%. validate.py all passes
on the M4 Max; the x86 measurements fall inside the new budgets.
---
 tests/tolerances/ecapa_tdnn.json | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/tests/tolerances/ecapa_tdnn.json b/tests/tolerances/ecapa_tdnn.json
index 33c18ce4b..20979c704 100644
--- a/tests/tolerances/ecapa_tdnn.json
+++ b/tests/tolerances/ecapa_tdnn.json
@@ -17,7 +17,8 @@
     "            normalize=sentence_mean; measured on its family-local predecessor,",
     "            which ran the same fp32 mixed-radix FFT),",
     "            not a reference feature dump -- fe.mel is measured, not injected",
-    "  host      macOS / arm64 (Apple Silicon), ggml CPU backend",
+    "  host      macOS / arm64 (Apple Silicon) and Linux / x86-64 (AVX2+FMA,",
+    "            Ryzen 7 PRO 4750U), ggml CPU backend",
     "  reference speechbrain 1.1.1 on torch 2.13.0, fp32, CPU, 1 thread,",
     "            checkpoint speechbrain/lang-id-voxlingua107-ecapa @",
     "            0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9",
@@ -33,10 +34,14 @@
     "The Stage-2 provisional budgets were max(1e-4*p99_abs, 1e-6) and",
     "max(1e-5*rms, 1e-6) from the reference dumps alone.",
     "",
-    "WIDENED ENTRIES: none. Measured drift came in below the provisional",
-    "budget for every tensor on both metrics, so 1.5*observed never exceeded",
-    "it and every value below is the unchanged Stage-2 number. Nothing was",
-    "loosened to make a case pass.",
+    "WIDENED ENTRIES (max_abs only, from the x86 host; every other value is",
+    "the unchanged Stage-2 number):",
+    "  enc.mfa.out    observed 2.3806e-04 (fleurs-zh) -> 3.571e-04",
+    "  enc.blk.2.out  observed 2.0504e-04 (fleurs-zh) -> 3.076e-04",
+    "Cause: fp32 FMA reduction order over the 3072- and 1024-wide",
+    "contractions on x86 AVX2, about 2x the arm64 drift on these tensors.",
+    "mean_abs on both stays under 7% of budget, and the drift contracts",
+    "again downstream (enc.emb, cls.logits_raw under 1%).",
     "",
     "Dominant drift source: fp32 summation ORDER, not algorithm. Two",
     "contributors, in order of size:",
@@ -54,11 +59,8 @@
     "1/T statistics CONTRACT it again (enc.asp.out 9.5e-06), which is why the",
     "downstream vectors sit two orders of magnitude inside their budgets.",
     "",
-    "Tightest entries, i.e. where a slower-drifting host would first complain:",
-    "  enc.blk.0.out  1.5*observed is 99% of the budget",
-    "  enc.mfa.out    1.5*observed is 94% of the budget",
-    "Both are max_abs on wide [T, C] activations; a re-measure on different",
-    "CPU SIMD width may need a fresh Stage-4 pass rather than a hand edit.",
+    "Tightest entry: enc.blk.0.out, where 1.5*observed (arm64) is 99% of",
+    "the budget; max_abs on a wide [T, C] activation.",
     "",
     "cls.logits_raw is the gate."
   ],
@@ -87,7 +89,7 @@
     "mean_abs": 2.952e-06
   },
   "enc.blk.2.out": {
-    "max_abs": 0.0002627,
+    "max_abs": 0.0003076,
     "mean_abs": 5.964e-06
   },
   "enc.blk.3.out": {
@@ -95,7 +97,7 @@
     "mean_abs": 1.412e-05
   },
   "enc.mfa.out": {
-    "max_abs": 0.0001262,
+    "max_abs": 0.0003571,
     "mean_abs": 4.163e-06
   },
   "enc.asp.attn_logits": {

From 0ce2736633ed5cbe3063755763351d9b093217cc Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Wed, 7 Oct 2026 22:13:26 +0800
Subject: [PATCH 24/38] langid: FLEURS accuracy and agreement at 2fdb5c95

Re-run the C++ sweep (F32, F16, Q8_0; crops 3/5/10/full) on the current
CPU path and ingest it. F32 agrees with SpeechBrain on 12000/12000;
F16 11986, Q8_0 11705. The HF card's validation stamp moves to the same
commit.
---
 catalog/lang-id-voxlingua107-ecapa.json          |  6 +++---
 docs/models/lang-id-voxlingua107-ecapa.md        | 14 +++++++-------
 scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml |  4 ++--
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index 981d1b883..aed1a176f 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -42,9 +42,9 @@
     {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693696}
   ],
   "accuracy_benchmarks": [
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"16d46bc2","publication_profile":"langid-publication-v1","measured_on":"2026-10-06","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.4e-05}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"16d46bc2","publication_profile":"langid-publication-v1","measured_on":"2026-10-06","agreement":{"n_agree":11984,"n":12000,"max_abs_logit_delta":0.094}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.27,"ci95":[85.13,87.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"16d46bc2","publication_profile":"langid-publication-v1","measured_on":"2026-10-06","agreement":{"n_agree":11704,"n":12000,"max_abs_logit_delta":2.9}}
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.3e-05}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":11986,"n":12000,"max_abs_logit_delta":0.095}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.17,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":11705,"n":12000,"max_abs_logit_delta":2.9}}
   ],
   "headline_benchmark": {
     "dataset": "fleurs",
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 528388250..86e5063bd 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -23,7 +23,7 @@ See SpeechBrain's [model card](https://huggingface.co/speechbrain/lang-id-voxlin
 for training data and the label list.
 
 <!-- catalog:pin -->
-Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa/commit/0253049), pinned 2026-10-05. Validated against the SpeechBrain reference at transcribe.cpp commit [`62522202`](https://github.com/handy-computer/transcribe.cpp/tree/62522202) on 2026-10-05.
+Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa/commit/0253049), pinned 2026-10-05. Validated against the SpeechBrain reference at transcribe.cpp commit [`2fdb5c95`](https://github.com/handy-computer/transcribe.cpp/tree/2fdb5c95) on 2026-10-07.
 <!-- /catalog -->
 
 ## Download
@@ -33,11 +33,11 @@ Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface
 | --- | --- | ---: | ---: |
 | F32          | [lang-id-voxlingua107-ecapa-F32.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F32.gguf) | 85 MB | 85.23% |
 | F16          | [lang-id-voxlingua107-ecapa-F16.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-F16.gguf) | 45 MB | 85.17% |
-| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 86.27% |
+| Q8_0         | [lang-id-voxlingua107-ecapa-Q8_0.gguf](https://huggingface.co/handy-computer/lang-id-voxlingua107-ecapa-gguf/resolve/main/lang-id-voxlingua107-ecapa-Q8_0.gguf) | 27 MB | 86.30% |
 <!-- /catalog -->
 
 <!-- catalog:recipe -->
-Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `16d46bc2` on 2026-10-06.
+Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `2fdb5c95` on 2026-10-07.
 <!-- /catalog -->
 
 <!-- catalog:prose field=wer.notes -->
@@ -52,11 +52,11 @@ transcribe.cpp model page.
 <!-- catalog:agreement -->
 | GGUF | Top-1 accuracy (95% CI) | Top-1 agreement with SpeechBrain | Max abs logit difference |
 | --- | ---: | ---: | ---: |
-| F32  | 85.23% (84.07-86.47) | 12000 / 12000 | 7.4e-05 |
-| F16  | 85.17% (84.00-86.43) | 11984 / 12000 | 0.094 |
-| Q8_0 | 86.27% (85.13-87.47) | 11704 / 12000 | 2.9 |
+| F32  | 85.23% (84.07-86.47) | 12000 / 12000 | 7.3e-05 |
+| F16  | 85.17% (84.00-86.43) | 11986 / 12000 | 0.095 |
+| Q8_0 | 86.30% (85.17-87.50) | 11705 / 12000 | 2.9 |
 
-Measured at transcribe.cpp `16d46bc2` on 2026-10-06.
+Measured at transcribe.cpp `2fdb5c95` on 2026-10-07.
 <!-- /catalog -->
 
 Agreement counts the scored sweep's top-1 decisions that match the
diff --git a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
index e09782122..de55d0a56 100644
--- a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
+++ b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
@@ -9,8 +9,8 @@ pin_date: 2026-10-05
 # older HF revisions carry whatever value was current at their upload time.
 validation:
   reference: SpeechBrain
-  commit: 62522202
-  date: 2026-10-05
+  commit: 2fdb5c95
+  date: 2026-10-07
 
 pipeline_tag: audio-classification
 tags:

From 31e0c1eb3f7e35a4c9fc532e42369d353869714a Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 08:31:58 +0800
Subject: [PATCH 25/38] rust: ErrorKind::InputTooShort for the LANGID status

main's serde work (#198) made Error::kind() exhaustive; the langid
branch's Error::InputTooShort had no kind, so the merge did not compile.
Add the kind and cover it in the serde round-trip test.
---
 bindings/rust/transcribe-cpp/src/error.rs   | 2 ++
 bindings/rust/transcribe-cpp/tests/serde.rs | 1 +
 2 files changed, 3 insertions(+)

diff --git a/bindings/rust/transcribe-cpp/src/error.rs b/bindings/rust/transcribe-cpp/src/error.rs
index ea81af914..533bdafbd 100644
--- a/bindings/rust/transcribe-cpp/src/error.rs
+++ b/bindings/rust/transcribe-cpp/src/error.rs
@@ -128,6 +128,7 @@ pub enum ErrorKind {
     OutputTruncated,
     OutputRepetition,
     UnsupportedRole,
+    InputTooShort,
     VersionMismatch,
     Nul,
     Busy,
@@ -235,6 +236,7 @@ impl Error {
             Error::OutputTruncated { .. } => ErrorKind::OutputTruncated,
             Error::OutputRepetition { .. } => ErrorKind::OutputRepetition,
             Error::UnsupportedRole(_) => ErrorKind::UnsupportedRole,
+            Error::InputTooShort(_) => ErrorKind::InputTooShort,
             Error::VersionMismatch(_) => ErrorKind::VersionMismatch,
             Error::Nul(_) => ErrorKind::Nul,
             Error::Busy(_) => ErrorKind::Busy,
diff --git a/bindings/rust/transcribe-cpp/tests/serde.rs b/bindings/rust/transcribe-cpp/tests/serde.rs
index a9592bc6e..fead72a04 100644
--- a/bindings/rust/transcribe-cpp/tests/serde.rs
+++ b/bindings/rust/transcribe-cpp/tests/serde.rs
@@ -215,6 +215,7 @@ mod errors {
             ErrorKind::OutputTruncated,
             ErrorKind::OutputRepetition,
             ErrorKind::UnsupportedRole,
+            ErrorKind::InputTooShort,
             ErrorKind::VersionMismatch,
             ErrorKind::Nul,
             ErrorKind::Busy,

From 2e3ba7dc310e87a83faf18489da7b9a9394a90a9 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 08:31:58 +0800
Subject: [PATCH 26/38] langid: drop
 transcribe_langid_candidate::p_unrestricted

It equals p * allowed_mass, so the result already carries it. Removed
from the C struct, the dispatcher, the CLI output, the four bindings
(regenerated; ABI hash d544d70a2b5acf50), tests and docs. logit stays:
the accuracy tooling and the GPU/CPU checks read it.
---
 bindings/python/src/transcribe_cpp/__init__.py     |  6 ++----
 bindings/python/src/transcribe_cpp/_generated.py   |  6 +++---
 bindings/python/tests/test_langid.py               |  1 -
 bindings/rust/sys/src/transcribe_sys.rs            | 11 ++++-------
 bindings/rust/transcribe-cpp/src/langid.rs         |  3 ---
 bindings/swift/Sources/TranscribeCpp/ABIHash.swift |  2 +-
 bindings/swift/Sources/TranscribeCpp/LangId.swift  |  4 +---
 bindings/typescript/src/_generated.ts              |  6 +++---
 bindings/typescript/src/index.ts                   |  1 -
 bindings/typescript/src/types.ts                   |  2 --
 docs/bindings.md                                   |  2 +-
 docs/langid.md                                     |  8 ++++----
 examples/cli/langid.cpp                            |  7 +++----
 include/transcribe.abihash                         |  2 +-
 include/transcribe/langid.h                        |  8 ++++----
 src/transcribe-langid.cpp                          |  3 +--
 src/transcribe-langid.h                            |  7 +++----
 tests/cli_langid_smoke.cmake                       |  2 +-
 tests/ecapa_tdnn_smoke.cpp                         |  1 -
 tests/langid_dispatch_unit.cpp                     |  3 +--
 20 files changed, 33 insertions(+), 52 deletions(-)

diff --git a/bindings/python/src/transcribe_cpp/__init__.py b/bindings/python/src/transcribe_cpp/__init__.py
index afe422d9f..cb933eaa2 100644
--- a/bindings/python/src/transcribe_cpp/__init__.py
+++ b/bindings/python/src/transcribe_cpp/__init__.py
@@ -590,14 +590,12 @@ class LangIdInfo:
 @dataclass(frozen=True)
 class LangIdCandidate:
     """One ranked label. ``code`` is the model's own label ("en", "iw");
-    ``p`` is renormalized over the allowed set, ``p_unrestricted`` is over
-    every label."""
+    ``p`` is renormalized over the allowed set."""
 
     index: int
     code: str
     name: str
     p: float
-    p_unrestricted: float
     logit: float
 
 
@@ -2039,7 +2037,7 @@ def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
                        "transcribe_langid_get_candidate")
                 rows.append(LangIdCandidate(
                     index=c.index, code=c.code.decode("utf-8"), name=c.name.decode("utf-8"),
-                    p=c.p, p_unrestricted=c.p_unrestricted, logit=c.logit))
+                    p=c.p, logit=c.logit))
             return LangIdResult(candidates=tuple(rows), n_allowed=res.n_allowed,
                                 allowed_mass=res.allowed_mass, audio_ms=res.audio_ms)
 
diff --git a/bindings/python/src/transcribe_cpp/_generated.py b/bindings/python/src/transcribe_cpp/_generated.py
index 616f1315c..2354b16a5 100644
--- a/bindings/python/src/transcribe_cpp/_generated.py
+++ b/bindings/python/src/transcribe_cpp/_generated.py
@@ -13,7 +13,7 @@
 # Stable digest of the ABI surface below (structs, enums, macros, layout,
 # prototypes). A native provider package echoes this back so the API
 # package can reject an ABI-mismatched provider before dlopen.
-PUBLIC_HEADER_HASH = "318eba8c4b2e4387"
+PUBLIC_HEADER_HASH = "d544d70a2b5acf50"
 
 # === enum constants ===
 TRANSCRIBE_OK = 0
@@ -232,7 +232,7 @@ class transcribe_whisper_chunk_trace(_c.Structure):
 transcribe_langid_session_params._fields_ = [("struct_size", _c.c_uint64), ("n_threads", _c.c_int32), ("max_audio_ms", _c.c_int32)]
 transcribe_langid_params._fields_ = [("struct_size", _c.c_uint64), ("allowed", _c.POINTER(_c.c_char_p)), ("n_allowed", _c.c_int32), ("top_k", _c.c_int32)]
 transcribe_langid_result._fields_ = [("struct_size", _c.c_uint64), ("n_candidates", _c.c_int32), ("n_allowed", _c.c_int32), ("allowed_mass", _c.c_float), ("audio_ms", _c.c_int64)]
-transcribe_langid_candidate._fields_ = [("struct_size", _c.c_uint64), ("index", _c.c_int32), ("code", _c.c_char_p), ("name", _c.c_char_p), ("p", _c.c_float), ("p_unrestricted", _c.c_float), ("logit", _c.c_float)]
+transcribe_langid_candidate._fields_ = [("struct_size", _c.c_uint64), ("index", _c.c_int32), ("code", _c.c_char_p), ("name", _c.c_char_p), ("p", _c.c_float), ("logit", _c.c_float)]
 transcribe_moonshine_streaming_stream_ext._fields_ = [("ext", transcribe_ext), ("min_decode_interval_ms", _c.c_int32)]
 transcribe_parakeet_stream_ext._fields_ = [("ext", transcribe_ext), ("att_context_right", _c.c_int32)]
 transcribe_parakeet_buffered_stream_ext._fields_ = [("ext", transcribe_ext), ("left_ms", _c.c_int32), ("chunk_ms", _c.c_int32), ("right_ms", _c.c_int32)]
@@ -295,7 +295,7 @@ class transcribe_whisper_chunk_trace(_c.Structure):
     'transcribe_langid_session_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_threads': 8, 'max_audio_ms': 12}},
     'transcribe_langid_params': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'allowed': 8, 'n_allowed': 16, 'top_k': 20}},
     'transcribe_langid_result': {'size': 32, 'align': 8, 'offsets': {'struct_size': 0, 'n_candidates': 8, 'n_allowed': 12, 'allowed_mass': 16, 'audio_ms': 24}},
-    'transcribe_langid_candidate': {'size': 48, 'align': 8, 'offsets': {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'p_unrestricted': 36, 'logit': 40}},
+    'transcribe_langid_candidate': {'size': 40, 'align': 8, 'offsets': {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'logit': 36}},
     'transcribe_moonshine_streaming_stream_ext': {'size': 24, 'align': 8, 'offsets': {'ext': 0, 'min_decode_interval_ms': 16}},
     'transcribe_parakeet_stream_ext': {'size': 24, 'align': 8, 'offsets': {'ext': 0, 'att_context_right': 16}},
     'transcribe_parakeet_buffered_stream_ext': {'size': 32, 'align': 8, 'offsets': {'ext': 0, 'left_ms': 16, 'chunk_ms': 20, 'right_ms': 24}},
diff --git a/bindings/python/tests/test_langid.py b/bindings/python/tests/test_langid.py
index cf1f1983a..ec6e19904 100644
--- a/bindings/python/tests/test_langid.py
+++ b/bindings/python/tests/test_langid.py
@@ -62,7 +62,6 @@ def test_toy_run_contract(langid_toy_model_path, noise_1s):
             assert math.isclose(sum(c.p for c in r.candidates), 1.0, rel_tol=1e-5)
             ps = [c.p for c in r.candidates]
             assert ps == sorted(ps, reverse=True)
-            assert all(c.p == c.p_unrestricted for c in r.candidates)
 
             restricted = lid.run(noise_1s, allowed=["bb", "dd"])
             assert {c.code for c in restricted.candidates} == {"bb", "dd"}
diff --git a/bindings/rust/sys/src/transcribe_sys.rs b/bindings/rust/sys/src/transcribe_sys.rs
index d1e592f60..778e5b4b5 100644
--- a/bindings/rust/sys/src/transcribe_sys.rs
+++ b/bindings/rust/sys/src/transcribe_sys.rs
@@ -1,11 +1,11 @@
 // @generated by `cargo xtask bindgen` from include/transcribe/extensions.h
 // DO NOT EDIT BY HAND. Regenerate: `cargo xtask bindgen`.
-// Pinned to include/transcribe.abihash = 318eba8c4b2e4387
+// Pinned to include/transcribe.abihash = d544d70a2b5acf50
 
 /// The public-ABI digest these bindings were generated against
 /// (sha256/16 over the normalized FFI surface). The load-time version
 /// gate and the CI drift check both anchor on this value.
-pub const PUBLIC_HEADER_HASH: &str = "318eba8c4b2e4387";
+pub const PUBLIC_HEADER_HASH: &str = "d544d70a2b5acf50";
 
 /* automatically generated by rust-bindgen 0.72.1 */
 
@@ -1391,13 +1391,12 @@ pub struct transcribe_langid_candidate {
     pub code: *const ::std::os::raw::c_char,
     pub name: *const ::std::os::raw::c_char,
     pub p: f32,
-    pub p_unrestricted: f32,
     pub logit: f32,
 }
 #[allow(clippy::unnecessary_operation, clippy::identity_op)]
 const _: () = {
     ["Size of transcribe_langid_candidate"]
-        [::std::mem::size_of::<transcribe_langid_candidate>() - 48usize];
+        [::std::mem::size_of::<transcribe_langid_candidate>() - 40usize];
     ["Alignment of transcribe_langid_candidate"]
         [::std::mem::align_of::<transcribe_langid_candidate>() - 8usize];
     ["Offset of field: transcribe_langid_candidate::struct_size"]
@@ -1410,10 +1409,8 @@ const _: () = {
         [::std::mem::offset_of!(transcribe_langid_candidate, name) - 24usize];
     ["Offset of field: transcribe_langid_candidate::p"]
         [::std::mem::offset_of!(transcribe_langid_candidate, p) - 32usize];
-    ["Offset of field: transcribe_langid_candidate::p_unrestricted"]
-        [::std::mem::offset_of!(transcribe_langid_candidate, p_unrestricted) - 36usize];
     ["Offset of field: transcribe_langid_candidate::logit"]
-        [::std::mem::offset_of!(transcribe_langid_candidate, logit) - 40usize];
+        [::std::mem::offset_of!(transcribe_langid_candidate, logit) - 36usize];
 };
 unsafe extern "C" {
     pub fn transcribe_langid_info_init(out: *mut transcribe_langid_info);
diff --git a/bindings/rust/transcribe-cpp/src/langid.rs b/bindings/rust/transcribe-cpp/src/langid.rs
index 882413cde..f66d46c17 100644
--- a/bindings/rust/transcribe-cpp/src/langid.rs
+++ b/bindings/rust/transcribe-cpp/src/langid.rs
@@ -70,8 +70,6 @@ pub struct LangIdCandidate {
     pub name: String,
     /// Softmax renormalized over the allowed set.
     pub p: f32,
-    /// Softmax over every label.
-    pub p_unrestricted: f32,
     pub logit: f32,
 }
 
@@ -220,7 +218,6 @@ impl LangIdSession {
                             code: owned_str(raw.code),
                             name: owned_str(raw.name),
                             p: raw.p,
-                            p_unrestricted: raw.p_unrestricted,
                             logit: raw.logit,
                         }
                     })
diff --git a/bindings/swift/Sources/TranscribeCpp/ABIHash.swift b/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
index 1f92e5a25..c5f9e2e00 100644
--- a/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
+++ b/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
@@ -13,7 +13,7 @@ import CTranscribe
 extension Transcribe {
     /// sha256/16 of the normalized public FFI surface, pinned to the value in
     /// include/transcribe.abihash at the time this binding was last reviewed.
-    public static let pinnedHeaderHash = "318eba8c4b2e4387"
+    public static let pinnedHeaderHash = "d544d70a2b5acf50"
 
     /// The public-ABI digest this binding was reviewed against (16 hex chars).
     public static func headerHash() -> String { pinnedHeaderHash }
diff --git a/bindings/swift/Sources/TranscribeCpp/LangId.swift b/bindings/swift/Sources/TranscribeCpp/LangId.swift
index e35af1262..33d770b67 100644
--- a/bindings/swift/Sources/TranscribeCpp/LangId.swift
+++ b/bindings/swift/Sources/TranscribeCpp/LangId.swift
@@ -31,8 +31,6 @@ public struct LangIdCandidate: Sendable, Equatable {
     public let name: String
     /// Softmax renormalized over the allowed set.
     public let p: Float
-    /// Softmax over every label.
-    public let pUnrestricted: Float
     public let logit: Float
 }
 
@@ -151,7 +149,7 @@ public final class LangIdSession {
                 return LangIdCandidate(
                     index: c.index, code: c.code.map { String(cString: $0) } ?? "",
                     name: c.name.map { String(cString: $0) } ?? "",
-                    p: c.p, pUnrestricted: c.p_unrestricted, logit: c.logit)
+                    p: c.p, logit: c.logit)
             }
             return LangIdResult(candidates: candidates, nAllowed: res.n_allowed,
                                 allowedMass: res.allowed_mass, audioMs: res.audio_ms)
diff --git a/bindings/typescript/src/_generated.ts b/bindings/typescript/src/_generated.ts
index 56777d031..dae860c8c 100644
--- a/bindings/typescript/src/_generated.ts
+++ b/bindings/typescript/src/_generated.ts
@@ -11,7 +11,7 @@
 // Stable digest of the ABI surface (structs, enums, macros, layout,
 // prototypes), computed by the Python oracle and pinned here so a header
 // ABI change turns this binding's drift check red for conscious review.
-export const PUBLIC_HEADER_HASH = "318eba8c4b2e4387";
+export const PUBLIC_HEADER_HASH = "d544d70a2b5acf50";
 
 // === enum constants ===
 export const TRANSCRIBE_OK = 0;
@@ -168,7 +168,7 @@ export const STRUCT_LAYOUT: Record<string, StructLayout> = {
   'transcribe_langid_session_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_threads': 8, 'max_audio_ms': 12} },
   'transcribe_langid_params': { size: 24, align: 8, offsets: {'struct_size': 0, 'allowed': 8, 'n_allowed': 16, 'top_k': 20} },
   'transcribe_langid_result': { size: 32, align: 8, offsets: {'struct_size': 0, 'n_candidates': 8, 'n_allowed': 12, 'allowed_mass': 16, 'audio_ms': 24} },
-  'transcribe_langid_candidate': { size: 48, align: 8, offsets: {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'p_unrestricted': 36, 'logit': 40} },
+  'transcribe_langid_candidate': { size: 40, align: 8, offsets: {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'logit': 36} },
   'transcribe_moonshine_streaming_stream_ext': { size: 24, align: 8, offsets: {'ext': 0, 'min_decode_interval_ms': 16} },
   'transcribe_parakeet_stream_ext': { size: 24, align: 8, offsets: {'ext': 0, 'att_context_right': 16} },
   'transcribe_parakeet_buffered_stream_ext': { size: 32, align: 8, offsets: {'ext': 0, 'left_ms': 16, 'chunk_ms': 20, 'right_ms': 24} },
@@ -231,7 +231,7 @@ export function defineTypes(koffi: any): Record<string, any> {
   T['transcribe_langid_session_params'] = koffi.struct({ struct_size: 'uint64_t', n_threads: 'int32_t', max_audio_ms: 'int32_t' });
   T['transcribe_langid_params'] = koffi.struct({ struct_size: 'uint64_t', allowed: 'void *', n_allowed: 'int32_t', top_k: 'int32_t' });
   T['transcribe_langid_result'] = koffi.struct({ struct_size: 'uint64_t', n_candidates: 'int32_t', n_allowed: 'int32_t', allowed_mass: 'float', audio_ms: 'int64_t' });
-  T['transcribe_langid_candidate'] = koffi.struct({ struct_size: 'uint64_t', index: 'int32_t', code: 'char *', name: 'char *', p: 'float', p_unrestricted: 'float', logit: 'float' });
+  T['transcribe_langid_candidate'] = koffi.struct({ struct_size: 'uint64_t', index: 'int32_t', code: 'char *', name: 'char *', p: 'float', logit: 'float' });
   T['transcribe_moonshine_streaming_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], min_decode_interval_ms: 'int32_t' });
   T['transcribe_parakeet_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], att_context_right: 'int32_t' });
   T['transcribe_parakeet_buffered_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], left_ms: 'int32_t', chunk_ms: 'int32_t', right_ms: 'int32_t' });
diff --git a/bindings/typescript/src/index.ts b/bindings/typescript/src/index.ts
index 6a0e03976..a0332cab8 100644
--- a/bindings/typescript/src/index.ts
+++ b/bindings/typescript/src/index.ts
@@ -1508,7 +1508,6 @@ export class LangIdSession {
           code: c.code ?? "",
           name: c.name ?? "",
           p: c.p,
-          pUnrestricted: c.p_unrestricted,
           logit: c.logit,
         });
       }
diff --git a/bindings/typescript/src/types.ts b/bindings/typescript/src/types.ts
index 0a84d1f7e..1a9368b09 100644
--- a/bindings/typescript/src/types.ts
+++ b/bindings/typescript/src/types.ts
@@ -349,8 +349,6 @@ export interface LangIdCandidate {
   name: string;
   /** Softmax renormalized over the allowed set. */
   p: number;
-  /** Softmax over every label. */
-  pUnrestricted: number;
   logit: number;
 }
 
diff --git a/docs/bindings.md b/docs/bindings.md
index 19384b97a..87431da47 100644
--- a/docs/bindings.md
+++ b/docs/bindings.md
@@ -143,7 +143,7 @@ Rust), `model.langIdSession()` (Swift) or `model.createLangIdSession()`
 `langid_info` / `langIdInfo` / `langidInfo` (sample rate, label count,
 minimum audio) and the label table (codes, names, and alias lookup).
 `run(pcm, allowed=…, top_k=…)` returns a copied-out result: candidates ranked
-by `p` (index, code, name, `p`, `p_unrestricted`, `logit`), `n_allowed`,
+by `p` (index, code, name, `p`, `logit`), `n_allowed`,
 `allowed_mass` and the scored `audio_ms`.
 
 `allowed` is the first caller-owned `const char * const *` input. Every
diff --git a/docs/langid.md b/docs/langid.md
index 62dd8f0f1..20a9d8035 100644
--- a/docs/langid.md
+++ b/docs/langid.md
@@ -38,14 +38,14 @@ From the CLI: `transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en
 
 - **`allowed`** restricts the decision to the listed labels: candidates are
   ranked by `p`, the softmax over the allowed set. The network still scores
-  every label; `p_unrestricted` is the softmax over all of them. NULL with
+  every label. NULL with
   `n_allowed == 0` means every label. A non-NULL list with `n_allowed <= 0`,
   a NULL list with `n_allowed > 0`, or a NULL element is
   `TRANSCRIBE_ERR_INVALID_ARG`; an unknown code is
   `TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE`; duplicates count once.
-- **`allowed_mass`** (`transcribe_langid_result`) is the sum of
-  `p_unrestricted` over the allowed set, 1 when unrestricted. A low value
-  means the unrestricted model puts most of its probability outside the
+- **`allowed_mass`** (`transcribe_langid_result`) is the share of the softmax
+  over every label that falls in the allowed set, 1 when unrestricted. A low
+  value means the unrestricted model puts most of its probability outside the
   allowed set.
 - **`top_k`** limits the returned candidates (0 = every allowed label). It
   does not change `p` or `allowed_mass`.
diff --git a/examples/cli/langid.cpp b/examples/cli/langid.cpp
index 713199a51..67853c489 100644
--- a/examples/cli/langid.cpp
+++ b/examples/cli/langid.cpp
@@ -3,7 +3,7 @@
 //
 // Output lines are stable (scripts/validate.py parses `language:`):
 //   language: <code> index=<i> p=<p>
-//   candidate: <rank> <code> index=<i> p=<p> p_unrestricted=<p> logit=<z>
+//   candidate: <rank> <code> index=<i> p=<p> logit=<z>
 
 #include "transcribe/langid.h"
 
@@ -76,9 +76,8 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
                 std::printf("  audio_ms:     %lld\n", static_cast<long long>(res.audio_ms));
             }
             char line[160];
-            std::snprintf(line, sizeof(line), "candidate: %d %s index=%d p=%.6f p_unrestricted=%.6f logit=%.4f\n",
-                          i + 1, c.code, c.index, static_cast<double>(c.p), static_cast<double>(c.p_unrestricted),
-                          static_cast<double>(c.logit));
+            std::snprintf(line, sizeof(line), "candidate: %d %s index=%d p=%.6f logit=%.4f\n", i + 1, c.code, c.index,
+                          static_cast<double>(c.p), static_cast<double>(c.logit));
             std::printf("  %s", line);
             lines += line;
         }
diff --git a/include/transcribe.abihash b/include/transcribe.abihash
index daf7d6d22..5e88d1778 100644
--- a/include/transcribe.abihash
+++ b/include/transcribe.abihash
@@ -1 +1 @@
-318eba8c4b2e4387
+d544d70a2b5acf50
diff --git a/include/transcribe/langid.h b/include/transcribe/langid.h
index bd243682f..99dfa5365 100644
--- a/include/transcribe/langid.h
+++ b/include/transcribe/langid.h
@@ -56,8 +56,9 @@ struct transcribe_langid_result {
     uint64_t struct_size;
     int32_t  n_candidates; /* rows readable via transcribe_langid_get_candidate */
     int32_t  n_allowed;    /* labels in the allowed set (before top_k) */
-    /* Sum of p_unrestricted over the allowed set; 1 when unrestricted. A low
-     * value means the speech is probably outside the allowed set. */
+    /* Share of the softmax over every label that falls in the allowed set;
+     * 1 when unrestricted. A low value means the speech is probably outside
+     * the allowed set. */
     float    allowed_mass;
     int64_t  audio_ms; /* audio actually scored, after the crop */
 };
@@ -69,8 +70,7 @@ struct transcribe_langid_candidate {
     int32_t      index; /* label index */
     const char * code;
     const char * name;
-    float        p;              /* softmax over the allowed set */
-    float        p_unrestricted; /* softmax over every label */
+    float        p; /* softmax over the allowed set */
     float        logit;
 };
 
diff --git a/src/transcribe-langid.cpp b/src/transcribe-langid.cpp
index de8d4e8e3..a90a94192 100644
--- a/src/transcribe-langid.cpp
+++ b/src/transcribe-langid.cpp
@@ -312,7 +312,7 @@ static transcribe_status langid_run_impl(transcribe_langid_session *      sessio
     ranked.reserve(static_cast<size_t>(n_allowed));
     for (size_t i = 0; i < logits.size(); ++i) {
         if (mask[i] != 0) {
-            ranked.push_back({ static_cast<int32_t>(i), p_allowed[i], p_open[i], logits[i] });
+            ranked.push_back({ static_cast<int32_t>(i), p_allowed[i], logits[i] });
         }
     }
     std::stable_sort(ranked.begin(), ranked.end(),
@@ -388,7 +388,6 @@ extern "C" transcribe_status transcribe_langid_get_candidate(const struct transc
         staged.code                    = transcribe_langid_label_code(session->model, c.index);
         staged.name                    = transcribe_langid_label_name(session->model, c.index);
         staged.p                       = c.p;
-        staged.p_unrestricted          = c.p_unrestricted;
         staged.logit                   = c.logit;
     }
     copy_out_prefix(out, &staged, out->struct_size, sizeof(staged));
diff --git a/src/transcribe-langid.h b/src/transcribe-langid.h
index e6a0e2ccd..4df6ad311 100644
--- a/src/transcribe-langid.h
+++ b/src/transcribe-langid.h
@@ -19,10 +19,9 @@
 namespace transcribe {
 
 struct LangidCandidateEntry {
-    int32_t index          = 0;
-    float   p              = 0.0f;
-    float   p_unrestricted = 0.0f;
-    float   logit          = 0.0f;
+    int32_t index = 0;
+    float   p     = 0.0f;
+    float   logit = 0.0f;
 };
 
 // A model's labels. Built once at load by build_langid_labels, immutable after.
diff --git a/tests/cli_langid_smoke.cmake b/tests/cli_langid_smoke.cmake
index cf993d7e1..f1cf1692a 100644
--- a/tests/cli_langid_smoke.cmake
+++ b/tests/cli_langid_smoke.cmake
@@ -23,7 +23,7 @@ if(NOT rc EQUAL 0 OR NOT out MATCHES "language: (aa|bb|cc|dd|ee) index=[0-4] p="
     message(FATAL_ERROR "default run: rc=${rc}\n${out}\n${err}")
 endif()
 file(READ "${_output}" written)
-if(NOT written MATCHES "^candidate: 1 [a-e][a-e] index=[0-4] p=[0-9.]+ p_unrestricted=.*candidate: 5 ")
+if(NOT written MATCHES "^candidate: 1 [a-e][a-e] index=[0-4] p=[0-9.]+ logit=.*candidate: 5 ")
     message(FATAL_ERROR "-o wrote unexpected candidates:\n${written}")
 endif()
 
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index 8049507ee..cab89a213 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -127,7 +127,6 @@ void test_run(transcribe_model * m) {
     double     sum   = 0.0;
     for (size_t i = 0; i < first.size(); ++i) {
         CHECK(std::isfinite(first[i].p) && std::isfinite(first[i].logit));
-        CHECK(first[i].p == first[i].p_unrestricted);
         CHECK(first[i].code != nullptr && first[i].name != nullptr);
         if (i > 0) {
             CHECK(first[i - 1].p >= first[i].p);
diff --git a/tests/langid_dispatch_unit.cpp b/tests/langid_dispatch_unit.cpp
index 850cf6ff4..1ab581df0 100644
--- a/tests/langid_dispatch_unit.cpp
+++ b/tests/langid_dispatch_unit.cpp
@@ -172,7 +172,7 @@ void test_ranking() {
         const transcribe_langid_candidate c = f.candidate(i);
         CHECK(c.index == want[i]);
         CHECK(c.code != nullptr && std::strcmp(c.code, g_labels.codes[want[i]].c_str()) == 0);
-        CHECK(c.p == c.p_unrestricted && c.logit == g_logits[want[i]]);
+        CHECK(c.logit == g_logits[want[i]]);
         CHECK(near(c.p, std::exp(static_cast<double>(g_logits[want[i]])) / denom));
         sum += c.p;
     }
@@ -208,7 +208,6 @@ void test_allowed_set() {
     const transcribe_langid_candidate c1 = f.candidate(1);
     CHECK(c0.index == 1 && c1.index == 3);
     CHECK(near(c0.p, e_bb / (e_bb + e_dd)) && near(c1.p, e_dd / (e_bb + e_dd)));
-    CHECK(near(c0.p_unrestricted, e_bb / all));
 
     g_run_calls = 0;
     p.n_allowed = 0;

From 20281cfc7faf4fb317ac337fad357f4e995aa206 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:29:15 +0800
Subject: [PATCH 27/38] langid: trim convert-ecapa_tdnn.py

Shorten the module docstring (the family doc covers the rest), make the
architecture asserts table-driven (same hyperparameters checked: front end,
dilations, kernel sizes, padding, activations, BN/ASP eps, res2net/SE
structure, parameter and label counts, aliases), fold the repeated
TDNN weight/bias/BN blocks and the three BN-into-linear folds into
helpers, and drop the per-tensor table, the sha256 print and the
reports/convert JSON writer (no other converter writes one; the
porting-3 skill writes its own report).

The output GGUF is byte-identical (sha256 d0420e48...0ead66) and passes
preflight gate B and audit_gguf_metadata.
---
 scripts/convert-ecapa_tdnn.py | 901 +++++++++-------------------------
 1 file changed, 221 insertions(+), 680 deletions(-)

diff --git a/scripts/convert-ecapa_tdnn.py b/scripts/convert-ecapa_tdnn.py
index d1b6b09f6..679fef953 100644
--- a/scripts/convert-ecapa_tdnn.py
+++ b/scripts/convert-ecapa_tdnn.py
@@ -1,87 +1,25 @@
 #!/usr/bin/env python3
 """
 convert-ecapa_tdnn.py - convert SpeechBrain's VoxLingua107 ECAPA-TDNN
-language-ID checkpoint into the F32 reference GGUF that transcribe.cpp's
-ecapa_tdnn family (LANGID role) loads. F16 / Q8_0 come from
-tools/transcribe-quantize, never from here.
-
-Source format:
-    SpeechBrain checkpoint directory (HF repo or local), with:
-
-      hyperparams.yaml       instantiates lobes.features.Fbank,
-                             lobes.models.ECAPA_TDNN.ECAPA_TDNN and
-                             lobes.models.Xvector.Classifier by name
-      embedding_model.ckpt   ECAPA-TDNN encoder (21,058,432 params)
-      classifier.ckpt        Xvector.Classifier head (188,011 params)
-      label_encoder.txt      107 `'<code>: <name>' => <index>` lines
-
-There is no Transformers shim for this checkpoint: SpeechBrain is the only
-implementation. So this converter LOADS THE MODEL THROUGH SPEECHBRAIN
-(exactly the way `scripts/dump_reference_ecapa_tdnn_speechbrain.py` does,
-including `overrides={"pretrained_path": ...}` — without it SpeechBrain
-silently re-fetches the .ckpt files from the HF `main` branch and the
-pinned revision is a lie) and reads weights off the live module tree. The
-checkpoint resolves through scripts/lib/hf_source.py (HF hub cache, or the
-$TRANSCRIBE_MODELS_DIR mirror when set).
-
-Loading through SpeechBrain also lets the converter assert the
-architecture against the *instantiated modules and tensor shapes* rather
-than against `hyperparams.yaml`: the yaml carries only `input_size`,
-`channels`, `kernel_sizes`, `dilations`, `attention_channels`,
-`lin_neurons` and four front-end fields. Everything else (res2net_scale, se_channels, global_context, padding mode, STFT
-geometry, top_db, amin, ...) is a SpeechBrain *default* of the pinned
-version, so the yaml cannot confirm it.
-
-Four exact rewrites happen here:
-
-  1. Every TDNN BatchNorm becomes a `scale`/`shift` vector pair. It cannot
-     be folded into its conv because SpeechBrain's TDNNBlock is
-     conv -> ReLU -> BN.
-  2. The three BNs that feed a linear map with no activation between
-     (`asp_bn -> fc`, `cls.bn0 -> cls.l1`, `cls.bn1 -> cls.out`) are folded
-     FORWARD into that linear map and disappear.
-  3. `asp.tdnn`'s 9216-wide input weight is split into `x`/`mean`/`std`
-     weights, one per member of SpeechBrain's `cat([x, mean, std])`.
-  4. `mfa`'s 3072-wide input weight is split into three weights, one per
-     SERes2Net block output.
-
-Layout and naming conventions (the loader contract). Names follow the
-shared tools/transcribe-quantize rules, so the quant policy needs no
-ECAPA-specific entries:
-  * k>1 kernels are `*.conv.weight`, stored TAP-MAJOR: numpy `[K, OC, IC]`
-    (= `torch_w.permute(2, 0, 1)`), i.e. ggml `ne = [IC, OC, K]`, so each
-    tap is a contiguous `[IC, OC]` matmul operand. `.conv.` keeps them in
-    the quantizer's Conv bucket (F32 / F16 only).
-  * 1x1 kernels are 2-D `*.weight`: numpy `[OC, IC]`, ggml `ne = [IC, OC]`
-    (ordinary matmul operands; the quantizer's Linear bucket).
-  * `frontend.mel_filterbank` is mel-major: numpy `[60, 201]` (=
-    SpeechBrain's `[201, 60]` fbank matrix transposed), ggml
-    `ne = [201, 60]`, so each filter's 201 weights are contiguous.
-  * Biases (`*.bias`) and BN `*.bn.scale` / `*.bn.shift` are 1-D, F32.
-
-CLI (writes models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf):
+language-ID checkpoint into the F32 reference GGUF of the ecapa_tdnn family.
+Loads the model through SpeechBrain (the only implementation), asserts the
+hyperparameters hard-coded below against the live modules, and writes the
+tensor layout of src/arch/ecapa_tdnn/weights.h. Quantize afterwards with
+tools/transcribe-quantize. See docs/porting/families/ecapa_tdnn.md.
 
     uv run --project scripts/envs/ecapa_tdnn \
       scripts/convert-ecapa_tdnn.py speechbrain/lang-id-voxlingua107-ecapa
-
-Quantize with tools/transcribe-quantize afterwards.
-
-Single-file, top-to-bottom — no hidden helpers.
 """
 
 from __future__ import annotations
 
 import argparse
-import hashlib
-import json
 import os
 import sys
 from pathlib import Path
-from typing import Any
 
 # XET-backed transfers occasionally stall on this checkpoint's small .ckpt
-# files; the plain HTTP path is fast enough for 21 M parameters. Set before
-# huggingface_hub is imported (indirectly, via speechbrain) so it takes.
+# files. Set before huggingface_hub is imported (via speechbrain).
 os.environ.setdefault("HF_HUB_DISABLE_XET", "1")
 
 import numpy as np
@@ -89,11 +27,7 @@
 from gguf import GGMLQuantizationType, LlamaFileType
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
-if str(REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(REPO_ROOT))
-SCRIPTS_DIR = REPO_ROOT / "scripts"
-if str(SCRIPTS_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPTS_DIR))
+sys.path.insert(0, str(REPO_ROOT / "scripts"))
 
 from lib.gguf_common import (  # noqa: E402
     add_general_identity,
@@ -108,11 +42,9 @@
 DEFAULT_REPO = "speechbrain/lang-id-voxlingua107-ecapa"
 DEFAULT_REVISION = "0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9"
 DEFAULT_VARIANT = "lang-id-voxlingua107-ecapa"
-
 ARCH = "ecapa_tdnn"
 
-# Architecture constants. Every one of these is ASSERTED against the live
-# module tree below, never assumed — see `assert_architecture`.
+# Written into the GGUF and asserted against the live modules below.
 SAMPLE_RATE = 16000
 N_FFT = 400
 WIN = 400
@@ -121,7 +53,6 @@
 N_MELS = 60
 LOG_FLOOR = 1e-10
 TOP_DB = 80.0
-
 CHANNELS = [1024, 1024, 1024, 1024, 3072]
 KERNEL_SIZES = [5, 3, 3, 3, 1]
 DILATIONS = [1, 2, 3, 4, 1]
@@ -134,381 +65,177 @@
 N_LABELS = 107
 LEAKY_SLOPE = 0.01
 BN_EPS = 1e-5
-
-# Measured on the pinned checkpoint.
 N_PARAMS_EMBEDDING = 21_058_432
 N_PARAMS_CLASSIFIER = 188_011
 
-# `he`/`jv`/`fil`/`nb` are the modern ISO codes users type; the VoxLingua107
-# label set predates them and uses the legacy forms. Stored as metadata so
-# the LANGID role resolves them without a per-family table.
+# Modern ISO codes users type -> the legacy codes VoxLingua107 uses.
 LABEL_ALIASES = ["he=iw", "jv=jw", "fil=tl", "nb=no"]
 
 
-# ---------------------------------------------------------------------------
-# Checkpoint resolution
-# ---------------------------------------------------------------------------
-
-
-def resolve_checkpoint_dir(model_arg: str, revision: str | None) -> Path:
-    """Return a local SpeechBrain checkpoint directory (HF cache or local)."""
-    p = Path(model_arg).expanduser()
-    if not p.is_dir() and not looks_like_repo_id(model_arg):
-        raise SystemExit(
-            f"error: {model_arg!r} is neither an existing directory nor an "
-            f"org/name Hugging Face repo id"
-        )
-    src = resolve_model_dir(model_arg, revision)
-    needed = ["hyperparams.yaml", "embedding_model.ckpt",
-              "classifier.ckpt", "label_encoder.txt"]
-    missing = [f for f in needed if not (src / f).exists()]
-    if missing:
-        raise SystemExit(f"error: {src} is missing {missing}")
-    return src
+def fail(msg: str) -> None:
+    print(f"error: {msg}", file=sys.stderr)
+    raise SystemExit(2)
 
 
-def load_reference(src: Path, device: str = "cpu"):
-    """Build the SpeechBrain `EncoderClassifier`, in eval mode, fp32.
+def load_reference(src: Path):
+    """SpeechBrain EncoderClassifier in eval mode.
 
