diff --git a/.gitignore b/.gitignore index 57f6fad..ee3109c 100644 --- a/.gitignore +++ b/.gitignore @@ -22,3 +22,4 @@ output/* !output/.gitkeep samples/* !samples/.gitkeep +reference_tracks/ diff --git a/config/moods/ethereal.yaml b/config/moods/ethereal.yaml index a2940b6..268ad00