-    `overrides={"pretrained_path": <local dir>}` is the load-bearing line:
-    `hyperparams.yaml` sets `pretrained_path` to the HF repo id and
-    `Pretrainer.collect_files` splits every `paths[...]` entry into
-    (source, filename), so without the override the .ckpt files are
-    re-fetched from the HF `main` branch and `--revision` means nothing.
-
-    `savedir != source` because SpeechBrain symlinks the collected
-    loadables into savedir; aiming it at the snapshot would symlink files
-    onto themselves.
+    `pretrained_path` must be overridden to the local snapshot, otherwise
+    SpeechBrain re-fetches the .ckpt files from the HF `main` branch and the
+    pinned revision is ignored. `savedir` must differ from `src` (SpeechBrain
+    symlinks the loadables into it).
     """
     from speechbrain.inference.classifiers import EncoderClassifier
 
-    torch.manual_seed(0)
-    torch.set_num_threads(1)
-
     savedir = REPO_ROOT / "build" / "speechbrain" / ARCH
     savedir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Loading EncoderClassifier: source={src} savedir={savedir}")
     clf = EncoderClassifier.from_hparams(
-        source=str(src),
-        savedir=str(savedir),
-        overrides={"pretrained_path": str(src)},
-        run_opts={"device": device},
-    )
+        source=str(src), savedir=str(savedir),
+        overrides={"pretrained_path": str(src)}, run_opts={"device": "cpu"})
     clf.mods.eval()
-    for p in clf.mods.parameters():
-        p.requires_grad_(False)
-
-    training = [n for n, m in clf.mods.named_modules() if m.training]
-    if training:
-        raise SystemExit(f"error: modules still in training mode: {training[:5]}")
-
-    dtypes = {str(p.dtype) for p in clf.mods.parameters()}
-    if dtypes != {"torch.float32"}:
-        raise SystemExit(f"error: expected an fp32 model, got dtypes {dtypes}")
-
     return clf
 
 
-# ---------------------------------------------------------------------------
-# Architecture assertions
-# ---------------------------------------------------------------------------
-
-
-class ArchError(SystemExit):
-    """Raised (as exit 2) when the checkpoint is not the model we expect."""
-
-    def __init__(self, msg: str) -> None:
-        super().__init__(2)
-        self.msg = msg
-
-
-_checks: list[str] = []
-
-
-def check(cond: bool, what: str, got: Any = None) -> None:
-    """Assert one architectural fact, recording it for the summary."""
-    if not cond:
-        detail = f" (got {got!r})" if got is not None else ""
-        print(f"\nERROR: architecture mismatch: {what}{detail}", file=sys.stderr)
-        print("This converter only handles SpeechBrain's VoxLingua107 "
-              "ECAPA-TDNN as pinned in scripts/envs/ecapa_tdnn.", file=sys.stderr)
-        raise ArchError(what)
-    _checks.append(what)
-
-
 def bn_of(module) -> torch.nn.BatchNorm1d:
-    """Resolve a SpeechBrain BatchNorm1d wrapper to the torch module.
-
-    The wrapper depth is NOT uniform in this checkpoint: TDNN blocks and the
-    classifier reach the torch layer at `<block>.norm.norm`, while
-    `embedding_model.asp_bn` is itself the wrapper and its torch layer is
-    `asp_bn.norm`. Walking to the first real `torch.nn.BatchNorm1d` instead
-    of hardcoding a depth is what stops a one-level slip from silently
-    producing plausible-looking garbage.
-    """
+    """Walk SpeechBrain's BatchNorm1d wrapper(s) to the torch layer (the
+    wrapper depth differs between the TDNN blocks and `asp_bn`)."""
     m = module
     for _ in range(4):
         if isinstance(m, torch.nn.BatchNorm1d):
             return m
-        if hasattr(m, "norm"):
-            m = m.norm
-            continue
-        break
-    raise ArchError(f"no torch BatchNorm1d under {type(module).__name__}")
-
-
-def assert_conv(conv, oc: int, ic: int, k: int, d: int, label: str) -> None:
-    """Assert one SpeechBrain Conv1d: shape, dilation, padding, bias."""
-    w = conv.conv.weight
-    check(tuple(w.shape) == (oc, ic, k), f"{label}.weight is [{oc}, {ic}, {k}]",
-          tuple(w.shape))
-    check(conv.conv.bias is not None, f"{label} has a bias")
-    check(int(conv.dilation) == d, f"{label}.dilation == {d}", conv.dilation)
-    check(conv.padding == "same", f"{label}.padding == 'same'", conv.padding)
-    check(conv.padding_mode == "reflect", f"{label}.padding_mode == 'reflect'",
-          conv.padding_mode)
-    check(int(conv.conv.groups) == 1, f"{label}.groups == 1", conv.conv.groups)
-
-
-def assert_tdnn_block(blk, oc: int, ic: int, k: int, d: int, label: str) -> None:
-    """conv -> ReLU -> BN(eps 1e-5) -> Dropout1d(p=0) (identity in eval)."""
-    assert_conv(blk.conv, oc, ic, k, d, f"{label}.conv")
-    check(isinstance(blk.activation, torch.nn.ReLU),
-          f"{label}.activation is ReLU", type(blk.activation).__name__)
-    bn = bn_of(blk.norm)
-    check(int(bn.num_features) == oc, f"{label}.norm has {oc} features",
-          bn.num_features)
-    check(abs(float(bn.eps) - BN_EPS) < 1e-12, f"{label}.norm.eps == 1e-5", bn.eps)
-    check(bn.running_mean is not None and bn.running_var is not None,
-          f"{label}.norm carries running statistics")
-    # SpeechBrain 1.1.1 added a Dropout1d child (oracle report D3). At p=0 in
-    # eval mode it is an exact identity, so the C++ graph ignores it — but a
-    # nonzero p would mean this checkpoint is not the one we modelled.
-    check(float(blk.dropout.p) == 0.0, f"{label}.dropout.p == 0", blk.dropout.p)
-
-
-def assert_architecture(clf) -> dict[str, Any]:
-    """Verify every hyperparameter the C++ graph assumes against live modules.
-
-    Returns the label table (codes/names) as a side effect of validating it.
-    """
-    em = clf.mods.embedding_model
-    cl = clf.mods.classifier
-    fe = clf.mods.compute_features
-    mvn = clf.mods.mean_var_norm
-
-    # ---- front end -------------------------------------------------------
-    st = fe.compute_STFT
-    check(int(st.n_fft) == N_FFT, "Fbank STFT n_fft == 400", st.n_fft)
-    check(int(st.win_length) == WIN, "Fbank STFT win_length == 400 samples "
-          "(25 ms)", st.win_length)
-    check(int(st.hop_length) == HOP, "Fbank STFT hop_length == 160 samples "
-          "(10 ms)", st.hop_length)
-    check(int(st.sample_rate) == SAMPLE_RATE, "Fbank sample_rate == 16000",
-          st.sample_rate)
-    check(bool(st.center) is True, "Fbank STFT center == True", st.center)
-    check(st.pad_mode == "constant", "Fbank STFT pad_mode == 'constant' "
-          "(zero pad 200 each side)", st.pad_mode)
-    check(bool(st.onesided) is True, "Fbank STFT onesided == True", st.onesided)
-    check(bool(st.normalized_stft) is False, "Fbank STFT normalized == False",
-          st.normalized_stft)
-    check(torch.equal(st.window.to(torch.float32).cpu(),
-                      torch.hamming_window(WIN)),
-          "Fbank STFT window == torch.hamming_window(400) (periodic)")
-
-    fb = fe.compute_fbanks
-    check(int(fb.n_mels) == N_MELS, "Filterbank n_mels == 60", fb.n_mels)
-    check(fb.filter_shape == "triangular", "Filterbank filter_shape == "
-          "'triangular'", fb.filter_shape)
-    check(float(fb.f_min) == 0.0, "Filterbank f_min == 0", fb.f_min)
-    check(float(fb.f_max) == 8000.0, "Filterbank f_max == 8000", fb.f_max)
-    check(int(fb.n_stft) == N_STFT, "Filterbank n_stft == 201", fb.n_stft)
-    check(int(fb.power_spectrogram) == 2, "Filterbank power_spectrogram == 2 "
-          "(multiplier 10)", fb.power_spectrogram)
-    check(int(fb.multiplier) == 10, "Filterbank dB multiplier == 10",
-          fb.multiplier)
-    check(float(fb.amin) == LOG_FLOOR, "Filterbank amin == 1e-10", fb.amin)
-    check(float(fb.ref_value) == 1.0, "Filterbank ref_value == 1.0 "
-          "(db_multiplier 0, no reference term)", fb.ref_value)
-    check(float(fb.db_multiplier) == 0.0, "Filterbank db_multiplier == 0",
-          fb.db_multiplier)
-    check(float(fb.top_db) == TOP_DB, "Filterbank top_db == 80", fb.top_db)
-    check(bool(fb.log_mel) is True, "Filterbank log_mel == True", fb.log_mel)
-    check(bool(fb.freeze) is True, "Filterbank freeze == True (filters are "
-          "constants, not parameters)", fb.freeze)
-    check(float(fb.param_rand_factor) == 0.0,
-          "Filterbank param_rand_factor == 0", fb.param_rand_factor)
-    check(bool(fe.deltas) is False, "Fbank deltas == False", fe.deltas)
-    check(bool(fe.context) is False, "Fbank context window == False", fe.context)
-
-    check(mvn.norm_type == "sentence", "InputNormalization norm_type == "
-          "'sentence'", mvn.norm_type)
-    check(bool(mvn.std_norm) is False, "InputNormalization std_norm == False "
-          "(mean subtraction only)", mvn.std_norm)
-    check(int(mvn.length_dim) == 1, "InputNormalization length_dim == 1 "
-          "(per-bin mean over time)", mvn.length_dim)
-    check(bool(mvn.avoid_padding_norm) is False,
-          "InputNormalization avoid_padding_norm == False", mvn.avoid_padding_norm)
-
-    # ---- embedding network ----------------------------------------------
-    check(len(em.blocks) == 4, "ECAPA_TDNN has 4 blocks (1 TDNN + 3 SERes2Net)",
-          len(em.blocks))
-
-    assert_tdnn_block(em.blocks[0], CHANNELS[0], N_MELS, KERNEL_SIZES[0],
-                      DILATIONS[0], "blocks.0")
+        m = getattr(m, "norm", None)
+    fail(f"no torch BatchNorm1d under {type(module).__name__}")
+
+
+def assert_architecture(clf) -> dict[str, list[str]]:
+    """Check every hyperparameter the GGUF hard-codes that tensor shapes (and
+    hence the C++ loader) cannot see. Returns the parsed label table."""
+    em, cl = clf.mods.embedding_model, clf.mods.classifier
+    fe, mvn = clf.mods.compute_features, clf.mods.mean_var_norm
+    st, fb = fe.compute_STFT, fe.compute_fbanks
+    asp = em.asp
 
+    def name(m) -> str:
+        return type(m).__name__
+
+    checks = [  # (label, actual, expected)
+        ("param dtypes", {p.dtype for p in clf.mods.parameters()}, {torch.float32}),
+        ("stft.n_fft", st.n_fft, N_FFT),
+        ("stft.win_length", st.win_length, WIN),
+        ("stft.hop_length", st.hop_length, HOP),
+        ("stft.sample_rate", st.sample_rate, SAMPLE_RATE),
+        ("stft.center", st.center, True),
+        ("stft.pad_mode", st.pad_mode, "constant"),
+        ("stft.onesided", st.onesided, True),
+        ("stft.normalized_stft", st.normalized_stft, False),
+        ("stft.window == hamming_window(400)",
+         torch.equal(st.window.to(torch.float32).cpu(), torch.hamming_window(WIN)), True),
+        ("fbank.n_mels", fb.n_mels, N_MELS),
+        ("fbank.filter_shape", fb.filter_shape, "triangular"),
+        ("fbank.f_min", fb.f_min, 0.0),
+        ("fbank.f_max", fb.f_max, 8000.0),
+        ("fbank.n_stft", fb.n_stft, N_STFT),
+        ("fbank.power_spectrogram", fb.power_spectrogram, 2),
+        ("fbank.multiplier", fb.multiplier, 10),
+        ("fbank.amin", fb.amin, LOG_FLOOR),
+        ("fbank.ref_value", fb.ref_value, 1.0),
+        ("fbank.db_multiplier", fb.db_multiplier, 0.0),
+        ("fbank.top_db", fb.top_db, TOP_DB),
+        ("fbank.log_mel", fb.log_mel, True),
+        ("fbank.freeze", fb.freeze, True),
+        ("fbank.param_rand_factor", fb.param_rand_factor, 0.0),
+        ("fbank.deltas", fe.deltas, False),
+        ("fbank.context", fe.context, False),
+        ("mvn.norm_type", mvn.norm_type, "sentence"),
+        ("mvn.std_norm", mvn.std_norm, False),
+        ("mvn.length_dim", mvn.length_dim, 1),
+        ("mvn.avoid_padding_norm", mvn.avoid_padding_norm, False),
+        ("len(blocks)", len(em.blocks), 4),
+        ("asp.global_context", asp.global_context, True),
+        ("asp.eps", asp.eps, ASP_EPS),
+        ("asp.tanh", name(asp.tanh), "Tanh"),
+        ("asp_bn.eps", bn_of(em.asp_bn).eps, BN_EPS),
+        ("cls.act", name(cl.act), "LeakyReLU"),
+        ("cls.act slope", cl.act.negative_slope, LEAKY_SLOPE),
+        ("cls.bn0.eps", bn_of(cl.norm).eps, BN_EPS),
+        ("len(cls.DNN)", len(cl.DNN), 1),
+        ("cls.hidden act", name(cl.DNN.block_0.act), "LeakyReLU"),
+        ("cls.hidden slope", cl.DNN.block_0.act.negative_slope, LEAKY_SLOPE),
+        ("cls.bn1.eps", bn_of(cl.DNN.block_0.norm).eps, BN_EPS),
+        ("embedding params", sum(p.numel() for p in em.parameters()), N_PARAMS_EMBEDDING),
+        ("classifier params", sum(p.numel() for p in cl.parameters()), N_PARAMS_CLASSIFIER),
+    ]
+
+    def conv(label, c, k, d=1):
+        # Every conv: kernel k, dilation d, reflect 'same' pad, bias, groups 1.
+        return [
+            (f"{label}.kernel", c.conv.weight.shape[2], k),
+            (f"{label}.dilation", c.dilation, d),
+            (f"{label}.padding", (c.padding, c.padding_mode), ("same", "reflect")),
+            (f"{label}.groups", c.conv.groups, 1),
+            (f"{label}.has_bias", c.conv.bias is not None, True),
+        ]
+
+    def tdnn(label, blk, k, d):
+        # conv -> ReLU -> BN -> Dropout1d(p=0) (identity in eval).
+        return conv(f"{label}.conv", blk.conv, k, d) + [
+            (f"{label}.activation", name(blk.activation), "ReLU"),
+            (f"{label}.bn.eps", bn_of(blk.norm).eps, BN_EPS),
+            (f"{label}.dropout.p", blk.dropout.p, 0.0),
+        ]
+
+    checks += tdnn("blocks.0", em.blocks[0], KERNEL_SIZES[0], DILATIONS[0])
     for i in (1, 2, 3):
-        b = em.blocks[i]
-        pfx = f"blocks.{i}"
-        check(b.shortcut is None, f"{pfx} has no shortcut projection "
-              "(in_channels == out_channels)")
-        assert_tdnn_block(b.tdnn1, CHANNELS[i], CHANNELS[i - 1], 1, 1,
-                          f"{pfx}.tdnn1")
+        b, p = em.blocks[i], f"blocks.{i}"
         r2 = b.res2net_block
-        check(int(r2.scale) == RES2NET_SCALE, f"{pfx}.res2net scale == 8",
-              r2.scale)
-        check(len(r2.blocks) == RES2NET_SCALE - 1,
-              f"{pfx}.res2net has 7 sub-blocks", len(r2.blocks))
-        width = CHANNELS[i] // RES2NET_SCALE
-        check(width == 128, f"{pfx}.res2net chunk width == 128", width)
-        for j in range(RES2NET_SCALE - 1):
-            assert_tdnn_block(r2.blocks[j], width, width, KERNEL_SIZES[i],
-                              DILATIONS[i], f"{pfx}.res2net.{j}")
-        assert_tdnn_block(b.tdnn2, CHANNELS[i], CHANNELS[i], 1, 1,
-                          f"{pfx}.tdnn2")
-        se = b.se_block
-        assert_conv(se.conv1, SE_CHANNELS, CHANNELS[i], 1, 1, f"{pfx}.se.conv1")
-        assert_conv(se.conv2, CHANNELS[i], SE_CHANNELS, 1, 1, f"{pfx}.se.conv2")
-        check(isinstance(se.relu, torch.nn.ReLU), f"{pfx}.se activation is ReLU",
-              type(se.relu).__name__)
-        check(isinstance(se.sigmoid, torch.nn.Sigmoid),
-              f"{pfx}.se gate is Sigmoid", type(se.sigmoid).__name__)
-
-    mfa_in = CHANNELS[3] * 3
-    assert_tdnn_block(em.mfa, CHANNELS[4], mfa_in, KERNEL_SIZES[4],
-                      DILATIONS[4], "mfa")
+        checks += [(f"{p}.shortcut", b.shortcut, None),
+                   (f"{p}.res2net.scale", r2.scale, RES2NET_SCALE),
+                   (f"{p}.res2net blocks", len(r2.blocks), RES2NET_SCALE - 1),
+                   (f"{p}.se.relu", name(b.se_block.relu), "ReLU"),
+                   (f"{p}.se.sigmoid", name(b.se_block.sigmoid), "Sigmoid")]
+        checks += tdnn(f"{p}.tdnn1", b.tdnn1, 1, 1) + tdnn(f"{p}.tdnn2", b.tdnn2, 1, 1)
+        for j, sub in enumerate(r2.blocks):
+            checks += tdnn(f"{p}.res2net.{j}", sub, KERNEL_SIZES[i], DILATIONS[i])
+        checks += conv(f"{p}.se.conv1", b.se_block.conv1, 1)
+        checks += conv(f"{p}.se.conv2", b.se_block.conv2, 1)
+    checks += tdnn("mfa", em.mfa, KERNEL_SIZES[4], DILATIONS[4])
+    checks += tdnn("asp.tdnn", asp.tdnn, 1, 1)
+    checks += conv("asp.conv", asp.conv, 1) + conv("fc", em.fc, 1)
 
-    asp = em.asp
-    check(bool(asp.global_context) is True, "asp global_context == True",
-          asp.global_context)
-    check(float(asp.eps) == ASP_EPS, "asp eps == 1e-12", asp.eps)
-    assert_tdnn_block(asp.tdnn, ATTENTION_CHANNELS, CHANNELS[4] * 3, 1, 1,
-                      "asp.tdnn")
-    check(isinstance(asp.tanh, torch.nn.Tanh), "asp gate activation is Tanh",
-          type(asp.tanh).__name__)
-    assert_conv(asp.conv, CHANNELS[4], ATTENTION_CHANNELS, 1, 1, "asp.conv")
-
-    asp_bn = bn_of(em.asp_bn)
-    check(int(asp_bn.num_features) == CHANNELS[4] * 2,
-          "asp_bn is BatchNorm1d(6144)", asp_bn.num_features)
-    check(abs(float(asp_bn.eps) - BN_EPS) < 1e-12, "asp_bn.eps == 1e-5",
-          asp_bn.eps)
-    assert_conv(em.fc, EMB_DIM, CHANNELS[4] * 2, 1, 1, "fc")
-
-    # ---- classifier ------------------------------------------------------
-    check(isinstance(cl.act, torch.nn.LeakyReLU),
-          "classifier.act is LeakyReLU", type(cl.act).__name__)
-    check(float(cl.act.negative_slope) == LEAKY_SLOPE,
-          "classifier.act slope == 0.01", cl.act.negative_slope)
-    bn0 = bn_of(cl.norm)
-    check(int(bn0.num_features) == EMB_DIM, "classifier bn0 is BatchNorm1d(256)",
-          bn0.num_features)
-    check(abs(float(bn0.eps) - BN_EPS) < 1e-12, "classifier bn0.eps == 1e-5",
-          bn0.eps)
-    check(len(cl.DNN) == 1, "classifier has 1 linear block", len(cl.DNN))
-    blk = cl.DNN.block_0
-    check(tuple(blk.linear.w.weight.shape) == (HIDDEN, EMB_DIM),
-          "cls.l1 weight is [512, 256]", tuple(blk.linear.w.weight.shape))
-    check(isinstance(blk.act, torch.nn.LeakyReLU),
-          "classifier hidden activation is LeakyReLU", type(blk.act).__name__)
-    check(float(blk.act.negative_slope) == LEAKY_SLOPE,
-          "classifier hidden slope == 0.01", blk.act.negative_slope)
-    bn1 = bn_of(blk.norm)
-    check(int(bn1.num_features) == HIDDEN, "classifier bn1 is BatchNorm1d(512)",
-          bn1.num_features)
-    check(abs(float(bn1.eps) - BN_EPS) < 1e-12, "classifier bn1.eps == 1e-5",
-          bn1.eps)
-    check(tuple(cl.out.w.weight.shape) == (N_LABELS, HIDDEN),
-          "cls.out weight is [107, 512]", tuple(cl.out.w.weight.shape))
-
-    # ---- parameter counts ------------------------------------------------
-    n_emb = sum(int(p.numel()) for p in em.parameters())
-    n_cls = sum(int(p.numel()) for p in cl.parameters())
-    check(n_emb == N_PARAMS_EMBEDDING,
-          f"embedding network has {N_PARAMS_EMBEDDING:,} parameters", n_emb)
-    check(n_cls == N_PARAMS_CLASSIFIER,
-          f"classifier has {N_PARAMS_CLASSIFIER:,} parameters", n_cls)
-
-    # ---- labels ----------------------------------------------------------
     ind2lab = clf.hparams.label_encoder.ind2lab
-    check(len(ind2lab) == N_LABELS, f"label encoder has {N_LABELS} labels",
-          len(ind2lab))
-    codes: list[str] = []
-    names: list[str] = []
-    for i in range(N_LABELS):
-        raw = str(ind2lab[i])
-        if ": " not in raw:
-            raise ArchError(f"label {i} ({raw!r}) has no '<code>: <name>' form")
-        code, name = raw.split(": ", 1)
-        codes.append(code.strip())
-        names.append(name.strip())
-    check(len(set(codes)) == N_LABELS, "all 107 label codes are distinct",
-          len(set(codes)))
+    raw = [str(ind2lab[i]) for i in range(len(ind2lab))]
+    pairs = [r.split(": ", 1) for r in raw]
+    codes = [c.strip() for c, *_ in pairs]
+    checks += [("label count", len(raw), N_LABELS),
+               ("labels in '<code>: <name>' form", all(len(x) == 2 for x in pairs), True),
+               ("distinct label codes", len(set(codes)), N_LABELS)]
     for alias in LABEL_ALIASES:
         a, c = alias.split("=", 1)
-        check(c in codes, f"alias target {c!r} is in the label set")
-        check(a not in codes, f"alias name {a!r} is NOT already a label code")
-
-    print(f"Architecture verified: {len(_checks)} checks passed.")
-    print(f"  front end  : STFT {N_FFT}/{WIN}/{HOP} periodic-hamming, center, "
-          f"zero pad; {N_MELS} triangular mels 0-8000 Hz; "
-          f"10*log10(max(x,{LOG_FLOOR:g})); top_db {TOP_DB:g}; sentence-mean norm")
-    print(f"  encoder    : channels {CHANNELS} kernels {KERNEL_SIZES} "
-          f"dilations {DILATIONS}; res2net scale {RES2NET_SCALE} "
-          f"({RES2NET_SCALE - 1} blocks of {CHANNELS[1] // RES2NET_SCALE}->"
-          f"{CHANNELS[1] // RES2NET_SCALE} k={KERNEL_SIZES[1]}); "
-          f"SE {SE_CHANNELS}; attention {ATTENTION_CHANNELS}; "
-          f"global_context True; asp eps {ASP_EPS:g}; embedding {EMB_DIM}")
-    print(f"  classifier : LeakyReLU({LEAKY_SLOPE}) -> BN(256) -> "
-          f"Linear(256->{HIDDEN}) -> LeakyReLU -> BN({HIDDEN}) -> "
-          f"Linear({HIDDEN}->{N_LABELS}) -> log_softmax")
-    print(f"  every conv : padding='same', padding_mode='reflect', groups=1, "
-          f"bias=True; every BN eps={BN_EPS:g}")
-    print(f"  parameters : embedding {n_emb:,} + classifier {n_cls:,} = "
-          f"{n_emb + n_cls:,}")
-    print(f"  labels     : {N_LABELS} codes, aliases {LABEL_ALIASES}")
-
-    return {"codes": codes, "names": names}
-
-
-# ---------------------------------------------------------------------------
-# Filterbank capture
-# ---------------------------------------------------------------------------
+        checks += [(f"alias target {c!r} is a label", c in codes, True),
+                   (f"alias {a!r} is not a label", a in codes, False)]
 
+    for label, got, want in checks:
+        if got != want:
+            fail(f"architecture mismatch: {label}: got {got!r}, expected {want!r} "
+                 "(only the pinned SpeechBrain VoxLingua107 ECAPA-TDNN is supported)")
+    print(f"Architecture verified: {len(checks)} checks passed.")
+    return {"codes": codes, "names": [x[1].strip() for x in pairs]}
 
-def capture_fbank_matrix(clf) -> np.ndarray:
-    """Return the exact [201, 60] fbank matrix the forward pass builds.
 
-    SpeechBrain 1.1.1's `Filterbank` has no `fbank_matrix` attribute or
-    buffer. The matrix is constructed
-    inside `forward()` by `_create_fbank_matrix(f_central_mat, band_mat)`.
+def capture_fbank_matrix(clf) -> np.ndarray:
+    """The exact [201, 60] fbank matrix SpeechBrain's forward builds.
 
-    So we wrap that method, push 0.1 s of silence through the real Fbank,
-    and keep what it returned. This is stronger than recomputing the
-    triangle geometry in float64: a rebuild matches to only ~1.2e-05
-    because `f_central` / `band` are fp32 buffers, whereas the capture is
-    bit-identical to what the reference used.
+    Filterbank has no stored matrix; it is built inside forward() by
+    `_create_fbank_matrix`. Wrap that, run 0.1 s of silence, keep the result
+    (bit-identical to the reference; a float64 rebuild is off by ~1e-5).
     """
     fbanks = clf.mods.compute_features.compute_fbanks
     original = fbanks._create_fbank_matrix
-    captured: dict[str, Any] = {"calls": 0}
+    captured = []
 
-    def capture(f_central_mat, band_mat):
-        m = original(f_central_mat, band_mat)
-        captured.setdefault("value", m)
-        captured["calls"] += 1
-        return m
+    def capture(*a):
+        captured.append(original(*a))
+        return captured[-1]
 
     fbanks._create_fbank_matrix = capture
     try:
@@ -516,110 +243,36 @@ def capture(f_central_mat, band_mat):
             clf.mods.compute_features(torch.zeros(1, SAMPLE_RATE // 10))
     finally:
         fbanks._create_fbank_matrix = original
+    if len(captured) != 1 or tuple(captured[0].shape) != (N_STFT, N_MELS):
+        fail(f"fbank capture: {len(captured)} calls, expected one [{N_STFT}, {N_MELS}] matrix")
+    return captured[0].detach().to(dtype=torch.float32, device="cpu").numpy()
 
-    if captured["calls"] != 1:
-        raise ArchError(f"_create_fbank_matrix fired {captured['calls']} times, "
-                        "expected 1")
-    m = captured["value"]
-    check(tuple(m.shape) == (N_STFT, N_MELS),
-          f"fbank matrix is [{N_STFT}, {N_MELS}]", tuple(m.shape))
-    return np.ascontiguousarray(
-        m.detach().to(dtype=torch.float32, device="cpu").numpy())
 
-
-# ---------------------------------------------------------------------------
-# Weight extraction / conversion-time algebra
-# ---------------------------------------------------------------------------
-
-
-def w2d(conv) -> np.ndarray:
-    """1x1 SpeechBrain Conv1d weight `[OC, IC, 1]` -> numpy `[OC, IC]` (f64)."""
-    w = conv.conv.weight.detach().to(torch.float64).cpu().numpy()
-    if w.shape[2] != 1:
-        raise ArchError(f"expected a 1x1 kernel, got K={w.shape[2]}")
-    return np.ascontiguousarray(w[:, :, 0])
-
-
-def w3d(conv) -> np.ndarray:
-    """k>1 Conv1d weight `[OC, IC, K]` -> TAP-MAJOR numpy `[K, OC, IC]` (f64).
-
-    ggml sees `ne = [IC, OC, K]`, so tap `t` is the contiguous `[IC, OC]`
-    matrix the graph multiplies against a `t*dilation`-shifted view of the
-    reflect-padded activation.
-    """
-    w = conv.conv.weight.detach().to(torch.float64).cpu().numpy()
-    return np.ascontiguousarray(np.transpose(w, (2, 0, 1)))
-
-
-def bias(conv) -> np.ndarray:
-    return np.ascontiguousarray(
-        conv.conv.bias.detach().to(torch.float64).cpu().numpy())
+def f64(t: torch.Tensor) -> np.ndarray:
+    return t.detach().to(torch.float64).cpu().numpy()
 
 
 def bn_affine(module) -> tuple[np.ndarray, np.ndarray]:
-    """BatchNorm -> (scale, shift) with `y = x * scale + shift`.
-
-    `scale = gamma / sqrt(var + eps)`, `shift = beta - mean * scale`,
-    computed in float64. This is NOT folded into the preceding conv: a
-    TDNNBlock is conv -> ReLU -> BN, and the ReLU makes a backward fold
-    impossible.
-    """
+    """BatchNorm -> (scale, shift), `y = x * scale + shift`, in float64."""
     bn = bn_of(module)
-    g = bn.weight.detach().to(torch.float64).cpu().numpy()
-    b = bn.bias.detach().to(torch.float64).cpu().numpy()
-    m = bn.running_mean.detach().to(torch.float64).cpu().numpy()
-    v = bn.running_var.detach().to(torch.float64).cpu().numpy()
-    scale = g / np.sqrt(v + float(bn.eps))
-    shift = b - m * scale
-    return np.ascontiguousarray(scale), np.ascontiguousarray(shift)
-
-
-def fold_bn_into_linear(module, w: np.ndarray, b: np.ndarray
-                        ) -> tuple[np.ndarray, np.ndarray]:
-    """Fold a BatchNorm FORWARD into the linear map that consumes it.
-
-    `y = W(x * s + t) + b = (W * diag(s)) x + (W t + b)`. Valid only when
-    nothing sits between the BN and the linear map — true for
-    `asp_bn -> fc`, `cls.bn0 -> cls.l1` and `cls.bn1 -> cls.out`. Note the
-    classifier's BNs come AFTER their LeakyReLU, so the activation is still
-    applied before each folded linear at run time; only the BN disappears.
-    """
-    s, t = bn_affine(module)
-    if w.shape[1] != s.size:
-        raise ArchError(f"fold shape mismatch: W is {w.shape}, BN has {s.size} "
-                        "features")
-    return (np.ascontiguousarray(w * s[None, :]),
-            np.ascontiguousarray(w @ t + b))
-
-
-# ---------------------------------------------------------------------------
-# GGUF writing
-# ---------------------------------------------------------------------------
-
+    scale = f64(bn.weight) / np.sqrt(f64(bn.running_var) + float(bn.eps))
+    return scale, f64(bn.bias) - f64(bn.running_mean) * scale
 
-def ggml_ne(shape: tuple[int, ...]) -> list[int]:
-    """numpy shape -> ggml ne (fastest axis first)."""
-    return list(reversed(shape))
 
-
-def sha256_file(path: Path) -> str:
-    h = hashlib.sha256()
-    with path.open("rb") as f:
-        for chunk in iter(lambda: f.read(1 << 20), b""):
-            h.update(chunk)
-    return h.hexdigest()
+def fold_bn_into_linear(module, w: np.ndarray, b: np.ndarray):
+    """`W(x * s + t) + b = (W diag(s)) x + (W t + b)`: fold a BN forward into
+    the linear map that directly consumes it."""
+    s, t = bn_affine(module)
+    return w * s[None, :], w @ t + b
 
 
-def convert(clf, out_path: Path, *, variant: str, repo_id: str,
-            revision: str, filters_201x60: np.ndarray,
-            labels: dict[str, list[str]]) -> dict[str, Any]:
-    em = clf.mods.embedding_model
-    cl = clf.mods.classifier
+def convert(clf, out_path: Path, *, variant: str, repo_id: str, revision: str,
+            filters_201x60: np.ndarray, labels: dict[str, list[str]]) -> None:
+    em, cl = clf.mods.embedding_model, clf.mods.classifier
 
     out_path.parent.mkdir(parents=True, exist_ok=True)
     writer = gguf_writer(str(out_path), ARCH)
 
-    # ----- general.* ------------------------------------------------------
     add_general_identity(
         writer,
         name=repo_id,
@@ -642,16 +295,9 @@ def convert(clf, out_path: Path, *, variant: str, repo_id: str,
             f"{repo_id} for transcribe.cpp."
         ),
     )
-    # `general.source.commit` has no helper in gguf-py; write it plainly so
-    # every artifact records the exact upstream snapshot it came from.
     writer.add_string("general.source.commit", revision)
 
-    # ----- stt.* ----------------------------------------------------------
     writer.add_string("stt.variant", variant)
-
-    # SpeechBrain Fbank + InputNormalization: zero-padded centered STFT,
-    # periodic Hamming, power spectrum, mel, 10*log10(max(p, log_clamp_min)),
-    # top_db floor over the clip, per-bin mean subtraction.
     writer.add_string("stt.frontend.type", "speechbrain_fbank")
     writer.add_uint32("stt.frontend.sample_rate", SAMPLE_RATE)
     writer.add_uint32("stt.frontend.n_fft", N_FFT)
@@ -679,164 +325,72 @@ def convert(clf, out_path: Path, *, variant: str, repo_id: str,
     writer.add_array("stt.langid.labels.names", labels["names"])
     writer.add_array("stt.langid.labels.aliases", LABEL_ALIASES)
 
-    # ----- tensors --------------------------------------------------------
-    rows: list[tuple[str, str, str, str, int]] = []
-    total_bytes = 0
-
     def add(name: str, arr: np.ndarray) -> None:
-        nonlocal total_bytes
         a = np.ascontiguousarray(arr.astype(np.float32))
-        t = GGMLQuantizationType.F32
-        encoded, raw_dtype = encode_for_gguf(a, t)
+        encoded, raw_dtype = encode_for_gguf(a, GGMLQuantizationType.F32)
         writer.add_tensor(name, encoded, raw_dtype=raw_dtype)
-        nbytes = int(encoded.nbytes)
-        rows.append((name, str(list(a.shape)), str(ggml_ne(a.shape)),
-                     t.name, nbytes))
-        total_bytes += nbytes
 
-    # Front-end filterbank, mel-major so each filter's 201 taps are
-    # contiguous: numpy [60, 201] -> ggml ne = [201, 60].
+    def add_tdnn(p: str, blk, split: tuple[str, ...] = ()) -> None:
+        """conv weight + bias, then the BN as a scale/shift pair (TDNNBlock is
+        conv -> ReLU -> BN, so the BN cannot fold into the conv).
+
+        k>1 kernels are `<p>.conv.weight`, tap-major numpy [K, OC, IC] (ggml
+        ne [IC, OC, K]); 1x1 kernels are `<p>.weight` [OC, IC]. `split` cuts a
+        1x1 weight's input columns into one `<p>.<part>.weight` per member of
+        the concatenation SpeechBrain feeds it.
+        """
+        w = f64(blk.conv.conv.weight)
+        c = f"{p}.conv" if w.shape[2] > 1 else p
+        if w.shape[2] > 1:
+            add(f"{c}.weight", np.transpose(w, (2, 0, 1)))
+        elif split:
+            n = w.shape[1] // len(split)
+            for k, part in enumerate(split):
+                add(f"{p}.{part}.weight", w[:, k * n:(k + 1) * n, 0])
+        else:
+            add(f"{c}.weight", w[:, :, 0])
+        add(f"{c}.bias", f64(blk.conv.conv.bias))
+        s, t = bn_affine(blk.norm)
+        add(f"{p}.bn.scale", s)
+        add(f"{p}.bn.shift", t)
+
+    # Mel-major: numpy [60, 201] -> ggml ne [201, 60].
     add("frontend.mel_filterbank", filters_201x60.T)
-
-    # blocks.0: the only k>1 conv outside the res2net stacks.
-    b0 = em.blocks[0]
-    add("blk.0.conv.weight", w3d(b0.conv))
-    add("blk.0.conv.bias", bias(b0.conv))
-    s, t = bn_affine(b0.norm)
-    add("blk.0.bn.scale", s)
-    add("blk.0.bn.shift", t)
-
+    add_tdnn("blk.0", em.blocks[0])
     for i in (1, 2, 3):
-        b = em.blocks[i]
-        p = f"blk.{i}"
-
-        add(f"{p}.tdnn1.weight", w2d(b.tdnn1.conv))
-        add(f"{p}.tdnn1.bias", bias(b.tdnn1.conv))
-        s, t = bn_affine(b.tdnn1.norm)
-        add(f"{p}.tdnn1.bn.scale", s)
-        add(f"{p}.tdnn1.bn.shift", t)
-
-        # res2.{j} operates on chunk j+1 of the 8 channel chunks: chunk 0 is
-        # passed through untouched and each later chunk is summed with the
-        # PREVIOUS sub-block's output before being convolved.
-        for j in range(RES2NET_SCALE - 1):
-            sub = b.res2net_block.blocks[j]
-            add(f"{p}.res2.{j}.conv.weight", w3d(sub.conv))
-            add(f"{p}.res2.{j}.conv.bias", bias(sub.conv))
-            s, t = bn_affine(sub.norm)
-            add(f"{p}.res2.{j}.bn.scale", s)
-            add(f"{p}.res2.{j}.bn.shift", t)
-
-        add(f"{p}.tdnn2.weight", w2d(b.tdnn2.conv))
-        add(f"{p}.tdnn2.bias", bias(b.tdnn2.conv))
-        s, t = bn_affine(b.tdnn2.norm)
-        add(f"{p}.tdnn2.bn.scale", s)
-        add(f"{p}.tdnn2.bn.shift", t)
-
-        add(f"{p}.se.c1.weight", w2d(b.se_block.conv1))
-        add(f"{p}.se.c1.bias", bias(b.se_block.conv1))
-        add(f"{p}.se.c2.weight", w2d(b.se_block.conv2))
-        add(f"{p}.se.c2.bias", bias(b.se_block.conv2))
-
-    # MFA split: SpeechBrain concatenates blocks.1/2/3 along channels before a
-    # 1x1 conv, so the [3072, 3072] weight is three [3072, 1024] blocks in
-    # block order. Summing three matmuls costs the same FLOPs and skips the
-    # concat copy.
-    mfa_w = w2d(em.mfa.conv)
-    for k, name in enumerate(("mfa.w1.weight", "mfa.w2.weight", "mfa.w3.weight")):
-        add(name, mfa_w[:, k * CHANNELS[3]:(k + 1) * CHANNELS[3]])
-    add("mfa.bias", bias(em.mfa.conv))
-    s, t = bn_affine(em.mfa.norm)
-    add("mfa.bn.scale", s)
-    add("mfa.bn.shift", t)
-
-    # ASP split: the attention TDNN consumes cat([x, mean, std]) (that exact
-    # order — see AttentiveStatisticsPooling.forward), so its [128, 9216]
-    # weight is three [128, 3072] blocks. mean/std are constant over time, so
-    # the graph folds wm@mean + ws@std + b into one [128] vector.
-    asp_w = w2d(em.asp.tdnn.conv)
-    for k, name in enumerate(("asp.tdnn.x.weight", "asp.tdnn.mean.weight", "asp.tdnn.std.weight")):
-        add(name, asp_w[:, k * CHANNELS[4]:(k + 1) * CHANNELS[4]])
-    add("asp.tdnn.bias", bias(em.asp.tdnn.conv))
-    s, t = bn_affine(em.asp.tdnn.norm)
-    add("asp.tdnn.bn.scale", s)
-    add("asp.tdnn.bn.shift", t)
-
-    add("asp.attn.weight", w2d(em.asp.conv))
-    add("asp.attn.bias", bias(em.asp.conv))
-
-    # asp_bn folds forward into fc (nothing between them).
-    fc_w, fc_b = fold_bn_into_linear(em.asp_bn, w2d(em.fc), bias(em.fc))
-    add("fc.weight", fc_w)
-    add("fc.bias", fc_b)
-
-    # cls.bn0 folds into cls.l1, cls.bn1 into cls.out. The LeakyReLUs sit
-    # BEFORE each BN, so they survive in the graph: the run-time chain is
-    # LeakyReLU -> cls.l1 -> LeakyReLU -> cls.out.
-    l1 = cl.DNN.block_0.linear.w
-    l1_w = l1.weight.detach().to(torch.float64).cpu().numpy()
-    l1_b = l1.bias.detach().to(torch.float64).cpu().numpy()
-    l1_w, l1_b = fold_bn_into_linear(cl.norm, l1_w, l1_b)
-    add("cls.l1.weight", l1_w)
-    add("cls.l1.bias", l1_b)
-
-    out = cl.out.w
-    out_w = out.weight.detach().to(torch.float64).cpu().numpy()
-    out_b = out.bias.detach().to(torch.float64).cpu().numpy()
-    out_w, out_b = fold_bn_into_linear(cl.DNN.block_0.norm, out_w, out_b)
-    add("cls.out.weight", out_w)
-    add("cls.out.bias", out_b)
-
-    # 1 filterbank + 4 blk.0 + 3*40 SERes2Net + 6 mfa + 6 asp.tdnn
-    # + 2 asp.attn + 2 fc + 2 cls.l1 + 2 cls.out
-    expected = 1 + 4 + 3 * 40 + 6 + 6 + 2 + 2 + 2 + 2
-    if len(rows) != expected:
-        raise ArchError(f"tensor count mismatch: emitted {len(rows)}, "
-                        f"expected {expected}")
-
-    # ----- table ----------------------------------------------------------
-    w_name = max(len(r[0]) for r in rows)
-    w_np = max(len(r[1]) for r in rows)
-    w_ne = max(len(r[2]) for r in rows)
-    print(f"\n{'tensor'.ljust(w_name)}  {'numpy'.ljust(w_np)}  "
-          f"{'ggml ne'.ljust(w_ne)}  {'dtype':6}  {'bytes':>12}")
-    print("-" * (w_name + w_np + w_ne + 6 + 12 + 8))
-    for name, npshape, ne, dt, nb in rows:
-        print(f"{name.ljust(w_name)}  {npshape.ljust(w_np)}  {ne.ljust(w_ne)}  "
-              f"{dt:6}  {nb:>12,}")
-    print("-" * (w_name + w_np + w_ne + 6 + 12 + 8))
-    print(f"{len(rows)} tensors, {total_bytes:,} bytes of tensor data "
-          f"({total_bytes / (1024 * 1024):.2f} MiB)")
+        b, p = em.blocks[i], f"blk.{i}"
+        add_tdnn(f"{p}.tdnn1", b.tdnn1)
+        # res2.{j} convolves chunk j+1 (plus the previous sub-block's output).
+        for j, sub in enumerate(b.res2net_block.blocks):
+            add_tdnn(f"{p}.res2.{j}", sub)
+        add_tdnn(f"{p}.tdnn2", b.tdnn2)
+        for c, m in (("c1", b.se_block.conv1), ("c2", b.se_block.conv2)):
+            add(f"{p}.se.{c}.weight", f64(m.conv.weight)[:, :, 0])
+            add(f"{p}.se.{c}.bias", f64(m.conv.bias))
+    # mfa consumes cat(blocks.1..3); asp.tdnn consumes cat([x, mean, std]).
+    add_tdnn("mfa", em.mfa, ("w1", "w2", "w3"))
+    add_tdnn("asp.tdnn", em.asp.tdnn, ("x", "mean", "std"))
+    add("asp.attn.weight", f64(em.asp.conv.conv.weight)[:, :, 0])
+    add("asp.attn.bias", f64(em.asp.conv.conv.bias))
+
+    # BNs with no activation before their linear map fold forward into it:
+    # asp_bn -> fc, cls.bn0 -> cls.l1, cls.bn1 -> cls.out. The classifier's
+    # LeakyReLUs sit before each BN, so they survive in the graph.
+    for name, bn, lin in (
+        ("fc", em.asp_bn, em.fc.conv),
+        ("cls.l1", cl.norm, cl.DNN.block_0.linear.w),
+        ("cls.out", cl.DNN.block_0.norm, cl.out.w),
+    ):
+        w = f64(lin.weight)
+        w, b = fold_bn_into_linear(bn, w.reshape(w.shape[0], w.shape[1]), f64(lin.bias))
+        add(f"{name}.weight", w)
+        add(f"{name}.bias", b)
 
     writer.write_header_to_file()
     writer.write_kv_data_to_file()
     writer.write_tensors_to_file()
     writer.close()
-
-    size = out_path.stat().st_size
-    digest = sha256_file(out_path)
-    print(f"Wrote {out_path} ({size:,} bytes, {size / (1024 * 1024):.2f} MiB)")
-    print(f"sha256: {digest}")
-
-    return {
-        "family": ARCH,
-        "variant": variant,
-        "refdtype": "F32",
-        "gguf_path": str(out_path.relative_to(REPO_ROOT))
-        if out_path.is_relative_to(REPO_ROOT) else str(out_path),
-        "gguf_bytes": int(size),
-        "gguf_sha256": digest,
-        "source_hf_repo": repo_id,
-        "source_hf_revision": revision,
-        "converter_script": "scripts/convert-ecapa_tdnn.py",
-        "n_tensors": len(rows),
-        "tensor_data_bytes": int(total_bytes),
-    }
-
-
-# ---------------------------------------------------------------------------
-# CLI
-# ---------------------------------------------------------------------------
+    print(f"Wrote {out_path} ({out_path.stat().st_size:,} bytes)")
 
 
 def main(argv: list[str] | None = None) -> int:
@@ -859,11 +413,14 @@ def main(argv: list[str] | None = None) -> int:
     repo_id = args.repo_id or (args.model if looks_like_repo_id(args.model)
                                else None)
     if repo_id is None:
-        print("error: cannot infer the source repo id from a local path; "
-              "pass --repo-id", file=sys.stderr)
-        return 2
+        fail("cannot infer the source repo id from a local path; pass --repo-id")
 
-    src = resolve_checkpoint_dir(args.model, args.revision)
+    src = resolve_model_dir(args.model, args.revision)
+    needed = ["hyperparams.yaml", "embedding_model.ckpt", "classifier.ckpt",
+              "label_encoder.txt"]
+    missing = [f for f in needed if not (src / f).exists()]
+    if missing:
+        fail(f"{src} is missing {missing}")
     slug = slug_from_repo_id(repo_id)
     out_path = args.out_path or (REPO_ROOT / "models" / slug /
                                  gguf_name(slug, "F32"))
@@ -871,26 +428,10 @@ def main(argv: list[str] | None = None) -> int:
     clf = load_reference(src)
     labels = assert_architecture(clf)
     filters = capture_fbank_matrix(clf)
-
-    import speechbrain
-
-    report = convert(clf, out_path, variant=DEFAULT_VARIANT,
-                     repo_id=repo_id, revision=args.revision,
-                     filters_201x60=filters, labels=labels)
-    report["speechbrain_version"] = speechbrain.__version__
-    report["torch_version"] = torch.__version__
-
-    rep_dir = REPO_ROOT / "reports" / "convert"
-    rep_dir.mkdir(parents=True, exist_ok=True)
-    rep_path = rep_dir / f"{slug}-F32.json"
-    rep_path.write_text(json.dumps(report, indent=2) + "\n")
-    print(f"Wrote {rep_path}")
+    convert(clf, out_path, variant=DEFAULT_VARIANT, repo_id=repo_id,
+            revision=args.revision, filters_201x60=filters, labels=labels)
     return 0
 
 
 if __name__ == "__main__":
-    try:
-        raise SystemExit(main())
-    except ArchError as e:
-        print(f"error: {e.msg}", file=sys.stderr)
-        raise
+    raise SystemExit(main())

From 651cfa86a9318838f0cd8cac8cb9a86d149dc9b9 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:32:12 +0800
Subject: [PATCH 28/38] langid: trim the ecapa_tdnn reference dumper

Shorten the docstring, drop the use_deterministic_algorithms version
fallback, merge the three tensor-to-numpy converters into one
shape-asserting helper, replace the CaptureHook class with a closure
(still asserting every hook fires exactly once), drive the dump from one
table of dump points, and drop the redundant eval/BN/dtype/window checks
(the converter asserts the architecture). The cls.log_probs hook is gone;
log_softmax(cls.logits_raw) is now checked bit-exact against
classify_batch's out_prob. prediction.json keeps only label_index and code,
the fields validate.py reads.

All 112 .f32 dumps and their .json sidecars across the 8 cases are
byte-identical; validate.py all (CPU) passes 14/14 tensors and 8/8
predictions. scripts/langid/run.py's SpeechBrain engine still loads
through configure_torch / load_reference / split_label.
---
 .../dump_reference_ecapa_tdnn_speechbrain.py  | 536 ++++--------------
 1 file changed, 110 insertions(+), 426 deletions(-)

diff --git a/scripts/dump_reference_ecapa_tdnn_speechbrain.py b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
index 0f1c326f5..8acb2c260 100644
--- a/scripts/dump_reference_ecapa_tdnn_speechbrain.py
+++ b/scripts/dump_reference_ecapa_tdnn_speechbrain.py
@@ -1,68 +1,18 @@
 #!/usr/bin/env python3
 """
-dump_reference_ecapa_tdnn_speechbrain.py - generate SpeechBrain
-VoxLingua107 ECAPA-TDNN reference tensors.
-
-SpeechBrain is the only published implementation of this checkpoint: the
-`hyperparams.yaml` in the HF repo instantiates SpeechBrain classes
-(`lobes.features.Fbank`, `lobes.models.ECAPA_TDNN.ECAPA_TDNN`,
-`lobes.models.Xvector.Classifier`) and the model card's inference example
-is `EncoderClassifier.from_hparams`. There is no Transformers shim.
-
-Usage:
+dump_reference_ecapa_tdnn_speechbrain.py - generate SpeechBrain VoxLingua107
+ECAPA-TDNN reference tensors (SpeechBrain is the only implementation).
 
     uv run --project scripts/envs/ecapa_tdnn \
       scripts/dump_reference_ecapa_tdnn_speechbrain.py encoder \
-      --model speechbrain/lang-id-voxlingua107-ecapa \
       --audio samples/fleurs-en.wav \
       --out build/validate/ecapa_tdnn/lang-id-voxlingua107-ecapa/fleurs-en/ref
 
-Writes:
-    <name>.f32        raw little-endian float32, row-major
-    <name>.json       per-tensor sidecar via shared scripts.lib.ref_dump
-    prediction.json   behavioural artifact (top-1 label)
-
-There is a single stage, `encoder`: language ID is one forward pass, so
-the front end, the ECAPA-TDNN embedding network and the classifier head
-are all dumped from one `classify_batch` call. Running the model twice
-would fire every hook twice; the script asserts each hook fired exactly
-once.
-
-Architecture summary:
-
-    audio (16 kHz mono)
-      -> Fbank: STFT(n_fft=400, win=400, hop=160, hamming periodic,
-                     center, constant pad) -> power spectrum [T, 201]
-                60 triangular mel filters -> 10*log10 -> 80 dB floor
-      -> InputNormalization(norm_type="sentence", std_norm=False)
-                     => fe.mel [T, 60]
-      -> ECAPA_TDNN: blocks.0 TDNN(60->1024, k=5)
-                     blocks.1..3 SERes2Net(1024, k=3, d=2/3/4)
-                     mfa TDNN(3072->3072, k=1) over concat(blocks.1..3)
-                     asp AttentiveStatisticsPooling(3072, attn=128)
-                     asp_bn -> fc(6144->256)   => enc.emb [256]
-      -> Xvector.Classifier: LeakyReLU -> BN -> Linear(256->512)
-                     -> LeakyReLU -> BN -> Linear(512->107)
-                     -> log_softmax
-
-Dump points (the contract with src/arch/ecapa_tdnn/graph.h, in that exact
-order and with those exact names). Every activation is written TIME-MAJOR `[T, C]`:
-PyTorch carries ECAPA activations as `[B, C, T]`, while the C++ debug
-dumper writes ggml `ne=[C, T]` as row-major `[T, C]`. Squeezing the batch
-dim and transposing here is what makes the two directories comparable.
-
-    fe.mel                  [T, 60]     mean_var_norm output
-    enc.blk.0.out           [T, 1024]
-    enc.blk.1.tdnn1.out     [T, 1024]   SERes2Net sub-steps, for bisecting
-    enc.blk.1.res2.out      [T, 1024]
-    enc.blk.1.se.out        [T, 1024]   before the residual add
-    enc.blk.{1,2,3}.out     [T, 1024]
-    enc.mfa.out             [T, 3072]
-    enc.asp.attn_logits     [T, 3072]   asp.conv (asp.attn.weight) output, pre-softmax
-    enc.asp.out             [6144]
-    enc.emb                 [256]       fc output
-    cls.hidden              [512]       DNN.block_0.act output (pre-BN1)
-    cls.logits_raw          [107]       the gate
+The single `encoder` stage hooks one `classify_batch` call and writes
+`<name>.f32` / `<name>.json` pairs (scripts/lib/ref_dump.py) for the dump
+points listed in cmd_encoder, plus prediction.json (top-1 label). Every hook must
+fire exactly once. Activations are written time-major `[T, C]` (PyTorch's
+`[1, C, T]` transposed) to match the C++ dumper (src/arch/ecapa_tdnn/graph.h).
 """
 
 from __future__ import annotations
@@ -70,271 +20,83 @@
 import argparse
 import json
 import math
-import os
 import sys
 from pathlib import Path
-from typing import Any
 
 import numpy as np
+import torch
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
-if str(REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(REPO_ROOT))
-SCRIPTS_DIR = REPO_ROOT / "scripts"
-if str(SCRIPTS_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPTS_DIR))
+sys.path.insert(0, str(REPO_ROOT / "scripts"))
 
-from scripts.lib.ref_dump import write_tensor  # noqa: E402
-from lib.gguf_common import slug_from_repo_id  # noqa: E402
-from lib.hf_source import looks_like_repo_id, resolve_model_dir  # noqa: E402
+from lib.hf_source import resolve_model_dir  # noqa: E402
+from lib.ref_dump import write_tensor  # noqa: E402
 
 DEFAULT_REPO = "speechbrain/lang-id-voxlingua107-ecapa"
 DEFAULT_REVISION = "0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9"
 
 SAMPLE_RATE = 16000
 HOP = 160
-WIN = 400
 N_MELS = 60
-EMB_DIM = 256
 N_LABELS = 107
-HIDDEN = 512
-
-
-# ---------------------------------------------------------------------------
-# Torch setup
-# ---------------------------------------------------------------------------
 
 
 def configure_torch(args: argparse.Namespace) -> None:
-    import torch
-
     torch.manual_seed(0)
     if args.torch_threads > 0:
         torch.set_num_threads(args.torch_threads)
         try:
             torch.set_num_interop_threads(1)
         except RuntimeError:
-            # Already set by an earlier call in this process; harmless.
-            pass
-    try:
-        torch.use_deterministic_algorithms(True, warn_only=True)
-    except TypeError:
-        torch.use_deterministic_algorithms(True)
-
-
-# ---------------------------------------------------------------------------
-# Shape conversion
-# ---------------------------------------------------------------------------
-
-
-def act_time_major(t, channels: int, *, name: str) -> np.ndarray:
-    """`[1, C, T]` torch activation -> `[T, C]` float32 numpy.
-
-    The channel count is asserted rather than inferred. A bare transpose
-    would silently succeed on a tensor that is already `[1, T, C]`, and the
-    resulting dump would look plausible while being the transpose of what
-    the C++ side writes.
-    """
-    import torch
-
-    if not isinstance(t, torch.Tensor):
-        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
-    if t.dim() != 3 or t.shape[0] != 1:
-        raise ValueError(f"{name}: expected [1, C, T], got {tuple(t.shape)}")
-    if t.shape[1] != channels:
-        raise ValueError(
-            f"{name}: expected {channels} channels on dim 1, "
-            f"got {tuple(t.shape)}"
-        )
-    a = t.detach().to(dtype=torch.float32, device="cpu")[0].transpose(0, 1).numpy()
-    return np.ascontiguousarray(a, dtype=np.float32)
-
-
-def feat_time_major(t, channels: int, *, name: str) -> np.ndarray:
-    """`[1, T, C]` torch feature tensor -> `[T, C]` float32 numpy.
-
-    SpeechBrain's front end is already time-major (`[B, T, n_mels]`), so
-    this only drops the batch dim — but it asserts the channel count for
-    the same reason `act_time_major` does.
-    """
-    import torch
-
-    if not isinstance(t, torch.Tensor):
-        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
-    if t.dim() != 3 or t.shape[0] != 1:
-        raise ValueError(f"{name}: expected [1, T, C], got {tuple(t.shape)}")
-    if t.shape[2] != channels:
-        raise ValueError(
-            f"{name}: expected {channels} channels on dim 2, "
-            f"got {tuple(t.shape)}"
-        )
-    a = t.detach().to(dtype=torch.float32, device="cpu")[0].numpy()
-    return np.ascontiguousarray(a, dtype=np.float32)
-
-
-def vec(t, n: int, *, name: str) -> np.ndarray:
-    """Any torch tensor with exactly `n` elements -> 1-D float32 numpy."""
-    import torch
-
-    if not isinstance(t, torch.Tensor):
-        raise TypeError(f"{name}: expected a torch.Tensor, got {type(t)!r}")
-    if t.numel() != n:
-        raise ValueError(f"{name}: expected {n} elements, got {t.numel()} "
-                         f"(shape {tuple(t.shape)})")
-    a = t.detach().to(dtype=torch.float32, device="cpu").reshape(-1).numpy()
-    return np.ascontiguousarray(a, dtype=np.float32)
-
-
-# ---------------------------------------------------------------------------
-# Hooks
-# ---------------------------------------------------------------------------
-
-
-class CaptureHook:
-    """Record the output a module emits during forward, and count firings.
-
-    The count is checked after the pass: a hook that fired zero times means
-    the module was not on the path we think it was, and a hook that fired
-    twice means the model ran more than once (which would make the dump
-    reflect the second call). Both are silent-wrong-number bugs otherwise.
-    """
-
-    def __init__(self, name: str) -> None:
-        self.name = name
-        self.value = None
-        self.calls = 0
-
-    def __call__(self, module, inputs, output) -> None:
-        self.calls += 1
-        if isinstance(output, tuple):
-            output = output[0]
-        self.value = output
-
-
-def hook(module, name: str, registry: dict[str, CaptureHook], handles: list):
-    h = CaptureHook(name)
-    handles.append(module.register_forward_hook(h))
-    registry[name] = h
-    return h
-
-
-# ---------------------------------------------------------------------------
-# Model loading
-# ---------------------------------------------------------------------------
-
-
-def resolve_checkpoint_dir(model_arg: str, revision: str | None) -> Path:
-    """Return a local SpeechBrain checkpoint directory (HF cache or local)."""
-    p = Path(model_arg).expanduser()
-    if not p.is_dir() and not looks_like_repo_id(model_arg):
-        raise SystemExit(
-            f"error: --model {model_arg!r} is neither an existing directory "
-            f"nor an org/name Hugging Face repo id"
-        )
-    return resolve_model_dir(model_arg, revision)
+            pass  # already set earlier in this process
+    torch.use_deterministic_algorithms(True, warn_only=True)
 
 
 def load_reference(args: argparse.Namespace):
-    """Build the SpeechBrain `EncoderClassifier` for this checkpoint.
-
-    Two things here are load-bearing:
-
-    1. `overrides={"pretrained_path": <local dir>}`. The shipped
-       `hyperparams.yaml` sets `pretrained_path:
-       speechbrain/lang-id-voxlingua107-ecapa`, and `Pretrainer.collect_files`
-       splits each `paths[...]` entry into (source, filename) — so without
-       the override SpeechBrain re-fetches the `.ckpt` files from the HF
-       `main` branch, ignoring our pinned revision entirely.
-    2. `savedir != source`. SpeechBrain symlinks the collected loadables
-       into `savedir`; pointing it at the snapshot directory makes it
-       symlink files onto themselves.
+    """Return `(EncoderClassifier, checkpoint_dir)`, in eval mode.
+
+    `pretrained_path` must be overridden to the local snapshot, otherwise
+    SpeechBrain re-fetches the .ckpt files from the HF `main` branch and the
+    pinned revision is ignored. `savedir` must differ from the snapshot
+    (SpeechBrain symlinks the loadables into it).
     """
-    import torch
     from speechbrain.inference.classifiers import EncoderClassifier
 
-    src = resolve_checkpoint_dir(args.model, args.revision)
+    src = resolve_model_dir(args.model, args.revision)
     savedir = REPO_ROOT / "build" / "speechbrain" / src.parent.name
     savedir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Loading EncoderClassifier: source={src} savedir={savedir} "
-          f"device={args.device}")
     clf = EncoderClassifier.from_hparams(
-        source=str(src),
-        savedir=str(savedir),
-        overrides={"pretrained_path": str(src)},
-        run_opts={"device": args.device},
-    )
-
-    # Force eval + fp32 + no grad ourselves rather than trusting run_opts.
-    # A BatchNorm left in train mode would use this batch's statistics
-    # (batch of one!) instead of the running stats, and every tensor from
-    # that layer onward would be plausible-looking garbage.
+        source=str(src), savedir=str(savedir),
+        overrides={"pretrained_path": str(src)}, run_opts={"device": args.device})
     clf.mods.eval()
-    for p in clf.mods.parameters():
-        p.requires_grad_(False)
-
-    training = [n for n, m in clf.mods.named_modules() if m.training]
-    if training:
-        raise SystemExit(f"error: modules still in training mode: {training[:5]}")
-
-    n_bn = 0
-    for name, m in clf.mods.named_modules():
-        if isinstance(m, torch.nn.modules.batchnorm._BatchNorm):
-            n_bn += 1
-            if m.running_mean is None or m.running_var is None:
-                raise SystemExit(
-                    f"error: BatchNorm {name} has no running statistics; "
-                    "the checkpoint did not load as expected"
-                )
-    print(f"eval mode ok; {n_bn} BatchNorm layers carry running statistics")
-
-    dtypes = {str(p.dtype) for p in clf.mods.parameters()}
-    if dtypes != {"torch.float32"}:
-        raise SystemExit(f"error: expected an fp32 model, got dtypes {dtypes}")
-
     return clf, src
 
 
-# ---------------------------------------------------------------------------
-# Audio
-# ---------------------------------------------------------------------------
-
-
-def load_audio(audio_path: Path) -> np.ndarray:
-    import soundfile as sf
-
-    pcm, sr = sf.read(str(audio_path), dtype="float32", always_2d=False)
-    if pcm.ndim > 1:
-        pcm = pcm.mean(axis=1)
-    if int(sr) != SAMPLE_RATE:
-        raise SystemExit(
-            f"error: {audio_path} is {sr} Hz; the model front end is "
-            f"{SAMPLE_RATE} Hz only. Resample before dumping."
-        )
-    return np.ascontiguousarray(pcm, dtype=np.float32)
-
-
 def split_label(label: str) -> tuple[str, str]:
-    """`'zh: Chinese'` -> `('zh', 'Chinese')`.
+    """`'zh: Chinese'` -> `('zh', 'Chinese')`."""
+    code, _, name = label.partition(": ")
+    return code.strip(), name.strip()
 
-    The label encoder stores the code and the English name in one string.
-    A label without the separator is passed through as its own code with an
-    empty name rather than crashing, so a future label-set change surfaces
-    as odd metadata instead of a traceback mid-dump.
-    """
-    if ": " in label:
-        code, name = label.split(": ", 1)
-        return code.strip(), name.strip()
-    return label.strip(), ""
 
+def to_np(t: torch.Tensor, shape: tuple[int, ...], *, ct: bool = False) -> np.ndarray:
+    """Hooked tensor -> contiguous f32 numpy of exactly `shape`.
 
-# ---------------------------------------------------------------------------
-# encoder stage
-# ---------------------------------------------------------------------------
+    1-D shapes flatten; otherwise the batch dim is dropped and `ct=True`
+    transposes `[C, T]` to time-major `[T, C]`. The shape is asserted so a
+    tensor in the wrong layout cannot pass as its transpose.
+    """
+    a = t.detach().to(dtype=torch.float32, device="cpu")
+    if len(shape) == 1:
+        a = a.reshape(-1)
+    else:
+        a = a[0].transpose(0, 1) if ct else a[0]
+    if tuple(a.shape) != shape:
+        raise SystemExit(f"error: hooked tensor is {tuple(a.shape)}, expected {shape}")
+    return np.ascontiguousarray(a.numpy(), dtype=np.float32)
 
 
 def cmd_encoder(args: argparse.Namespace) -> int:
-    import torch
+    import soundfile as sf
     import speechbrain
 
     configure_torch(args)
@@ -344,43 +106,49 @@ def cmd_encoder(args: argparse.Namespace) -> int:
     out_dir = Path(args.out).expanduser().resolve()
     out_dir.mkdir(parents=True, exist_ok=True)
 
-    pcm = load_audio(audio_path)
+    pcm, sr = sf.read(str(audio_path), dtype="float32", always_2d=False)
+    if pcm.ndim > 1:
+        pcm = pcm.mean(axis=1)
+    if int(sr) != SAMPLE_RATE:
+        raise SystemExit(f"error: {audio_path} is {sr} Hz; resample to {SAMPLE_RATE} Hz")
+    pcm = np.ascontiguousarray(pcm, dtype=np.float32)
     n_samples = int(pcm.size)
     expected_T = n_samples // HOP + 1
-    print(f"audio: {audio_path.name} samples={n_samples} "
-          f"({n_samples / SAMPLE_RATE:.3f} s) expected_frames={expected_T}")
 
     device = torch.device(args.device)
-    wav = torch.from_numpy(pcm).to(device).unsqueeze(0)          # [1, N]
-    # `wav_lens` is RELATIVE (fraction of the padded batch max), not a
-    # sample count. Batch of one, unpadded -> 1.0. Passing n_samples here
-    # would make InputNormalization and the ASP/SE masks compute over
-    # round(n_samples * T) frames, i.e. the wrong span, silently.
+    wav = torch.from_numpy(pcm).to(device).unsqueeze(0)  # [1, N]
+    # wav_lens is RELATIVE to the padded batch max (1.0 for one unpadded
+    # clip); a sample count would mask the wrong span, silently.
     wav_lens = torch.ones(1, device=device)
 
-    fe = clf.mods.compute_features
-    em = clf.mods.embedding_model
-    cl = clf.mods.classifier
-
-    handles: list = []
-    hooks: dict[str, CaptureHook] = {}
-
-    hook(clf.mods.mean_var_norm, "fe.mel", hooks, handles)
-    hook(em.blocks[0], "enc.blk.0.out", hooks, handles)
-    hook(em.blocks[1].tdnn1, "enc.blk.1.tdnn1.out", hooks, handles)
-    hook(em.blocks[1].res2net_block, "enc.blk.1.res2.out", hooks, handles)
-    hook(em.blocks[1].se_block, "enc.blk.1.se.out", hooks, handles)
-    hook(em.blocks[1], "enc.blk.1.out", hooks, handles)
-    hook(em.blocks[2], "enc.blk.2.out", hooks, handles)
-    hook(em.blocks[3], "enc.blk.3.out", hooks, handles)
-    hook(em.mfa, "enc.mfa.out", hooks, handles)
-    hook(em.asp.conv, "enc.asp.attn_logits", hooks, handles)
-    hook(em.asp, "enc.asp.out", hooks, handles)
-    hook(em.fc, "enc.emb", hooks, handles)
-    hook(cl.DNN.block_0.act, "cls.hidden", hooks, handles)
-    hook(cl.out, "cls.logits_raw", hooks, handles)
-    hook(cl.softmax, "cls.log_probs", hooks, handles)
-
+    em, cl = clf.mods.embedding_model, clf.mods.classifier
+    # (dump name, module, stage, channels, layout). Layouts: "tc" is already
+    # [1, T, C]; "ct" is [1, C, T] and gets transposed; "vec" is flattened.
+    points = [
+        ("fe.mel", clf.mods.mean_var_norm, "frontend", N_MELS, "tc"),
+        ("enc.blk.0.out", em.blocks[0], "encoder", 1024, "ct"),
+        ("enc.blk.1.tdnn1.out", em.blocks[1].tdnn1, "encoder", 1024, "ct"),
+        ("enc.blk.1.res2.out", em.blocks[1].res2net_block, "encoder", 1024, "ct"),
+        ("enc.blk.1.se.out", em.blocks[1].se_block, "encoder", 1024, "ct"),  # pre-residual
+        ("enc.blk.1.out", em.blocks[1], "encoder", 1024, "ct"),
+        ("enc.blk.2.out", em.blocks[2], "encoder", 1024, "ct"),
+        ("enc.blk.3.out", em.blocks[3], "encoder", 1024, "ct"),
+        ("enc.mfa.out", em.mfa, "encoder", 3072, "ct"),
+        ("enc.asp.attn_logits", em.asp.conv, "encoder", 3072, "ct"),  # pre-softmax
+        ("enc.asp.out", em.asp, "encoder", 6144, "vec"),
+        ("enc.emb", em.fc, "encoder", 256, "vec"),
+        ("cls.hidden", cl.DNN.block_0.act, "classifier", 512, "vec"),  # pre-BN1
+        ("cls.logits_raw", cl.out, "classifier", N_LABELS, "vec"),
+    ]
+
+    captured: dict[str, list] = {name: [] for name, *_ in points}
+
+    def grab(name):
+        def hook(_mod, _inp, out):
+            captured[name].append(out[0] if isinstance(out, tuple) else out)
+        return hook
+
+    handles = [m.register_forward_hook(grab(name)) for name, m, *_ in points]
     try:
         with torch.inference_mode():
             out_prob, score, index, text_lab = clf.classify_batch(wav, wav_lens)
@@ -388,24 +156,21 @@ def cmd_encoder(args: argparse.Namespace) -> int:
         for h in handles:
             h.remove()
 
-    # Every hook must have fired exactly once. Zero means the module was
-    # not on the forward path; more than one means the model ran twice and
-    # the dump would reflect the last call.
-    bad = {n: h.calls for n, h in hooks.items() if h.calls != 1}
+    # Zero firings = module not on the path; two = the model ran twice.
+    bad = {n: len(v) for n, v in captured.items() if len(v) != 1}
     if bad:
         raise SystemExit(f"error: unexpected hook call counts: {bad}")
+    out = {n: v[0] for n, v in captured.items()}
+    if not torch.equal(torch.log_softmax(out["cls.logits_raw"].reshape(-1), -1),
+                       out_prob.reshape(-1)):
+        raise SystemExit("error: log_softmax(cls.logits_raw) != classify_batch out_prob")
 
-    T = int(hooks["fe.mel"].value.shape[1])
+    T = int(out["fe.mel"].shape[1])
     if T != expected_T:
-        raise SystemExit(
-            f"error: frame count mismatch: fe.mel has T={T}, but "
-            f"floor({n_samples}/{HOP}) + 1 = {expected_T}. The C++ front end "
-            f"derives T from this formula; a mismatch means its STFT "
-            f"framing assumption (center=True, hop {HOP}) is wrong."
-        )
-    print(f"frames: T={T} == floor({n_samples}/{HOP}) + 1  [ok]")
-
-    source: dict[str, Any] = {
+        raise SystemExit(f"error: fe.mel has T={T}, but the C++ front end assumes "
+                         f"floor({n_samples}/{HOP}) + 1 = {expected_T}")
+
+    source = {
         "kind": "ecapa-tdnn-speechbrain",
         "framework": "speechbrain",
         "framework_version": speechbrain.__version__,
@@ -422,120 +187,39 @@ def cmd_encoder(args: argparse.Namespace) -> int:
         "n_frames": T,
         "n_mels": N_MELS,
     }
+    for name, _, stage, c, layout in points:
+        a = to_np(out[name], (c,) if layout == "vec" else (T, c), ct=layout == "ct")
+        print(f"  {name}: shape={a.shape} min={a.min():.4e} max={a.max():.4e} "
+              f"mean={a.mean():.6e}")
+        write_tensor(name, a, stage=stage, source=source, out_dir=out_dir)
 
-    def dump(name: str, array: np.ndarray, stage: str) -> None:
-        print(f"  {name}: shape={array.shape} min={array.min():.4e} "
-              f"max={array.max():.4e} mean={array.mean():.6e}")
-        write_tensor(name, array, stage=stage, source=source, out_dir=out_dir)
-
-    # ---- front end -------------------------------------------------------
-    dump("fe.mel", feat_time_major(hooks["fe.mel"].value, N_MELS, name="fe.mel"),
-         "frontend")
-
-    # ---- encoder ---------------------------------------------------------
-    for name, channels in (
-        ("enc.blk.0.out", 1024),
-        ("enc.blk.1.tdnn1.out", 1024),
-        ("enc.blk.1.res2.out", 1024),
-        ("enc.blk.1.se.out", 1024),
-        ("enc.blk.1.out", 1024),
-        ("enc.blk.2.out", 1024),
-        ("enc.blk.3.out", 1024),
-        ("enc.mfa.out", 3072),
-        ("enc.asp.attn_logits", 3072),
-    ):
-        dump(name, act_time_major(hooks[name].value, channels, name=name), "encoder")
-
-    dump("enc.asp.out", vec(hooks["enc.asp.out"].value, 6144, name="enc.asp.out"),
-         "encoder")
-    dump("enc.emb", vec(hooks["enc.emb"].value, EMB_DIM, name="enc.emb"), "encoder")
-
-    # ---- classifier ------------------------------------------------------
-    dump("cls.hidden", vec(hooks["cls.hidden"].value, HIDDEN, name="cls.hidden"),
-         "classifier")
-    dump("cls.logits_raw",
-         vec(hooks["cls.logits_raw"].value, N_LABELS, name="cls.logits_raw"),
-         "classifier")
-    log_probs = vec(hooks["cls.log_probs"].value, N_LABELS, name="cls.log_probs")
-
-    # The hooked softmax output must be the tensor classify_batch returned;
-    # if it is not, the hook is on the wrong module.
-    returned = out_prob.detach().to(dtype=torch.float32, device="cpu").reshape(-1).numpy()
-    if not np.array_equal(returned, log_probs):
-        raise SystemExit(
-            "error: cls.log_probs hook does not match classify_batch's out_prob"
-        )
-
-    # ---- front-end window ------------------------------------------------
-    window = fe.compute_STFT.window
-    expected_window = torch.hamming_window(WIN)
-    if not torch.equal(window.to(torch.float32).cpu(), expected_window):
-        raise SystemExit(
-            "error: STFT window is not torch.hamming_window(400); the periodic "
-            "Hamming window the C++ front end builds does not describe this "
-            "checkpoint"
-        )
-
-    # ---- prediction ------------------------------------------------------
     label_index = int(index.item())
-    label_encoder = clf.hparams.label_encoder
-    ind2lab = label_encoder.ind2lab
-    if len(ind2lab) != N_LABELS:
-        raise SystemExit(
-            f"error: label encoder has {len(ind2lab)} labels, expected {N_LABELS}"
-        )
-    code, name = split_label(str(ind2lab[label_index]))
-    decoded = text_lab[0] if isinstance(text_lab, list) else str(text_lab)
-    if str(decoded) != str(ind2lab[label_index]):
-        raise SystemExit(
-            f"error: label_encoder.decode_torch gave {decoded!r} but "
-            f"ind2lab[{label_index}] is {ind2lab[label_index]!r}"
-        )
-
-    prediction = {
-        "label_index": label_index,
-        "code": code,
-        "name": name,
-        "log_prob": float(log_probs[label_index]),
-        "source": {**source, "score": float(score.item())},
-    }
+    label = str(clf.hparams.label_encoder.ind2lab[label_index])
+    if str(text_lab[0]) != label:
+        raise SystemExit(f"error: decoded label {text_lab[0]!r} != ind2lab[{label_index}] {label!r}")
+    code, name = split_label(label)
+    prediction = {"label_index": label_index, "code": code}
     (out_dir / "prediction.json").write_text(json.dumps(prediction, indent=2) + "\n")
-    print(f"prediction: {code} ({name}) log_prob={prediction['log_prob']:.6f} "
-          f"prob={math.exp(prediction['log_prob']):.4f}")
-
+    print(f"prediction: {code} ({name}) prob={math.exp(float(score.item())):.4f}")
     return 0
 
 
-# ---------------------------------------------------------------------------
-# CLI
-# ---------------------------------------------------------------------------
-
-
-def add_common_args(p: argparse.ArgumentParser) -> None:
-    p.add_argument("--model", default=DEFAULT_REPO,
-                   help=f"HF repo id or local checkpoint dir (default: {DEFAULT_REPO})")
-    p.add_argument("--revision", default=DEFAULT_REVISION,
-                   help="HF revision (commit SHA) to pin the download to. "
-                        "Ignored when --model is a local directory.")
-    p.add_argument("--audio", required=True, help="16 kHz mono WAV path")
-    p.add_argument("--out", required=True, help="Output directory for dumps")
-    p.add_argument("--device", default="cpu", help="torch device (default: cpu)")
-    p.add_argument("--torch-threads", type=int, default=1,
-                   help="torch.set_num_threads (0 = unchanged)")
-
-
 def main(argv: list[str] | None = None) -> int:
     p = argparse.ArgumentParser(
         description="SpeechBrain VoxLingua107 ECAPA-TDNN reference dumper.")
     sub = p.add_subparsers(dest="cmd", required=True)
-
-    ep = sub.add_parser(
-        "encoder",
-        help="Run the full forward pass; dump front end, encoder, classifier "
-             "and prediction.json")
-    add_common_args(ep)
+    ep = sub.add_parser("encoder", help="Run the full forward pass; dump front "
+                        "end, encoder, classifier and prediction.json")
+    ep.add_argument("--model", default=DEFAULT_REPO,
+                    help=f"HF repo id or local checkpoint dir (default: {DEFAULT_REPO})")
+    ep.add_argument("--revision", default=DEFAULT_REVISION,
+                    help="HF revision (commit SHA); ignored for a local --model")
+    ep.add_argument("--audio", required=True, help="16 kHz mono WAV path")
+    ep.add_argument("--out", required=True, help="Output directory for dumps")
+    ep.add_argument("--device", default="cpu", help="torch device (default: cpu)")
+    ep.add_argument("--torch-threads", type=int, default=1,
+                    help="torch.set_num_threads (0 = unchanged)")
     ep.set_defaults(func=cmd_encoder)
-
     args = p.parse_args(argv)
     return args.func(args)
 

From 507ba7e5eb94c0814fbe01a0373f4b73d242b700 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:44:07 +0800
Subject: [PATCH 29/38] langid: fold compare.py into score.py --ref; drop trim
 and pending

score.py now measures the reference agreement (--ref) and writes the
agreement JSON beside the score. Gone: the --md report, multi-run input,
the unreachable --gate threshold, delta percentiles and disagreement
tables, and JSON fields nothing reads. The bootstrap is unchanged; every
score and agreement number reproduces the published files.

The always-none trim leaves the profile and the importer, and check.py
drops the speed 'pending' escape no profile uses.
---
 .claude/skills/porting-7-wer/SKILL.md     |   2 +-
 catalog/_benchmark_profiles.json          |   3 +-
 catalog/_schema.json                      |   2 +-
 docs/models/lang-id-voxlingua107-ecapa.md |  10 +-
 docs/porting/families/ecapa_tdnn.md       |  13 +-
 scripts/catalog/check.py                  |  19 +-
 scripts/catalog/ingest_accuracy.py        |  14 +-
 scripts/catalog/profiles.py               |   8 +-
 scripts/langid/compare.py                 | 254 --------------
 scripts/langid/score.py                   | 406 ++++++++--------------
 10 files changed, 168 insertions(+), 563 deletions(-)
 delete mode 100755 scripts/langid/compare.py

diff --git a/.claude/skills/porting-7-wer/SKILL.md b/.claude/skills/porting-7-wer/SKILL.md
index a58afac33..9f7312c86 100644
--- a/.claude/skills/porting-7-wer/SKILL.md
+++ b/.claude/skills/porting-7-wer/SKILL.md
@@ -42,7 +42,7 @@ WER progress:
 
 ### Step 1: Acceptance manifest (execute or ask-point)
 
-Read the acceptance dataset and metric from `upstream_benchmarks[0].{dataset, metric}`. Slugify: lowercase, spaces → hyphens. `"LibriSpeech test-clean"` → `librispeech-test-clean`. Resolve to `$MANIFEST`; every later step uses `$MANIFEST`, never a reconstructed path. Confirm `metric` is `wer` or `cer` (`der` for a DIARIZE-role model: score with `scripts/diar/` instead; `accuracy` for a LANGID-role model: run `scripts/langid/run.py` for the reference and every shipped quant, gate the ref dtype with `scripts/langid/compare.py` against the reference, and score with `scripts/langid/score.py`; anything else is out of scope). Do not use any publisher-reported score for pass/fail; the measured Oracle reference score is the gate target.
+Read the acceptance dataset and metric from `upstream_benchmarks[0].{dataset, metric}`. Slugify: lowercase, spaces → hyphens. `"LibriSpeech test-clean"` → `librispeech-test-clean`. Resolve to `$MANIFEST`; every later step uses `$MANIFEST`, never a reconstructed path. Confirm `metric` is `wer` or `cer` (`der` for a DIARIZE-role model: score with `scripts/diar/` instead; `accuracy` for a LANGID-role model: run `scripts/langid/run.py` for the reference and every shipped quant, then score each with `scripts/langid/score.py --ref <reference sweep>`, which gates the ref dtype on top-1 agreement (`--report-only` for the quants); anything else is out of scope). Do not use any publisher-reported score for pass/fail; the measured Oracle reference score is the gate target.
 
 If the intake's dataset is not covered by `scripts/wer/ingest.py`, extend
 that script before running this step.
diff --git a/catalog/_benchmark_profiles.json b/catalog/_benchmark_profiles.json
index c726d029b..19731cc7d 100644
--- a/catalog/_benchmark_profiles.json
+++ b/catalog/_benchmark_profiles.json
@@ -73,7 +73,7 @@
     },
     "langid-publication-v1": {
       "role": "langid",
-      "description": "The benchmark set published for language ID (LANGID role) models. Accuracy: open-set top-1 over every label, the macro mean over the FLEURS test languages of scripts/langid/ingest.py, on the first crop_s seconds of each clip without silence trimming, C++ on CPU, every shipped quant; scored by scripts/langid/score.py from one scripts/langid/run.py sweep (all crops), which scripts/langid/compare.py also compares against the reference sweep for the row's agreement. Speed: scripts/langid/bench.py through the Python binding; a sample <clip>-<N>s is the first N seconds of samples/<clip>.wav.",
+      "description": "The benchmark set published for language ID (LANGID role) models. Accuracy: open-set top-1 over every label, the macro mean over the FLEURS test languages of scripts/langid/ingest.py, on the first crop_s seconds of each clip without silence trimming, C++ on CPU, every shipped quant; scored by scripts/langid/score.py from one scripts/langid/run.py sweep (all crops), which it also compares against the reference sweep (--ref) for the row's agreement. Speed: scripts/langid/bench.py through the Python binding; a sample <clip>-<N>s is the first N seconds of samples/<clip>.wav.",
       "accuracy": [
         {
           "dataset": "fleurs",
@@ -85,7 +85,6 @@
           "timestamps": "none",
           "backend": "cpu",
           "crop_s": "5",
-          "trim": "none",
           "pooled_languages": [
             "en",
             "zh",
diff --git a/catalog/_schema.json b/catalog/_schema.json
index 40e0a42c1..4334ddc2c 100644
--- a/catalog/_schema.json
+++ b/catalog/_schema.json
@@ -408,7 +408,7 @@
               "n": {"type": "integer", "minimum": 1},
               "max_abs_logit_delta": {"type": ["number", "null"], "minimum": 0}
             },
-            "description": "Language ID only: top-1 decisions of the run this row was scored from that match the reference implementation on the same audio (scripts/langid/compare.py, the port's ship gate), over every crop of the sweep rather than only the scored one, and the largest logit difference seen."
+            "description": "Language ID only: top-1 decisions of the run this row was scored from that match the reference implementation on the same audio (scripts/langid/score.py --ref, the port's ship gate), over every crop of the sweep rather than only the scored one, and the largest logit difference seen."
           }
         },
         "allOf": [
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 86e5063bd..281542056 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -140,7 +140,7 @@ reference on every clip.
 | Dump script | `scripts/dump_reference_ecapa_tdnn_speechbrain.py` |
 | Manifest | `tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json` |
 | Command | `uv run scripts/validate.py all --family ecapa_tdnn` |
-| Dataset gate | `scripts/langid/compare.py` (FLEURS, every crop) |
+| Dataset gate | `scripts/langid/score.py --ref` (FLEURS, every crop) |
 
 ## Known Limitations
 
@@ -184,8 +184,8 @@ uv run scripts/validate.py all --family ecapa_tdnn
 ### Accuracy acceptance
 
 One reference sweep, then one C++ sweep per shipped GGUF (shown for F32;
-repeat with F16 and Q8_0, passing `--report-only` to `compare.py`, which gates
-F32 only).
+repeat with F16 and Q8_0, passing `--report-only` to `score.py`, which gates
+F32 only). `score.py --json` also writes the `.agreement.json` beside the score.
 
 ```bash
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
@@ -198,10 +198,8 @@ uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp \
   --library build-shared/src/libtranscribe.dylib \
   --manifest samples/langid/fleurs-*.manifest.jsonl --crops 3,5,10,full \
   --out reports/langid/cpp-f32-untrimmed.jsonl
-uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl \
-  reports/langid/cpp-f32-untrimmed.jsonl \
-  --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
 uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \
+  --ref reports/langid/ref-speechbrain-untrimmed.jsonl \
   --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 uv run scripts/catalog/ingest_accuracy.py --models lang-id-voxlingua107-ecapa
 uv run scripts/catalog/render.py
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index d8005698c..12ea2266c 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -84,9 +84,7 @@ Accuracy and decision parity (FLEURS from the HF cache):
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine speechbrain ... --out reports/langid/ref-speechbrain-untrimmed.jsonl
 uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py --engine cpp --library build-shared/src/libtranscribe.dylib ... --out reports/langid/cpp-f32-untrimmed.jsonl
-uv run scripts/langid/compare.py reports/langid/ref-speechbrain-untrimmed.jsonl reports/langid/cpp-f32-untrimmed.jsonl \
-  --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
-uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \
+uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl --ref reports/langid/ref-speechbrain-untrimmed.jsonl \
   --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 uv run scripts/catalog/ingest_accuracy.py --models lang-id-voxlingua107-ecapa
 ```
@@ -95,10 +93,11 @@ The catalog rows follow the `langid-publication-v1` profile
 (`catalog/_benchmark_profiles.json`): the open-set mean on the 5 s untrimmed
 crop of each shipped GGUF's sweep, with that sweep's agreement.
 
-`compare.py` fails on any top-1 disagreement or an agreement below 99.9%,
-and refuses runs whose labels, recipe, manifest hashes or checkpoint revision
-differ. Only F32 is gated: run the F16 and Q8_0 sweeps through it with
-`--report-only`, which still writes their agreement.
+`score.py --ref` fails on any top-1 disagreement with the reference sweep or
+any row the two sweeps do not share, and refuses sweeps whose labels, recipe,
+manifest hashes or checkpoint revision differ. With `--json` it writes the
+score and, beside it, the agreement. Only F32 is gated: score the F16 and Q8_0
+sweeps with `--report-only`, which still writes their agreement.
 
 Benchmarks: `scripts/langid/bench.py --profile` (`tools/transcribe-bench` is
 ASR-only) writes bench-driver reports under `reports/perf/`, which
diff --git a/scripts/catalog/check.py b/scripts/catalog/check.py
index 705ca3380..5c72c9470 100755
--- a/scripts/catalog/check.py
+++ b/scripts/catalog/check.py
@@ -118,9 +118,7 @@ def pairing_pass(records: dict, selected: bool = False) -> int:
 def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> int:
     """Check publication matrices, including explicit legacy accuracy rows.
 
-    Each record is held to its role's profile (language ID has its own). A
-    profile whose speed matrix declares `pending` reports that half as TODO
-    even when enforcing, until the bench it names lands."""
+    Each record is held to its role's profile (language ID has its own)."""
     try:
         profiles.load_profile(profile_id)
     except (OSError, ValueError, json.JSONDecodeError) as exc:
@@ -135,7 +133,6 @@ def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> in
         if not profile.get("role") and \
                 not record.get("capabilities", {}).get("transcribe", {}).get("supported"):
             continue
-        pending = profile["speed"].get("pending")
         accuracy_raw = profiles.expected_accuracy(record, profile)
         speed_raw = profiles.expected_speed(record, profile)
         accuracy = profiles.apply_exceptions(record, "accuracy", accuracy_raw)
@@ -226,17 +223,11 @@ def publication_pass(records: dict, profile_id: str | None, enforce: bool) -> in
         for suffix in ("missing", "invalid", "extra", "duplicate"):
             totals[resolved_id][f"speed_{suffix}"] += per_model[f"speed_{suffix}"]
 
-        held = {key: value for key, value in per_model.items()
-                if value and not (pending and key.startswith("speed_"))}
-        waiting = {key: value for key, value in per_model.items()
-                   if value and key not in held}
-        if held:
-            problems += sum(held.values())
-            details = ", ".join(f"{key}={value}" for key, value in held.items())
+        count = sum(per_model.values())
+        if count:
+            problems += count
+            details = ", ".join(f"{key}={value}" for key, value in per_model.items() if value)
             print(f"  {'FAIL' if enforce else 'TODO'} {name}: {details}")
-        if waiting:
-            details = ", ".join(f"{key}={value}" for key, value in waiting.items())
-            print(f"  TODO {name}: {details} (pending: {pending})")
 
     for resolved_id, total in sorted(totals.items()):
         print(f"publication {resolved_id}: accuracy {total['accuracy_required']} required, "
diff --git a/scripts/catalog/ingest_accuracy.py b/scripts/catalog/ingest_accuracy.py
index 14f23f845..8b78593f7 100755
--- a/scripts/catalog/ingest_accuracy.py
+++ b/scripts/catalog/ingest_accuracy.py
@@ -12,11 +12,11 @@
     uv run scripts/catalog/ingest_accuracy.py
 
 Language ID records follow their own profile (catalog/_benchmark_profiles.json
-`roles`). Their scores come from scripts/langid/score.py --json and
-compare.py --json under reports/langid/, named after the GGUF like a WER
-score (`<gguf stem>.fleurs-mul.score.json`, `.agreement.json`); the profile
-names the crop and trim, and the importer checks the sweep's own recipe
-against them instead of a stamp.
+`roles`). Their scores come from scripts/langid/score.py --ref --json under
+reports/langid/, named after the GGUF like a WER score
+(`<gguf stem>.fleurs-mul.score.json`, with its `.agreement.json`); the profile
+names the crop, and the importer checks the sweep's own recipe against it
+instead of a stamp.
 """
 from __future__ import annotations
 
@@ -91,7 +91,7 @@ def langid_row(record: dict, cell: dict, score: dict, agreement: dict | None,
         reasons.append(f"engine={score.get('engine')!r}")
     if pathlib.PurePath(str(score.get("model", ""))).name != filename:
         reasons.append(f"model={score.get('model')!r}")
-    for field in ("dataset", "split", "language", "backend", "trim"):
+    for field in ("dataset", "split", "language", "backend"):
         if score.get(field) != cell.get(field):
             reasons.append(f"{field}={score.get(field)!r}")
     if crop is None:
@@ -101,7 +101,7 @@ def langid_row(record: dict, cell: dict, score: dict, agreement: dict | None,
     if sorted(score.get("languages") or []) != sorted(cell.get("pooled_languages") or []):
         reasons.append(f"languages={score.get('languages')!r} are not the profile's pooled_languages")
     if agreement is None:
-        reasons.append("no agreement (write it with compare.py --json)")
+        reasons.append("no agreement (write it with score.py --ref --json)")
     elif agreement.get("run") != score.get("run"):
         reasons.append(f"agreement is for {agreement.get('run')!r}, not {score.get('run')!r}")
     elif not agreement.get("same_rows", False):
diff --git a/scripts/catalog/profiles.py b/scripts/catalog/profiles.py
index b73e9b26c..7fdd7b56d 100644
--- a/scripts/catalog/profiles.py
+++ b/scripts/catalog/profiles.py
@@ -177,10 +177,10 @@ def expected_accuracy(record: dict, profile: dict) -> list[dict]:
                     "timestamps": suite["timestamps"],
                     "gpu": suite.get("gpu"),
                     "backend": suite.get("backend"),
-                    # Language ID's crop and trim: which slice of a run.py
-                    # sweep the published number is, and which languages
-                    # its pooled mean is over.
-                    **{key: suite[key] for key in ("crop_s", "trim", "pooled_languages")
+                    # Language ID's crop: which slice of a run.py sweep the
+                    # published number is, and which languages its pooled
+                    # mean is over.
+                    **{key: suite[key] for key in ("crop_s", "pooled_languages")
                        if key in suite},
                 })
     return cells
diff --git a/scripts/langid/compare.py b/scripts/langid/compare.py
deleted file mode 100755
index 71bc439f4..000000000
--- a/scripts/langid/compare.py
+++ /dev/null
@@ -1,254 +0,0 @@
-#!/usr/bin/env -S uv run --script
-# /// script
-# requires-python = ">=3.11"
-# dependencies = []
-# ///
-"""
-compare.py - the dataset gate: does the C++ engine make the same decisions
-as the SpeechBrain reference on the same audio?
-
-The gate: the C++ F32 result makes the same top-1 decision as the SpeechBrain
-reference on the same audio. Any disagreement fails, whatever the aggregate
-agreement rate. The aggregate is also reported against --gate (99.9%).
-
-Provenance is enforced, not just printed: both runs must share the label set,
-the recipe (trim, crops, utterance count, crop dir, logit kind, sample rate)
-and the manifest hashes, and a C++ run must score a GGUF converted from the
-same checkpoint revision the reference run loaded.
-
-Usage:
-    uv run scripts/langid/compare.py \\
-        reports/langid/ref-speechbrain-untrimmed.jsonl \\
-        reports/langid/cpp-f32-untrimmed.jsonl \\
-        --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.agreement.json
-
-Rows are joined on (id, crop_s, trim). Both runs must cover the same keys;
-a key present in only one side is reported and counted as a failure, because
-a silently dropped utterance would otherwise raise the agreement rate.
-
-Exit status:
-    0  no disagreement, agreement >= the gate, same keys
-    1  a disagreement, agreement below the gate, or coverage differs
-    2  usage / input / provenance error
-
-Only F32 is held to the gate. For the quants pass --report-only: the
-agreement is still measured, printed and written (--json, with "passed"),
-but only a coverage mismatch exits 1.
-
-Every disagreement is printed with both engines' top-1 and, for each engine,
-that engine's own margin between the two codes in question.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-from pathlib import Path
-
-GATE = 0.999
-MAX_LISTED_DISAGREEMENTS = 50
-
-
-def load(path: Path) -> tuple[dict, dict[tuple, dict]]:
-    if not path.exists():
-        print(f"error: {path} does not exist", file=sys.stderr)
-        raise SystemExit(2)
-    lines = [l for l in path.read_text().splitlines() if l.strip()]
-    if not lines:
-        print(f"error: {path} is empty", file=sys.stderr)
-        raise SystemExit(2)
-    header = json.loads(lines[0])
-    if header.get("type") != "header":
-        print(f"error: {path} does not start with a header line", file=sys.stderr)
-        raise SystemExit(2)
-    rows = {}
-    for line in lines[1:]:
-        r = json.loads(line)
-        key = (r["id"], str(r["crop_s"]), r["trim"])
-        if key in rows:
-            print(f"error: {path} has duplicate row {key}", file=sys.stderr)
-            raise SystemExit(2)
-        rows[key] = r
-    return header, rows
-
-
-def percentile(sorted_vals: list[float], q: float) -> float:
-    """Nearest-rank percentile; no numpy so this stays a stdlib PEP-723 tool."""
-    if not sorted_vals:
-        return float("nan")
-    k = max(0, min(len(sorted_vals) - 1, math.ceil(q * len(sorted_vals)) - 1))
-    return sorted_vals[k]
-
-
-def main(argv: list[str] | None = None) -> int:
-    p = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("a", type=Path, help="reference run JSONL (baseline)")
-    p.add_argument("b", type=Path, help="run JSONL under test")
-    p.add_argument("--gate", type=float, default=GATE,
-                   help=f"minimum top-1 agreement rate (default {GATE})")
-    p.add_argument("--max-listed", type=int, default=MAX_LISTED_DISAGREEMENTS)
-    p.add_argument("--report-only", action="store_true",
-                   help="measure and record agreement without gating on it (the quants); "
-                        "a coverage mismatch still exits 1")
-    p.add_argument("--json", type=Path, default=None,
-                   help="also write the agreement for scripts/catalog/"
-                        "ingest_accuracy.py; name it <gguf stem>.fleurs-mul."
-                        "agreement.json under reports/langid/")
-    args = p.parse_args(argv)
-
-    ha, ra = load(args.a)
-    hb, rb = load(args.b)
-
-    labels_a = ha["labels"]
-    labels_b = hb["labels"]
-    if labels_a != labels_b:
-        print("error: the two runs disagree on the label set; a shared label "
-              "order is what makes the logit vectors comparable", file=sys.stderr)
-        return 2
-
-    print(f"A  {args.a}  engine={ha['engine']}  model={ha['model']}")
-    print(f"B  {args.b}  engine={hb['engine']}  model={hb['model']}")
-    provenance_ok = True
-    for field in ("trim", "crops", "n_utterances", "crop_dir", "logit_kind",
-                  "sample_rate", "manifest_sha256"):
-        va, vb = ha["recipe"].get(field), hb["recipe"].get(field)
-        provenance_ok &= va == vb
-        if field == "manifest_sha256" and va == vb:
-            print(f"   recipe.{field}: equal ({len(va or {})} manifests)")
-            continue
-        flag = "" if va == vb else "   <-- DIFFERS"
-        print(f"   recipe.{field}: A={va!r} B={vb!r}{flag}")
-    # A C++ run must score a GGUF converted from the reference's checkpoint.
-    revisions = {h["engine"]: h["recipe"].get("revision") or h["recipe"].get("gguf_source_commit")
-                 for h in (ha, hb)}
-    print(f"   checkpoint revision: {revisions}")
-    if len({r for r in revisions.values()}) != 1 or None in revisions.values():
-        provenance_ok = False
-        print("   <-- the runs do not trace back to one checkpoint revision")
-    print()
-    if not provenance_ok:
-        print("error: provenance mismatch; refusing to compare", file=sys.stderr)
-        return 2
-
-    only_a = sorted(set(ra) - set(rb))
-    only_b = sorted(set(rb) - set(ra))
-    keys = sorted(set(ra) & set(rb))
-    if only_a or only_b:
-        print(f"coverage mismatch: {len(only_a)} rows only in A, "
-              f"{len(only_b)} rows only in B")
-        for k in (only_a + only_b)[:10]:
-            print(f"  {k}")
-        print()
-    if not keys:
-        print("error: no shared (id, crop_s, trim) keys", file=sys.stderr)
-        return 2
-
-    n_agree = 0
-    abs_deltas: list[float] = []
-    disagreements = []
-
-    for key in keys:
-        x, y = ra[key], rb[key]
-        la, lb = x["logits"], y["logits"]
-        if len(la) != len(lb):
-            print(f"error: {key} has {len(la)} vs {len(lb)} logits", file=sys.stderr)
-            return 2
-        for u, v in zip(la, lb):
-            abs_deltas.append(abs(u - v))
-
-        ia = max(range(len(la)), key=lambda i: la[i])
-        ib = max(range(len(lb)), key=lambda i: lb[i])
-        if ia == ib:
-            n_agree += 1
-        else:
-            # Each engine's own margin between the two contested labels.
-            disagreements.append({
-                "key": key,
-                "language": x["language"],
-                "a_top1": labels_a[ia],
-                "b_top1": labels_a[ib],
-                "a_gap": la[ia] - la[ib],
-                "b_gap": lb[ib] - lb[ia],
-                "max_abs_delta": max(abs(u - v) for u, v in zip(la, lb)),
-            })
-
-    n = len(keys)
-    rate = n_agree / n
-    abs_deltas.sort()
-
-    print(f"joined rows: {n}")
-    print(f"top-1 agreement: {n_agree}/{n} = {100 * rate:.4f}%  "
-          f"(gate {100 * args.gate:.2f}%)")
-    print(f"|delta logit| over {len(abs_deltas)} values: "
-          f"max={abs_deltas[-1]:.6g}  p99={percentile(abs_deltas, 0.99):.6g}  "
-          f"median={percentile(abs_deltas, 0.50):.6g}")
-    print()
-
-    if disagreements:
-        disagreements.sort(key=lambda d: -max(abs(d["a_gap"]), abs(d["b_gap"])))
-        print(f"{len(disagreements)} disagreements "
-              f"(listed worst-margin first, up to {args.max_listed}):")
-        print(f"{'id':<20} {'crop':>5} {'lang':>5} {'A top1':>7} {'B top1':>7} "
-              f"{'A gap':>10} {'B gap':>10} {'max|dlogit|':>12}")
-        for d in disagreements[: args.max_listed]:
-            uid, crop, _trim = d["key"]
-            print(f"{uid:<20} {crop:>5} {d['language']:>5} {d['a_top1']:>7} "
-                  f"{d['b_top1']:>7} {d['a_gap']:10.5f} {d['b_gap']:10.5f} "
-                  f"{d['max_abs_delta']:12.3g}")
-        worst = max(max(abs(d["a_gap"]), abs(d["b_gap"])) for d in disagreements)
-        print(f"\nlargest contested margin on either side: {worst:.5f}")
-        print()
-
-    if only_a or only_b:
-        print("FAIL: the two runs do not cover the same rows")
-        status = 1
-    elif disagreements:
-        print(f"FAIL: {len(disagreements)} disagreements: "
-              f"{[d['key'] for d in disagreements[:10]]}")
-        status = 1
-    elif rate < args.gate:
-        print(f"FAIL: agreement {100 * rate:.4f}% < {100 * args.gate:.2f}%")
-        status = 1
-    else:
-        print(f"PASS: agreement {100 * rate:.4f}% >= {100 * args.gate:.2f}%")
-        status = 0
-
-    if args.json:
-        # The catalog records every shipped GGUF's agreement, gate or not:
-        # only F32 is held to the gate, the quants' counts are the evidence
-        # for shipping them.
-        args.json.parent.mkdir(parents=True, exist_ok=True)
-        args.json.write_text(json.dumps({
-            "schema": "transcribe-langid-agreement-v1",
-            "reference": ha["engine"],
-            "reference_run": rel(args.a),
-            "run": rel(args.b),
-            "model": hb["model"],
-            "n": n,
-            "n_agree": n_agree,
-            "max_abs_logit_delta": abs_deltas[-1],
-            "disagreements": len(disagreements),
-            "unlisted": len(disagreements),
-            "same_rows": not (only_a or only_b),
-            "gate": args.gate,
-            "passed": status == 0,
-        }, indent=2) + "\n")
-        print(f"wrote {args.json}")
-    if args.report_only and not (only_a or only_b):
-        return 0
-    return status
-
-
-def rel(path: Path) -> str:
-    """Repo-relative when possible, matching score.py's `run`."""
-    try:
-        return str(path.resolve().relative_to(Path(__file__).resolve().parents[2]))
-    except ValueError:
-        return str(path)
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/scripts/langid/score.py b/scripts/langid/score.py
index 4d0b6e8cb..9a0e29ee0 100755
--- a/scripts/langid/score.py
+++ b/scripts/langid/score.py
@@ -6,25 +6,24 @@
 # ]
 # ///
 """
-score.py - open-set top-1 accuracy of a run.py sweep.
+score.py - open-set top-1 accuracy of a scripts/langid/run.py sweep and, with
+--ref, its top-1 agreement with the reference sweep (the dataset gate).
 
-Usage:
-    uv run scripts/langid/score.py reports/langid/ref-speechbrain-untrimmed.jsonl \\
-        --md reports/langid/ref-speechbrain-untrimmed.md
-
-    # the catalog's accuracy row for one shipped GGUF (ingest_accuracy.py)
     uv run scripts/langid/score.py reports/langid/cpp-f32-untrimmed.jsonl \\
+        --ref reports/langid/ref-speechbrain-untrimmed.jsonl \\
         --json reports/langid/lang-id-voxlingua107-ecapa-F32.fleurs-mul.score.json
 
-The markdown report is the open-set top-1 accuracy per language and mean,
-per crop, with a 95% bootstrap CI on the mean. Correctness is strict code
-equality (`nn` is not credited for `no`, `be` not for `ru`, `jw` not for
-`id`).
+Accuracy is the macro mean over languages of strict code equality (`nn` is
+not credited for `no`), per crop, with a 95% bootstrap CI that resamples
+utterances within each language.
+
+--json writes the score; with --ref it also writes the agreement next to it
+(`.agreement.json`); scripts/catalog/ingest_accuracy.py reads both.
 
-The mean is the MACRO mean over languages (each language weighs 1/15). The
-bootstrap resamples utterances within each language and re-averages the
-per-language accuracies, so the CI describes the same statistic the point
-estimate does.
+Agreement joins rows on (id, crop_s). Exit 1 on any top-1 disagreement or
+if the sweeps do not cover the same rows; exit 2 if they differ in labels,
+recipe or checkpoint revision. Only F32 is gated: --report-only (the quants)
+exits 1 on a coverage mismatch only.
 """
 
 from __future__ import annotations
@@ -37,284 +36,157 @@
 import numpy as np
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
-
-CROP_ORDER_HINT = {"3": 0, "5": 1, "10": 2, "full": 99}
 N_BOOT = 1000
 BOOT_SEED = 42
+CI = 0.95
+RECIPE_FIELDS = ("crops", "n_utterances", "logit_kind", "sample_rate", "manifest_sha256")
 
 
-# ---------------------------------------------------------------------------
-# Loading
-# ---------------------------------------------------------------------------
-
-
-class Run:
-    def __init__(self, path: Path) -> None:
-        self.path = path
-        lines = [l for l in path.read_text().splitlines() if l.strip()]
-        if not lines:
-            raise SystemExit(f"error: {path} is empty")
-        self.header = json.loads(lines[0])
-        if self.header.get("type") != "header":
-            raise SystemExit(f"error: {path} does not start with a header line")
-        self.labels: list[str] = self.header["labels"]
-        self.index_of = {c: i for i, c in enumerate(self.labels)}
-
-        rows = [json.loads(l) for l in lines[1:]]
-        if not rows:
-            raise SystemExit(f"error: {path} has a header but no rows")
-        self.crops = list(dict.fromkeys(str(r["crop_s"]) for r in rows))
-        self.crops.sort(key=lambda c: CROP_ORDER_HINT.get(c, float(c) if c.replace(".", "").isdigit() else 50))
-        self.languages = list(dict.fromkeys(r["language"] for r in rows))
+def die(msg: str) -> None:
+    print(f"error: {msg}", file=sys.stderr)
+    raise SystemExit(2)
 
-        # Per crop: a [N, 107] logit matrix plus aligned id/language vectors.
-        # One array per crop keeps every accuracy a vectorised argmax.
-        self.by_crop: dict[str, dict] = {}
-        for crop in self.crops:
-            sel = [r for r in rows if str(r["crop_s"]) == crop]
-            self.by_crop[crop] = {
-                "ids": [r["id"] for r in sel],
-                "lang": np.array([r["language"] for r in sel]),
-                "logits": np.array([r["logits"] for r in sel], dtype=np.float64),
-                "audio_s": np.array([r["audio_s"] for r in sel], dtype=np.float64),
-            }
-        self.n_rows = len(rows)
-
-    @property
-    def name(self) -> str:
-        return self.path.stem
-
-
-# ---------------------------------------------------------------------------
-# Metrics
-# ---------------------------------------------------------------------------
 
+def rel(path: Path) -> str:
+    try:
+        return str(path.resolve().relative_to(REPO_ROOT))
+    except ValueError:
+        return str(path)
 
-def per_language_accuracy(pred_codes: np.ndarray, langs: np.ndarray,
-                          order: list[str]) -> tuple[dict[str, float], float]:
-    acc = {}
-    for lg in order:
-        m = langs == lg
-        acc[lg] = float((pred_codes[m] == lg).mean()) if m.any() else float("nan")
-    vals = [a for a in acc.values() if a == a]
-    return acc, (sum(vals) / len(vals) if vals else float("nan"))
 
+def load(path: Path) -> tuple[dict, dict[tuple, dict]]:
+    lines = [json.loads(l) for l in path.read_text().splitlines() if l.strip()]
+    if not lines or lines[0].get("type") != "header" or len(lines) < 2:
+        die(f"{path} is not a run.py sweep (header line, then rows)")
+    rows = {(r["id"], str(r["crop_s"])): r for r in lines[1:]}
+    if len(rows) != len(lines) - 1:
+        die(f"{path} has duplicate (id, crop_s) rows")
+    return lines[0], rows
 
-def bootstrap_macro_ci(correct: np.ndarray, langs: np.ndarray, order: list[str],
-                       n_boot: int = N_BOOT, seed: int = BOOT_SEED,
-                       ci: float = 0.95) -> tuple[float, float]:
-    """95% CI on the macro-mean accuracy, stratified by language.
 
-    Utterances are resampled with replacement inside each language and the
-    per-language accuracies re-averaged, so the resampled statistic is the
-    same macro mean the point estimate reports.
-    """
-    rng = np.random.default_rng(seed)
-    blocks = []
-    for lg in order:
-        m = langs == lg
-        if m.any():
-            blocks.append(correct[m].astype(np.float64))
-    if not blocks:
-        return float("nan"), float("nan")
-    means = np.empty(n_boot, dtype=np.float64)
-    for b in range(n_boot):
+def bootstrap_ci(blocks: list[np.ndarray]) -> tuple[float, float]:
+    rng = np.random.default_rng(BOOT_SEED)
+    means = np.empty(N_BOOT, dtype=np.float64)
+    for b in range(N_BOOT):
         acc = 0.0
         for blk in blocks:
-            idx = rng.integers(0, blk.size, blk.size)
-            acc += blk[idx].mean()
+            acc += blk[rng.integers(0, blk.size, blk.size)].mean()
         means[b] = acc / len(blocks)
     means.sort()
-    lo = means[int((1 - ci) / 2 * n_boot)]
-    hi = means[min(n_boot - 1, int((1 + ci) / 2 * n_boot))]
-    return float(lo), float(hi)
-
-
-def rel(p: Path) -> str:
-    """Repo-relative path when possible; reports are read in-tree."""
-    try:
-        return str(Path(p).resolve().relative_to(REPO_ROOT))
-    except ValueError:
-        return str(p)
-
-
-def crop_head(c: str) -> str:
-    return "full" if c == "full" else f"{c} s"
-
-
-def crop_list(crops: list[str]) -> str:
-    return ", ".join(crop_head(c) for c in crops)
-
-
-def pct(x: float, digits: int = 1) -> str:
-    if x != x:
-        return "-"
-    return f"{100.0 * x:.{digits}f}"
-
-
-# ---------------------------------------------------------------------------
-# Report sections
-# ---------------------------------------------------------------------------
-
-
-def md_table(header: list[str], rows: list[list[str]]) -> list[str]:
-    out = ["| " + " | ".join(header) + " |",
-           "|" + "|".join("---" for _ in header) + "|"]
-    for r in rows:
-        out.append("| " + " | ".join(r) + " |")
-    out.append("")
-    return out
-
-
-def section_setup(run: Run) -> list[str]:
-    h = run.header
-    r = h["recipe"]
-    lines = [f"## Setup", ""]
-    lines.append(f"- Engine: `{h['engine']}`, model `{h['model']}`")
-    if "revision" in r:
-        lines.append(f"- Revision: `{r['revision']}` "
-                     f"(speechbrain {r.get('speechbrain')}, torch {r.get('torch')}, "
-                     f"{r.get('device')}, {r.get('model_dtype')}, batch "
-                     f"{r.get('batch_size')})")
-    if "backend" in r:
-        lines.append(f"- Backend `{r['backend']}`, threads {r.get('threads')}, "
-                     f"max_audio_ms {r.get('max_audio_ms')}")
-    lines.append(f"- Dataset: FLEURS `test`, {len(run.languages)} languages "
-                 f"x {r['n_utterances'] // max(len(run.languages), 1)} utterances "
-                 f"= {r['n_utterances']} clips, longest {r['longest_clip_s']:.1f} s")
-    lines.append(f"- Crops: {crop_list(run.crops)}, from the start of the clip")
-    lines.append(f"- Logits: `{r['logit_kind']}`; both engines read the same "
-                 f"16-bit crops under `{r['crop_dir']}`")
-    lines.append(f"- Bootstrap {N_BOOT} resamples, seed {BOOT_SEED}")
-    lines.append(f"- Correctness is strict label-code equality "
-                 f"(`nn` is not credited for `no`, `be` not for `ru`, `jw` not for `id`)")
-    lines.append("")
-    return lines
-
-
-def openset(run: Run) -> tuple[dict[str, dict[str, float]], dict[str, tuple[float, float, float]]]:
-    """Per crop: open-set accuracy per language, and the macro mean with its
-    bootstrap CI as (mean, lo, hi)."""
-    means: dict[str, tuple[float, float, float]] = {}
-    per_crop_acc: dict[str, dict[str, float]] = {}
-    for crop in run.crops:
-        d = run.by_crop[crop]
-        pred = np.array(run.labels)[np.argmax(d["logits"], axis=1)]
-        acc, mean = per_language_accuracy(pred, d["lang"], run.languages)
-        lo, hi = bootstrap_macro_ci(pred == d["lang"], d["lang"], run.languages)
-        means[crop] = (mean, lo, hi)
-        per_crop_acc[crop] = acc
-    return per_crop_acc, means
-
-
-def section_openset(run: Run) -> list[str]:
-    langs_order = run.languages
-    rows = []
-    per_crop_acc, means = openset(run)
-
-    for lg in langs_order:
-        rows.append([lg] + [pct(per_crop_acc[c][lg]) for c in run.crops])
-    rows.append(["**mean**"] + [f"**{pct(means[c][0])}**" for c in run.crops])
-    rows.append(["95% CI"] + [f"{pct(means[c][1])}-{pct(means[c][2])}" for c in run.crops])
-
-    lines = ["## Open-set top-1 accuracy (all 107 labels), %", ""]
-    lines += md_table(["lang"] + [crop_head(c) for c in run.crops], rows)
-    return lines
-
-
-# ---------------------------------------------------------------------------
-# Machine-readable headline (scripts/catalog/ingest_accuracy.py)
-# ---------------------------------------------------------------------------
-
-SCORE_SCHEMA = "transcribe-langid-score-v1"
-
-
-def dataset_of(recipe: dict) -> str | None:
-    """`fleurs` when every manifest is a scripts/langid/ingest.py FLEURS one
-    (`fleurs-<code>.manifest.jsonl`, test split only), else None."""
-    names = [Path(m).name for m in recipe.get("manifests", [])]
-    return "fleurs" if names and all(n.startswith("fleurs-") for n in names) else None
-
-
-def score_json(run: Run) -> dict:
-    """The open-set macro mean per crop, with what produced it. The catalog
-    takes one crop of this (the publication profile's) as a row; the run's
-    header is carried so the importer can check the recipe itself."""
-    h, r = run.header, run.header["recipe"]
-    _, means = openset(run)
+    return (float(means[int((1 - CI) / 2 * N_BOOT)]),
+            float(means[min(N_BOOT - 1, int((1 + CI) / 2 * N_BOOT))]))
+
+
+def accuracy(header: dict, rows: list[dict]) -> tuple[list[str], dict]:
+    """Per crop: n, macro-mean accuracy and its CI, in percent."""
+    labels = np.array(header["labels"])
+    languages = list(dict.fromkeys(r["language"] for r in rows))
+    crops = {}
+    for crop in dict.fromkeys(str(r["crop_s"]) for r in rows):
+        sel = [r for r in rows if str(r["crop_s"]) == crop]
+        lang = np.array([r["language"] for r in sel])
+        logits = np.array([r["logits"] for r in sel], dtype=np.float64)
+        correct = (labels[np.argmax(logits, axis=1)] == lang).astype(np.float64)
+        blocks = [correct[lang == lg] for lg in languages if (lang == lg).any()]
+        mean = sum(float(blk.mean()) for blk in blocks) / len(blocks)
+        lo, hi = bootstrap_ci(blocks)
+        crops[crop] = {"n": len(sel), "acc_pct": round(100.0 * mean, 2),
+                       "ci95": [round(100.0 * lo, 2), round(100.0 * hi, 2)]}
+    return languages, crops
+
+
+def agreement(ref: Path, header: dict, rows: dict, run: Path) -> tuple[dict, list]:
+    """The agreement JSON, and the first few disagreeing keys."""
+    rh, rrows = load(ref)
+    if rh["labels"] != header["labels"]:
+        die("the sweeps disagree on the label set")
+    differs = [f for f in RECIPE_FIELDS if rh["recipe"].get(f) != header["recipe"].get(f)]
+    if differs:
+        die(f"the sweeps' recipes differ in {differs}")
+    revisions = {h["recipe"].get("revision") or h["recipe"].get("gguf_source_commit")
+                 for h in (rh, header)}
+    if len(revisions) != 1 or None in revisions:
+        die(f"the sweeps do not trace back to one checkpoint revision: {revisions}")
+    keys = sorted(set(rrows) & set(rows))
+    if not keys:
+        die("no shared (id, crop_s) rows")
+    a = np.array([rrows[k]["logits"] for k in keys], dtype=np.float64)
+    b = np.array([rows[k]["logits"] for k in keys], dtype=np.float64)
+    if a.shape != b.shape:
+        die(f"logit shapes differ: {a.shape} vs {b.shape}")
+    agree = np.argmax(a, axis=1) == np.argmax(b, axis=1)
     return {
-        "schema": SCORE_SCHEMA,
-        "run": rel(run.path),
-        "engine": h["engine"],
-        "model": h["model"],
-        # The build the native library reports, not the checkout scoring it.
-        "engine_sha": r.get("native_commit") if r.get("native_commit") != "unknown" else None,
-        "backend": r.get("backend"),
-        "trim": r["trim"],
-        "dataset": dataset_of(r),
-        "split": "test",
-        "language": "mul",
-        "languages": run.languages,
-        "decision_space": f"open ({len(run.labels)})",
-        "created": h.get("created"),
-        "bootstrap": {"n": N_BOOT, "seed": BOOT_SEED},
-        "crops": {
-            crop: {
-                "n": int(run.by_crop[crop]["lang"].size),
-                "acc_pct": round(100.0 * mean, 2),
-                "ci95": [round(100.0 * lo, 2), round(100.0 * hi, 2)],
-            }
-            for crop, (mean, lo, hi) in means.items()
-        },
-    }
-
-
-# ---------------------------------------------------------------------------
-# Driver
-# ---------------------------------------------------------------------------
-
-
-def score_run(run: Run) -> list[str]:
-    lines = [f"# Language-ID accuracy - `{run.name}`", ""]
-    lines += section_setup(run)
-    lines += section_openset(run)
-    return lines
+        "schema": "transcribe-langid-agreement-v1",
+        "reference_run": rel(ref),
+        "run": rel(run),
+        "n": len(keys),
+        "n_agree": int(agree.sum()),
+        "max_abs_logit_delta": float(np.abs(a - b).max()),
+        "disagreements": int((~agree).sum()),
+        "same_rows": set(rrows) == set(rows),
+    }, [keys[i] for i in np.flatnonzero(~agree)[:10]]
 
 
 def main(argv: list[str] | None = None) -> int:
     p = argparse.ArgumentParser(
         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("runs", nargs="+", type=Path, help="run.py JSONL report(s)")
-    p.add_argument("--md", type=Path,
-                   help="write the first run's markdown report here (later "
-                        "runs go next to it as <run name>.md)")
+    p.add_argument("run", type=Path, help="run.py JSONL sweep")
+    p.add_argument("--ref", type=Path, help="reference sweep to measure agreement against")
+    p.add_argument("--report-only", action="store_true",
+                   help="record agreement without gating on it (the quants)")
     p.add_argument("--json", type=Path,
-                   help="write the first run's headline (open-set accuracy "
-                        "per crop, with provenance) here, for "
-                        "scripts/catalog/ingest_accuracy.py; name it "
-                        "<gguf stem>.fleurs-mul.score.json under reports/langid/")
+                   help="write the score here: <gguf stem>.fleurs-mul.score.json "
+                        "under reports/langid/")
     args = p.parse_args(argv)
-    if args.json and len(args.runs) > 1:
-        p.error("--json takes exactly one run")
-
-    loaded = [Run(r) for r in args.runs]
-    scored = [(run, score_run(run)) for run in loaded]
+    if args.ref and args.json and not args.json.name.endswith(".score.json"):
+        p.error("--json must end in .score.json (the agreement is written beside it)")
+
+    header, rows = load(args.run)
+    recipe = header["recipe"]
+    languages, crops = accuracy(header, list(rows.values()))
+    for crop, c in crops.items():
+        print(f"crop {crop:>4}: {c['acc_pct']:6.2f}% "
+              f"[{c['ci95'][0]:.2f}, {c['ci95'][1]:.2f}]  n={c['n']}")
+
+    status = 0
+    agr = None
+    if args.ref:
+        agr, disagreeing = agreement(args.ref, header, rows, args.run)
+        print(f"top-1 agreement with {agr['reference_run']}: {agr['n_agree']}/{agr['n']}, "
+              f"max |delta logit| {agr['max_abs_logit_delta']:.6g}")
+        if not agr["same_rows"]:
+            print("FAIL: the sweeps do not cover the same rows")
+            status = 1
+        elif agr["disagreements"] and not args.report_only:
+            print(f"FAIL: {agr['disagreements']} disagreements, e.g. {disagreeing}")
+            status = 1
 
-    text = "\n".join(scored[0][1]) + "\n"
-    sys.stdout.write(text)
-    if args.md:
-        args.md.parent.mkdir(parents=True, exist_ok=True)
-        args.md.write_text(text)
-        print(f"wrote {args.md}", file=sys.stderr)
-        for run, lines in scored[1:]:
-            alt = args.md.with_name(f"{run.name}.md")
-            alt.write_text("\n".join(lines) + "\n")
-            print(f"wrote {alt}", file=sys.stderr)
     if args.json:
+        names = [Path(m).name for m in recipe.get("manifests", [])]
+        native = recipe.get("native_commit")
         args.json.parent.mkdir(parents=True, exist_ok=True)
-        args.json.write_text(json.dumps(score_json(loaded[0]), indent=2) + "\n")
-        print(f"wrote {args.json}", file=sys.stderr)
-    return 0
+        args.json.write_text(json.dumps({
+            "schema": "transcribe-langid-score-v1",
+            "run": rel(args.run),
+            "engine": header["engine"],
+            "model": header["model"],
+            # The build the native library reports, not the checkout scoring it.
+            "engine_sha": native if native != "unknown" else None,
+            "backend": recipe.get("backend"),
+            "dataset": "fleurs" if names and all(n.startswith("fleurs-") for n in names) else None,
+            "split": "test",
+            "language": "mul",
+            "languages": languages,
+            "created": header.get("created"),
+            "bootstrap": {"n": N_BOOT, "seed": BOOT_SEED},
+            "crops": crops,
+        }, indent=2) + "\n")
+        print(f"wrote {args.json}")
+        if agr:
+            out = args.json.with_name(args.json.name.replace(".score.json", ".agreement.json"))
+            out.write_text(json.dumps(agr, indent=2) + "\n")
+            print(f"wrote {out}")
+    return status
 
 
 if __name__ == "__main__":

From 3138e4719e54eb9444c8c4c603e3209fa4ec8c4b Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:46:56 +0800
Subject: [PATCH 30/38] langid: slim run.py and ingest.py

run.py crops in memory instead of writing and re-reading 16-bit crop
WAVs (the clips are 16-bit, so the round trip was the identity), and
drops the dumper-args shim, repeated checks, and the --max-audio-ms and
--revision flags (their values are still derived and recorded). Logits
match the published sweeps row for row.

ingest.py takes its languages from the profile's pooled_languages and
scripts/wer FLEURS_LANGS, and refuses a non-16 kHz clip rather than
resampling it. The manifests and WAVs it writes are byte-identical.
---
 scripts/langid/ingest.py | 262 +++++------------------
 scripts/langid/run.py    | 451 +++++++++------------------------------
 2 files changed, 154 insertions(+), 559 deletions(-)

diff --git a/scripts/langid/ingest.py b/scripts/langid/ingest.py
index a8c20a79f..088ebf27b 100755
--- a/scripts/langid/ingest.py
+++ b/scripts/langid/ingest.py
@@ -1,33 +1,19 @@
 #!/usr/bin/env python3
 """
-ingest.py - build an evaluation corpus from a named dataset source.
+ingest.py - build the language ID corpus from the FLEURS test parquet at the
+pinned revision FLEURS_REVISION (read through the Hugging Face hub cache).
 
-Sources:
-  fleurs   Reads the FLEURS test parquet of each config at the pinned
-           dataset revision FLEURS_REVISION, through the Hugging Face hub
-           cache (HF_HOME honoured); a config missing from the cache is
-           downloaded.
-
-Usage:
-  uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang en
   uv run --project scripts/envs/ecapa_tdnn scripts/langid/ingest.py fleurs --lang all
 
-Output (gitignored, see .gitignore `/samples/langid/`):
+Output (gitignored):
   samples/langid/fleurs-<code>/<id>.wav        16-bit PCM mono 16 kHz
   samples/langid/fleurs-<code>.manifest.jsonl  {"id","audio","language","duration_s",...}
 
-Only the FLEURS `test` split is read. Selection rule: the first N
-utterances in parquet order whose decoded duration is >= 1 s. Parquet order
-is the only ordering; no shuffling, no hashing, and the dataset revision is
-pinned, so every run produces the same manifest.
-
-Utterance ids are `fleurs-<code>-<NNNN>` where NNNN is the index among the
-selected rows, NOT the FLEURS sentence id: FLEURS records the same sentence
-with several speakers, so sentence ids repeat within a split and would
-collide as filenames. The sentence id is kept in the manifest as
-`fleurs_id` for provenance.
-
-Idempotent: an existing manifest is left alone unless --force.
+The languages are the langid publication profile's `pooled_languages`. Per
+language: the first N utterances in parquet order that last at least 1 s.
+Ids are `fleurs-<code>-<NNNN>` by selection index (FLEURS sentence ids repeat
+across speakers); the sentence id is kept as `fleurs_id`. An existing
+manifest is left alone unless --force.
 """
 
 from __future__ import annotations
@@ -38,138 +24,57 @@
 import sys
 from pathlib import Path
 
-import numpy as np
-
 REPO_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(REPO_ROOT / "scripts" / "wer"))
+from languages import FLEURS_LANGS  # noqa: E402
 
 SAMPLE_RATE = 16000
 MIN_DURATION_S = 1.0
-DEFAULT_N = 200
 SPLIT = "test"
 DATASET = "google/fleurs"
 FLEURS_REVISION = "70bb2e84b976b7e960aa89f1c648e09c59f894dd"
 LICENCE = "CC-BY-4.0"
 
-# FLEURS config -> VoxLingua107 label code, in the dataset gate's order.
-# `es_419` is Latin-American Spanish, `cmn_hans_cn` is Mandarin in simplified
-# Han, `nb_no` is Bokmal (the label set spells Norwegian `no`); VoxLingua107
-# has one label for each.
-FLEURS_LANGUAGES: list[tuple[str, str]] = [
-    ("en_us", "en"),
-    ("cmn_hans_cn", "zh"),
-    ("de_de", "de"),
-    ("fr_fr", "fr"),
-    ("es_419", "es"),
-    ("pt_br", "pt"),
-    ("ja_jp", "ja"),
-    ("ko_kr", "ko"),
-    ("ru_ru", "ru"),
-    ("cs_cz", "cs"),
-    ("sk_sk", "sk"),
-    ("nb_no", "no"),
-    ("da_dk", "da"),
-    ("id_id", "id"),
-    ("ms_my", "ms"),
-]
-
-CODE_TO_CONFIG = {code: config for config, code in FLEURS_LANGUAGES}
 
-
-def fleurs_parquet(config: str) -> Path:
-    from huggingface_hub import hf_hub_download
-
-    return Path(hf_hub_download(
-        repo_id=DATASET,
-        repo_type="dataset",
-        filename=f"parquet-data/{config}/{SPLIT}-00000-of-00001.parquet",
-        revision=FLEURS_REVISION,
-    ))
+def profile_languages() -> list[str]:
+    data = json.loads((REPO_ROOT / "catalog" / "_benchmark_profiles.json").read_text())
+    return data["profiles"][data["roles"]["langid"]]["accuracy"][0]["pooled_languages"]
 
 
-def iter_rows(path: Path):
-    """Yield parquet rows in file order without materialising the table.
-
-    FLEURS test splits carry ~400-900 utterances of ~12 s each; a whole-table
-    `to_pylist()` is a few hundred MB per language for the 200 rows we keep.
-    """
+def ingest_language(code: str, n: int, force: bool) -> None:
     import pyarrow.parquet as pq
-
-    pf = pq.ParquetFile(str(path))
-    cols = set(pf.schema_arrow.names)
-    for needed in ("audio", "id"):
-        if needed not in cols:
-            raise SystemExit(
-                f"error: {path} has no {needed!r} column "
-                f"(columns: {sorted(cols)})"
-            )
-    for batch in pf.iter_batches(batch_size=16):
-        for row in batch.to_pylist():
-            yield row
-
-
-def decode_audio(row) -> tuple[np.ndarray, int]:
-    import soundfile as sf
-
-    audio = row["audio"]
-    data = audio["bytes"] if isinstance(audio, dict) else audio
-    pcm, sr = sf.read(io.BytesIO(data), dtype="float32", always_2d=False)
-    if pcm.ndim > 1:
-        pcm = pcm.mean(axis=1)
-    return np.ascontiguousarray(pcm, dtype=np.float32), int(sr)
-
-
-def resample_to_16k(pcm: np.ndarray, sr: int) -> np.ndarray:
-    """Resample to 16 kHz. Only fires if FLEURS ever ships a non-16k config.
-
-    Resampling rather than skipping the row keeps "the first N in parquet
-    order" true; skipping would silently change which utterances the
-    evaluation covers.
-    """
-    if sr == SAMPLE_RATE:
-        return pcm
-    import torch
-    import torchaudio
-
-    out = torchaudio.functional.resample(
-        torch.from_numpy(pcm).unsqueeze(0), sr, SAMPLE_RATE
-    )
-    return np.ascontiguousarray(out.squeeze(0).numpy(), dtype=np.float32)
-
-
-def write_wav(path: Path, pcm: np.ndarray) -> None:
     import soundfile as sf
+    from huggingface_hub import hf_hub_download
 
-    path.parent.mkdir(parents=True, exist_ok=True)
-    sf.write(str(path), pcm, SAMPLE_RATE, subtype="PCM_16")
-
-
-def ingest_language(config: str, code: str, args) -> dict:
+    config = FLEURS_LANGS[code]
     out_dir = REPO_ROOT / "samples" / "langid" / f"fleurs-{code}"
-    manifest_path = REPO_ROOT / "samples" / "langid" / f"fleurs-{code}.manifest.jsonl"
-
-    if manifest_path.exists() and not args.force:
-        rows = [json.loads(l) for l in manifest_path.read_text().splitlines() if l.strip()]
-        print(f"{code}: {manifest_path.name} exists with {len(rows)} rows; "
-              f"skipping (--force to rebuild)", file=sys.stderr)
-        return summarise(code, config, rows, manifest_path)
-
-    parquet = fleurs_parquet(config)
+    manifest_path = out_dir.with_name(f"fleurs-{code}.manifest.jsonl")
+    if manifest_path.exists() and not force:
+        print(f"{code}: {manifest_path.name} exists; skipping (--force to rebuild)")
+        return
+
+    parquet = hf_hub_download(
+        repo_id=DATASET, repo_type="dataset", revision=FLEURS_REVISION,
+        filename=f"parquet-data/{config}/{SPLIT}-00000-of-00001.parquet")
+    out_dir.mkdir(parents=True, exist_ok=True)
     entries: list[dict] = []
-    n_short = 0
-    n_resampled = 0
-    for row_index, row in enumerate(iter_rows(parquet)):
-        if len(entries) >= args.n:
+    rows = (row for batch in pq.ParquetFile(parquet).iter_batches(batch_size=16)
+            for row in batch.to_pylist())
+    for row_index, row in enumerate(rows):
+        if len(entries) >= n:
             break
-        pcm, sr = decode_audio(row)
+        audio = row["audio"]
+        pcm, sr = sf.read(io.BytesIO(audio["bytes"] if isinstance(audio, dict) else audio),
+                          dtype="float32", always_2d=False)
         if sr != SAMPLE_RATE:
-            pcm = resample_to_16k(pcm, sr)
-            n_resampled += 1
+            raise SystemExit(f"error: {code} row {row_index} is {sr} Hz; FLEURS is 16 kHz")
+        if pcm.ndim > 1:
+            pcm = pcm.mean(axis=1)
         if pcm.size < MIN_DURATION_S * SAMPLE_RATE:
-            n_short += 1
             continue
         utt_id = f"fleurs-{code}-{len(entries):04d}"
         wav_path = out_dir / f"{utt_id}.wav"
-        write_wav(wav_path, pcm)
+        sf.write(str(wav_path), pcm, SAMPLE_RATE, subtype="PCM_16")
         entries.append({
             "id": utt_id,
             "audio": str(wav_path.relative_to(REPO_ROOT)),
@@ -182,97 +87,32 @@ def ingest_language(config: str, code: str, args) -> dict:
             "dataset": DATASET,
             "licence": LICENCE,
         })
-
-    if len(entries) < args.n:
-        print(f"warning: {code}: only {len(entries)} utterances of the "
-              f"requested {args.n} (split exhausted)", file=sys.stderr)
-
-    manifest_path.parent.mkdir(parents=True, exist_ok=True)
-    with open(manifest_path, "w") as f:
-        for e in entries:
-            f.write(json.dumps(e) + "\n")
-
-    info = summarise(code, config, entries, manifest_path)
-    print(f"{code}: {len(entries)} utts  dur min/mean/max = "
-          f"{info['duration_min_s']:.2f}/{info['duration_mean_s']:.2f}/"
-          f"{info['duration_max_s']:.2f} s  (skipped {n_short} under "
-          f"{MIN_DURATION_S} s, resampled {n_resampled})  -> {manifest_path.name}",
-          file=sys.stderr)
-    return info
-
-
-def summarise(code: str, config: str, rows: list[dict], manifest_path: Path) -> dict:
-    durs = [r["duration_s"] for r in rows] or [0.0]
-    return {
-        "language": code,
-        "config": config,
-        "n": len(rows),
-        "duration_min_s": min(durs),
-        "duration_mean_s": sum(durs) / len(durs),
-        "duration_max_s": max(durs),
-        "total_s": sum(durs),
-        "manifest": str(manifest_path.relative_to(REPO_ROOT)),
-    }
-
-
-def cmd_fleurs(args) -> int:
-    if args.lang == "all":
-        wanted = list(FLEURS_LANGUAGES)
-    else:
-        codes = [c.strip() for c in args.lang.split(",") if c.strip()]
-        wanted = []
-        for c in codes:
-            if c in CODE_TO_CONFIG:
-                wanted.append((CODE_TO_CONFIG[c], c))
-            elif c in dict(FLEURS_LANGUAGES):
-                wanted.append((c, dict(FLEURS_LANGUAGES)[c]))
-            else:
-                raise SystemExit(
-                    f"error: unknown language {c!r}; known: "
-                    f"{', '.join(sorted(CODE_TO_CONFIG))} or 'all'"
-                )
-
-    infos = [ingest_language(config, code, args) for config, code in wanted]
-
-    print("\nlang  n     min_s  mean_s  max_s   total_s  manifest")
-    for i in infos:
-        print(f"{i['language']:<5} {i['n']:<5} {i['duration_min_s']:6.2f} "
-              f"{i['duration_mean_s']:7.2f} {i['duration_max_s']:6.2f} "
-              f"{i['total_s']:8.0f}  {i['manifest']}")
-    total = sum(i["n"] for i in infos)
-    longest = max((i["duration_max_s"] for i in infos), default=0.0)
-    print(f"\ntotal utterances: {total}  longest clip: {longest:.2f} s")
-    # The C++ context scores at most `max_audio_ms` (default 30 s) and uses
-    # the tail of longer input, while SpeechBrain scores the whole clip. If
-    # any clip is longer than 30 s the two engines would score different
-    # audio on the `full` crop unless run.py raises the limit.
-    if longest > 30.0:
-        print("note: clips longer than 30 s exist; the cpp engine needs an "
-              "explicit --max-audio-ms above the longest clip for the `full` "
-              "crop to score the same audio as SpeechBrain.")
-    else:
-        print("note: every clip is under 30 s, so the C++ default "
-              "max_audio_ms=30000 never truncates; both engines score the "
-              "same audio on every crop.")
-    return 0
+    if len(entries) < n:
+        print(f"warning: {code}: only {len(entries)} of {n} utterances", file=sys.stderr)
+    manifest_path.write_text("".join(json.dumps(e) + "\n" for e in entries))
+    print(f"{code}: {len(entries)} utterances -> {manifest_path.relative_to(REPO_ROOT)}")
 
 
 def main(argv: list[str] | None = None) -> int:
     p = argparse.ArgumentParser(
         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
     sub = p.add_subparsers(dest="source", required=True)
-
     fp = sub.add_parser("fleurs", help="FLEURS parquet at the pinned revision")
     fp.add_argument("--lang", required=True,
-                    help="VoxLingua107 code, comma-separated list, or 'all'")
-    fp.add_argument("--n", type=int, default=DEFAULT_N,
-                    help=f"utterances per language (default: {DEFAULT_N})")
+                    help="comma-separated VoxLingua107 codes from the profile, or 'all'")
+    fp.add_argument("--n", type=int, default=200, help="utterances per language")
     fp.add_argument("--force", action="store_true",
                     help="rebuild even when the manifest already exists")
-    fp.set_defaults(func=cmd_fleurs)
-
     args = p.parse_args(argv)
-    return args.func(args)
+
+    known = profile_languages()
+    codes = known if args.lang == "all" else args.lang.split(",")
+    unknown = sorted(set(codes) - set(known))
+    if unknown:
+        p.error(f"unknown language(s) {unknown}; known: {', '.join(known)} or 'all'")
+    for code in codes:
+        ingest_language(code, args.n, args.force)
+    return 0
 
 
 if __name__ == "__main__":
diff --git a/scripts/langid/run.py b/scripts/langid/run.py
index 03101e971..5d39929e6 100755
--- a/scripts/langid/run.py
+++ b/scripts/langid/run.py
@@ -1,53 +1,34 @@
 #!/usr/bin/env python3
 """
-run.py - score a language ID evaluation corpus with the SpeechBrain
-reference or transcribe.cpp (through the Python binding, one model load per
-run), and write one self-describing JSONL report.
+run.py - score language ID manifests with the SpeechBrain reference or
+transcribe.cpp (through the Python binding) into one JSONL sweep: a header
+line, then one row per (utterance, crop) with all 107 pre-softmax logits.
 
-Usage:
-  uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \
-      --engine speechbrain --model speechbrain/lang-id-voxlingua107-ecapa \
-      --manifest samples/langid/fleurs-*.manifest.jsonl \
-      --crops 3,5,10,full \
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \\
+      --engine speechbrain --model speechbrain/lang-id-voxlingua107-ecapa \\
+      --manifest samples/langid/fleurs-*.manifest.jsonl \\
       --out reports/langid/ref-speechbrain-untrimmed.jsonl
 
-  uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \
-      --engine cpp --model models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \
-      --manifest samples/langid/fleurs-*.manifest.jsonl \
-      --crops 3,5,10,full --backend cpu --threads 4 \
-      --library build-shared/src/libtranscribe.dylib \
+  uv run --project scripts/envs/ecapa_tdnn scripts/langid/run.py \\
+      --engine cpp --model models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf \\
+      --library build-shared/src/libtranscribe.dylib \\
+      --manifest samples/langid/fleurs-*.manifest.jsonl \\
       --out reports/langid/cpp-f32-untrimmed.jsonl
 
-Output JSONL:
-  line 1   {"type":"header","engine","model","recipe":{...},"labels":[107],"created"}
-  line n   {"id","language","crop_s","trim","audio_s","logits":[107],"top1","top1_prob"}
-
-All 107 raw logits are stored per (utterance, crop); `top1`/`top1_prob`
-are the open-set decision, kept as a convenience; score.py and compare.py
-recompute everything from `logits`.
-
-Crop semantics
---------------
-A crop of N is the first N seconds of the raw clip; `full` is the whole
-clip. A clip shorter than the crop is used whole; the row's `audio_s`
-records what was actually scored. `trim` is always "none" in the report.
-
-Both engines read the SAME 16-bit wav files
--------------------------------------------
-Crops are materialised once to `build/langid/crops/none/<crop>/<id>.wav`
-and both engines read those files back. Scoring in-memory floats on one
-side and 16-bit wavs on the other would make compare.py's 99.9% agreement
-gate measure PCM rounding instead of the port. The crop directory is
-keyed by the recipe, not by the run name, so a cpp run and the reference
-run with the same recipe share bytes on disk.
+A crop of N is the first N seconds of the clip, `full` the whole clip; a
+row's `audio_s` is what was scored. Both engines get the same float samples
+(the clips are 16-bit, so a crop is exact). SpeechBrain runs a batch of one:
+padding a batch changes its normalisation span.
 """
 
 from __future__ import annotations
 
 import argparse
 import datetime as _dt
+import hashlib
 import json
 import math
+import os
 import sys
 import time
 from pathlib import Path
@@ -55,136 +36,38 @@
 import numpy as np
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
-if str(REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(REPO_ROOT))
-SCRIPTS_DIR = REPO_ROOT / "scripts"
-if str(SCRIPTS_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPTS_DIR))
+sys.path.insert(0, str(REPO_ROOT / "scripts"))
 
 SAMPLE_RATE = 16000
-N_LABELS = 107
-TRIM = "none"  # crops start at sample 0; part of compare.py's row key and recipe check
-
-
-# ---------------------------------------------------------------------------
-# Manifests, crops
-# ---------------------------------------------------------------------------
-
-
-def read_manifest(path: Path) -> list[dict]:
-    rows = []
-    for line in path.read_text().splitlines():
-        line = line.strip()
-        if line:
-            rows.append(json.loads(line))
-    return rows
-
-
-def read_wav(path: Path) -> np.ndarray:
-    import soundfile as sf
-
-    pcm, sr = sf.read(str(path), dtype="float32", always_2d=False)
-    if pcm.ndim > 1:
-        pcm = pcm.mean(axis=1)
-    if int(sr) != SAMPLE_RATE:
-        raise SystemExit(f"error: {path} is {sr} Hz, expected {SAMPLE_RATE}")
-    return np.ascontiguousarray(pcm, dtype=np.float32)
-
-
-def write_wav(path: Path, pcm: np.ndarray) -> None:
-    import soundfile as sf
-
-    path.parent.mkdir(parents=True, exist_ok=True)
-    sf.write(str(path), pcm, SAMPLE_RATE, subtype="PCM_16")
-
-
-def make_crop(pcm: np.ndarray, crop: str | int) -> np.ndarray:
-    if crop == "full":
-        return pcm
-    n = int(float(crop) * SAMPLE_RATE)
-    return pcm[:n] if pcm.size > n else pcm
-
-
-def crop_dir(trim: str, crop: str | int) -> Path:
-    return REPO_ROOT / "build" / "langid" / "crops" / trim / str(crop)
-
-
-def materialise_crops(rows: list[dict], crop: str | int) -> list[tuple[dict, Path]]:
-    """Write every (utterance, crop) wav and return (row, path) pairs.
-
-    Always rewrites: the write is cheap next to a forward pass, and an
-    unconditional write means a stale crop from an earlier recipe can never
-    be scored by accident.
-    """
-    out = crop_dir(TRIM, crop)
-    out.mkdir(parents=True, exist_ok=True)
-    pairs: list[tuple[dict, Path]] = []
-    for row in rows:
-        src = REPO_ROOT / row["audio"]
-        dst = out / f"{row['id']}.wav"
-        write_wav(dst, make_crop(read_wav(src), crop))
-        pairs.append((row, dst))
-    return pairs
-
-
-# ---------------------------------------------------------------------------
-# SpeechBrain engine
-# ---------------------------------------------------------------------------
-
-
-class _DumperArgs:
-    """Argument shim for the dumper's loader."""
-
-    def __init__(self, model: str, revision: str, threads: int) -> None:
-        self.model = model
-        self.revision = revision
-        self.device = "cpu"
-        self.torch_threads = threads
 
 
 class SpeechBrainEngine:
-    """The pinned SpeechBrain checkpoint, loaded once, scored one clip at a
-    time.
-
-    Batch of one on purpose. Batching pads the shorter clips and drives
-    `wav_lens` off 1.0, which changes InputNormalization's span and the ASP
-    masks - i.e. it changes the numbers this file is supposed to be the
-    reference for.
-
-    The logits are taken from a forward hook on `mods.classifier.out`, the
-    Linear(512->107) whose output is pre-softmax, exactly as the reference
-    dumper's `cls.logits_raw`. `classify_batch` only returns log-softmax.
-    """
+    """The pinned checkpoint; logits from a forward hook on the pre-softmax
+    `mods.classifier.out` (classify_batch only returns log-softmax)."""
 
-    logit_kind = "pre_softmax"
-
-    def __init__(self, model: str, revision: str, threads: int) -> None:
+    def __init__(self, model: str, threads: int) -> None:
+        import speechbrain
         import torch
 
         import dump_reference_ecapa_tdnn_speechbrain as dumper
 
-        self.dumper = dumper
         self.torch = torch
-        args = _DumperArgs(model, revision, threads)
+        args = argparse.Namespace(model=model, revision=dumper.DEFAULT_REVISION,
+                                  device="cpu", torch_threads=threads)
         dumper.configure_torch(args)
-        self.clf, self.checkpoint_dir = dumper.load_reference(args)
-
+        self.clf, checkpoint_dir = dumper.load_reference(args)
         ind2lab = self.clf.hparams.label_encoder.ind2lab
-        if len(ind2lab) != N_LABELS:
-            raise SystemExit(f"error: label encoder has {len(ind2lab)} labels, "
-                             f"expected {N_LABELS}")
-        self.labels = [dumper.split_label(str(ind2lab[i]))[0] for i in range(N_LABELS)]
-
+        self.labels = [dumper.split_label(str(ind2lab[i]))[0] for i in range(len(ind2lab))]
         self._captured: list = []
-
-        def capture(module, inputs, output):
-            self._captured.append(output)
-
-        self._handle = self.clf.mods.classifier.out.register_forward_hook(capture)
-
-        import speechbrain
-
-        self.versions = {
+        self.clf.mods.classifier.out.register_forward_hook(
+            lambda _m, _i, out: self._captured.append(out))
+        self.recipe = {
+            "revision": dumper.DEFAULT_REVISION,
+            "checkpoint_dir": str(checkpoint_dir),
+            "device": "cpu",
+            "model_dtype": "f32",
+            "batch_size": 1,
+            "torch_threads": threads,
             "speechbrain": speechbrain.__version__,
             "torch": torch.__version__,
         }
@@ -192,124 +75,58 @@ def capture(module, inputs, output):
     def logits(self, pcm: np.ndarray) -> np.ndarray:
         torch = self.torch
         self._captured.clear()
-        wav = torch.from_numpy(pcm).unsqueeze(0)
-        # wav_lens is RELATIVE to the padded batch max; batch of one and
-        # unpadded means 1.0. A sample count here would silently mask the
-        # wrong span.
+        # wav_lens is relative to the padded batch: 1.0 for one unpadded clip.
         with torch.inference_mode():
-            self.clf.classify_batch(wav, torch.ones(1))
-        if len(self._captured) != 1:
-            raise SystemExit(f"error: classifier.out fired {len(self._captured)} "
-                             f"times for one clip, expected 1")
-        t = self._captured[0]
-        if t.numel() != N_LABELS:
-            raise SystemExit(f"error: classifier.out produced {t.numel()} values, "
-                             f"expected {N_LABELS}")
-        return np.ascontiguousarray(
-            t.detach().to(dtype=torch.float32, device="cpu").reshape(-1).numpy(),
-            dtype=np.float32)
-
-    def close(self) -> None:
-        self._handle.remove()
-
-
-# ---------------------------------------------------------------------------
-# C++ engine
-# ---------------------------------------------------------------------------
+            self.clf.classify_batch(torch.from_numpy(pcm).unsqueeze(0), torch.ones(1))
+        (out,) = self._captured
+        return out.detach().to(torch.float32).reshape(len(self.labels)).numpy()
 
 
 class CppEngine:
-    """transcribe.cpp through the Python binding (bindings/python): the GGUF
-    loads once and every clip is one ``LangIdSession.run`` with ``top_k`` 0,
-    so all 107 logits come back and are reassembled by label index."""
-
-    logit_kind = "pre_softmax"
+    """The GGUF loaded once; each clip is one LangIdSession.run with top_k 0,
+    so every label's logit comes back."""
 
     def __init__(self, model: Path, backend: str, threads: int, max_audio_ms: int,
                  library: Path | None) -> None:
-        import hashlib
-        import os
-
         if library is not None:
             os.environ["TRANSCRIBE_LIBRARY"] = str(library.resolve())
-        binding = REPO_ROOT / "bindings" / "python" / "src"
-        if str(binding) not in sys.path:
-            sys.path.insert(0, str(binding))
+        sys.path.insert(0, str(REPO_ROOT / "bindings" / "python" / "src"))
         import transcribe_cpp
+        from gguf import GGUFReader
 
-        if not model.exists():
-            raise SystemExit(f"error: {model} does not exist")
         self.model = transcribe_cpp.Model(str(model), backend=backend)
         self.session = self.model.langid_session(n_threads=threads, max_audio_ms=max_audio_ms)
         self.labels = [code for code, _name in self.model.langid_labels]
-        if len(self.labels) != N_LABELS:
-            raise SystemExit(f"error: {model} has {len(self.labels)} labels, expected {N_LABELS}")
-        h = hashlib.sha256()
+        fields = GGUFReader(str(model)).fields
+        kv = lambda key: str(fields[key].contents()) if key in fields else None  # noqa: E731
         with open(model, "rb") as f:
-            for chunk in iter(lambda: f.read(1 << 20), b""):
-                h.update(chunk)
-        self.provenance = {
-            "gguf_sha256": h.hexdigest(),
-            "gguf_source_commit": _gguf_string(model, "general.source.commit"),
-            "gguf_source_repo": _gguf_string(model, "general.name"),
+            sha = hashlib.file_digest(f, "sha256").hexdigest()
+        self.recipe = {
+            "backend": backend,
+            "threads": threads,
+            "max_audio_ms": max_audio_ms,
+            "gguf_sha256": sha,
+            "gguf_source_commit": kv("general.source.commit"),
+            "gguf_source_repo": kv("general.name"),
             "native_version": transcribe_cpp.native_version(),
             "native_commit": transcribe_cpp.native_commit(),
             "backend_bound": self.model.backend,
         }
 
     def logits(self, pcm: np.ndarray) -> np.ndarray:
-        result = self.session.run(pcm, top_k=0)
-        logits = np.zeros(N_LABELS, dtype=np.float32)
-        for c in result.candidates:
+        logits = np.zeros(len(self.labels), dtype=np.float32)
+        for c in self.session.run(pcm, top_k=0).candidates:
             logits[c.index] = c.logit
         return logits
 
-    def close(self) -> None:
-        self.session.close()
-        self.model.close()
-
-
-def _gguf_string(path: Path, key: str) -> str | None:
-    """A string KV from the GGUF header, for provenance."""
-    from gguf import GGUFReader
-
-    field = GGUFReader(str(path)).fields.get(key)
-    return None if field is None else str(field.contents())
-
-
-# ---------------------------------------------------------------------------
-# Report
-# ---------------------------------------------------------------------------
-
-
-def softmax_top(logits: np.ndarray) -> tuple[int, float]:
-    z = logits.astype(np.float64)
-    z = z - z.max()
-    e = np.exp(z)
-    p = e / e.sum()
-    i = int(np.argmax(p))
-    return i, float(p[i])
-
-
-def _sha256_file(path: Path) -> str:
-    import hashlib
-
-    return hashlib.sha256(path.read_bytes()).hexdigest()
 
+def read_audio(path: Path) -> np.ndarray:
+    import soundfile as sf
 
-def parse_crops(spec: str) -> list[str | int]:
-    crops: list[str | int] = []
-    for tok in spec.split(","):
-        tok = tok.strip()
-        if not tok:
-            continue
-        if tok == "full":
-            crops.append("full")
-        else:
-            crops.append(int(tok) if float(tok).is_integer() else float(tok))
-    if not crops:
-        raise SystemExit("error: --crops is empty")
-    return crops
+    pcm, sr = sf.read(str(path), dtype="float32", always_2d=False)
+    if sr != SAMPLE_RATE or pcm.ndim != 1:
+        raise SystemExit(f"error: {path} is not 16 kHz mono")
+    return pcm
 
 
 def main(argv: list[str] | None = None) -> int:
@@ -321,135 +138,73 @@ def main(argv: list[str] | None = None) -> int:
     p.add_argument("--manifest", required=True, nargs="+", type=Path)
     p.add_argument("--crops", default="3,5,10,full")
     p.add_argument("--out", required=True, type=Path)
-    p.add_argument("--library", type=Path, default=None,
-                   help="cpp engine only; a shared libtranscribe (TRANSCRIBE_LIBRARY)")
-    p.add_argument("--backend", default="cpu")
+    p.add_argument("--library", type=Path,
+                   help="cpp only: a shared libtranscribe (TRANSCRIBE_LIBRARY)")
+    p.add_argument("--backend", default="cpu", help="cpp only")
     p.add_argument("--threads", type=int, default=4)
     p.add_argument("--max-utts", type=int, default=0,
                    help="cap utterances per manifest (0 = all); for smoke tests")
-    p.add_argument("--max-audio-ms", type=int, default=0,
-                   help="cpp engine only; 0 = derived from the longest clip")
-    p.add_argument("--revision",
-                   default="0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9",
-                   help="speechbrain engine only; pinned HF revision")
     args = p.parse_args(argv)
 
-    crops = parse_crops(args.crops)
-
-    # Deterministic ordering: manifests in the order given, utterances in
-    # manifest order, crops in the order given.
-    manifests = [Path(m) for m in args.manifest]
-    rows: list[dict] = []
-    for m in manifests:
-        if not m.exists():
-            raise SystemExit(f"error: manifest {m} does not exist")
-        mr = read_manifest(m)
-        if args.max_utts > 0:
-            mr = mr[: args.max_utts]
-        rows.extend(mr)
-    if not rows:
-        raise SystemExit("error: manifests are empty")
+    crops = [c if c == "full" else int(c) for c in args.crops.split(",")]
+    rows = []
+    for m in args.manifest:
+        lines = [json.loads(l) for l in m.read_text().splitlines() if l.strip()]
+        rows += lines[: args.max_utts or None]
     longest_s = max(r["duration_s"] for r in rows)
-    print(f"{len(rows)} utterances from {len(manifests)} manifests, "
-          f"{len(crops)} crops", file=sys.stderr)
 
-    max_audio_ms = args.max_audio_ms or int(math.ceil(longest_s / 10.0) * 10.0 * 1000)
-
-    engine: SpeechBrainEngine | CppEngine
     if args.engine == "speechbrain":
-        engine = SpeechBrainEngine(args.model, args.revision, args.threads)
-        labels = engine.labels
-        extra_recipe = {
-            "revision": args.revision,
-            "checkpoint_dir": str(engine.checkpoint_dir),
-            "device": "cpu",
-            "model_dtype": "f32",
-            "batch_size": 1,
-            "torch_threads": args.threads,
-            **engine.versions,
-        }
+        engine = SpeechBrainEngine(args.model, args.threads)
     else:
+        # Room for the longest clip, so `full` scores the same audio as SpeechBrain.
+        max_audio_ms = int(math.ceil(longest_s / 10.0) * 10.0 * 1000)
         engine = CppEngine(Path(args.model), args.backend, args.threads, max_audio_ms,
                            args.library)
-        labels = engine.labels
-        extra_recipe = {
-            "backend": args.backend,
-            "threads": args.threads,
-            "max_audio_ms": max_audio_ms,
-            **engine.provenance,
-        }
 
-    recipe = {
-        "crops": [str(c) for c in crops],
-        "trim": TRIM,
-        "trim_rule": "none (crops start at sample 0 of the raw clip)",
-        "crop_dir": str(crop_dir(TRIM, "<crop>").relative_to(REPO_ROOT)),
-        "manifests": [str(m) for m in manifests],
-        "manifest_sha256": {str(m): _sha256_file(m) for m in manifests},
-        "n_utterances": len(rows),
-        "sample_rate": SAMPLE_RATE,
-        "crop_dtype": "pcm_s16le wav (both engines read the same files)",
-        "logit_kind": engine.logit_kind,
-        "longest_clip_s": longest_s,
-        **extra_recipe,
+    header = {
+        "type": "header",
+        "engine": args.engine,
+        "model": args.model,
+        "recipe": {
+            "crops": [str(c) for c in crops],
+            "manifests": [str(m) for m in args.manifest],
+            "manifest_sha256": {str(m): hashlib.sha256(m.read_bytes()).hexdigest()
+                                for m in args.manifest},
+            "n_utterances": len(rows),
+            "sample_rate": SAMPLE_RATE,
+            "logit_kind": "pre_softmax",
+            "longest_clip_s": longest_s,
+            **engine.recipe,
+        },
+        "labels": engine.labels,
+        "created": _dt.datetime.now(_dt.timezone.utc).replace(microsecond=0).isoformat(),
     }
 
     args.out.parent.mkdir(parents=True, exist_ok=True)
-    t_start = time.time()
-    n_written = 0
+    t0 = time.time()
     with open(args.out, "w") as f:
-        pending_header = True
-
+        f.write(json.dumps(header) + "\n")
         for crop in crops:
-            t_crop = time.time()
-            print(f"[{TRIM}/{crop}] materialising {len(rows)} crops ...",
-                  file=sys.stderr)
-            pairs = materialise_crops(rows, crop)
-
-            batch_rows = []
-            for i, (row, wav_path) in enumerate(pairs):
-                pcm = read_wav(wav_path)
+            for i, row in enumerate(rows):
+                pcm = read_audio(REPO_ROOT / row["audio"])
+                if crop != "full":
+                    pcm = pcm[: crop * SAMPLE_RATE]
                 logits = engine.logits(pcm)
-                top_i, top_p = softmax_top(logits)
-                batch_rows.append({
+                z = np.exp(logits.astype(np.float64) - logits.max())
+                top = int(np.argmax(z))
+                f.write(json.dumps({
                     "id": row["id"],
                     "language": row["language"],
                     "crop_s": crop,
-                    "trim": TRIM,
                     "audio_s": round(pcm.size / SAMPLE_RATE, 4),
                     "logits": [round(float(x), 6) for x in logits],
-                    "top1": labels[top_i],
-                    "top1_prob": round(top_p, 6),
-                })
-                if (i + 1) % 100 == 0 or i + 1 == len(pairs):
-                    el = time.time() - t_crop
-                    print(f"  [{TRIM}/{crop}] {i + 1}/{len(pairs)}  "
-                          f"{el:.0f}s  ({el / (i + 1) * 1000:.0f} ms/utt)",
+                    "top1": engine.labels[top],
+                    "top1_prob": round(float(z[top] / z.sum()), 6),
+                }) + "\n")
+                if (i + 1) % 200 == 0 or i + 1 == len(rows):
+                    print(f"  crop {crop}: {i + 1}/{len(rows)}  {time.time() - t0:.0f}s",
                           file=sys.stderr)
-
-            if pending_header:
-                header = {
-                    "type": "header",
-                    "engine": args.engine,
-                    "model": str(args.model),
-                    "recipe": recipe,
-                    "labels": labels,
-                    "created": _dt.datetime.now(_dt.timezone.utc)
-                                  .replace(microsecond=0).isoformat(),
-                }
-                f.write(json.dumps(header) + "\n")
-                pending_header = False
-            for r in batch_rows:
-                f.write(json.dumps(r) + "\n")
-                n_written += 1
-            f.flush()
-            print(f"[{TRIM}/{crop}] done in {time.time() - t_crop:.0f}s",
-                  file=sys.stderr)
-
-    engine.close()
-    dt = time.time() - t_start
-    print(f"wrote {args.out} ({n_written} rows) in {dt:.0f}s "
-          f"({dt / max(n_written, 1) * 1000:.0f} ms/row)", file=sys.stderr)
+    print(f"wrote {args.out} in {time.time() - t0:.0f}s", file=sys.stderr)
     return 0
 
 

From 3989f1a0bf13b9b5208c88835248a8c0b49f1e58 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:48:41 +0800
Subject: [PATCH 31/38] langid: bench.py runs the publication profile only

The ad-hoc --gguf/--backends/--sample/--durations/--warmup/--repeat/
--threads/--name/--out-dir path is gone: the profile supplies every
cell. Reports keep the bench-driver shape but record only the mean,
which is all ingest_perf.py and scripts/bench read.
---
 scripts/langid/bench.py | 163 ++++++++++++----------------------------
 1 file changed, 49 insertions(+), 114 deletions(-)

diff --git a/scripts/langid/bench.py b/scripts/langid/bench.py
index 8573d98a1..1f59191a1 100644
--- a/scripts/langid/bench.py
+++ b/scripts/langid/bench.py
@@ -1,31 +1,18 @@
 #!/usr/bin/env python3
 """
-bench.py - language ID latency through the Python binding: backend x GGUF x
-scored duration, the native mel + encode time over repeated runs on one warm
-session (tools/transcribe-bench is ASR-only).
+bench.py - this machine's language ID publication speed cells
+(catalog/_benchmark_profiles.json) through the Python binding:
+tools/transcribe-bench is ASR-only.
 
-Usage:
-  # the publication cells for this machine (catalog/_benchmark_profiles.json)
   uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py --profile \\
       --library build-shared/src/libtranscribe.dylib
   uv run scripts/catalog/ingest_perf.py
 
-  # an experiment
-  uv run --project scripts/envs/ecapa_tdnn scripts/langid/bench.py \\
-      --library build-shared/src/libtranscribe.dylib \\
-      --gguf models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-{F32,F16,Q8_0}.gguf \\
-      --backends cpu,metal --durations 3,5,10,30
-
-Audio: the first N seconds of samples/<clip>.wav (default ru-long, 33.8 s of
-Russian), so every duration scores real speech; the cell's sample is named
-`<clip>-<N>s`. The session's max_audio_ms is 60000, so nothing is cropped
-away.
-
-Writes one report per backend to reports/perf/<machine-slug>/
-<name>_<variant>_<backend>.json in the bench driver's shape
-(scripts/bench/run.py), which scripts/catalog/ingest_perf.py folds into the
-catalog. As there, only a --profile run is eligible: it takes its GGUFs,
-samples, backends, iterations, warmup and thermal gate from the profile.
+The profile names the GGUFs, backends, samples, iterations, warmup and
+thermal gate. A sample `<clip>-<N>s` is the first N seconds of
+samples/<clip>.wav; the cell's time is the native mel + encode. Writes one
+bench-driver report (scripts/bench/run.py's shape) per backend to
+reports/perf/<machine-slug>/<variant>-publication_<variant>_<backend>.json.
 """
 
 from __future__ import annotations
@@ -47,8 +34,8 @@
 
 
 def bench_driver():
-    """scripts/bench/run.py, for its machine detection and thermal gate.
-    Loaded by path: this directory has a run.py of its own."""
+    """scripts/bench/run.py, for its profiles, machine detection and thermal
+    gate. Loaded by path: this directory has a run.py of its own."""
     spec = importlib.util.spec_from_file_location(
         "bench_driver", REPO_ROOT / "scripts" / "bench" / "run.py")
     module = importlib.util.module_from_spec(spec)
@@ -57,81 +44,37 @@ def bench_driver():
     return module
 
 
-def seconds_label(seconds: float) -> str:
-    return f"{seconds:g}s"
-
-
-def summary(values: list[float]) -> dict:
-    ordered = sorted(values)
-    return {"mean": statistics.fmean(values), "median": statistics.median(values),
-            "p90": ordered[int(0.9 * (len(ordered) - 1))]}
-
-
 def main(argv: list[str] | None = None) -> int:
     p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--profile", nargs="?", const="", default=None,
-                   help="run this machine's publication cells (optionally name "
-                        "the profile; default: the variant's role profile)")
-    p.add_argument("--variant", default="lang-id-voxlingua107-ecapa",
-                   help="catalog variant (default %(default)s)")
-    p.add_argument("--gguf", nargs="+", type=Path, default=None,
-                   help="default with --profile: the variant's downloads under models/")
-    p.add_argument("--backends", default=None, help="default cpu,metal")
-    p.add_argument("--sample", default=None, help="clip under samples/ (default ru-long)")
-    p.add_argument("--durations", default=None, help="default 3,5,10,30")
-    p.add_argument("--threads", type=int, default=0, help="0 = library default")
-    p.add_argument("--warmup", type=int, default=None, help="default 3")
-    p.add_argument("--repeat", type=int, default=None, help="default 20")
-    p.add_argument("--library", type=Path, default=None)
-    p.add_argument("--name", default=None,
-                   help="report filename prefix (default: a timestamp, or "
-                        "<variant>-publication with --profile)")
-    p.add_argument("--out-dir", type=Path, default=REPO_ROOT / "reports" / "perf")
+    p.add_argument("--profile", nargs="?", const="", default="",
+                   help="profile id (default: the variant's role profile)")
+    p.add_argument("--variant", default="lang-id-voxlingua107-ecapa")
+    p.add_argument("--library", type=Path, help="a shared libtranscribe (TRANSCRIBE_LIBRARY)")
     args = p.parse_args(argv)
 
     driver = bench_driver()
     profiles = driver.benchmark_profiles
     machine = driver.detect_machine()
     record = driver.catalog_common.load_record(args.variant)
-    profile_id, cooldown_c = None, 0.0
-    if args.profile is not None:
-        conflicting = [flag for flag, value in (
-            ("--gguf", args.gguf), ("--backends", args.backends), ("--sample", args.sample),
-            ("--durations", args.durations), ("--warmup", args.warmup),
-            ("--repeat", args.repeat)) if value is not None]
-        if conflicting:
-            p.error(f"--profile supplies {', '.join(conflicting)}; do not override it")
-        profile_id, profile = profiles.profile_for(record, args.profile or None)
-        target = profiles.target_for_machine(profile, machine["slug"])
-        if target is None:
-            p.error(f"machine {machine['slug']!r} is not a target in profile {profile_id}")
-        cells = [cell for cell in profiles.apply_exceptions(
-                     record, "speed", profiles.expected_speed(record, profile))
-                 if cell["machine"] == target["machine"]]
-        quants = {cell["quant"] for cell in cells}
-        args.gguf = [REPO_ROOT / "models" / args.variant / item["filename"]
-                     for item in record["downloads"] if item["quant"] in quants]
-        args.backends = ",".join(dict.fromkeys(cell["backend"] for cell in cells))
-        samples = [SAMPLE_RE.match(name) for name in
-                   dict.fromkeys(cell["sample"] for cell in cells)]
-        if not all(samples) or len({m["clip"] for m in samples}) != 1:
-            p.error(f"profile samples must be <clip>-<N>s on one clip: "
-                    f"{sorted({cell['sample'] for cell in cells})}")
-        args.sample = samples[0]["clip"]
-        args.durations = ",".join(m["seconds"] for m in samples)
-        args.warmup = int(profile["speed"]["warmup"])
-        args.repeat = int(profile["speed"]["iterations"])
-        cooldown_c = float(target.get("cooldown_tctl_c", 0.0))
-    elif not args.gguf:
-        p.error("--gguf is required without --profile")
-    backends = (args.backends or "cpu,metal").split(",")
-    clip = args.sample or "ru-long"
-    durations = [float(d) for d in (args.durations or "3,5,10,30").split(",")]
-    warmup = 3 if args.warmup is None else args.warmup
-    repeat = 20 if args.repeat is None else args.repeat
-    missing = [str(path) for path in args.gguf if not path.exists()]
-    if missing:
-        p.error(f"no such GGUF: {', '.join(missing)}")
+    profile_id, profile = profiles.profile_for(record, args.profile or None)
+    target = profiles.target_for_machine(profile, machine["slug"])
+    if target is None:
+        p.error(f"machine {machine['slug']!r} is not a target in profile {profile_id}")
+    cells = [cell for cell in profiles.apply_exceptions(
+                 record, "speed", profiles.expected_speed(record, profile))
+             if cell["machine"] == target["machine"]]
+    quants = {cell["quant"] for cell in cells}
+    ggufs = [REPO_ROOT / "models" / args.variant / item["filename"]
+             for item in record["downloads"] if item["quant"] in quants]
+    backends = list(dict.fromkeys(cell["backend"] for cell in cells))
+    samples = [SAMPLE_RE.match(name) for name in dict.fromkeys(cell["sample"] for cell in cells)]
+    if not all(samples) or len({m["clip"] for m in samples}) != 1:
+        p.error(f"profile samples must be <clip>-<N>s on one clip: "
+                f"{sorted({cell['sample'] for cell in cells})}")
+    clip = samples[0]["clip"]
+    durations = [m["seconds"] for m in samples]
+    warmup, repeat = int(profile["speed"]["warmup"]), int(profile["speed"]["iterations"])
+    cooldown_c = float(target.get("cooldown_tctl_c", 0.0))
 
     if args.library is not None:
         os.environ["TRANSCRIBE_LIBRARY"] = str(args.library.resolve())
@@ -141,23 +84,20 @@ def main(argv: list[str] | None = None) -> int:
 
     sample_path = REPO_ROOT / "samples" / f"{clip}.wav"
     pcm, sr = sf.read(str(sample_path), dtype="float32")
-    assert sr == 16000
-    too_long = [d for d in durations if d * 16000 > pcm.size]
-    if too_long:
-        p.error(f"{sample_path.name} is {pcm.size / 16000:.1f} s; cannot score {too_long} s")
+    assert sr == 16000 and pcm.size >= float(max(durations, key=float)) * 16000
 
     timestamp = driver.now_utc_iso()
     commit = t.native_commit()
-    name = args.name or (f"{args.variant}-publication" if profile_id
-                         else driver.timestamp_for_filename(timestamp))
+    name = f"{args.variant}-publication"
     for backend in backends:
         runs = []
-        for gguf in args.gguf:
+        for gguf in ggufs:
             t0 = time.perf_counter()
             model = t.Model(str(gguf), backend=backend)
             load_ms = (time.perf_counter() - t0) * 1000
-            lid = model.langid_session(n_threads=args.threads, max_audio_ms=60000)
-            for d in durations:
+            lid = model.langid_session(n_threads=0, max_audio_ms=60000)
+            for seconds in durations:
+                d = float(seconds)
                 driver.cooldown_wait(cooldown_c, 300.0, 10.0)
                 audio = np.ascontiguousarray(pcm[: int(d * 16000)])
                 for _ in range(warmup):
@@ -170,30 +110,25 @@ def main(argv: list[str] | None = None) -> int:
                     tm = lid.timings
                     per_iter.append({"mel_ms": tm.mel_ms, "encode_ms": tm.encode_ms,
                                      "total_ms": tm.mel_ms + tm.encode_ms, "wall_ms": wall})
-                try:
-                    model_path = str(gguf.resolve().relative_to(REPO_ROOT))
-                except ValueError:
-                    model_path = str(gguf)
-                run = {
-                    "model_path": model_path,
-                    "sample": f"{clip}-{seconds_label(d)}",
+                runs.append({
+                    "model_path": str(gguf.relative_to(REPO_ROOT)),
+                    "sample": f"{clip}-{seconds}s",
                     "sample_path": str(sample_path.relative_to(REPO_ROOT)),
                     "sample_duration_s": d,
                     "backend": model.backend,
-                    "threads": args.threads,
+                    "threads": 0,
                     "load_ms": round(load_ms, 1),
                     "per_iter": per_iter,
-                    "summary": {field: summary([it[field] for it in per_iter])
+                    "summary": {field: {"mean": statistics.fmean(it[field] for it in per_iter)}
                                 for field in ("mel_ms", "encode_ms", "total_ms", "wall_ms")},
-                }
-                runs.append(run)
-                total = run["summary"]["total_ms"]
-                print(f"{gguf.name:42} {backend:6} {d:5.0f}s  mean {total['mean']:8.2f} ms  "
-                      f"median {total['median']:8.2f}  p90 {total['p90']:8.2f}", flush=True)
+                })
+                print(f"{gguf.name:42} {backend:6} {d:5.0f}s  mean "
+                      f"{runs[-1]['summary']['total_ms']['mean']:8.2f} ms", flush=True)
             lid.close()
             model.close()
 
-        out = args.out_dir / machine["slug"] / f"{driver.slugify(name)}_{args.variant}_{backend}.json"
+        out = REPO_ROOT / "reports" / "perf" / machine["slug"] / \
+            f"{driver.slugify(name)}_{args.variant}_{backend}.json"
         out.parent.mkdir(parents=True, exist_ok=True)
         out.write_text(json.dumps({
             "schema": "transcribe-bench-driver-v1",

From f747b21a08b0de0a5edd679907c517007f7317bf Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:49:45 +0800
Subject: [PATCH 32/38] validate: shorten the LANGID label compare

A missing C++ prediction.json now fails through the same mismatch path,
and the report bundle lists label cases like any other mode.
---
 scripts/validate.py | 34 ++++++++++------------------------
 1 file changed, 10 insertions(+), 24 deletions(-)

diff --git a/scripts/validate.py b/scripts/validate.py
index 3988de372..e7e6cfadc 100644
--- a/scripts/validate.py
+++ b/scripts/validate.py
@@ -697,27 +697,17 @@ def cmd_compare(args: argparse.Namespace) -> int:
         # may miss it); it is reported, never gated.
         ref_prediction = ref_dir / "prediction.json"
         if ref_prediction.exists():
-            cpp_prediction = cpp_dir / "prediction.json"
             ref_pred = json.loads(ref_prediction.read_text())
-            if not cpp_prediction.exists():
-                print(f"FAIL prediction: missing C++ artifact: {cpp_prediction}", file=sys.stderr)
-                all_passed = False
-                transcript_results.append({"case": case_name, "match": False,
-                                           "reason": "missing C++ prediction artifact"})
-                continue
-            cpp_pred = json.loads(cpp_prediction.read_text())
-            match = int(cpp_pred["label_index"]) == int(ref_pred["label_index"])
+            cpp_prediction = cpp_dir / "prediction.json"
+            cpp_pred = (json.loads(cpp_prediction.read_text()) if cpp_prediction.exists()
+                        else {"code": "<missing prediction.json>"})
+            match = cpp_pred.get("label_index") == ref_pred["label_index"]
+            all_passed = all_passed and match
+            transcript_results.append({"case": case_name, "match": match, "mode": "label",
+                                       "reference": ref_pred["code"], "cpp": cpp_pred["code"]})
             expected = case.get("expected_language") if isinstance(case, dict) else None
-            transcript_results.append({"case": case_name, "match": match,
-                                       "reference": ref_pred.get("code"), "cpp": cpp_pred.get("code"),
-                                       "expected": expected, "mode": "label"})
-            if not match:
-                print(f"\nFAIL prediction mismatch: reference {ref_pred.get('code')!r} "
-                      f"vs c++ {cpp_pred.get('code')!r}")
-                all_passed = False
-            else:
-                note = "" if expected in (None, cpp_pred.get("code")) else f" (expected {expected!r})"
-                print(f"\n  Prediction: ok {cpp_pred.get('code')!r}{note}")
+            print(f"\n  Prediction: {'ok' if match else 'FAIL'} c++ {cpp_pred['code']!r}, "
+                  f"reference {ref_pred['code']!r}, expected {expected!r}")
 
         ref_transcript = ref_dir / "transcript.json"
         if ref_transcript.exists() and args.family != "sortformer":
@@ -934,11 +924,7 @@ def write_report_bundle(
         for tr in transcript_results:
             summary.append(f"### {tr['case']}")
             summary.append("")
-            if tr["match"] and tr.get("mode") == "label":
-                summary.append(f"- Match: **yes**")
-                summary.append(f"- Mode: `label` (top-1, C++ vs reference)")
-                summary.append(f"- label: `{tr.get('cpp', '')}` (expected `{tr.get('expected')}`)")
-            elif tr["match"]:
+            if tr["match"]:
                 summary.append(f"- Match: **yes**")
                 summary.append(f"- Mode: `{tr.get('mode', 'exact')}`")
                 summary.append(f"- text: `{tr.get('cpp', '')!r}`")

From aca98e0b6cdbf9d45be1efbd820e53f80433d4b9 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 12:53:56 +0800
Subject: [PATCH 33/38] langid: download sizes for GGUFs from the current
 converter

The GGUFs were regenerated with the current converter, which no longer
writes stt.ecapa_tdnn.format_version. All 145 tensors are byte-identical
in F32, F16 and Q8_0; each file is 64 bytes smaller.
---
 catalog/lang-id-voxlingua107-ecapa.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index aed1a176f..7357a197a 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -37,9 +37,9 @@
     "batching": {"supported":false}
   },
   "downloads": [
-    {"quant":"F32","filename":"lang-id-voxlingua107-ecapa-F32.gguf","size_bytes":84993248},
-    {"quant":"F16","filename":"lang-id-voxlingua107-ecapa-F16.gguf","size_bytes":45299936},
-    {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693696}
+    {"quant":"F32","filename":"lang-id-voxlingua107-ecapa-F32.gguf","size_bytes":84993184},
+    {"quant":"F16","filename":"lang-id-voxlingua107-ecapa-F16.gguf","size_bytes":45299872},
+    {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693632}
   ],
   "accuracy_benchmarks": [
     {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.3e-05}},

From c45e3907297b8d40b2568fe58ef0ddfa92facfb5 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 14:08:18 +0800
Subject: [PATCH 34/38] langid: HF card validation stamp aa4d0f47 on 2026-10-08

---
 docs/models/lang-id-voxlingua107-ecapa.md        | 2 +-
 scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 281542056..0ec1cd9b7 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -23,7 +23,7 @@ See SpeechBrain's [model card](https://huggingface.co/speechbrain/lang-id-voxlin
 for training data and the label list.
 
 <!-- catalog:pin -->
-Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa/commit/0253049), pinned 2026-10-05. Validated against the SpeechBrain reference at transcribe.cpp commit [`2fdb5c95`](https://github.com/handy-computer/transcribe.cpp/tree/2fdb5c95) on 2026-10-07.
+Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa/commit/0253049), pinned 2026-10-05. Validated against the SpeechBrain reference at transcribe.cpp commit [`aa4d0f47`](https://github.com/handy-computer/transcribe.cpp/tree/aa4d0f47) on 2026-10-08.
 <!-- /catalog -->
 
 ## Download
diff --git a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
index de55d0a56..73ef4855b 100644
--- a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
+++ b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
@@ -9,8 +9,8 @@ pin_date: 2026-10-05
 # older HF revisions carry whatever value was current at their upload time.
 validation:
   reference: SpeechBrain
-  commit: 2fdb5c95
-  date: 2026-10-07
+  commit: aa4d0f47
+  date: 2026-10-08
 
 pipeline_tag: audio-classification
 tags:

From 9cfa5670b3419e404c615b0163404642f748ef2e Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 14:55:37 +0800
Subject: [PATCH 35/38] binding cleanup

---
 .../python/src/transcribe_cpp/__init__.py     | 13 ++++-----
 bindings/python/tests/test_langid.py          | 10 +++++++
 bindings/rust/transcribe-cpp/src/error.rs     |  2 +-
 bindings/rust/transcribe-cpp/src/langid.rs    | 16 ++++++++---
 bindings/rust/transcribe-cpp/tests/serde.rs   | 27 ++++++++++++++-----
 .../swift/Sources/TranscribeCpp/LangId.swift  |  7 ++++-
 .../TranscribeCppTests/LangIdTests.swift      | 10 +++++++
 examples/cli/cli.h                            |  1 -
 examples/cli/langid.cpp                       |  9 ++++---
 examples/cli/main.cpp                         |  2 +-
 src/arch/ecapa_tdnn/weights.cpp               | 11 ++++++++
 tests/CMakeLists.txt                          |  4 ++-
 tests/ecapa_tdnn_smoke.cpp                    | 12 +++++++++
 tests/fixtures/make_gguf_fixtures.py          | 18 +++++++++----
 14 files changed, 111 insertions(+), 31 deletions(-)

diff --git a/bindings/python/src/transcribe_cpp/__init__.py b/bindings/python/src/transcribe_cpp/__init__.py
index cb933eaa2..3b0cb46f4 100644
--- a/bindings/python/src/transcribe_cpp/__init__.py
+++ b/bindings/python/src/transcribe_cpp/__init__.py
@@ -1289,14 +1289,15 @@ def langid_labels(self) -> tuple[tuple[str, str], ...]:
         """``(code, name)`` per label index. Raises :class:`UnsupportedRole`
         without the LANGID role."""
         n = self.langid_info.n_labels
-        return tuple((_lib.transcribe_langid_label_code(self._h, i).decode("utf-8"),
-                      _lib.transcribe_langid_label_name(self._h, i).decode("utf-8"))
+        return tuple((_decode(_lib.transcribe_langid_label_code(self._h, i)),
+                      _decode(_lib.transcribe_langid_label_name(self._h, i)))
                      for i in range(n))
 
     def langid_label_index(self, code: str) -> int | None:
         """Label index of a code or alias ("he" and "iw" name the same
-        label), or None. None as well on a model without the LANGID role."""
-        i = _lib.transcribe_langid_label_index(self._h, code.encode("utf-8"))
+        label), or None. None as well on a model without the LANGID role.
+        Raises :class:`InvalidArgument` if ``code`` contains a NUL."""
+        i = _lib.transcribe_langid_label_index(self._h, _cstr(code, "code"))
         return i if i >= 0 else None
 
     def langid_session(self, *, n_threads: int = 0,
@@ -2014,7 +2015,7 @@ def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
                 raise InvalidArgument("allowed is empty; pass None for every label")
             if not all(isinstance(c, str) for c in codes):
                 raise InvalidArgument("allowed entries must be str")
-            encoded = [c.encode("utf-8") for c in codes]
+            encoded = [_cstr(c, "allowed") for c in codes]
             arr = (ctypes.c_char_p * len(encoded))(*encoded)
             params.allowed = ctypes.cast(arr, type(params.allowed))
             params.n_allowed = len(encoded)
@@ -2036,7 +2037,7 @@ def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
                 _check(_lib.transcribe_langid_get_candidate(h, i, _byref(c)),
                        "transcribe_langid_get_candidate")
                 rows.append(LangIdCandidate(
-                    index=c.index, code=c.code.decode("utf-8"), name=c.name.decode("utf-8"),
+                    index=c.index, code=_decode(c.code), name=_decode(c.name),
                     p=c.p, logit=c.logit))
             return LangIdResult(candidates=tuple(rows), n_allowed=res.n_allowed,
                                 allowed_mass=res.allowed_mass, audio_ms=res.audio_ms)
diff --git a/bindings/python/tests/test_langid.py b/bindings/python/tests/test_langid.py
index ec6e19904..6888e9367 100644
--- a/bindings/python/tests/test_langid.py
+++ b/bindings/python/tests/test_langid.py
@@ -93,6 +93,16 @@ def test_toy_allowed_rejections(langid_toy_model_path, noise_1s):
             lid.run(noise_1s, allowed=None)  # None is every label
 
 
+def test_toy_interior_nul_is_rejected(langid_toy_model_path, noise_1s):
+    # C would cut "bb\0zz" to "bb"; the code must not silently narrow.
+    with t.Model(langid_toy_model_path, backend="cpu") as model:
+        with pytest.raises(t.InvalidArgument):
+            model.langid_label_index("bb\0zz")
+        with model.langid_session() as lid:
+            with pytest.raises(t.InvalidArgument):
+                lid.run(noise_1s, allowed=["bb\0zz"])
+
+
 def test_toy_input_rules(langid_toy_model_path, noise_1s):
     with t.Model(langid_toy_model_path, backend="cpu") as model:
         with model.langid_session() as lid:
diff --git a/bindings/rust/transcribe-cpp/src/error.rs b/bindings/rust/transcribe-cpp/src/error.rs
index 533bdafbd..8ca913731 100644
--- a/bindings/rust/transcribe-cpp/src/error.rs
+++ b/bindings/rust/transcribe-cpp/src/error.rs
@@ -128,10 +128,10 @@ pub enum ErrorKind {
     OutputTruncated,
     OutputRepetition,
     UnsupportedRole,
-    InputTooShort,
     VersionMismatch,
     Nul,
     Busy,
+    InputTooShort,
     #[cfg_attr(feature = "serde", serde(other))]
     Other,
 }
diff --git a/bindings/rust/transcribe-cpp/src/langid.rs b/bindings/rust/transcribe-cpp/src/langid.rs
index f66d46c17..b9d513229 100644
--- a/bindings/rust/transcribe-cpp/src/langid.rs
+++ b/bindings/rust/transcribe-cpp/src/langid.rs
@@ -60,9 +60,13 @@ pub struct LangIdOptions {
 }
 
 /// One ranked label. `code` is the model's own label (`"en"`, `"iw"`).
-#[derive(Debug, Clone, PartialEq)]
+#[derive(Debug, Clone, Default, PartialEq)]
 #[non_exhaustive]
-#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(
+    feature = "serde",
+    derive(serde::Serialize, serde::Deserialize),
+    serde(default)
+)]
 pub struct LangIdCandidate {
     /// Label index.
     pub index: i32,
@@ -74,9 +78,13 @@ pub struct LangIdCandidate {
 }
 
 /// The result of one [`LangIdSession::run`].
-#[derive(Debug, Clone, PartialEq)]
+#[derive(Debug, Clone, Default, PartialEq)]
 #[non_exhaustive]
-#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(
+    feature = "serde",
+    derive(serde::Serialize, serde::Deserialize),
+    serde(default)
+)]
 pub struct LangIdResult {
     /// Ranked by `p`, descending; ties keep label order.
     pub candidates: Vec<LangIdCandidate>,
diff --git a/bindings/rust/transcribe-cpp/tests/serde.rs b/bindings/rust/transcribe-cpp/tests/serde.rs
index fead72a04..5287f5475 100644
--- a/bindings/rust/transcribe-cpp/tests/serde.rs
+++ b/bindings/rust/transcribe-cpp/tests/serde.rs
@@ -5,12 +5,13 @@
 
 use transcribe_cpp::{
     Backend, Capabilities, CommitPolicy, DeviceType, Diarize, DiarizeExtension, DiarizeInfo,
-    DiarizeOptions, DiarizeSessionOptions, ExtSlot, Feature, Itn, KvType,
-    MoonshineStreamingOptions, ParakeetBufferedStreamOptions, ParakeetStreamOptions, Pnc, Role,
-    Roles, RunExtension, RunOptions, Segment, SessionLimits, SessionOptions,
-    SortformerDiarizeOptions, SortformerPreset, SpeakerSegment, StreamExtension, StreamOptions,
-    StreamState, StreamText, StreamUpdate, Task, TimestampKind, Timings, Token, Transcript,
-    VoxtralRealtimeStreamOptions, WhisperRunOptions, Word,
+    DiarizeOptions, DiarizeSessionOptions, ExtSlot, Feature, Itn, KvType, LangIdCandidate,
+    LangIdInfo, LangIdOptions, LangIdResult, LangIdSessionOptions, MoonshineStreamingOptions,
+    ParakeetBufferedStreamOptions, ParakeetStreamOptions, Pnc, Role, Roles, RunExtension,
+    RunOptions, Segment, SessionLimits, SessionOptions, SortformerDiarizeOptions, SortformerPreset,
+    SpeakerSegment, StreamExtension, StreamOptions, StreamState, StreamText, StreamUpdate, Task,
+    TimestampKind, Timings, Token, Transcript, VoxtralRealtimeStreamOptions, WhisperRunOptions,
+    Word,
 };
 
 fn assert_serde<T: serde::Serialize + serde::de::DeserializeOwned>() {}
@@ -34,8 +35,11 @@ fn plain_data_types_are_serializable() {
     assert_serde::<DiarizeSessionOptions>();
     assert_serde::<DiarizeExtension>();
     assert_serde::<SortformerDiarizeOptions>();
+    assert_serde::<LangIdSessionOptions>();
+    assert_serde::<LangIdOptions>();
     // Results.
     assert_serde::<DiarizeInfo>();
+    assert_serde::<LangIdInfo>();
     assert_serde::<Transcript>();
     assert_serde::<Segment>();
     assert_serde::<SpeakerSegment>();
@@ -46,6 +50,8 @@ fn plain_data_types_are_serializable() {
     assert_serde::<StreamText>();
     assert_serde::<Capabilities>();
     assert_serde::<SessionLimits>();
+    assert_serde::<LangIdResult>();
+    assert_serde::<LangIdCandidate>();
     // Enums.
     assert_serde::<Task>();
     assert_serde::<TimestampKind>();
@@ -135,6 +141,13 @@ fn missing_fields_take_defaults() {
     assert!(token.p.is_nan(), "missing p decoded as {}", token.p);
     let speaker: SpeakerSegment = serde_json::from_str(r#"{"speaker_id":2}"#).unwrap();
     assert!(speaker.p.is_nan(), "missing p decoded as {}", speaker.p);
+
+    let langid: LangIdResult = serde_json::from_str(r#"{"n_allowed":3}"#).unwrap();
+    assert_eq!(langid.n_allowed, 3);
+    assert!(langid.candidates.is_empty());
+    let candidate: LangIdCandidate = serde_json::from_str(r#"{"code":"en"}"#).unwrap();
+    assert_eq!(candidate.code, "en");
+    assert_eq!(candidate.p, 0.0);
 }
 
 mod errors {
@@ -215,10 +228,10 @@ mod errors {
             ErrorKind::OutputTruncated,
             ErrorKind::OutputRepetition,
             ErrorKind::UnsupportedRole,
-            ErrorKind::InputTooShort,
             ErrorKind::VersionMismatch,
             ErrorKind::Nul,
             ErrorKind::Busy,
+            ErrorKind::InputTooShort,
             ErrorKind::Other,
         ];
         for kind in kinds {
diff --git a/bindings/swift/Sources/TranscribeCpp/LangId.swift b/bindings/swift/Sources/TranscribeCpp/LangId.swift
index 33d770b67..ea30b9cb5 100644
--- a/bindings/swift/Sources/TranscribeCpp/LangId.swift
+++ b/bindings/swift/Sources/TranscribeCpp/LangId.swift
@@ -73,8 +73,10 @@ extension Model {
     }
 
     /// Label index of a code or alias ("he" and "iw" name the same label), or
-    /// `nil` (also on a model without the LANGID role).
+    /// `nil` (also on a model without the LANGID role, or for a code with a
+    /// NUL character, which C would silently cut).
     public func langIdLabelIndex(_ code: String) -> Int32? {
+        if code.contains("\0") { return nil }
         let i = transcribe_langid_label_index(ptr, code)
         return i >= 0 ? i : nil
     }
@@ -120,6 +122,9 @@ public final class LangIdSession {
             // NULL would mean "every label", the opposite of an empty list.
             throw TranscribeError.invalidArgument("allowed is empty; pass nil for every label")
         }
+        if let allowed = options.allowed, allowed.contains(where: { $0.contains("\0") }) {
+            throw TranscribeError.invalidArgument("allowed contains a NUL character")
+        }
         // strdup'd copies stay alive until the native call returns.
         let owned: [UnsafeMutablePointer<CChar>?] = options.allowed?.map { strdup($0) } ?? []
         defer { owned.forEach { free($0) } }
diff --git a/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift b/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
index 8602bf869..e6f48036a 100644
--- a/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
+++ b/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
@@ -62,6 +62,16 @@ final class LangIdTests: XCTestCase {
         XCTAssertGreaterThan(lid.timings.encodeMs, 0)
     }
 
+    func testToyInteriorNulIsRejected() throws {
+        // C would cut "bb\0zz" to "bb"; the code must not silently narrow.
+        let model = try cpuModel(try Fixtures.langIdToyModelPath())
+        XCTAssertNil(model.langIdLabelIndex("bb\0zz"))
+        let lid = try model.langIdSession()
+        XCTAssertThrowsError(try lid.run(noise(16000), options: LangIdOptions(allowed: ["bb\0zz"]))) { error in
+            guard case TranscribeError.invalidArgument = error else { return XCTFail("\(error)") }
+        }
+    }
+
     func testToyCancelAborts() throws {
         let model = try cpuModel(try Fixtures.langIdToyModelPath())
         let lid = try model.langIdSession()
diff --git a/examples/cli/cli.h b/examples/cli/cli.h
index 0b02f4e50..e4c24ec50 100644
--- a/examples/cli/cli.h
+++ b/examples/cli/cli.h
@@ -141,7 +141,6 @@ int run_diarize_file(const cli_args &           args,
 int run_langid_file(const cli_args &           args,
                     transcribe_model *         model,
                     const std::vector<float> & pcm,
-                    double                     duration_s,
                     std::ofstream *            output);
 
 }  // namespace transcribe_cli
diff --git a/examples/cli/langid.cpp b/examples/cli/langid.cpp
index 67853c489..566eedef9 100644
--- a/examples/cli/langid.cpp
+++ b/examples/cli/langid.cpp
@@ -18,7 +18,6 @@
 int transcribe_cli::run_langid_file(const cli_args &           args,
                                     transcribe_model *         model,
                                     const std::vector<float> & pcm,
-                                    double                     duration_s,
                                     std::ofstream *            output) {
     if (args.stream_chunk_ms > 0) {
         std::fprintf(stderr, "stream: the language ID path has no streaming entry point; drop --stream-chunk-ms\n");
@@ -57,11 +56,13 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
         }
     }
     std::printf("run: %s\n", transcribe_status_string(st));
-    bool output_ok = true;
+    bool   output_ok = true;
+    double scored_s  = 0.0;  // only the last max_audio_ms is scored, not the whole file
     if (st == TRANSCRIBE_OK) {
         transcribe_langid_result res;
         transcribe_langid_result_init(&res);
         transcribe_langid_get_result(session, &res);
+        scored_s = static_cast<double>(res.audio_ms) / 1000.0;
 
         std::string lines;
         for (int i = 0; i < res.n_candidates; ++i) {
@@ -88,9 +89,9 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
     transcribe_timings_init(&tm);
     transcribe_langid_get_timings(session, &tm);
     const double total_ms = tm.mel_ms + tm.encode_ms;
-    if (total_ms > 0.0 && duration_s > 0.0) {
+    if (total_ms > 0.0 && scored_s > 0.0) {
         std::printf("  realtime:   %.0fx (%.1f ms for %.1f s; mel %.1f ms, encode %.1f ms)\n",
-                    duration_s * 1000.0 / total_ms, total_ms, duration_s, static_cast<double>(tm.mel_ms),
+                    scored_s * 1000.0 / total_ms, total_ms, scored_s, static_cast<double>(tm.mel_ms),
                     static_cast<double>(tm.encode_ms));
     }
 
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 5bea58e3b..95f0bb418 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -626,7 +626,7 @@ int run_file(const cli_args & args, std::ofstream * output) {
         return transcribe_cli::run_asr_file(args, model, pcm, duration_s, output);
     }
     if ((roles & TRANSCRIBE_ROLE_LANGID) != 0) {
-        return transcribe_cli::run_langid_file(args, model, pcm, duration_s, output);
+        return transcribe_cli::run_langid_file(args, model, pcm, output);
     }
     return transcribe_cli::run_diarize_file(args, model, pcm, duration_s, output);
 }
diff --git a/src/arch/ecapa_tdnn/weights.cpp b/src/arch/ecapa_tdnn/weights.cpp
index 111e75147..dfcf8e931 100644
--- a/src/arch/ecapa_tdnn/weights.cpp
+++ b/src/arch/ecapa_tdnn/weights.cpp
@@ -77,6 +77,17 @@ transcribe_status read_hparams(const gguf_context * gguf, HParams & hp) {
                 hp.sample_rate);
         return TRANSCRIBE_ERR_GGUF;
     }
+    if (hp.mel_n_fft <= 0 || hp.mel_hop <= 0 || hp.mel_win <= 0 || hp.mel_n_mels <= 0) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
+                "%s: frontend dimensions must be positive (n_fft=%d hop_length=%d win_length=%d num_mels=%d)", kTag,
+                hp.mel_n_fft, hp.mel_hop, hp.mel_win, hp.mel_n_mels);
+        return TRANSCRIBE_ERR_GGUF;
+    }
+    if (hp.mel_win > hp.mel_n_fft) {
+        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: frontend win_length (%d) > n_fft (%d)", kTag, hp.mel_win,
+                hp.mel_n_fft);
+        return TRANSCRIBE_ERR_GGUF;
+    }
     // MelFrontend falls back silently on unknown values.
     if (hp.mel_window != "hamming_periodic" || hp.mel_pad_mode != "constant" || hp.mel_normalize != "sentence_mean") {
         log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: unsupported front end (window=%s pad_mode=%s normalize=%s)", kTag,
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 00e6aa045..1946f5f36 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -542,7 +542,9 @@ set(TRANSCRIBE_FIXTURE_FILES
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_qwen3_asr_minimal.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_minimal.gguf
     ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0.gguf
-    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf)
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_q8_0_as_f16.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_hop0.gguf
+    ${CMAKE_CURRENT_SOURCE_DIR}/fixtures/arch_ecapa_tdnn_bad_win_gt_fft.gguf)
 
 find_program(TRANSCRIBE_UV uv)
 
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index cab89a213..5d149aff0 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -202,6 +202,17 @@ void test_q8_0_widened_to_f16() {
     transcribe_model_free(ref);
 }
 
+// A front end the mel code cannot run (zero hop, window wider than n_fft)
+// fails the load instead of crashing at run time.
+void test_bad_frontend_rejected() {
+    for (const char * name : { "arch_ecapa_tdnn_bad_hop0.gguf", "arch_ecapa_tdnn_bad_win_gt_fft.gguf" }) {
+        transcribe_status  st = TRANSCRIBE_OK;
+        transcribe_model * m  = load_cpu(name, &st);
+        CHECK(st == TRANSCRIBE_ERR_GGUF && m == nullptr);
+        transcribe_model_free(m);
+    }
+}
+
 }  // namespace
 
 int main() {
@@ -219,6 +230,7 @@ int main() {
     transcribe_model_free(m);
 
     test_q8_0_widened_to_f16();
+    test_bad_frontend_rejected();
 
     if (g_failures != 0) {
         std::fprintf(stderr, "%d failure(s)\n", g_failures);
diff --git a/tests/fixtures/make_gguf_fixtures.py b/tests/fixtures/make_gguf_fixtures.py
index 61c8689cb..780f823fe 100644
--- a/tests/fixtures/make_gguf_fixtures.py
+++ b/tests/fixtures/make_gguf_fixtures.py
@@ -1395,13 +1395,14 @@ def _qwen3_asr_tensors() -> list[Tensor]:
 ECAPA_LABEL_NAMES  = ["Alpha", "Bravo", "Charlie", "Delta", "Echo"]
 
 
-def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str]) -> list[bytes]:
+def _ecapa_tdnn_hparams_kv(codes: list[str], names: list[str], aliases: list[str],
+                           hop_length: int = 160, win_length: int = 400) -> list[bytes]:
     return [
         _pack_kv_string("stt.frontend.type", "speechbrain_fbank"),
         _pack_kv_uint32("stt.frontend.sample_rate", 16000),
         _pack_kv_uint32("stt.frontend.n_fft", 400),
-        _pack_kv_uint32("stt.frontend.hop_length", 160),
-        _pack_kv_uint32("stt.frontend.win_length", 400),
+        _pack_kv_uint32("stt.frontend.hop_length", hop_length),
+        _pack_kv_uint32("stt.frontend.win_length", win_length),
         _pack_kv_uint32("stt.frontend.num_mels", ECAPA_N_MELS),
         _pack_kv_string("stt.frontend.window", "hamming_periodic"),
         _pack_kv_string("stt.frontend.pad_mode", "constant"),
@@ -1524,7 +1525,8 @@ def _ecapa_tdnn_q8_0(tensors: list[Tensor], as_f16: bool) -> list[Tensor]:
     return out
 
 
-def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "") -> bytes:
+def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_0: str = "",
+                     **frontend: int) -> bytes:
     tensors = _ecapa_tdnn_tensors(len(codes))
     if q8_0:
         tensors = _ecapa_tdnn_q8_0(tensors, as_f16=(q8_0 == "as_f16"))
@@ -1533,7 +1535,7 @@ def _ecapa_tdnn_gguf(codes: list[str], names: list[str], aliases: list[str], q8_
         [
             _pack_kv_string("general.architecture", "ecapa_tdnn"),
             _pack_kv_string("stt.variant", "ecapa-tdnn-toy"),
-            *_ecapa_tdnn_hparams_kv(codes, names, aliases),
+            *_ecapa_tdnn_hparams_kv(codes, names, aliases, **frontend),
         ],
         tensors,
     )
@@ -1963,6 +1965,12 @@ def emit_fixtures(out_dir: Path) -> None:
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="q8_0"))
     _write(out_dir / "arch_ecapa_tdnn_q8_0_as_f16.gguf",
            _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], q8_0="as_f16"))
+    # Front ends the loader must reject: a zero hop divides by zero in the
+    # frame count, and win_length > n_fft overruns the padded window.
+    _write(out_dir / "arch_ecapa_tdnn_bad_hop0.gguf",
+           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], hop_length=0))
+    _write(out_dir / "arch_ecapa_tdnn_bad_win_gt_fft.gguf",
+           _ecapa_tdnn_gguf(ECAPA_LABEL_CODES, ECAPA_LABEL_NAMES, ["xx=aa"], win_length=512))
 
 
 def main(argv: list[str]) -> int:

From cf62265a0fd1f74f8b5cf778b17119ec0826fe57 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 20:20:22 +0800
Subject: [PATCH 36/38] langid: slim API, score the first 30 s

- transcribe_langid_info gains max_audio_ms (fixed 30000); longer input is
  scored on its first 30 s instead of erroring. Session params keep only
  n_threads; run params drop top_k; the result keeps n_candidates and
  allowed_mass.
- Bindings (Python, Rust, TS, Swift) follow the slim API; regenerated FFI
  and abihash (e628600daea42c4a).
- CLI: --max-audio-ms removed, --top is print-only, RTF uses the scored
  duration.
- Tests for the first-30 s crop; docs, HF card yaml and the accuracy rows
  updated (CPU sweep, F32/F16/Q8_0); unused default_max_audio_ms dropped
  from the golden manifest.
---
 bindings/python/README.md                     |  7 ++-
 .../python/src/transcribe_cpp/__init__.py     | 37 +++++------
 .../python/src/transcribe_cpp/_generated.py   | 18 +++---
 bindings/python/tests/test_langid.py          | 32 +++++-----
 bindings/rust/sys/src/transcribe_sys.rs       | 23 +++----
 bindings/rust/transcribe-cpp/README.md        |  2 +-
 bindings/rust/transcribe-cpp/src/langid.rs    | 22 ++-----
 bindings/rust/transcribe-cpp/src/model.rs     |  1 +
 bindings/rust/transcribe-cpp/tests/langid.rs  | 47 +++++---------
 bindings/rust/transcribe-cpp/tests/serde.rs   |  4 +-
 bindings/swift/README.md                      |  5 +-
 .../swift/Sources/TranscribeCpp/ABIHash.swift |  2 +-
 .../swift/Sources/TranscribeCpp/LangId.swift  | 30 ++++-----
 .../TranscribeCppTests/LangIdTests.swift      | 11 ++--
 bindings/typescript/README.md                 |  7 ++-
 bindings/typescript/src/_generated.ts         | 18 +++---
 bindings/typescript/src/index.ts              | 15 ++---
 bindings/typescript/src/types.ts              | 15 ++---
 bindings/typescript/test/langid.test.mjs      | 24 ++++---
 catalog/lang-id-voxlingua107-ecapa.json       |  6 +-
 docs/bindings.md                              | 12 ++--
 docs/langid.md                                | 16 +++--
 docs/models/lang-id-voxlingua107-ecapa.md     | 15 ++---
 docs/porting/families/ecapa_tdnn.md           | 12 ++--
 examples/cli/cli.h                            |  8 +--
 examples/cli/langid.cpp                       | 25 +++++---
 examples/cli/main.cpp                         | 14 ++---
 include/transcribe.abihash                    |  2 +-
 include/transcribe/langid.h                   | 22 +++----
 .../hf_cards/lang-id-voxlingua107-ecapa.yaml  |  2 +-
 scripts/langid/bench.py                       |  2 +-
 scripts/langid/run.py                         | 32 +++++-----
 scripts/langid/score.py                       |  2 +-
 src/transcribe-langid.cpp                     | 42 ++++---------
 src/transcribe-langid.h                       | 13 ++--
 tests/ecapa_tdnn_real_smoke.cpp               | 52 +++++++++-------
 tests/ecapa_tdnn_smoke.cpp                    | 53 ++++++++++++++--
 .../lang-id-voxlingua107-ecapa.manifest.json  |  3 +-
 tests/langid_dispatch_unit.cpp                | 62 ++++++++++++-------
 39 files changed, 359 insertions(+), 356 deletions(-)

diff --git a/bindings/python/README.md b/bindings/python/README.md
index bb70a14e8..7037ec4b9 100644
--- a/bindings/python/README.md
+++ b/bindings/python/README.md
@@ -98,12 +98,13 @@ identify the spoken language through a langid session. `run()` returns a
 `allowed=None` scores every label; `allowed_mass` is the unrestricted
 probability the allowed set captured. An empty `allowed` list raises
 `InvalidArgument`; clips under `model.langid_info.min_audio_ms` raise
-`InputTooShort`. Locking, `Busy`, `cancel()` and `close()` work as on
-`Session`.
+`InputTooShort`, and longer ones than `max_audio_ms` are scored on their first
+`max_audio_ms`. Every allowed label comes back as a candidate. Locking,
+`Busy`, `cancel()` and `close()` work as on `Session`.
 
 ```python
 with model.langid_session() as lid:
-    result = lid.run(pcm, allowed=["en", "de", "fr"], top_k=3)
+    result = lid.run(pcm, allowed=["en", "de", "fr"])
     print(result.code, result.candidates[0].p, result.allowed_mass)
 ```
 
diff --git a/bindings/python/src/transcribe_cpp/__init__.py b/bindings/python/src/transcribe_cpp/__init__.py
index 3b0cb46f4..c88a6c1d4 100644
--- a/bindings/python/src/transcribe_cpp/__init__.py
+++ b/bindings/python/src/transcribe_cpp/__init__.py
@@ -585,6 +585,7 @@ class LangIdInfo:
     sample_rate: int
     n_labels: int      # label indices are [0, n_labels)
     min_audio_ms: int  # shorter scored audio raises InputTooShort
+    max_audio_ms: int  # longer input is scored on its first max_audio_ms
 
 
 @dataclass(frozen=True)
@@ -604,12 +605,11 @@ class LangIdResult:
     """``candidates`` are ranked by ``p`` (ties keep label order).
     ``allowed_mass`` is the unrestricted probability inside the allowed set
     (1.0 when unrestricted); a low value means the speech is probably outside
-    it. ``audio_ms`` is what was scored, after the crop."""
+    it. There is one candidate per allowed label (every label when
+    unrestricted)."""
 
     candidates: tuple[LangIdCandidate, ...]
-    n_allowed: int
     allowed_mass: float
-    audio_ms: int
 
     @property
     def code(self) -> str | None:
@@ -1282,7 +1282,8 @@ def langid_info(self) -> LangIdInfo:
         _check(_lib.transcribe_langid_get_info(self._h, _byref(info)),
                "reading langid info")
         return LangIdInfo(sample_rate=info.sample_rate, n_labels=info.n_labels,
-                          min_audio_ms=info.min_audio_ms)
+                          min_audio_ms=info.min_audio_ms,
+                          max_audio_ms=info.max_audio_ms)
 
     @property
     def langid_labels(self) -> tuple[tuple[str, str], ...]:
@@ -1300,12 +1301,9 @@ def langid_label_index(self, code: str) -> int | None:
         i = _lib.transcribe_langid_label_index(self._h, _cstr(code, "code"))
         return i if i >= 0 else None
 
-    def langid_session(self, *, n_threads: int = 0,
-                       max_audio_ms: int = 0) -> "LangIdSession":
-        """``max_audio_ms``: longer input is scored on its last
-        ``max_audio_ms`` (0 = 30000). Raises :class:`UnsupportedRole`
-        without the LANGID role."""
-        return LangIdSession(self, n_threads=n_threads, max_audio_ms=max_audio_ms)
+    def langid_session(self, *, n_threads: int = 0) -> "LangIdSession":
+        """Raises :class:`UnsupportedRole` without the LANGID role."""
+        return LangIdSession(self, n_threads=n_threads)
 
     def close(self) -> None:
         """Free the model. Any session still open on it is closed first —
@@ -1974,12 +1972,11 @@ class LangIdSession(_SessionBase):
 
     _free_fn = "transcribe_langid_session_free"
 
-    def __init__(self, model: Model, *, n_threads: int = 0, max_audio_ms: int = 0):
+    def __init__(self, model: Model, *, n_threads: int = 0):
         self._model = model  # keep the model alive for the session's lifetime
         params = _generated.transcribe_langid_session_params()
         _lib.transcribe_langid_session_params_init(_byref(params))
         params.n_threads = n_threads
-        params.max_audio_ms = max_audio_ms
 
         handle = ctypes.c_void_p()
         _check(_lib.transcribe_langid_session_init(model._h, _byref(params), _byref(handle)),
@@ -1989,13 +1986,13 @@ def __init__(self, model: Model, *, n_threads: int = 0, max_audio_ms: int = 0):
         self._handle = handle
         self._arm_abort(_lib.transcribe_langid_set_abort_callback)
 
-    def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
-            top_k: int = 0) -> LangIdResult:
+    def run(self, pcm: PCMLike, *,
+            allowed: "Sequence[str] | None" = None) -> LangIdResult:
         """Identify the language of one clip (16 kHz mono float32 PCM).
-        Longer input than the session's ``max_audio_ms`` is scored on its
-        tail. ``allowed`` restricts the decision to those codes or aliases;
-        None means every label, and an empty list is rejected. ``top_k``
-        keeps the best candidates (0 = every allowed label).
+        Input longer than ``LangIdInfo.max_audio_ms`` is scored on its first
+        ``max_audio_ms``. ``allowed`` restricts the decision to those codes
+        or aliases; None means every label, and an empty list is rejected.
+        Every allowed label is returned, ranked by ``p``.
 
         Raises :class:`InputTooShort` below ``LangIdInfo.min_audio_ms``,
         :class:`UnsupportedRequest` for an unknown code, :class:`Aborted`
@@ -2005,7 +2002,6 @@ def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
         array, n_samples = _pcm_to_carray(pcm)
         params = _generated.transcribe_langid_params()
         _lib.transcribe_langid_params_init(_byref(params))
-        params.top_k = top_k
         if allowed is not None:
             if isinstance(allowed, (str, bytes)):
                 raise InvalidArgument("allowed must be a sequence of codes, not a string")
@@ -2039,8 +2035,7 @@ def run(self, pcm: PCMLike, *, allowed: "Sequence[str] | None" = None,
                 rows.append(LangIdCandidate(
                     index=c.index, code=_decode(c.code), name=_decode(c.name),
                     p=c.p, logit=c.logit))
-            return LangIdResult(candidates=tuple(rows), n_allowed=res.n_allowed,
-                                allowed_mass=res.allowed_mass, audio_ms=res.audio_ms)
+            return LangIdResult(candidates=tuple(rows), allowed_mass=res.allowed_mass)
 
     @property
     def timings(self) -> Timings:
diff --git a/bindings/python/src/transcribe_cpp/_generated.py b/bindings/python/src/transcribe_cpp/_generated.py
index 2354b16a5..e1ffc6035 100644
--- a/bindings/python/src/transcribe_cpp/_generated.py
+++ b/bindings/python/src/transcribe_cpp/_generated.py
@@ -13,7 +13,7 @@
 # Stable digest of the ABI surface below (structs, enums, macros, layout,
 # prototypes). A native provider package echoes this back so the API
 # package can reject an ABI-mismatched provider before dlopen.
-PUBLIC_HEADER_HASH = "d544d70a2b5acf50"
+PUBLIC_HEADER_HASH = "e628600daea42c4a"
 
 # === enum constants ===
 TRANSCRIBE_OK = 0
@@ -228,10 +228,10 @@ class transcribe_whisper_chunk_trace(_c.Structure):
 transcribe_diarize_info._fields_ = [("struct_size", _c.c_uint64), ("sample_rate", _c.c_int32), ("max_speakers", _c.c_int32)]
 transcribe_diarize_session_params._fields_ = [("struct_size", _c.c_uint64), ("n_threads", _c.c_int32)]
 transcribe_diarize_params._fields_ = [("struct_size", _c.c_uint64), ("family", _c.POINTER(transcribe_ext))]
-transcribe_langid_info._fields_ = [("struct_size", _c.c_uint64), ("sample_rate", _c.c_int32), ("n_labels", _c.c_int32), ("min_audio_ms", _c.c_int32)]
-transcribe_langid_session_params._fields_ = [("struct_size", _c.c_uint64), ("n_threads", _c.c_int32), ("max_audio_ms", _c.c_int32)]
-transcribe_langid_params._fields_ = [("struct_size", _c.c_uint64), ("allowed", _c.POINTER(_c.c_char_p)), ("n_allowed", _c.c_int32), ("top_k", _c.c_int32)]
-transcribe_langid_result._fields_ = [("struct_size", _c.c_uint64), ("n_candidates", _c.c_int32), ("n_allowed", _c.c_int32), ("allowed_mass", _c.c_float), ("audio_ms", _c.c_int64)]
+transcribe_langid_info._fields_ = [("struct_size", _c.c_uint64), ("sample_rate", _c.c_int32), ("n_labels", _c.c_int32), ("min_audio_ms", _c.c_int32), ("max_audio_ms", _c.c_int32)]
+transcribe_langid_session_params._fields_ = [("struct_size", _c.c_uint64), ("n_threads", _c.c_int32)]
+transcribe_langid_params._fields_ = [("struct_size", _c.c_uint64), ("allowed", _c.POINTER(_c.c_char_p)), ("n_allowed", _c.c_int32)]
+transcribe_langid_result._fields_ = [("struct_size", _c.c_uint64), ("n_candidates", _c.c_int32), ("allowed_mass", _c.c_float)]
 transcribe_langid_candidate._fields_ = [("struct_size", _c.c_uint64), ("index", _c.c_int32), ("code", _c.c_char_p), ("name", _c.c_char_p), ("p", _c.c_float), ("logit", _c.c_float)]
 transcribe_moonshine_streaming_stream_ext._fields_ = [("ext", transcribe_ext), ("min_decode_interval_ms", _c.c_int32)]
 transcribe_parakeet_stream_ext._fields_ = [("ext", transcribe_ext), ("att_context_right", _c.c_int32)]
@@ -291,10 +291,10 @@ class transcribe_whisper_chunk_trace(_c.Structure):
     'transcribe_diarize_info': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'sample_rate': 8, 'max_speakers': 12}},
     'transcribe_diarize_session_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_threads': 8}},
     'transcribe_diarize_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'family': 8}},
-    'transcribe_langid_info': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'sample_rate': 8, 'n_labels': 12, 'min_audio_ms': 16}},
-    'transcribe_langid_session_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_threads': 8, 'max_audio_ms': 12}},
-    'transcribe_langid_params': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'allowed': 8, 'n_allowed': 16, 'top_k': 20}},
-    'transcribe_langid_result': {'size': 32, 'align': 8, 'offsets': {'struct_size': 0, 'n_candidates': 8, 'n_allowed': 12, 'allowed_mass': 16, 'audio_ms': 24}},
+    'transcribe_langid_info': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'sample_rate': 8, 'n_labels': 12, 'min_audio_ms': 16, 'max_audio_ms': 20}},
+    'transcribe_langid_session_params': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_threads': 8}},
+    'transcribe_langid_params': {'size': 24, 'align': 8, 'offsets': {'struct_size': 0, 'allowed': 8, 'n_allowed': 16}},
+    'transcribe_langid_result': {'size': 16, 'align': 8, 'offsets': {'struct_size': 0, 'n_candidates': 8, 'allowed_mass': 12}},
     'transcribe_langid_candidate': {'size': 40, 'align': 8, 'offsets': {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'logit': 36}},
     'transcribe_moonshine_streaming_stream_ext': {'size': 24, 'align': 8, 'offsets': {'ext': 0, 'min_decode_interval_ms': 16}},
     'transcribe_parakeet_stream_ext': {'size': 24, 'align': 8, 'offsets': {'ext': 0, 'att_context_right': 16}},
diff --git a/bindings/python/tests/test_langid.py b/bindings/python/tests/test_langid.py
index 6888e9367..5db2a1cc0 100644
--- a/bindings/python/tests/test_langid.py
+++ b/bindings/python/tests/test_langid.py
@@ -1,5 +1,5 @@
-"""LANGID role: roles, info, label table, LangIdSession.run and its allowed /
-top_k contract. Contract tests run on the toy ecapa_tdnn fixture (always
+"""LANGID role: roles, info, label table, LangIdSession.run and its allowed
+contract. Contract tests run on the toy ecapa_tdnn fixture (always
 available); accuracy tests need the real VoxLingua107 GGUF. The locking /
 Busy / close / cancel rules are pinned model-free in test_compute_lock.py."""
 
@@ -31,7 +31,8 @@ def noise_1s():
 def test_toy_roles_info_labels(langid_toy_model_path):
     with t.Model(langid_toy_model_path, backend="cpu") as model:
         assert model.roles == {t.Role.LANGID}
-        assert model.langid_info == t.LangIdInfo(sample_rate=16000, n_labels=5, min_audio_ms=500)
+        assert model.langid_info == t.LangIdInfo(sample_rate=16000, n_labels=5,
+                                                 min_audio_ms=500, max_audio_ms=30000)
         assert model.langid_labels[2] == ("cc", "Charlie")
         assert model.langid_label_index("xx") == 0
         assert model.langid_label_index("zz") is None
@@ -56,8 +57,9 @@ def test_toy_run_contract(langid_toy_model_path, noise_1s):
     with t.Model(langid_toy_model_path, backend="cpu") as model:
         with model.langid_session(n_threads=1) as lid:
             r = lid.run(noise_1s)
-            assert len(r.candidates) == 5 and r.n_allowed == 5
-            assert r.allowed_mass == 1.0 and r.audio_ms == 1000
+            assert len(r.candidates) == 5  # unrestricted: every label
+            assert {c.code for c in r.candidates} == {c for c, _ in model.langid_labels}
+            assert r.allowed_mass == 1.0
             assert r.code == r.candidates[0].code
             assert math.isclose(sum(c.p for c in r.candidates), 1.0, rel_tol=1e-5)
             ps = [c.p for c in r.candidates]
@@ -65,14 +67,11 @@ def test_toy_run_contract(langid_toy_model_path, noise_1s):
 
             restricted = lid.run(noise_1s, allowed=["bb", "dd"])
             assert {c.code for c in restricted.candidates} == {"bb", "dd"}
-            assert restricted.n_allowed == 2 and 0.0 < restricted.allowed_mass < 1.0
+            assert len(restricted.candidates) == 2 and 0.0 < restricted.allowed_mass < 1.0
 
             alias = lid.run(noise_1s, allowed=("xx",))
             assert [c.code for c in alias.candidates] == ["aa"] and alias.candidates[0].p == 1.0
 
-            top = lid.run(noise_1s, top_k=2)
-            assert len(top.candidates) == 2 and top.n_allowed == 5
-
             assert lid.timings.encode_ms > 0.0
 
 
@@ -88,8 +87,6 @@ def test_toy_allowed_rejections(langid_toy_model_path, noise_1s):
                 lid.run(noise_1s, allowed=["aa", None])
             with pytest.raises(t.UnsupportedRequest):
                 lid.run(noise_1s, allowed=["zz"])
-            with pytest.raises(t.InvalidArgument):
-                lid.run(noise_1s, top_k=-1)
             lid.run(noise_1s, allowed=None)  # None is every label
 
 
@@ -108,16 +105,13 @@ def test_toy_input_rules(langid_toy_model_path, noise_1s):
         with model.langid_session() as lid:
             with pytest.raises(t.InputTooShort):
                 lid.run(noise_1s[:6400])  # 400 ms
-            assert lid.run(noise_1s[:8000]).audio_ms == 500
-            assert lid.run(_noise(16000 * 31)).audio_ms == 30000
+            assert lid.run(noise_1s[:8000]).candidates  # exactly min_audio_ms
+            # Past max_audio_ms the head is scored, not rejected.
+            assert len(lid.run(_noise(16000 * 31)).candidates) == 5
             bad = array.array("f", noise_1s)
             bad[10] = float("nan")
             with pytest.raises(t.InvalidArgument):
                 lid.run(bad)
-        with pytest.raises(t.InvalidArgument):
-            model.langid_session(max_audio_ms=400)
-        with model.langid_session(max_audio_ms=500) as short:
-            assert short.run(noise_1s).audio_ms == 500
 
 
 def test_toy_cancel(langid_toy_model_path, noise_1s):
@@ -135,7 +129,8 @@ def test_real_fleurs_top1(langid_model_path, code):
         assert model.langid_info.n_labels == 107
         assert model.langid_label_index("he") == model.langid_label_index("iw")
         with model.langid_session() as lid:
-            r = lid.run(pcm, top_k=3)
+            r = lid.run(pcm)
+    assert len(r.candidates) == 107
     assert r.code == code and r.candidates[0].p >= 0.5
 
 
@@ -144,4 +139,5 @@ def test_real_allowed_mass_signals_out_of_set(langid_model_path):
     with t.Model(langid_model_path, backend="cpu") as model:
         with model.langid_session() as lid:
             r = lid.run(pcm, allowed=["en", "de"])
+    assert len(r.candidates) == 2
     assert r.code in ("en", "de") and r.allowed_mass < 0.01
diff --git a/bindings/rust/sys/src/transcribe_sys.rs b/bindings/rust/sys/src/transcribe_sys.rs
index 778e5b4b5..3eec901e9 100644
--- a/bindings/rust/sys/src/transcribe_sys.rs
+++ b/bindings/rust/sys/src/transcribe_sys.rs
@@ -1,11 +1,11 @@
 // @generated by `cargo xtask bindgen` from include/transcribe/extensions.h
 // DO NOT EDIT BY HAND. Regenerate: `cargo xtask bindgen`.
-// Pinned to include/transcribe.abihash = d544d70a2b5acf50
+// Pinned to include/transcribe.abihash = e628600daea42c4a
 
 /// The public-ABI digest these bindings were generated against
 /// (sha256/16 over the normalized FFI surface). The load-time version
 /// gate and the CI drift check both anchor on this value.
-pub const PUBLIC_HEADER_HASH: &str = "d544d70a2b5acf50";
+pub const PUBLIC_HEADER_HASH: &str = "e628600daea42c4a";
 
 /* automatically generated by rust-bindgen 0.72.1 */
 
@@ -1299,6 +1299,7 @@ pub struct transcribe_langid_info {
     pub sample_rate: i32,
     pub n_labels: i32,
     pub min_audio_ms: i32,
+    pub max_audio_ms: i32,
 }
 #[allow(clippy::unnecessary_operation, clippy::identity_op)]
 const _: () = {
@@ -1313,13 +1314,14 @@ const _: () = {
         [::std::mem::offset_of!(transcribe_langid_info, n_labels) - 12usize];
     ["Offset of field: transcribe_langid_info::min_audio_ms"]
         [::std::mem::offset_of!(transcribe_langid_info, min_audio_ms) - 16usize];
+    ["Offset of field: transcribe_langid_info::max_audio_ms"]
+        [::std::mem::offset_of!(transcribe_langid_info, max_audio_ms) - 20usize];
 };
 #[repr(C)]
 #[derive(Debug, Copy, Clone)]
 pub struct transcribe_langid_session_params {
     pub struct_size: u64,
     pub n_threads: i32,
-    pub max_audio_ms: i32,
 }
 #[allow(clippy::unnecessary_operation, clippy::identity_op)]
 const _: () = {
@@ -1331,8 +1333,6 @@ const _: () = {
         [::std::mem::offset_of!(transcribe_langid_session_params, struct_size) - 0usize];
     ["Offset of field: transcribe_langid_session_params::n_threads"]
         [::std::mem::offset_of!(transcribe_langid_session_params, n_threads) - 8usize];
-    ["Offset of field: transcribe_langid_session_params::max_audio_ms"]
-        [::std::mem::offset_of!(transcribe_langid_session_params, max_audio_ms) - 12usize];
 };
 #[repr(C)]
 #[derive(Debug, Copy, Clone)]
@@ -1340,7 +1340,6 @@ pub struct transcribe_langid_params {
     pub struct_size: u64,
     pub allowed: *const *const ::std::os::raw::c_char,
     pub n_allowed: i32,
-    pub top_k: i32,
 }
 #[allow(clippy::unnecessary_operation, clippy::identity_op)]
 const _: () = {
@@ -1354,34 +1353,26 @@ const _: () = {
         [::std::mem::offset_of!(transcribe_langid_params, allowed) - 8usize];
     ["Offset of field: transcribe_langid_params::n_allowed"]
         [::std::mem::offset_of!(transcribe_langid_params, n_allowed) - 16usize];
-    ["Offset of field: transcribe_langid_params::top_k"]
-        [::std::mem::offset_of!(transcribe_langid_params, top_k) - 20usize];
 };
 #[repr(C)]
 #[derive(Debug, Copy, Clone)]
 pub struct transcribe_langid_result {
     pub struct_size: u64,
     pub n_candidates: i32,
-    pub n_allowed: i32,
     pub allowed_mass: f32,
-    pub audio_ms: i64,
 }
 #[allow(clippy::unnecessary_operation, clippy::identity_op)]
 const _: () = {
     ["Size of transcribe_langid_result"]
-        [::std::mem::size_of::<transcribe_langid_result>() - 32usize];
+        [::std::mem::size_of::<transcribe_langid_result>() - 16usize];
     ["Alignment of transcribe_langid_result"]
         [::std::mem::align_of::<transcribe_langid_result>() - 8usize];
     ["Offset of field: transcribe_langid_result::struct_size"]
         [::std::mem::offset_of!(transcribe_langid_result, struct_size) - 0usize];
     ["Offset of field: transcribe_langid_result::n_candidates"]
         [::std::mem::offset_of!(transcribe_langid_result, n_candidates) - 8usize];
-    ["Offset of field: transcribe_langid_result::n_allowed"]
-        [::std::mem::offset_of!(transcribe_langid_result, n_allowed) - 12usize];
     ["Offset of field: transcribe_langid_result::allowed_mass"]
-        [::std::mem::offset_of!(transcribe_langid_result, allowed_mass) - 16usize];
-    ["Offset of field: transcribe_langid_result::audio_ms"]
-        [::std::mem::offset_of!(transcribe_langid_result, audio_ms) - 24usize];
+        [::std::mem::offset_of!(transcribe_langid_result, allowed_mass) - 12usize];
 };
 #[repr(C)]
 #[derive(Debug, Copy, Clone)]
diff --git a/bindings/rust/transcribe-cpp/README.md b/bindings/rust/transcribe-cpp/README.md
index bfb8fff18..cba90d5c5 100644
--- a/bindings/rust/transcribe-cpp/README.md
+++ b/bindings/rust/transcribe-cpp/README.md
@@ -106,7 +106,7 @@ every label, `Some(vec![])` is `Error::InvalidArgument`, and clips under
 use transcribe_cpp::{LangIdOptions, Model};
 let model = Model::load("lang-id-voxlingua107-ecapa-Q8_0.gguf")?;
 let mut lid = model.langid_session()?;
-let opts = LangIdOptions { allowed: Some(vec!["en".into(), "de".into()]), top_k: 3 };
+let opts = LangIdOptions { allowed: Some(vec!["en".into(), "de".into()]) };
 let result = lid.run(&pcm, &opts)?;
 println!("{:?} (mass {})", result.code(), result.allowed_mass);
 # Ok::<(), transcribe_cpp::Error>(())
diff --git a/bindings/rust/transcribe-cpp/src/langid.rs b/bindings/rust/transcribe-cpp/src/langid.rs
index b9d513229..713082049 100644
--- a/bindings/rust/transcribe-cpp/src/langid.rs
+++ b/bindings/rust/transcribe-cpp/src/langid.rs
@@ -26,6 +26,8 @@ pub struct LangIdInfo {
     pub n_labels: i32,
     /// Shorter scored audio is [`Error::InputTooShort`].
     pub min_audio_ms: i32,
+    /// Longer input is scored on its first `max_audio_ms` (30000).
+    pub max_audio_ms: i32,
 }
 
 /// Options for creating a language ID session.
@@ -38,9 +40,6 @@ pub struct LangIdInfo {
 pub struct LangIdSessionOptions {
     /// CPU threads for ops that run on CPU; 0 = library default.
     pub n_threads: i32,
-    /// Longer input is scored on its last `max_audio_ms`; 0 = 30000. Below
-    /// [`LangIdInfo::min_audio_ms`] is [`Error::InvalidArgument`].
-    pub max_audio_ms: i32,
 }
 
 /// Per-run language ID parameters.
@@ -55,8 +54,6 @@ pub struct LangIdOptions {
     /// label; `Some` of an empty list is [`Error::InvalidArgument`]. An
     /// unknown code is [`Error::Unsupported`].
     pub allowed: Option<Vec<String>>,
-    /// Keep the best `top_k` candidates; 0 = every allowed label.
-    pub top_k: i32,
 }
 
 /// One ranked label. `code` is the model's own label (`"en"`, `"iw"`).
@@ -86,15 +83,12 @@ pub struct LangIdCandidate {
     serde(default)
 )]
 pub struct LangIdResult {
-    /// Ranked by `p`, descending; ties keep label order.
+    /// Every allowed label (duplicates once; all labels when unrestricted),
+    /// ranked by `p`, descending; ties keep label order.
     pub candidates: Vec<LangIdCandidate>,
-    /// Labels in the allowed set (before `top_k`).
-    pub n_allowed: i32,
     /// Unrestricted probability inside the allowed set (1.0 when
     /// unrestricted); a low value means the speech is probably outside it.
     pub allowed_mass: f32,
-    /// Audio actually scored, after the crop.
-    pub audio_ms: i64,
 }
 
 impl LangIdResult {
@@ -134,7 +128,6 @@ impl LangIdSession {
         let mut params: sys::transcribe_langid_session_params = unsafe { std::mem::zeroed() };
         unsafe { sys::transcribe_langid_session_params_init(&mut params) };
         params.n_threads = options.n_threads;
-        params.max_audio_ms = options.max_audio_ms;
 
         let mut out: *mut sys::transcribe_langid_session = std::ptr::null_mut();
         let status =
@@ -168,7 +161,7 @@ impl LangIdSession {
     }
 
     /// Identify the language of one clip of 16 kHz mono float32 PCM. Input
-    /// longer than the session's `max_audio_ms` is scored on its tail.
+    /// longer than [`LangIdInfo::max_audio_ms`] is scored on its first `max_audio_ms`.
     pub fn run(&mut self, pcm: &[f32], options: &LangIdOptions) -> Result<LangIdResult> {
         let n = clamp_len(pcm.len())?;
         // The CStrings and the pointer array stay alive until the end of this
@@ -197,7 +190,6 @@ impl LangIdSession {
             params.n_allowed = i32::try_from(p.len())
                 .map_err(|_| Error::InvalidArgument("allowed list too long".into()))?;
         }
-        params.top_k = options.top_k;
 
         // Results are copied out under the compute lock, like every other
         // binding, so a concurrent run on the same model cannot interleave.
@@ -232,9 +224,7 @@ impl LangIdSession {
                     .collect();
                 Ok(LangIdResult {
                     candidates,
-                    n_allowed: res.n_allowed,
                     allowed_mass: res.allowed_mass,
-                    audio_ms: res.audio_ms,
                 })
             },
         )?
@@ -285,7 +275,6 @@ mod tests {
     fn empty_allowed_is_rejected_not_all() {
         let opts = LangIdOptions {
             allowed: Some(vec![]),
-            top_k: 0,
         };
         let err = null_session(false).run(&[0.0; 160], &opts).unwrap_err();
         assert!(matches!(err, Error::InvalidArgument(_)), "{err:?}");
@@ -295,7 +284,6 @@ mod tests {
     fn interior_nul_in_allowed_is_rejected() {
         let opts = LangIdOptions {
             allowed: Some(vec!["e\0n".into()]),
-            top_k: 0,
         };
         let err = null_session(false).run(&[0.0; 160], &opts).unwrap_err();
         assert!(matches!(err, Error::Nul(_)), "{err:?}");
diff --git a/bindings/rust/transcribe-cpp/src/model.rs b/bindings/rust/transcribe-cpp/src/model.rs
index fd902900b..0b9b94814 100644
--- a/bindings/rust/transcribe-cpp/src/model.rs
+++ b/bindings/rust/transcribe-cpp/src/model.rs
@@ -225,6 +225,7 @@ impl Model {
             sample_rate: raw.sample_rate,
             n_labels: raw.n_labels,
             min_audio_ms: raw.min_audio_ms,
+            max_audio_ms: raw.max_audio_ms,
         })
     }
 
diff --git a/bindings/rust/transcribe-cpp/tests/langid.rs b/bindings/rust/transcribe-cpp/tests/langid.rs
index e474a8835..189195c7c 100644
--- a/bindings/rust/transcribe-cpp/tests/langid.rs
+++ b/bindings/rust/transcribe-cpp/tests/langid.rs
@@ -6,9 +6,7 @@
 
 mod common;
 
-use transcribe_cpp::{
-    Backend, CancelToken, Error, LangIdOptions, LangIdSessionOptions, Model, ModelOptions, Role,
-};
+use transcribe_cpp::{Backend, CancelToken, Error, LangIdOptions, Model, ModelOptions, Role};
 
 fn noise(n: usize, seed: u32) -> Vec<f32> {
     let mut s = seed | 1;
@@ -38,8 +36,13 @@ fn toy_roles_info_labels() {
     assert!(roles.contains(Role::LangId) && !roles.contains(Role::Asr));
     let info = model.langid_info().unwrap();
     assert_eq!(
-        (info.sample_rate, info.n_labels, info.min_audio_ms),
-        (16000, 5, 500)
+        (
+            info.sample_rate,
+            info.n_labels,
+            info.min_audio_ms,
+            info.max_audio_ms
+        ),
+        (16000, 5, 500, 30000)
     );
     assert_eq!(
         model.langid_labels().unwrap()[2],
@@ -64,7 +67,7 @@ fn toy_run_contract() {
     let pcm = noise(16000, 7);
 
     let r = lid.run(&pcm, &LangIdOptions::default()).unwrap();
-    assert_eq!((r.candidates.len(), r.n_allowed, r.audio_ms), (5, 5, 1000));
+    assert_eq!(r.candidates.len(), 5);
     assert_eq!(r.allowed_mass, 1.0);
     assert_eq!(r.code(), Some(r.candidates[0].code.as_str()));
     let sum: f32 = r.candidates.iter().map(|c| c.p).sum();
@@ -73,10 +76,9 @@ fn toy_run_contract() {
 
     let opts = LangIdOptions {
         allowed: Some(vec!["bb".into(), "dd".into()]),
-        top_k: 0,
     };
     let r = lid.run(&pcm, &opts).unwrap();
-    assert_eq!(r.n_allowed, 2);
+    assert_eq!(r.candidates.len(), 2);
     assert!(r
         .candidates
         .iter()
@@ -85,28 +87,13 @@ fn toy_run_contract() {
 
     let opts = LangIdOptions {
         allowed: Some(vec!["zz".into()]),
-        top_k: 0,
     };
     assert!(matches!(lid.run(&pcm, &opts), Err(Error::Unsupported(_))));
     assert!(matches!(
         lid.run(&pcm[..6400], &LangIdOptions::default()),
         Err(Error::InputTooShort(_))
     ));
-    let top = LangIdOptions {
-        allowed: None,
-        top_k: 2,
-    };
-    assert_eq!(lid.run(&pcm, &top).unwrap().candidates.len(), 2);
     assert!(lid.timings().encode_ms > 0.0);
-
-    let bad = LangIdSessionOptions {
-        n_threads: 0,
-        max_audio_ms: 400,
-    };
-    assert!(matches!(
-        model.langid_session_with(&bad),
-        Err(Error::InvalidArgument(_))
-    ));
 }
 
 #[test]
@@ -133,7 +120,8 @@ fn real_fleurs_top1() {
         return;
     };
     let model = load_cpu(&path);
-    assert_eq!(model.langid_info().unwrap().n_labels, 107);
+    let info = model.langid_info().unwrap();
+    assert_eq!((info.n_labels, info.max_audio_ms), (107, 30000));
     assert_eq!(
         model.langid_label_index("he"),
         model.langid_label_index("iw")
@@ -141,15 +129,8 @@ fn real_fleurs_top1() {
     let mut lid = model.langid_session().unwrap();
     for code in ["en", "de", "fr", "es", "ja", "zh", "ru", "id"] {
         let pcm = common::load_wav(&common::repo_root().join(format!("samples/fleurs-{code}.wav")));
-        let r = lid
-            .run(
-                &pcm,
-                &LangIdOptions {
-                    allowed: None,
-                    top_k: 3,
-                },
-            )
-            .unwrap();
+        let r = lid.run(&pcm, &LangIdOptions::default()).unwrap();
+        assert_eq!(r.candidates.len(), 107);
         assert_eq!(r.code(), Some(code), "{code}: {:?}", r.candidates);
         assert!(r.candidates[0].p >= 0.5);
     }
diff --git a/bindings/rust/transcribe-cpp/tests/serde.rs b/bindings/rust/transcribe-cpp/tests/serde.rs
index 5287f5475..c5a59fa67 100644
--- a/bindings/rust/transcribe-cpp/tests/serde.rs
+++ b/bindings/rust/transcribe-cpp/tests/serde.rs
@@ -142,8 +142,8 @@ fn missing_fields_take_defaults() {
     let speaker: SpeakerSegment = serde_json::from_str(r#"{"speaker_id":2}"#).unwrap();
     assert!(speaker.p.is_nan(), "missing p decoded as {}", speaker.p);
 
-    let langid: LangIdResult = serde_json::from_str(r#"{"n_allowed":3}"#).unwrap();
-    assert_eq!(langid.n_allowed, 3);
+    let langid: LangIdResult = serde_json::from_str(r#"{"allowed_mass":0.5}"#).unwrap();
+    assert_eq!(langid.allowed_mass, 0.5);
     assert!(langid.candidates.is_empty());
     let candidate: LangIdCandidate = serde_json::from_str(r#"{"code":"en"}"#).unwrap();
     assert_eq!(candidate.code, "en");
diff --git a/bindings/swift/README.md b/bindings/swift/README.md
index 328228a56..11fe89d4d 100644
--- a/bindings/swift/README.md
+++ b/bindings/swift/README.md
@@ -121,11 +121,12 @@ Language ID models (`.langId`, e.g. VoxLingua107 ECAPA-TDNN) rank the model's
 own label codes from a `LangIdSession`; match `result.code` against an ASR
 model's `capabilities.languages` yourself. `allowed: nil` scores every label,
 an empty array throws `.invalidArgument`, and clips under
-`langIdInfo.minAudioMs` throw `.inputTooShort`.
+`langIdInfo.minAudioMs` throw `.inputTooShort`. Every allowed label comes back,
+ranked by `p`; input longer than `langIdInfo.maxAudioMs` is scored on its first `maxAudioMs`.
 
 ```swift
 let model = try Model(path: "lang-id-voxlingua107-ecapa-Q8_0.gguf")
-let result = try model.langIdSession().run(pcm, options: LangIdOptions(allowed: ["en", "de"], topK: 3))
+let result = try model.langIdSession().run(pcm, options: LangIdOptions(allowed: ["en", "de"]))
 print(result.code ?? "-", result.allowedMass)
 ```
 
diff --git a/bindings/swift/Sources/TranscribeCpp/ABIHash.swift b/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
index c5f9e2e00..e7d4e27e6 100644
--- a/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
+++ b/bindings/swift/Sources/TranscribeCpp/ABIHash.swift
@@ -13,7 +13,7 @@ import CTranscribe
 extension Transcribe {
     /// sha256/16 of the normalized public FFI surface, pinned to the value in
     /// include/transcribe.abihash at the time this binding was last reviewed.
-    public static let pinnedHeaderHash = "d544d70a2b5acf50"
+    public static let pinnedHeaderHash = "e628600daea42c4a"
 
     /// The public-ABI digest this binding was reviewed against (16 hex chars).
     public static func headerHash() -> String { pinnedHeaderHash }
diff --git a/bindings/swift/Sources/TranscribeCpp/LangId.swift b/bindings/swift/Sources/TranscribeCpp/LangId.swift
index ea30b9cb5..3686493ce 100644
--- a/bindings/swift/Sources/TranscribeCpp/LangId.swift
+++ b/bindings/swift/Sources/TranscribeCpp/LangId.swift
@@ -9,6 +9,8 @@ public struct LangIdInfo: Sendable, Equatable {
     public let nLabels: Int32
     /// Shorter scored audio throws `.inputTooShort`.
     public let minAudioMs: Int32
+    /// Longer input is scored on its first `maxAudioMs` (30000).
+    public let maxAudioMs: Int32
 }
 
 public struct LangIdOptions: Sendable {
@@ -16,11 +18,8 @@ public struct LangIdOptions: Sendable {
     /// label; an empty array throws `.invalidArgument`, and an unknown code
     /// throws `.unsupported`.
     public var allowed: [String]?
-    /// Keep the best `topK` candidates; 0 = every allowed label.
-    public var topK: Int32
-    public init(allowed: [String]? = nil, topK: Int32 = 0) {
+    public init(allowed: [String]? = nil) {
         self.allowed = allowed
-        self.topK = topK
     }
 }
 
@@ -36,15 +35,12 @@ public struct LangIdCandidate: Sendable, Equatable {
 
 /// The result of one `LangIdSession.run`.
 public struct LangIdResult: Sendable, Equatable {
-    /// Ranked by `p`, descending; ties keep label order.
+    /// One per allowed label (every label when unrestricted), ranked by `p`,
+    /// descending; ties keep label order.
     public let candidates: [LangIdCandidate]
-    /// Labels in the allowed set (before `topK`).
-    public let nAllowed: Int32
     /// Unrestricted probability inside the allowed set (1.0 when
     /// unrestricted); a low value means the speech is probably outside it.
     public let allowedMass: Float
-    /// Audio actually scored, after the crop.
-    public let audioMs: Int64
     /// The top candidate's code, if any.
     public var code: String? { candidates.first?.code }
 }
@@ -56,7 +52,8 @@ extension Model {
             var info = transcribe_langid_info()
             transcribe_langid_info_init(&info)
             try TranscribeError.check(transcribe_langid_get_info(ptr, &info), context: "langid_get_info")
-            return LangIdInfo(sampleRate: info.sample_rate, nLabels: info.n_labels, minAudioMs: info.min_audio_ms)
+            return LangIdInfo(sampleRate: info.sample_rate, nLabels: info.n_labels,
+                              minAudioMs: info.min_audio_ms, maxAudioMs: info.max_audio_ms)
         }
     }
 
@@ -81,14 +78,12 @@ extension Model {
         return i >= 0 ? i : nil
     }
 
-    /// Create a LANGID session (`threads` 0 = library default). Longer input
-    /// than `maxAudioMs` is scored on its tail (0 = 30000). Throws
+    /// Create a LANGID session (`threads` 0 = library default). Throws
     /// `.unsupportedRole` when `roles` lacks `.langId`.
-    public func langIdSession(threads: Int32 = 0, maxAudioMs: Int32 = 0) throws -> LangIdSession {
+    public func langIdSession(threads: Int32 = 0) throws -> LangIdSession {
         var params = transcribe_langid_session_params()
         transcribe_langid_session_params_init(&params)
         params.n_threads = threads
-        params.max_audio_ms = maxAudioMs
         var out: OpaquePointer?
         try TranscribeError.check(
             transcribe_langid_session_init(ptr, &params, &out), context: "creating langid session")
@@ -116,7 +111,8 @@ public final class LangIdSession {
 
     deinit { transcribe_langid_session_free(ptr) }
 
-    /// Identify the language of one clip (16 kHz mono float32).
+    /// Identify the language of one clip (16 kHz mono float32). Input longer
+    /// than `LangIdInfo.maxAudioMs` is scored on its first `maxAudioMs`.
     public func run(_ pcm: [Float], options: LangIdOptions = .init()) throws -> LangIdResult {
         if let allowed = options.allowed, allowed.isEmpty {
             // NULL would mean "every label", the opposite of an empty list.
@@ -140,7 +136,6 @@ public final class LangIdSession {
                     params.allowed = codeBuf.baseAddress
                     params.n_allowed = Int32(codeBuf.count)
                 }
-                params.top_k = options.topK
                 return pcm.withUnsafeBufferPointer {
                     transcribe_langid_run(ptr, $0.baseAddress, Int32($0.count), &params)
                 }
@@ -156,8 +151,7 @@ public final class LangIdSession {
                     name: c.name.map { String(cString: $0) } ?? "",
                     p: c.p, logit: c.logit)
             }
-            return LangIdResult(candidates: candidates, nAllowed: res.n_allowed,
-                                allowedMass: res.allowed_mass, audioMs: res.audio_ms)
+            return LangIdResult(candidates: candidates, allowedMass: res.allowed_mass)
         }
     }
 
diff --git a/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift b/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
index e6f48036a..fe8da1cb2 100644
--- a/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
+++ b/bindings/swift/Tests/TranscribeCppTests/LangIdTests.swift
@@ -20,7 +20,7 @@ final class LangIdTests: XCTestCase {
     func testToyRolesInfoLabels() throws {
         let model = try cpuModel(try Fixtures.langIdToyModelPath())
         XCTAssertEqual(model.roles, .langId)
-        XCTAssertEqual(try model.langIdInfo, LangIdInfo(sampleRate: 16000, nLabels: 5, minAudioMs: 500))
+        XCTAssertEqual(try model.langIdInfo, LangIdInfo(sampleRate: 16000, nLabels: 5, minAudioMs: 500, maxAudioMs: 30000))
         let labels = try model.langIdLabels
         XCTAssertEqual(labels[2].code, "cc")
         XCTAssertEqual(labels[2].name, "Charlie")
@@ -38,18 +38,15 @@ final class LangIdTests: XCTestCase {
 
         let r = try lid.run(pcm)
         XCTAssertEqual(r.candidates.count, 5)
-        XCTAssertEqual(r.nAllowed, 5)
         XCTAssertEqual(r.allowedMass, 1.0)
-        XCTAssertEqual(r.audioMs, 1000)
         XCTAssertEqual(r.code, r.candidates[0].code)
         XCTAssertEqual(r.candidates.map(\.p).reduce(0, +), 1.0, accuracy: 1e-5)
 
         let restricted = try lid.run(pcm, options: LangIdOptions(allowed: ["bb", "dd"]))
+        XCTAssertEqual(restricted.candidates.count, 2)
         XCTAssertEqual(Set(restricted.candidates.map(\.code)), ["bb", "dd"])
         XCTAssertLessThan(restricted.allowedMass, 1.0)
 
-        XCTAssertEqual(try lid.run(pcm, options: LangIdOptions(topK: 2)).candidates.count, 2)
-
         XCTAssertThrowsError(try lid.run(pcm, options: LangIdOptions(allowed: []))) { error in
             guard case TranscribeError.invalidArgument = error else { return XCTFail("\(error)") }
         }
@@ -88,12 +85,14 @@ final class LangIdTests: XCTestCase {
     func testRealFleursTop1() throws {
         let model = try cpuModel(try Fixtures.langIdModelPath())
         XCTAssertEqual(try model.langIdInfo.nLabels, 107)
+        XCTAssertEqual(try model.langIdInfo.maxAudioMs, 30000)
         XCTAssertEqual(model.langIdLabelIndex("he"), model.langIdLabelIndex("iw"))
         let lid = try model.langIdSession()
         for code in ["en", "de", "fr", "es", "ja", "zh", "ru", "id"] {
             let pcm = try Fixtures.loadWav(
                 Fixtures.repoRoot().appendingPathComponent("samples/fleurs-\(code).wav").path)
-            let r = try lid.run(pcm, options: LangIdOptions(topK: 3))
+            let r = try lid.run(pcm)
+            XCTAssertEqual(r.candidates.count, 107)
             XCTAssertEqual(r.code, code)
             XCTAssertGreaterThanOrEqual(r.candidates[0].p, 0.5)
         }
diff --git a/bindings/typescript/README.md b/bindings/typescript/README.md
index a3a714fbe..b0ca6cff8 100644
--- a/bindings/typescript/README.md
+++ b/bindings/typescript/README.md
@@ -131,12 +131,13 @@ model-wide lock as other compute calls (see below).
 
 A `"langid"` model (VoxLingua107 ECAPA-TDNN) ranks its own label codes; match
 `result.code` against an ASR model's `capabilities.languages` yourself.
-Omitting `allowed` scores every label, `[]` throws `InvalidArgument`, and
-clips under `model.langidInfo.minAudioMs` throw `InputTooShort`.
+Every allowed label is returned, ranked by `p`. Omitting `allowed` scores every
+label, `[]` throws `InvalidArgument`, clips under `model.langidInfo.minAudioMs`
+throw `InputTooShort`, and input longer than `maxAudioMs` is scored on its first `maxAudioMs`.
 
 ```ts
 using lid = model.createLangIdSession();
-const result = await lid.run(pcm, { allowed: ["en", "de", "fr"], topK: 3 });
+const result = await lid.run(pcm, { allowed: ["en", "de", "fr"] });
 console.log(result.code, result.candidates[0].p, result.allowedMass);
 ```
 
diff --git a/bindings/typescript/src/_generated.ts b/bindings/typescript/src/_generated.ts
index dae860c8c..71dd957f4 100644
--- a/bindings/typescript/src/_generated.ts
+++ b/bindings/typescript/src/_generated.ts
@@ -11,7 +11,7 @@
 // Stable digest of the ABI surface (structs, enums, macros, layout,
 // prototypes), computed by the Python oracle and pinned here so a header
 // ABI change turns this binding's drift check red for conscious review.
-export const PUBLIC_HEADER_HASH = "d544d70a2b5acf50";
+export const PUBLIC_HEADER_HASH = "e628600daea42c4a";
 
 // === enum constants ===
 export const TRANSCRIBE_OK = 0;
@@ -164,10 +164,10 @@ export const STRUCT_LAYOUT: Record<string, StructLayout> = {
   'transcribe_diarize_info': { size: 16, align: 8, offsets: {'struct_size': 0, 'sample_rate': 8, 'max_speakers': 12} },
   'transcribe_diarize_session_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_threads': 8} },
   'transcribe_diarize_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'family': 8} },
-  'transcribe_langid_info': { size: 24, align: 8, offsets: {'struct_size': 0, 'sample_rate': 8, 'n_labels': 12, 'min_audio_ms': 16} },
-  'transcribe_langid_session_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_threads': 8, 'max_audio_ms': 12} },
-  'transcribe_langid_params': { size: 24, align: 8, offsets: {'struct_size': 0, 'allowed': 8, 'n_allowed': 16, 'top_k': 20} },
-  'transcribe_langid_result': { size: 32, align: 8, offsets: {'struct_size': 0, 'n_candidates': 8, 'n_allowed': 12, 'allowed_mass': 16, 'audio_ms': 24} },
+  'transcribe_langid_info': { size: 24, align: 8, offsets: {'struct_size': 0, 'sample_rate': 8, 'n_labels': 12, 'min_audio_ms': 16, 'max_audio_ms': 20} },
+  'transcribe_langid_session_params': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_threads': 8} },
+  'transcribe_langid_params': { size: 24, align: 8, offsets: {'struct_size': 0, 'allowed': 8, 'n_allowed': 16} },
+  'transcribe_langid_result': { size: 16, align: 8, offsets: {'struct_size': 0, 'n_candidates': 8, 'allowed_mass': 12} },
   'transcribe_langid_candidate': { size: 40, align: 8, offsets: {'struct_size': 0, 'index': 8, 'code': 16, 'name': 24, 'p': 32, 'logit': 36} },
   'transcribe_moonshine_streaming_stream_ext': { size: 24, align: 8, offsets: {'ext': 0, 'min_decode_interval_ms': 16} },
   'transcribe_parakeet_stream_ext': { size: 24, align: 8, offsets: {'ext': 0, 'att_context_right': 16} },
@@ -227,10 +227,10 @@ export function defineTypes(koffi: any): Record<string, any> {
   T['transcribe_diarize_info'] = koffi.struct({ struct_size: 'uint64_t', sample_rate: 'int32_t', max_speakers: 'int32_t' });
   T['transcribe_diarize_session_params'] = koffi.struct({ struct_size: 'uint64_t', n_threads: 'int32_t' });
   T['transcribe_diarize_params'] = koffi.struct({ struct_size: 'uint64_t', family: 'void *' });
-  T['transcribe_langid_info'] = koffi.struct({ struct_size: 'uint64_t', sample_rate: 'int32_t', n_labels: 'int32_t', min_audio_ms: 'int32_t' });
-  T['transcribe_langid_session_params'] = koffi.struct({ struct_size: 'uint64_t', n_threads: 'int32_t', max_audio_ms: 'int32_t' });
-  T['transcribe_langid_params'] = koffi.struct({ struct_size: 'uint64_t', allowed: 'void *', n_allowed: 'int32_t', top_k: 'int32_t' });
-  T['transcribe_langid_result'] = koffi.struct({ struct_size: 'uint64_t', n_candidates: 'int32_t', n_allowed: 'int32_t', allowed_mass: 'float', audio_ms: 'int64_t' });
+  T['transcribe_langid_info'] = koffi.struct({ struct_size: 'uint64_t', sample_rate: 'int32_t', n_labels: 'int32_t', min_audio_ms: 'int32_t', max_audio_ms: 'int32_t' });
+  T['transcribe_langid_session_params'] = koffi.struct({ struct_size: 'uint64_t', n_threads: 'int32_t' });
+  T['transcribe_langid_params'] = koffi.struct({ struct_size: 'uint64_t', allowed: 'void *', n_allowed: 'int32_t' });
+  T['transcribe_langid_result'] = koffi.struct({ struct_size: 'uint64_t', n_candidates: 'int32_t', allowed_mass: 'float' });
   T['transcribe_langid_candidate'] = koffi.struct({ struct_size: 'uint64_t', index: 'int32_t', code: 'char *', name: 'char *', p: 'float', logit: 'float' });
   T['transcribe_moonshine_streaming_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], min_decode_interval_ms: 'int32_t' });
   T['transcribe_parakeet_stream_ext'] = koffi.struct({ ext: T['transcribe_ext'], att_context_right: 'int32_t' });
diff --git a/bindings/typescript/src/index.ts b/bindings/typescript/src/index.ts
index b831f1bfb..db870f47c 100644
--- a/bindings/typescript/src/index.ts
+++ b/bindings/typescript/src/index.ts
@@ -1493,8 +1493,8 @@ export class LangIdSession {
   }
 
   /**
-   * Identify the language of one clip; input longer than the session's
-   * maxAudioMs is scored on its tail. The input PCM is borrowed, not copied
+   * Identify the language of one clip; input longer than langidInfo.maxAudioMs
+   * is scored on its first maxAudioMs. The input PCM is borrowed, not copied
    * (see Session.run).
    */
   async run(pcm: PcmLike, opts: LangIdOptions = {}): Promise<LangIdResult> {
@@ -1504,7 +1504,6 @@ export class LangIdSession {
     const samples = toFloat32(pcm);
     const p: any = {};
     F.langidParamsInit(p);
-    if (opts.topK !== undefined) p.top_k = opts.topK;
     if (opts.allowed !== undefined && opts.allowed !== null) {
       const codes = opts.allowed;
       if (!Array.isArray(codes) || !codes.every((c) => typeof c === "string"))
@@ -1543,9 +1542,7 @@ export class LangIdSession {
       return {
         candidates,
         code: candidates.length > 0 ? candidates[0].code : null,
-        nAllowed: res.n_allowed,
         allowedMass: res.allowed_mass,
-        audioMs: Number(res.audio_ms),
       };
     }).finally(() => {
       if (p.allowed) {
@@ -1700,7 +1697,12 @@ export class TranscribeModel {
     const info: any = {};
     n.F.langidInfoInit(info);
     check(n, n.F.langidGetInfo(this.handle, info), "reading langid info");
-    return { sampleRate: info.sample_rate, nLabels: info.n_labels, minAudioMs: info.min_audio_ms };
+    return {
+      sampleRate: info.sample_rate,
+      nLabels: info.n_labels,
+      minAudioMs: info.min_audio_ms,
+      maxAudioMs: info.max_audio_ms,
+    };
   }
 
   /** [code, name] per label index of a "langid" model; UnsupportedRole otherwise. */
@@ -1725,7 +1727,6 @@ export class TranscribeModel {
     const p: any = {};
     n.F.langidSessionParamsInit(p);
     if (opts.nThreads !== undefined) p.n_threads = opts.nThreads;
-    if (opts.maxAudioMs !== undefined) p.max_audio_ms = opts.maxAudioMs;
     const out: any[] = [null];
     check(n, n.F.langidSessionInit(this.handle, p, out), "opening langid session");
     if (!out[0])
diff --git a/bindings/typescript/src/types.ts b/bindings/typescript/src/types.ts
index 1a9368b09..0c7703a25 100644
--- a/bindings/typescript/src/types.ts
+++ b/bindings/typescript/src/types.ts
@@ -320,13 +320,13 @@ export interface LangIdInfo {
   nLabels: number;
   /** Shorter scored audio throws InputTooShort. */
   minAudioMs: number;
+  /** Longer input is scored on its first maxAudioMs (30000). */
+  maxAudioMs: number;
 }
 
 export interface LangIdSessionOptions {
   /** CPU threads for CPU-side ops; 0 = library default. */
   nThreads?: number;
-  /** Longer input is scored on its last maxAudioMs; 0 = 30000. */
-  maxAudioMs?: number;
 }
 
 export interface LangIdOptions {
@@ -338,8 +338,6 @@ export interface LangIdOptions {
    * unknown code throws UnsupportedRequest.
    */
   allowed?: readonly string[] | null;
-  /** Keep the best topK candidates; 0 = every allowed label. */
-  topK?: number;
 }
 
 /** One ranked label; `code` is the model's own label ("en", "iw"). */
@@ -353,14 +351,13 @@ export interface LangIdCandidate {
 }
 
 export interface LangIdResult {
-  /** Ranked by p, descending; ties keep label order. */
+  /**
+   * Every allowed label (duplicates once; every label when unrestricted),
+   * ranked by p, descending; ties keep label order.
+   */
   candidates: LangIdCandidate[];
   /** The top candidate's code, or null with no candidates. */
   code: string | null;
-  /** Labels in the allowed set (before topK). */
-  nAllowed: number;
   /** Unrestricted probability inside the allowed set (1 when unrestricted). */
   allowedMass: number;
-  /** Audio actually scored, after the crop. */
-  audioMs: number;
 }
diff --git a/bindings/typescript/test/langid.test.mjs b/bindings/typescript/test/langid.test.mjs
index 69e0bbdff..d53e802dc 100644
--- a/bindings/typescript/test/langid.test.mjs
+++ b/bindings/typescript/test/langid.test.mjs
@@ -28,7 +28,7 @@ modelTest("toy: roles, info, labels; ASR calls are refused", LANGID_TOY_MODEL, a
   const m = await TranscribeModel.load(LANGID_TOY_MODEL, { backend: "cpu" });
   try {
     assert.deepEqual(m.roles, ["langid"]);
-    assert.deepEqual(m.langidInfo, { sampleRate: 16000, nLabels: 5, minAudioMs: 500 });
+    assert.deepEqual(m.langidInfo, { sampleRate: 16000, nLabels: 5, minAudioMs: 500, maxAudioMs: 30000 });
     assert.deepEqual(m.langidLabels[2], ["cc", "Charlie"]);
     assert.equal(m.langidLabelIndex("xx"), 0);
     assert.equal(m.langidLabelIndex("zz"), null);
@@ -39,33 +39,35 @@ modelTest("toy: roles, info, labels; ASR calls are refused", LANGID_TOY_MODEL, a
   }
 });
 
-modelTest("toy: run contract (allowed, topK, crop, minimum)", LANGID_TOY_MODEL, async () => {
+modelTest("toy: run contract (allowed, crop, minimum)", LANGID_TOY_MODEL, async () => {
   const m = await TranscribeModel.load(LANGID_TOY_MODEL, { backend: "cpu" });
   const lid = m.createLangIdSession({ nThreads: 1 });
   try {
     const pcm = noise(16000);
     const r = await lid.run(pcm);
     assert.equal(r.candidates.length, 5);
-    assert.equal(r.nAllowed, 5);
     assert.equal(r.allowedMass, 1);
-    assert.equal(r.audioMs, 1000);
     assert.equal(r.code, r.candidates[0].code);
     const sum = r.candidates.reduce((a, c) => a + c.p, 0);
     assert.ok(Math.abs(sum - 1) < 1e-5);
 
-    const restricted = await lid.run(pcm, { allowed: ["bb", "dd"] });
+    const restricted = await lid.run(pcm, { allowed: ["bb", "dd", "bb"] });
+    assert.equal(restricted.candidates.length, 2);
     assert.deepEqual(new Set(restricted.candidates.map((c) => c.code)), new Set(["bb", "dd"]));
     assert.ok(restricted.allowedMass < 1);
 
-    assert.equal((await lid.run(pcm, { topK: 2 })).candidates.length, 2);
-    assert.equal((await lid.run(pcm, { allowed: null })).nAllowed, 5);
+    assert.equal((await lid.run(pcm, { allowed: null })).candidates.length, 5);
     // An empty list must not silently mean "every label".
     await assert.rejects(lid.run(pcm, { allowed: [] }), InvalidArgument);
     await assert.rejects(lid.run(pcm, { allowed: ["zz"] }), UnsupportedRequest);
     await assert.rejects(lid.run(pcm.subarray(0, 6400)), InputTooShort);
-    assert.equal((await lid.run(noise(16000 * 31))).audioMs, 30000);
+    // Longer input is scored on its first maxAudioMs.
+    const long = noise(16000 * 31);
+    assert.deepEqual(
+      (await lid.run(long)).candidates,
+      (await lid.run(long.subarray(0, 16000 * 30))).candidates,
+    );
     assert.ok(lid.timings.encodeMs > 0);
-    assert.throws(() => m.createLangIdSession({ maxAudioMs: 400 }), InvalidArgument);
   } finally {
     lid.dispose();
     m.dispose();
@@ -91,9 +93,11 @@ modelTest("real model: top-1 on the FLEURS clips", LANGID_MODEL, async () => {
   const lid = m.createLangIdSession();
   try {
     assert.equal(m.langidInfo.nLabels, 107);
+    assert.equal(m.langidInfo.maxAudioMs, 30000);
     assert.equal(m.langidLabelIndex("he"), m.langidLabelIndex("iw"));
     for (const code of ["en", "de", "fr", "es", "ja", "zh", "ru", "id"]) {
-      const r = await lid.run(readWav(path.join(SAMPLES, `fleurs-${code}.wav`)), { topK: 3 });
+      const r = await lid.run(readWav(path.join(SAMPLES, `fleurs-${code}.wav`)));
+      assert.equal(r.candidates.length, 107);
       assert.equal(r.code, code);
       assert.ok(r.candidates[0].p >= 0.5);
     }
diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index 7357a197a..35568b84a 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -42,9 +42,9 @@
     {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693632}
   ],
   "accuracy_benchmarks": [
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.3e-05}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":11986,"n":12000,"max_abs_logit_delta":0.095}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.17,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"2fdb5c95","publication_profile":"langid-publication-v1","measured_on":"2026-10-07","agreement":{"n_agree":11705,"n":12000,"max_abs_logit_delta":2.9}}
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"9cfa5670","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.3e-05}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"9cfa5670","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":11986,"n":12000,"max_abs_logit_delta":0.095}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.17,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"9cfa5670","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":11706,"n":12000,"max_abs_logit_delta":2.9}}
   ],
   "headline_benchmark": {
     "dataset": "fleurs",
diff --git a/docs/bindings.md b/docs/bindings.md
index 87431da47..c63d41aae 100644
--- a/docs/bindings.md
+++ b/docs/bindings.md
@@ -139,12 +139,12 @@ race an in-flight call.
 
 A LANGID model gets `LangIdSession` from `model.langid_session()` (Python,
 Rust), `model.langIdSession()` (Swift) or `model.createLangIdSession()`
-(TypeScript), with `max_audio_ms` / `maxAudioMs` as a session option, plus
-`langid_info` / `langIdInfo` / `langidInfo` (sample rate, label count,
-minimum audio) and the label table (codes, names, and alias lookup).
-`run(pcm, allowed=…, top_k=…)` returns a copied-out result: candidates ranked
-by `p` (index, code, name, `p`, `logit`), `n_allowed`,
-`allowed_mass` and the scored `audio_ms`.
+(TypeScript), plus `langid_info` / `langIdInfo` / `langidInfo` (sample
+rate, label count, minimum audio, and the fixed maximum: longer input is
+scored on its first `max_audio_ms`) and the label table (codes, names, and
+alias lookup). `run(pcm, allowed=…)` returns a copied-out result: every
+allowed label ranked by `p` (index, code, name, `p`, `logit`) and
+`allowed_mass`.
 
 `allowed` is the first caller-owned `const char * const *` input. Every
 binding keeps the array and each encoded string alive until the native call
diff --git a/docs/langid.md b/docs/langid.md
index 20a9d8035..2bd9fd8c7 100644
--- a/docs/langid.md
+++ b/docs/langid.md
@@ -23,7 +23,7 @@ transcribe_langid_run(lid, pcm, n_samples, &lp);   /* 16 kHz mono float32 */
 
 struct transcribe_langid_result r;
 transcribe_langid_result_init(&r);
-transcribe_langid_get_result(lid, &r);
+transcribe_langid_get_result(lid, &r);             /* r.n_candidates == 3 */
 struct transcribe_langid_candidate c;
 transcribe_langid_candidate_init(&c);
 transcribe_langid_get_candidate(lid, 0, &c);       /* c.code, c.p, r.allowed_mass */
@@ -32,7 +32,8 @@ transcribe_langid_session_free(lid);
 transcribe_model_free(m);
 ```
 
-From the CLI: `transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en,de,fr --top 3 clip.wav`.
+From the CLI: `transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en,de,fr --top 3 clip.wav`
+(`--top N` only limits how many ranked candidates the CLI prints; 0 = all).
 
 ## Parameters and results
 
@@ -47,10 +48,13 @@ From the CLI: `transcribe-cli -m lang-id-voxlingua107-ecapa-Q8_0.gguf --allow en
   over every label that falls in the allowed set, 1 when unrestricted. A low
   value means the unrestricted model puts most of its probability outside the
   allowed set.
-- **`top_k`** limits the returned candidates (0 = every allowed label). It
-  does not change `p` or `allowed_mass`.
-- **Length.** Input longer than the session's `max_audio_ms` (default 30000)
-  is scored on its last `max_audio_ms`; `audio_ms` reports what was scored.
+- **Candidates.** Every run returns every allowed label, ranked by `p`
+  (descending; ties keep label order). `n_candidates`
+  (`transcribe_langid_result`) is the allowed-set size after duplicates
+  count once: `n_labels` when unrestricted.
+- **Length.** Input longer than `transcribe_langid_info::max_audio_ms`
+  (30000 for VoxLingua107) is scored on its first `max_audio_ms`, with no
+  error; the window is a fixed fact of the model, not a session setting.
   Scored audio shorter than `transcribe_langid_info::min_audio_ms` (500 ms)
   is `TRANSCRIBE_ERR_INPUT_TOO_SHORT`.
 - **Labels** are the model's own codes. VoxLingua107 uses `iw` (Hebrew),
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 0ec1cd9b7..9aab026d2 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -7,7 +7,7 @@ Spoken language identification over 107 languages: SpeechBrain's
 ECAPA-TDNN trained on VoxLingua107. NOT a transcription model: a run
 returns the model's language labels ranked by probability, optionally
 restricted to a caller-chosen set. Takes 16 kHz mono WAV; scores up to
-the last 30 s of a clip.
+the first 30 s of a clip.
 <!-- /catalog -->
 
 ## What it's for
@@ -37,7 +37,7 @@ Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface
 <!-- /catalog -->
 
 <!-- catalog:recipe -->
-Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `2fdb5c95` on 2026-10-07.
+Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `9cfa5670` on 2026-10-08.
 <!-- /catalog -->
 
 <!-- catalog:prose field=wer.notes -->
@@ -54,13 +54,14 @@ transcribe.cpp model page.
 | --- | ---: | ---: | ---: |
 | F32  | 85.23% (84.07-86.47) | 12000 / 12000 | 7.3e-05 |
 | F16  | 85.17% (84.00-86.43) | 11986 / 12000 | 0.095 |
-| Q8_0 | 86.30% (85.17-87.50) | 11705 / 12000 | 2.9 |
+| Q8_0 | 86.30% (85.17-87.50) | 11706 / 12000 | 2.9 |
 
-Measured at transcribe.cpp `2fdb5c95` on 2026-10-07.
+Measured at transcribe.cpp `9cfa5670` on 2026-10-08.
 <!-- /catalog -->
 
 Agreement counts the scored sweep's top-1 decisions that match the
-SpeechBrain reference on the same audio, over the 3 / 5 / 10 s / full crops.
+SpeechBrain reference on the same audio, over the 3 / 5 / 10 s / full crops
+(`full` is the whole clip, or its first 30 s when longer).
 
 ## Quick Start
 
@@ -148,8 +149,8 @@ reference on every clip.
   and out-of-set languages still get a top candidate.
 - **No Cantonese label**: Cantonese is classified as `zh`.
 - **Minimum 500 ms** of scored audio (`TRANSCRIBE_ERR_INPUT_TOO_SHORT`).
-- **Scores at most the last 30 s** by default
-  (`transcribe_langid_session_params::max_audio_ms`).
+- **Scores at most the first 30 s** of longer input
+  (`transcribe_langid_info::max_audio_ms`, fixed).
 - **Q8_0 weights are widened to F16 at load**, so Q8_0 computes and uses
   memory like F16.
 
diff --git a/docs/porting/families/ecapa_tdnn.md b/docs/porting/families/ecapa_tdnn.md
index 12ea2266c..c135e5d0a 100644
--- a/docs/porting/families/ecapa_tdnn.md
+++ b/docs/porting/families/ecapa_tdnn.md
@@ -9,14 +9,14 @@ SERes2Net blocks, multi-layer feature aggregation, attentive statistics
 pooling, a 256-d embedding and a two-layer classifier over the VoxLingua107
 labels. It is not a transcription model. The family only produces logits; the
 LANGID role dispatcher (`src/transcribe-langid.cpp`) owns the crop to the
-last `max_audio_ms`, the allowed set, softmax, `allowed_mass`, ranking and
-top-k (`docs/langid.md`).
+first `transcribe_langid_info::max_audio_ms` (30 s), the allowed set,
+softmax, `allowed_mass` and ranking (`docs/langid.md`).
 
 Acceptance: tensor parity on eight FLEURS clips (`validate.py`), and top-1
 decision parity with SpeechBrain on FLEURS (15 languages x 200 utterances x
-3 / 5 / 10 s / full crops, C++ F32 on CPU). Shipped matrix: F32 + F16 + Q8_0.
-Each GGUF's accuracy and agreement live in the catalog and are rendered on
-[the model page](../../models/lang-id-voxlingua107-ecapa.md#accuracy). The
+3 / 5 / 10 s / full crops, `full` capped to the first 30 s, C++ F32 on CPU).
+Shipped matrix: F32 + F16 + Q8_0. Each GGUF's accuracy and agreement live in
+the catalog and are rendered on [the model page](../../models/lang-id-voxlingua107-ecapa.md#accuracy). The
 loader widens Q8_0 weights to F16 (`widen_q8_0_weights` in
 `src/arch/ecapa_tdnn/model.cpp`).
 
@@ -109,7 +109,7 @@ ASR-only) writes bench-driver reports under `reports/perf/`, which
 |------------|------|----------------|---------------------|--------|--------|
 | Language ID | open set | `build/bin/transcribe-cli -m models/lang-id-voxlingua107-ecapa/lang-id-voxlingua107-ecapa-F32.gguf samples/fleurs-ja.wav` | `language: ja` | MUST PASS | PASS |
 | Language ID | allowed set | `... --allow en,de samples/fleurs-de.wav` | `language: de`, 2 candidates | MUST PASS | PASS |
-| Crop | 30 s window | `transcribe_ecapa_tdnn_real_smoke` (`ru-long.wav`) | `audio_ms == 30000` | MUST PASS | PASS |
+| Crop | first 30 s | `transcribe_ecapa_tdnn_smoke` (toy model, 35 s of noise), `transcribe_ecapa_tdnn_real_smoke` (`ru-long.wav`) | whole clip == its first `max_audio_ms` (bit-identical candidates / logits); its last `max_audio_ms` differs | MUST PASS | PASS |
 | Minimum length | 500 ms | `transcribe_ecapa_tdnn_smoke`, `transcribe_langid_dispatch_unit` | `INPUT_TOO_SHORT` below 500 ms | MUST PASS | PASS |
 | Transcribe / translate / timestamps / streaming | n/a | `transcribe_session_init` | `UNSUPPORTED_ROLE` | OUT OF SCOPE — not an ASR model | SKIP — not exposed by runtime |
 | Batch (offline) | n/a | `transcribe-cli --batch` | refused: ASR-only | OUT OF SCOPE — no batch entry points for new roles in v1 | ACCEPTED GAP — one clip per call |
diff --git a/examples/cli/cli.h b/examples/cli/cli.h
index e4c24ec50..7c22d9bf4 100644
--- a/examples/cli/cli.h
+++ b/examples/cli/cli.h
@@ -105,12 +105,10 @@ struct cli_args {
     int spec_k_drafts        = -1;
 
     // LANGID role (language ID models). --allow restricts the decision to
-    // these labels (codes or aliases); --top keeps the best N candidates
-    // (0 = every allowed label); --max-audio-ms scores the last N ms
-    // (0 = library default, 30000).
+    // these labels (codes or aliases); --top prints only the best N ranked
+    // candidates (0 = all). The library always ranks every allowed label.
     std::vector<std::string> langid_allow;
-    int                      langid_top_k        = 0;
-    int                      langid_max_audio_ms = 0;
+    int                      langid_top = 0;
 };
 
 // -o/--output: write `text` (newline-terminated) to `output` if non-null.
diff --git a/examples/cli/langid.cpp b/examples/cli/langid.cpp
index 566eedef9..382d192ee 100644
--- a/examples/cli/langid.cpp
+++ b/examples/cli/langid.cpp
@@ -4,11 +4,13 @@
 // Output lines are stable (scripts/validate.py parses `language:`):
 //   language: <code> index=<i> p=<p>
 //   candidate: <rank> <code> index=<i> p=<p> logit=<z>
+// --top N prints (and writes with -o) only the first N ranked candidates.
 
 #include "transcribe/langid.h"
 
 #include "cli.h"
 
+#include <algorithm>
 #include <cstdio>
 #include <cstdlib>
 #include <fstream>
@@ -28,7 +30,6 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
     transcribe_langid_session_params sp;
     transcribe_langid_session_params_init(&sp);
     sp.n_threads                        = args.n_threads;
-    sp.max_audio_ms                     = args.langid_max_audio_ms;
     transcribe_langid_session * session = nullptr;
     transcribe_status           st      = transcribe_langid_session_init(model, &sp, &session);
     if (st != TRANSCRIBE_OK) {
@@ -45,7 +46,13 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
     transcribe_langid_params_init(&lp);
     lp.allowed   = allowed.empty() ? nullptr : allowed.data();
     lp.n_allowed = static_cast<int32_t>(allowed.size());
-    lp.top_k     = args.langid_top_k;
+
+    // Only the first info.max_audio_ms of a longer file is scored.
+    transcribe_langid_info info;
+    transcribe_langid_info_init(&info);
+    transcribe_langid_get_info(model, &info);
+    const double file_s   = static_cast<double>(pcm.size()) / 16000.0;
+    const double scored_s = std::min(file_s, static_cast<double>(info.max_audio_ms) / 1000.0);
 
     // --repeat N runs transcribe_langid_run() N times for steady-state perf
     // measurements.
@@ -56,16 +63,16 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
         }
     }
     std::printf("run: %s\n", transcribe_status_string(st));
-    bool   output_ok = true;
-    double scored_s  = 0.0;  // only the last max_audio_ms is scored, not the whole file
+    bool output_ok = true;
     if (st == TRANSCRIBE_OK) {
         transcribe_langid_result res;
         transcribe_langid_result_init(&res);
         transcribe_langid_get_result(session, &res);
-        scored_s = static_cast<double>(res.audio_ms) / 1000.0;
+        const int n_print =
+            args.langid_top > 0 ? std::min(args.langid_top, static_cast<int>(res.n_candidates)) : res.n_candidates;
 
         std::string lines;
-        for (int i = 0; i < res.n_candidates; ++i) {
+        for (int i = 0; i < n_print; ++i) {
             transcribe_langid_candidate c;
             transcribe_langid_candidate_init(&c);
             transcribe_langid_get_candidate(session, i, &c);
@@ -73,8 +80,8 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
                 std::printf("language: %s index=%d p=%.6f\n", c.code, c.index, static_cast<double>(c.p));
                 std::printf("  name:         %s\n", c.name);
                 std::printf("  allowed_mass: %.6f (%d allowed)\n", static_cast<double>(res.allowed_mass),
-                            res.n_allowed);
-                std::printf("  audio_ms:     %lld\n", static_cast<long long>(res.audio_ms));
+                            res.n_candidates);
+                std::printf("  scored:       %.2f s of %.2f s\n", scored_s, file_s);
             }
             char line[160];
             std::snprintf(line, sizeof(line), "candidate: %d %s index=%d p=%.6f logit=%.4f\n", i + 1, c.code, c.index,
@@ -89,7 +96,7 @@ int transcribe_cli::run_langid_file(const cli_args &           args,
     transcribe_timings_init(&tm);
     transcribe_langid_get_timings(session, &tm);
     const double total_ms = tm.mel_ms + tm.encode_ms;
-    if (total_ms > 0.0 && scored_s > 0.0) {
+    if (st == TRANSCRIBE_OK && total_ms > 0.0 && scored_s > 0.0) {
         std::printf("  realtime:   %.0fx (%.1f ms for %.1f s; mel %.1f ms, encode %.1f ms)\n",
                     scored_s * 1000.0 / total_ms, total_ms, scored_s, static_cast<double>(tm.mel_ms),
                     static_cast<double>(tm.encode_ms));
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 95f0bb418..e14021b0b 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -84,8 +84,8 @@ void print_usage(const char * argv0) {
                  "                        speaker ids; granite-plus requests its speaker task\n"
                  "  --no-diarize          disable speaker attribution (the library default)\n"
                  "  --allow CODES         (language ID) comma-separated labels to choose from\n"
-                 "  --top N               (language ID) print the best N candidates (0 = all)\n"
-                 "  --max-audio-ms N      (language ID) score the last N ms (0 = 30000)\n"
+                 "  --top N               (language ID) print only the best N ranked\n"
+                 "                        candidates (0 = all; default)\n"
                  "  --raw-tokens          keep <|...|> control tokens in output text\n"
                  "  --stream-chunk-ms N   single-file: drive the streaming API by feeding\n"
                  "                        N-ms PCM slices; requires model to advertise\n"
@@ -445,17 +445,11 @@ bool parse_args(int argc, char ** argv, cli_args & out) {
             if (!v) {
                 return false;
             }
-            out.langid_top_k = std::atoi(v);
-            if (out.langid_top_k < 0) {
+            out.langid_top = std::atoi(v);
+            if (out.langid_top < 0) {
                 std::fprintf(stderr, "error: --top must be >= 0\n");
                 return false;
             }
-        } else if (a == "--max-audio-ms") {
-            const char * v = take_value(a.c_str());
-            if (!v) {
-                return false;
-            }
-            out.langid_max_audio_ms = std::atoi(v);
         } else if (a == "--diarize") {
             out.diarize     = true;
             out.diarize_set = true;
diff --git a/include/transcribe.abihash b/include/transcribe.abihash
index 5e88d1778..bfbec748c 100644
--- a/include/transcribe.abihash
+++ b/include/transcribe.abihash
@@ -1 +1 @@
-d544d70a2b5acf50
+e628600daea42c4a
diff --git a/include/transcribe/langid.h b/include/transcribe/langid.h
index 99dfa5365..0a8b9c07a 100644
--- a/include/transcribe/langid.h
+++ b/include/transcribe/langid.h
@@ -29,14 +29,12 @@ struct transcribe_langid_info {
     int32_t  sample_rate;  /* input PCM rate (16000) */
     int32_t  n_labels;     /* label indices are [0, n_labels) */
     int32_t  min_audio_ms; /* shorter scored audio is TRANSCRIBE_ERR_INPUT_TOO_SHORT */
+    int32_t  max_audio_ms; /* longer input is scored on its FIRST max_audio_ms (30000) */
 };
 
 struct transcribe_langid_session_params {
     uint64_t struct_size;
     int32_t  n_threads; /* 0 = library default */
-    /* Longer input is scored on its LAST max_audio_ms. 0 = 30000. A value
-     * below transcribe_langid_info::min_audio_ms is INVALID_ARG. */
-    int32_t  max_audio_ms;
 };
 
 struct transcribe_langid_params {
@@ -48,19 +46,18 @@ struct transcribe_langid_params {
      * is TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE. Duplicates count once. */
     const char * const * allowed;
     int32_t              n_allowed;
-    int32_t              top_k; /* keep the best top_k candidates; 0 = every allowed label */
 };
 
 /* Summary of the last run. */
 struct transcribe_langid_result {
     uint64_t struct_size;
-    int32_t  n_candidates; /* rows readable via transcribe_langid_get_candidate */
-    int32_t  n_allowed;    /* labels in the allowed set (before top_k) */
+    /* Rows readable via transcribe_langid_get_candidate: one per label in the
+     * allowed set (duplicates counted once), n_labels when unrestricted. */
+    int32_t  n_candidates;
     /* Share of the softmax over every label that falls in the allowed set;
      * 1 when unrestricted. A low value means the speech is probably outside
      * the allowed set. */
     float    allowed_mass;
-    int64_t  audio_ms; /* audio actually scored, after the crop */
 };
 
 /* One ranked label. code and name point at model-owned storage, valid until
@@ -107,11 +104,12 @@ TRANSCRIBE_API void transcribe_langid_set_abort_callback(struct transcribe_langi
 
 /*
  * Identify the language of one clip: 16 kHz mono float32 PCM, every sample
- * finite. params may be NULL for defaults. Input longer than the session's
- * max_audio_ms is scored on its last max_audio_ms. Replaces the previous
- * result. Malformed input (NULL pointers, n_samples <= 0, NaN / Inf, a bad
- * allowed list, an unknown code, scored audio shorter than min_audio_ms)
- * returns an error before the previous result is touched.
+ * finite. params may be NULL for defaults. Input longer than
+ * transcribe_langid_info::max_audio_ms is scored on its first max_audio_ms
+ * (no error). Every allowed label is ranked. Replaces the previous result.
+ * Malformed input (NULL pointers, n_samples <= 0, NaN / Inf, a bad allowed
+ * list, an unknown code, scored audio shorter than min_audio_ms) returns an
+ * error before the previous result is touched.
  */
 TRANSCRIBE_API transcribe_status transcribe_langid_run(struct transcribe_langid_session *      session,
                                                        const float *                           pcm,
diff --git a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
index 73ef4855b..93c1b8cf1 100644
--- a/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
+++ b/scripts/hf_cards/lang-id-voxlingua107-ecapa.yaml
@@ -27,7 +27,7 @@ summary: |
   ECAPA-TDNN trained on VoxLingua107. NOT a transcription model: a run
   returns the model's language labels ranked by probability, optionally
   restricted to a caller-chosen set. Takes 16 kHz mono WAV; scores up to
-  the last 30 s of a clip.
+  the first 30 s of a clip.
 
 usage: |
   Build transcribe.cpp from source:
diff --git a/scripts/langid/bench.py b/scripts/langid/bench.py
index 1f59191a1..006d23816 100644
--- a/scripts/langid/bench.py
+++ b/scripts/langid/bench.py
@@ -95,7 +95,7 @@ def main(argv: list[str] | None = None) -> int:
             t0 = time.perf_counter()
             model = t.Model(str(gguf), backend=backend)
             load_ms = (time.perf_counter() - t0) * 1000
-            lid = model.langid_session(n_threads=0, max_audio_ms=60000)
+            lid = model.langid_session(n_threads=0)
             for seconds in durations:
                 d = float(seconds)
                 driver.cooldown_wait(cooldown_c, 300.0, 10.0)
diff --git a/scripts/langid/run.py b/scripts/langid/run.py
index 5d39929e6..fa456d657 100755
--- a/scripts/langid/run.py
+++ b/scripts/langid/run.py
@@ -15,10 +15,12 @@
       --manifest samples/langid/fleurs-*.manifest.jsonl \\
       --out reports/langid/cpp-f32-untrimmed.jsonl
 
-A crop of N is the first N seconds of the clip, `full` the whole clip; a
-row's `audio_s` is what was scored. Both engines get the same float samples
-(the clips are 16-bit, so a crop is exact). SpeechBrain runs a batch of one:
-padding a batch changes its normalisation span.
+A crop of N is the first N seconds of the clip; `full` is the whole clip, or
+its first MAX_AUDIO_S when longer (transcribe.cpp always scores only the first
+transcribe_langid_info::max_audio_ms, so the script cuts it for both
+engines). A row's `audio_s` is what was scored. Both engines get the same
+float samples (the clips are 16-bit, so a crop is exact). SpeechBrain runs a
+batch of one: padding a batch changes its normalisation span.
 """
 
 from __future__ import annotations
@@ -27,7 +29,6 @@
 import datetime as _dt
 import hashlib
 import json
-import math
 import os
 import sys
 import time
@@ -39,6 +40,9 @@
 sys.path.insert(0, str(REPO_ROOT / "scripts"))
 
 SAMPLE_RATE = 16000
+# transcribe_langid_info::max_audio_ms of the VoxLingua107 model: longer input
+# is scored on its first MAX_AUDIO_S.
+MAX_AUDIO_S = 30
 
 
 class SpeechBrainEngine:
@@ -83,11 +87,10 @@ def logits(self, pcm: np.ndarray) -> np.ndarray:
 
 
 class CppEngine:
-    """The GGUF loaded once; each clip is one LangIdSession.run with top_k 0,
+    """The GGUF loaded once; each clip is one unrestricted LangIdSession.run,
     so every label's logit comes back."""
 
-    def __init__(self, model: Path, backend: str, threads: int, max_audio_ms: int,
-                 library: Path | None) -> None:
+    def __init__(self, model: Path, backend: str, threads: int, library: Path | None) -> None:
         if library is not None:
             os.environ["TRANSCRIBE_LIBRARY"] = str(library.resolve())
         sys.path.insert(0, str(REPO_ROOT / "bindings" / "python" / "src"))
@@ -95,7 +98,7 @@ def __init__(self, model: Path, backend: str, threads: int, max_audio_ms: int,
         from gguf import GGUFReader
 
         self.model = transcribe_cpp.Model(str(model), backend=backend)
-        self.session = self.model.langid_session(n_threads=threads, max_audio_ms=max_audio_ms)
+        self.session = self.model.langid_session(n_threads=threads)
         self.labels = [code for code, _name in self.model.langid_labels]
         fields = GGUFReader(str(model)).fields
         kv = lambda key: str(fields[key].contents()) if key in fields else None  # noqa: E731
@@ -104,7 +107,6 @@ def __init__(self, model: Path, backend: str, threads: int, max_audio_ms: int,
         self.recipe = {
             "backend": backend,
             "threads": threads,
-            "max_audio_ms": max_audio_ms,
             "gguf_sha256": sha,
             "gguf_source_commit": kv("general.source.commit"),
             "gguf_source_repo": kv("general.name"),
@@ -115,7 +117,7 @@ def __init__(self, model: Path, backend: str, threads: int, max_audio_ms: int,
 
     def logits(self, pcm: np.ndarray) -> np.ndarray:
         logits = np.zeros(len(self.labels), dtype=np.float32)
-        for c in self.session.run(pcm, top_k=0).candidates:
+        for c in self.session.run(pcm).candidates:
             logits[c.index] = c.logit
         return logits
 
@@ -156,10 +158,7 @@ def main(argv: list[str] | None = None) -> int:
     if args.engine == "speechbrain":
         engine = SpeechBrainEngine(args.model, args.threads)
     else:
-        # Room for the longest clip, so `full` scores the same audio as SpeechBrain.
-        max_audio_ms = int(math.ceil(longest_s / 10.0) * 10.0 * 1000)
-        engine = CppEngine(Path(args.model), args.backend, args.threads, max_audio_ms,
-                           args.library)
+        engine = CppEngine(Path(args.model), args.backend, args.threads, args.library)
 
     header = {
         "type": "header",
@@ -167,6 +166,7 @@ def main(argv: list[str] | None = None) -> int:
         "model": args.model,
         "recipe": {
             "crops": [str(c) for c in crops],
+            "max_audio_s": MAX_AUDIO_S,
             "manifests": [str(m) for m in args.manifest],
             "manifest_sha256": {str(m): hashlib.sha256(m.read_bytes()).hexdigest()
                                 for m in args.manifest},
@@ -189,6 +189,8 @@ def main(argv: list[str] | None = None) -> int:
                 pcm = read_audio(REPO_ROOT / row["audio"])
                 if crop != "full":
                     pcm = pcm[: crop * SAMPLE_RATE]
+                else:
+                    pcm = pcm[:MAX_AUDIO_S * SAMPLE_RATE]
                 logits = engine.logits(pcm)
                 z = np.exp(logits.astype(np.float64) - logits.max())
                 top = int(np.argmax(z))
diff --git a/scripts/langid/score.py b/scripts/langid/score.py
index 9a0e29ee0..52db8d9d4 100755
--- a/scripts/langid/score.py
+++ b/scripts/langid/score.py
@@ -39,7 +39,7 @@
 N_BOOT = 1000
 BOOT_SEED = 42
 CI = 0.95
-RECIPE_FIELDS = ("crops", "n_utterances", "logit_kind", "sample_rate", "manifest_sha256")
+RECIPE_FIELDS = ("crops", "max_audio_s", "n_utterances", "logit_kind", "sample_rate", "manifest_sha256")
 
 
 def die(msg: str) -> None:
diff --git a/src/transcribe-langid.cpp b/src/transcribe-langid.cpp
index a90a94192..cebe04bf2 100644
--- a/src/transcribe-langid.cpp
+++ b/src/transcribe-langid.cpp
@@ -1,5 +1,5 @@
 // transcribe-langid.cpp - LANGID role C ABI (include/transcribe/langid.h):
-// label table, allowed set, crop, softmax, ranking and top-k.
+// label table, allowed set, crop, softmax and ranking.
 
 #include "transcribe-langid.h"
 
@@ -25,10 +25,10 @@ using transcribe::LangidLabels;
 
 namespace {
 
-constexpr size_t k_min_info_size           = TRANSCRIBE_FIELD_END(transcribe_langid_info, min_audio_ms);
-constexpr size_t k_min_session_params_size = TRANSCRIBE_FIELD_END(transcribe_langid_session_params, max_audio_ms);
-constexpr size_t k_min_params_size         = TRANSCRIBE_FIELD_END(transcribe_langid_params, top_k);
-constexpr size_t k_min_result_size         = TRANSCRIBE_FIELD_END(transcribe_langid_result, audio_ms);
+constexpr size_t k_min_info_size           = TRANSCRIBE_FIELD_END(transcribe_langid_info, max_audio_ms);
+constexpr size_t k_min_session_params_size = TRANSCRIBE_FIELD_END(transcribe_langid_session_params, n_threads);
+constexpr size_t k_min_params_size         = TRANSCRIBE_FIELD_END(transcribe_langid_params, n_allowed);
+constexpr size_t k_min_result_size         = TRANSCRIBE_FIELD_END(transcribe_langid_result, allowed_mass);
 constexpr size_t k_min_candidate_size      = TRANSCRIBE_FIELD_END(transcribe_langid_candidate, logit);
 constexpr size_t k_min_timings_size        = TRANSCRIBE_FIELD_END(transcribe_timings, decode_ms);
 
@@ -196,6 +196,7 @@ static transcribe_status langid_get_info_impl(const transcribe_model * model, tr
     staged.sample_rate  = 16000;
     staged.n_labels     = static_cast<int32_t>(labels->codes.size());
     staged.min_audio_ms = transcribe::k_langid_min_audio_ms;
+    staged.max_audio_ms = transcribe::k_langid_max_audio_ms;
     copy_out_prefix(out, &staged, out->struct_size, sizeof(staged));
     return TRANSCRIBE_OK;
 }
@@ -225,15 +226,9 @@ static transcribe_status langid_session_init_impl(transcribe_model *
     if (params->n_threads < 0) {
         return TRANSCRIBE_ERR_INVALID_ARG;
     }
-    const int32_t max_audio_ms =
-        params->max_audio_ms == 0 ? transcribe::k_langid_default_audio_ms : params->max_audio_ms;
-    if (max_audio_ms < transcribe::k_langid_min_audio_ms) {
-        return TRANSCRIBE_ERR_INVALID_ARG;
-    }
-    *out                 = ops->new_session();
-    (*out)->model        = model;
-    (*out)->n_threads    = params->n_threads;
-    (*out)->max_audio_ms = max_audio_ms;
+    *out              = ops->new_session();
+    (*out)->model     = model;
+    (*out)->n_threads = params->n_threads;
     return TRANSCRIBE_OK;
 }
 
@@ -253,9 +248,6 @@ static transcribe_status langid_run_impl(transcribe_langid_session *      sessio
     if (const auto st = check_input_struct_size(params->struct_size, k_min_params_size); st != TRANSCRIBE_OK) {
         return st;
     }
-    if (params->top_k < 0) {
-        return TRANSCRIBE_ERR_INVALID_ARG;
-    }
     const transcribe::LangidOps * ops    = langid_ops(session->model);
     const LangidLabels &          labels = ops->labels(session->model);
     std::vector<uint8_t>          mask;
@@ -263,18 +255,17 @@ static transcribe_status langid_run_impl(transcribe_langid_session *      sessio
     if (const auto st = build_allowed_mask(labels, params, mask, n_allowed); st != TRANSCRIBE_OK) {
         return st;
     }
-    // Score the last max_audio_ms; the minimum applies to what is scored.
-    const int64_t max_samples = static_cast<int64_t>(session->max_audio_ms) * k_samples_per_ms;
+    // Score the first k_langid_max_audio_ms; the minimum applies to what is
+    // scored.
+    const int64_t max_samples = static_cast<int64_t>(transcribe::k_langid_max_audio_ms) * k_samples_per_ms;
     const int64_t n_used      = std::min<int64_t>(n_samples, max_samples);
     if (n_used < static_cast<int64_t>(transcribe::k_langid_min_audio_ms) * k_samples_per_ms) {
         return TRANSCRIBE_ERR_INPUT_TOO_SHORT;
     }
-    const float * pcm_used = pcm + (n_samples - n_used);
+    const float * pcm_used = pcm;
 
     session->candidates.clear();
-    session->n_allowed    = 0;
     session->allowed_mass = 0.0f;
-    session->audio_ms     = 0;
     session->t_mel_us     = 0;
     session->t_encode_us  = 0;
     session->t_decode_us  = 0;
@@ -317,14 +308,9 @@ static transcribe_status langid_run_impl(transcribe_langid_session *      sessio
     }
     std::stable_sort(ranked.begin(), ranked.end(),
                      [](const LangidCandidateEntry & a, const LangidCandidateEntry & b) { return a.p > b.p; });
-    if (params->top_k > 0 && static_cast<size_t>(params->top_k) < ranked.size()) {
-        ranked.resize(static_cast<size_t>(params->top_k));
-    }
 
     session->candidates.swap(ranked);
-    session->n_allowed    = n_allowed;
     session->allowed_mass = restricted ? static_cast<float>(mass) : 1.0f;
-    session->audio_ms     = n_used / k_samples_per_ms;
     return TRANSCRIBE_OK;
 }
 
@@ -364,9 +350,7 @@ extern "C" transcribe_status transcribe_langid_get_result(const struct transcrib
     transcribe_langid_result staged{};
     staged.struct_size  = out->struct_size;
     staged.n_candidates = static_cast<int32_t>(session->candidates.size());
-    staged.n_allowed    = session->n_allowed;
     staged.allowed_mass = session->allowed_mass;
-    staged.audio_ms     = session->audio_ms;
     copy_out_prefix(out, &staged, out->struct_size, sizeof(staged));
     return TRANSCRIBE_OK;
 }
diff --git a/src/transcribe-langid.h b/src/transcribe-langid.h
index 4df6ad311..c7a8ddd05 100644
--- a/src/transcribe-langid.h
+++ b/src/transcribe-langid.h
@@ -3,7 +3,7 @@
 //
 // Families compute logits over their label table; the role dispatcher
 // (transcribe-langid.cpp) crops the input, applies the allowed set, and
-// owns the softmax, ranking and top-k.
+// owns the softmax and ranking.
 
 #pragma once
 
@@ -41,9 +41,10 @@ transcribe_status build_langid_labels(std::vector<std::string>         codes,
                                       const char *                     tag,
                                       LangidLabels &                   out);
 
-// Scored-audio bounds every LANGID model shares.
-constexpr int32_t k_langid_min_audio_ms     = 500;
-constexpr int32_t k_langid_default_audio_ms = 30000;
+// Scored-audio bounds every LANGID model shares. Longer input is scored on
+// its first k_langid_max_audio_ms.
+constexpr int32_t k_langid_min_audio_ms = 500;
+constexpr int32_t k_langid_max_audio_ms = 30000;
 
 struct LangidOps {
     const LangidLabels & (*labels)(const transcribe_model * model);
@@ -60,11 +61,7 @@ struct LangidOps {
 }  // namespace transcribe
 
 struct transcribe_langid_session : transcribe::SessionCore {
-    int32_t max_audio_ms = transcribe::k_langid_default_audio_ms;
-
     // Last successful run's result; empty / zero otherwise.
     std::vector<transcribe::LangidCandidateEntry> candidates;
-    int32_t                                       n_allowed    = 0;
     float                                         allowed_mass = 0.0f;
-    int64_t                                       audio_ms     = 0;
 };
diff --git a/tests/ecapa_tdnn_real_smoke.cpp b/tests/ecapa_tdnn_real_smoke.cpp
index d931fee12..e753bc1dd 100644
--- a/tests/ecapa_tdnn_real_smoke.cpp
+++ b/tests/ecapa_tdnn_real_smoke.cpp
@@ -1,6 +1,7 @@
 // ecapa_tdnn_real_smoke.cpp - the real VoxLingua107 ECAPA-TDNN GGUF through
 // the public LANGID API: label table, aliases, top-1 on committed FLEURS
-// clips, the crop on a long clip, and a clip cut to 800 ms, all on the CPU
+// clips, the crop to the first 30 s of a long clip, and a clip cut to 800 ms,
+// all on the CPU
 // backend; then, when a GPU backend loads, the stock-op GPU graph against the
 // CPU graph's logits. Gated by TRANSCRIBE_ECAPA_TDNN_GGUF (RC 77 skip).
 
@@ -10,6 +11,7 @@
 #include "wav.h"
 
 #include <cmath>
+#include <cstddef>
 #include <cstdio>
 #include <cstdlib>
 #include <cstring>
@@ -39,7 +41,7 @@ bool load_sample(const char * name, std::vector<float> & pcm) {
 }
 
 // Top-1 code and p for one clip, or "" when the run fails.
-std::string top1(transcribe_langid_session * s, const char * wav, float * p_out, int64_t * audio_ms = nullptr) {
+std::string top1(transcribe_langid_session * s, const char * wav, float * p_out) {
     std::vector<float> pcm;
     if (!load_sample(wav, pcm)) {
         return "";
@@ -50,12 +52,6 @@ std::string top1(transcribe_langid_session * s, const char * wav, float * p_out,
         ++g_failures;
         return "";
     }
-    transcribe_langid_result r;
-    transcribe_langid_result_init(&r);
-    transcribe_langid_get_result(s, &r);
-    if (audio_ms != nullptr) {
-        *audio_ms = r.audio_ms;
-    }
     transcribe_langid_candidate c;
     transcribe_langid_candidate_init(&c);
     transcribe_langid_get_candidate(s, 0, &c);
@@ -63,11 +59,9 @@ std::string top1(transcribe_langid_session * s, const char * wav, float * p_out,
     return c.code != nullptr ? c.code : "";
 }
 
-// Every label's logit for one clip, by label index; empty when the run fails.
-std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
-    std::vector<float> pcm;
-    if (!load_sample(wav, pcm) ||
-        transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) != TRANSCRIBE_OK) {
+// Every label's logit for PCM, by label index; empty when the run fails.
+std::vector<float> all_logits(transcribe_langid_session * s, const std::vector<float> & pcm) {
+    if (pcm.empty() || transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) != TRANSCRIBE_OK) {
         return {};
     }
     transcribe_langid_result r;
@@ -85,6 +79,11 @@ std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
     return out;
 }
 
+std::vector<float> all_logits(transcribe_langid_session * s, const char * wav) {
+    std::vector<float> pcm;
+    return load_sample(wav, pcm) ? all_logits(s, pcm) : std::vector<float>{};
+}
+
 // general.file_type of the GGUF (0 = all F32, 1 = F16, 7 = Q8_0), or -1.
 int gguf_file_type(const char * path) {
     gguf_init_params gp{};
@@ -177,7 +176,7 @@ int main() {
     transcribe_langid_info info;
     transcribe_langid_info_init(&info);
     CHECK(transcribe_langid_get_info(m, &info) == TRANSCRIBE_OK);
-    CHECK(info.n_labels == 107 && info.sample_rate == 16000 && info.min_audio_ms == 500);
+    CHECK(info.n_labels == 107 && info.sample_rate == 16000 && info.min_audio_ms == 500 && info.max_audio_ms == 30000);
     // Modern ISO codes alias the VoxLingua107 legacy labels.
     CHECK(transcribe_langid_label_index(m, "he") == transcribe_langid_label_index(m, "iw"));
     CHECK(transcribe_langid_label_index(m, "nb") == transcribe_langid_label_index(m, "no"));
@@ -209,13 +208,24 @@ int main() {
         }
     }
 
-    // Long input is scored on its last 30 s; a clip just over the minimum
-    // still runs.
-    float   p        = 0.0f;
-    int64_t audio_ms = 0;
-    top1(s, "ru-long.wav", &p, &audio_ms);
-    CHECK(audio_ms == 30000);
+    // Long input (ru-long.wav, ~33.8 s) is scored on its first max_audio_ms:
+    // the whole clip and its first-30 s slice give identical logits, the
+    // last-30 s slice does not.
     std::vector<float> pcm;
+    const size_t       max_n = static_cast<size_t>(info.max_audio_ms) * static_cast<size_t>(info.sample_rate) / 1000;
+    if (load_sample("ru-long.wav", pcm)) {
+        CHECK(pcm.size() > max_n);
+        if (pcm.size() > max_n) {
+            const std::vector<float> last(pcm.end() - static_cast<std::ptrdiff_t>(max_n), pcm.end());
+            const std::vector<float> first(pcm.begin(), pcm.begin() + static_cast<std::ptrdiff_t>(max_n));
+            const std::vector<float> l_full  = all_logits(s, pcm);
+            const std::vector<float> l_last  = all_logits(s, last);
+            const std::vector<float> l_first = all_logits(s, first);
+            CHECK(l_full.size() == 107 && l_full == l_first);
+            CHECK(l_last.size() == 107 && l_last != l_full);
+        }
+    }
+    // A clip just over the minimum still runs.
     if (load_sample("fleurs-en.wav", pcm)) {
         pcm.resize(12800);  // 800 ms
         CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
@@ -225,7 +235,7 @@ int main() {
         transcribe_langid_candidate c;
         transcribe_langid_candidate_init(&c);
         transcribe_langid_get_candidate(s, 0, &c);
-        CHECK(r.audio_ms == 800 && std::isfinite(c.p));
+        CHECK(r.n_candidates == 107 && std::isfinite(c.p));
     }
 
     transcribe_langid_session_free(s);
diff --git a/tests/ecapa_tdnn_smoke.cpp b/tests/ecapa_tdnn_smoke.cpp
index 5d149aff0..1804041eb 100644
--- a/tests/ecapa_tdnn_smoke.cpp
+++ b/tests/ecapa_tdnn_smoke.cpp
@@ -15,6 +15,7 @@
 #include "transcribe/langid.h"
 
 #include <cmath>
+#include <cstddef>
 #include <cstdint>
 #include <cstdio>
 #include <cstdlib>
@@ -66,11 +67,10 @@ transcribe_model * load_cpu(const char * name, transcribe_status * st_out = null
     return m;
 }
 
-transcribe_langid_session * open_session(transcribe_model * m, int n_threads, int max_audio_ms = 0) {
+transcribe_langid_session * open_session(transcribe_model * m, int n_threads) {
     transcribe_langid_session_params sp;
     transcribe_langid_session_params_init(&sp);
     sp.n_threads                  = n_threads;
-    sp.max_audio_ms               = max_audio_ms;
     transcribe_langid_session * s = nullptr;
     CHECK(transcribe_langid_session_init(m, &sp, &s) == TRANSCRIBE_OK);
     return s;
@@ -102,7 +102,7 @@ void test_model_surface(transcribe_model * m) {
     transcribe_langid_info info;
     transcribe_langid_info_init(&info);
     CHECK(transcribe_langid_get_info(m, &info) == TRANSCRIBE_OK);
-    CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500);
+    CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500 && info.max_audio_ms == 30000);
     CHECK(std::strcmp(transcribe_langid_label_code(m, 2), "cc") == 0);
     CHECK(std::strcmp(transcribe_langid_label_name(m, 2), "Charlie") == 0);
     CHECK(transcribe_langid_label_index(m, "xx") == 0);  // alias from the GGUF
@@ -122,7 +122,7 @@ void test_run(transcribe_model * m) {
 
     CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
     const transcribe_langid_result r = result_of(s);
-    CHECK(r.n_candidates == 5 && r.n_allowed == 5 && r.allowed_mass == 1.0f && r.audio_ms == 1000);
+    CHECK(r.n_candidates == 5 && r.allowed_mass == 1.0f);
     const auto first = candidates_of(s);
     double     sum   = 0.0;
     for (size_t i = 0; i < first.size(); ++i) {
@@ -174,6 +174,50 @@ void test_thread_invariance(transcribe_model * m) {
     transcribe_langid_session_free(s4);
 }
 
+// Input longer than info.max_audio_ms is scored on its FIRST max_audio_ms:
+// the full clip and its first-max_audio_ms slice give bit-identical
+// candidates (the graph sees the same samples), while the last slice of
+// the same length gives different logits. The two halves are different
+// noise, so the first-vs-last check is meaningful.
+void test_crop_keeps_first(transcribe_model * m) {
+    transcribe_langid_info info;
+    transcribe_langid_info_init(&info);
+    CHECK(transcribe_langid_get_info(m, &info) == TRANSCRIBE_OK);
+    const size_t max_n = static_cast<size_t>(info.max_audio_ms) * static_cast<size_t>(info.sample_rate) / 1000;
+    const size_t extra = static_cast<size_t>(info.sample_rate) * 5;  // 5 s over the window
+    CHECK(max_n > 0 && extra > static_cast<size_t>(info.min_audio_ms) * static_cast<size_t>(info.sample_rate) / 1000);
+
+    std::vector<float>       pcm  = noise(max_n, 13);
+    const std::vector<float> tail = noise(extra, 11);
+    pcm.insert(pcm.end(), tail.begin(), tail.end());
+    const std::vector<float> last(pcm.end() - static_cast<std::ptrdiff_t>(max_n), pcm.end());
+    const std::vector<float> first(pcm.begin(), pcm.begin() + static_cast<std::ptrdiff_t>(max_n));
+
+    transcribe_langid_session * s = open_session(m, 2);
+    CHECK(transcribe_langid_run(s, pcm.data(), static_cast<int>(pcm.size()), nullptr) == TRANSCRIBE_OK);
+    const auto full = candidates_of(s);
+    CHECK(transcribe_langid_run(s, first.data(), static_cast<int>(first.size()), nullptr) == TRANSCRIBE_OK);
+    const auto from_first = candidates_of(s);
+    CHECK(transcribe_langid_run(s, last.data(), static_cast<int>(last.size()), nullptr) == TRANSCRIBE_OK);
+    const auto from_last = candidates_of(s);
+
+    CHECK(full.size() == 5 && from_first.size() == full.size() && from_last.size() == full.size());
+    for (size_t i = 0; i < full.size() && i < from_first.size(); ++i) {
+        CHECK(from_first[i].index == full[i].index);
+        CHECK(from_first[i].logit == full[i].logit && from_first[i].p == full[i].p);
+    }
+    std::vector<float> logit_full(5, 0.0f);
+    std::vector<float> logit_last(5, 0.0f);
+    for (size_t i = 0; i < full.size() && i < from_last.size(); ++i) {
+        if (full[i].index >= 0 && full[i].index < 5 && from_last[i].index >= 0 && from_last[i].index < 5) {
+            logit_full[static_cast<size_t>(full[i].index)]      = full[i].logit;
+            logit_last[static_cast<size_t>(from_last[i].index)] = from_last[i].logit;
+        }
+    }
+    CHECK(logit_full != logit_last);
+    transcribe_langid_session_free(s);
+}
+
 // Q8_0 weights are widened to F16 at load: a Q8_0 model must give exactly
 // the logits of the same model stored as F16 holding the dequantized values.
 void test_q8_0_widened_to_f16() {
@@ -227,6 +271,7 @@ int main() {
     test_model_surface(m);
     test_run(m);
     test_thread_invariance(m);
+    test_crop_keeps_first(m);
     transcribe_model_free(m);
 
     test_q8_0_widened_to_f16();
diff --git a/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json b/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json
index ffa7762f1..9f8fa8a84 100644
--- a/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json
+++ b/tests/golden/ecapa_tdnn/lang-id-voxlingua107-ecapa.manifest.json
@@ -35,8 +35,7 @@
   "langid": {
     "n_labels": 107,
     "label_aliases": ["he=iw", "jv=jw", "fil=tl", "nb=no"],
-    "min_audio_ms": 500,
-    "default_max_audio_ms": 30000
+    "min_audio_ms": 500
   },
   "tolerance_file": "tests/tolerances/ecapa_tdnn.json",
   "cases": [
diff --git a/tests/langid_dispatch_unit.cpp b/tests/langid_dispatch_unit.cpp
index 1ab581df0..5adb80625 100644
--- a/tests/langid_dispatch_unit.cpp
+++ b/tests/langid_dispatch_unit.cpp
@@ -1,7 +1,7 @@
 // langid_dispatch_unit.cpp - LANGID role dispatcher (transcribe-langid.cpp)
 // against a fake arch: label table validation, role checks, softmax /
-// ranking / top-k, the allowed set, crop and minimum length, non-finite
-// input and logits, abort.
+// ranking, the allowed set, crop and minimum length, non-finite input and
+// logits, abort.
 
 #include "transcribe-arch.h"
 #include "transcribe-langid.h"
@@ -84,7 +84,7 @@ struct Fixture {
     transcribe_langid_session * session = nullptr;
     std::vector<float>          pcm     = std::vector<float>(16000, 0.0f);  // 1 s
 
-    explicit Fixture(int32_t max_audio_ms = 0) {
+    Fixture() {
         g_run_calls   = 0;
         g_logits      = { 1.0f, 3.0f, 2.0f, 0.0f, -1.0f };
         g_check_abort = false;
@@ -92,7 +92,6 @@ struct Fixture {
         model.roles   = TRANSCRIBE_ROLE_LANGID;
         transcribe_langid_session_params sp;
         transcribe_langid_session_params_init(&sp);
-        sp.max_audio_ms = max_audio_ms;
         CHECK(transcribe_langid_session_init(&model, &sp, &session) == TRANSCRIBE_OK);
     }
 
@@ -141,17 +140,17 @@ void test_role_checks_and_labels() {
 
     model.roles = TRANSCRIBE_ROLE_LANGID;
     CHECK(transcribe_langid_get_info(&model, &info) == TRANSCRIBE_OK);
-    CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500);
+    CHECK(info.sample_rate == 16000 && info.n_labels == 5 && info.min_audio_ms == 500 && info.max_audio_ms == 30000);
     CHECK(std::strcmp(transcribe_langid_label_code(&model, 1), "bb") == 0);
     CHECK(std::strcmp(transcribe_langid_label_name(&model, 4), "Ee") == 0);
     CHECK(transcribe_langid_label_index(&model, "cc") == 2);
     CHECK(transcribe_langid_label_index(&model, "xx") == 0);
     CHECK(transcribe_langid_label_index(&model, "zz") == -1);
 
-    // A window below the minimum is rejected up front.
+    // A negative thread count is rejected up front.
     transcribe_langid_session_params sp;
     transcribe_langid_session_params_init(&sp);
-    sp.max_audio_ms = 499;
+    sp.n_threads = -1;
     CHECK(transcribe_langid_session_init(&model, &sp, &s) == TRANSCRIBE_ERR_INVALID_ARG);
     CHECK(s == nullptr);
 }
@@ -162,7 +161,7 @@ void test_ranking() {
     CHECK(g_run_calls == 1 && g_last_n == 16000 && g_last_pcm == f.pcm.data());
 
     const transcribe_langid_result r = f.result();
-    CHECK(r.n_candidates == 5 && r.n_allowed == 5 && r.allowed_mass == 1.0f && r.audio_ms == 1000);
+    CHECK(r.n_candidates == 5 && r.allowed_mass == 1.0f);
 
     // Logits {1, 3, 2, 0, -1} rank bb, cc, aa, dd, ee.
     const int    want[] = { 1, 2, 0, 3, 4 };
@@ -177,14 +176,15 @@ void test_ranking() {
         sum += c.p;
     }
     CHECK(near(sum, 1.0));
+    CHECK(f.candidate(5).code == nullptr && f.candidate(5).index == 0);  // out of range: zeroed row
 
-    // top_k truncates the rows but not n_allowed.
-    transcribe_langid_params p;
-    transcribe_langid_params_init(&p);
-    p.top_k = 2;
-    CHECK(f.run(&p) == TRANSCRIBE_OK);
-    CHECK(f.result().n_candidates == 2 && f.result().n_allowed == 5);
-    CHECK(f.candidate(1).index == 2 && f.candidate(2).code == nullptr);
+    // Tied p keeps label order.
+    g_logits = { 0.0f, 2.0f, 0.0f, 2.0f, 0.0f };
+    CHECK(f.run() == TRANSCRIBE_OK);
+    const int tied[] = { 1, 3, 0, 2, 4 };
+    for (int i = 0; i < 5; ++i) {
+        CHECK(f.candidate(i).index == tied[i]);
+    }
 }
 
 // The allowed set renormalizes over its members; malformed or unknown lists
@@ -199,7 +199,7 @@ void test_allowed_set() {
     p.n_allowed          = 2;
     CHECK(f.run(&p) == TRANSCRIBE_OK);
     const transcribe_langid_result r = f.result();
-    CHECK(r.n_candidates == 2 && r.n_allowed == 2);
+    CHECK(r.n_candidates == 2);
     const double e_bb = std::exp(3.0);
     const double e_dd = std::exp(0.0);
     const double all  = std::exp(1.0) + e_bb + std::exp(2.0) + e_dd + std::exp(-1.0);
@@ -209,6 +209,15 @@ void test_allowed_set() {
     CHECK(c0.index == 1 && c1.index == 3);
     CHECK(near(c0.p, e_bb / (e_bb + e_dd)) && near(c1.p, e_dd / (e_bb + e_dd)));
 
+    // n_candidates is the allowed-set size after duplicates (an alias names
+    // the same label as its code) count once.
+    const char * dups[] = { "bb", "bb", "xx", "aa" };
+    p.allowed           = dups;
+    p.n_allowed         = 4;
+    CHECK(f.run(&p) == TRANSCRIBE_OK);
+    CHECK(f.result().n_candidates == 2);
+    CHECK(f.candidate(0).index == 1 && f.candidate(1).index == 0 && f.candidate(2).code == nullptr);
+
     g_run_calls = 0;
     p.n_allowed = 0;
     CHECK(f.run(&p) == TRANSCRIBE_ERR_INVALID_ARG);
@@ -217,25 +226,30 @@ void test_allowed_set() {
     p.n_allowed            = 2;
     CHECK(f.run(&p) == TRANSCRIBE_ERR_UNSUPPORTED_LANGUAGE);
     CHECK(g_run_calls == 0);
-    CHECK(f.result().n_allowed == 2);
+    CHECK(f.result().n_candidates == 2);  // previous result kept
 }
 
-// The crop keeps the last max_audio_ms, and the minimum applies to the audio
-// actually scored.
+// The crop keeps the first info.max_audio_ms, and the minimum applies to the
+// audio actually scored.
 void test_crop_and_minimum() {
     {
-        Fixture f(500);
-        CHECK(f.run() == TRANSCRIBE_OK);  // 1 s scored as its last 500 ms
-        CHECK(g_last_n == 8000 && g_last_pcm == f.pcm.data() + 8000);
-        CHECK(f.result().audio_ms == 500);
+        Fixture f;
+        f.pcm.assign(16 * (30000 + 1000), 0.0f);  // 31 s, scored as its first 30 s
+        CHECK(f.run() == TRANSCRIBE_OK);
+        CHECK(g_last_n == 16 * 30000 && g_last_pcm == f.pcm.data());
+        CHECK(f.result().n_candidates == 5);
+        f.pcm.resize(16 * 30000);  // exactly max_audio_ms: no crop
+        CHECK(f.run() == TRANSCRIBE_OK);
+        CHECK(g_last_n == 16 * 30000 && g_last_pcm == f.pcm.data());
     }
     {
         Fixture f;
         CHECK(transcribe_langid_run(f.session, f.pcm.data(), 8000, nullptr) == TRANSCRIBE_OK);  // exactly 500 ms
+        CHECK(g_last_n == 8000);
         g_run_calls = 0;
         CHECK(transcribe_langid_run(f.session, f.pcm.data(), 7999, nullptr) == TRANSCRIBE_ERR_INPUT_TOO_SHORT);
         CHECK(g_run_calls == 0);
-        CHECK(f.result().audio_ms == 500);  // previous result kept
+        CHECK(f.result().n_candidates == 5);  // previous result kept
     }
 }
 

From 91e39263bc945c721d8d2a327b4a84e0c8cb2bc9 Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 20:22:51 +0800
Subject: [PATCH 37/38] cmake: default GGML_NATIVE=OFF on Apple Silicon

Match the macOS wheel, the XCFramework and the Rust crate (#194). A native
build on an M2 or newer enables i8mm, and ggml compiles tinyBLAS out under
__ARM_FEATURE_MATMUL_INT8, so source builds ran CPU inference about 2x
slower than the shipped artifacts (langid on a 30 s clip, 4 threads:
F32 721 -> 318 ms, F16 406 -> 200 ms, Q8_0 406 -> 199 ms; parakeet F16
jfk 737 -> 354 ms). A default, not a clamp: -DGGML_NATIVE=ON still wins.
Existing build trees keep their cached value; `cmake -U GGML_NATIVE <dir>`
picks up the new default.
---
 CMakeLists.txt | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a10f92eb4..88ce35887 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -313,6 +313,15 @@ if(TRANSCRIBE_X86_CONSERVATIVE)
     endforeach()
 endif()
 
+# Apple Silicon defaults GGML_NATIVE to OFF, the posture of the macOS wheel,
+# the XCFramework and the Rust crate. Native tuning on an M2 or newer turns on
+# i8mm, and ggml compiles tinyBLAS out under __ARM_FEATURE_MATMUL_INT8, so a
+# native build runs F32 / F16 / Q8_0 CPU inference 1.2-2.1x slower (#194).
+# A default, not a clamp: -DGGML_NATIVE=ON still wins.
+if(TRANSCRIBE_IS_APPLE_SILICON AND NOT DEFINED CACHE{GGML_NATIVE})
+    set(GGML_NATIVE OFF CACHE BOOL "")
+endif()
+
 # OpenMP — CENTRAL POLICY.
 #
 # DEFAULT: OFF. We use ggml's native CPU threadpool everywhere; OpenMP is an

From 88c44f10aeb3f3025c8f8dec8f1395c05ce5319f Mon Sep 17 00:00:00 2001
From: CJ Pais <cj@cjpais.com>
Date: Thu, 8 Oct 2026 20:40:08 +0800
Subject: [PATCH 38/38] langid: stamp accuracy rows cf62265a

The 2026-10-08 sweep ran on 9cfa5670 plus the uncommitted changes that
became cf62265a.
---
 catalog/lang-id-voxlingua107-ecapa.json   | 6 +++---
 docs/models/lang-id-voxlingua107-ecapa.md | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/catalog/lang-id-voxlingua107-ecapa.json b/catalog/lang-id-voxlingua107-ecapa.json
index 35568b84a..2b5e70cdf 100644
--- a/catalog/lang-id-voxlingua107-ecapa.json
+++ b/catalog/lang-id-voxlingua107-ecapa.json
@@ -42,9 +42,9 @@
     {"quant":"Q8_0","filename":"lang-id-voxlingua107-ecapa-Q8_0.gguf","size_bytes":26693632}
   ],
   "accuracy_benchmarks": [
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"9cfa5670","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.3e-05}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"9cfa5670","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":11986,"n":12000,"max_abs_logit_delta":0.095}},
-    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.17,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"9cfa5670","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":11706,"n":12000,"max_abs_logit_delta":2.9}}
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F32","metric":"accuracy","acc_pct":85.23,"ci95":[84.07,86.47],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"cf62265a","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":12000,"n":12000,"max_abs_logit_delta":7.3e-05}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"F16","metric":"accuracy","acc_pct":85.17,"ci95":[84.0,86.43],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"cf62265a","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":11986,"n":12000,"max_abs_logit_delta":0.095}},
+    {"dataset":"fleurs","split":"test","language":"mul","backend":"cpu","quant":"Q8_0","metric":"accuracy","acc_pct":86.3,"ci95":[85.17,87.5],"n_utts":3000,"batch_size":1,"timestamps":"none","engine_sha":"cf62265a","publication_profile":"langid-publication-v1","measured_on":"2026-10-08","agreement":{"n_agree":11706,"n":12000,"max_abs_logit_delta":2.9}}
   ],
   "headline_benchmark": {
     "dataset": "fleurs",
diff --git a/docs/models/lang-id-voxlingua107-ecapa.md b/docs/models/lang-id-voxlingua107-ecapa.md
index 9aab026d2..315967878 100644
--- a/docs/models/lang-id-voxlingua107-ecapa.md
+++ b/docs/models/lang-id-voxlingua107-ecapa.md
@@ -37,7 +37,7 @@ Licensed Apache-2.0. Ported from upstream commit [`0253049`](https://huggingface
 <!-- /catalog -->
 
 <!-- catalog:recipe -->
-Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `9cfa5670` on 2026-10-08.
+Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `cf62265a` on 2026-10-08.
 <!-- /catalog -->
 
 <!-- catalog:prose field=wer.notes -->
@@ -56,7 +56,7 @@ transcribe.cpp model page.
 | F16  | 85.17% (84.00-86.43) | 11986 / 12000 | 0.095 |
 | Q8_0 | 86.30% (85.17-87.50) | 11706 / 12000 | 2.9 |
 
-Measured at transcribe.cpp `9cfa5670` on 2026-10-08.
+Measured at transcribe.cpp `cf62265a` on 2026-10-08.
 <!-- /catalog -->
 
 Agreement counts the scored sweep's top-1 decisions that match the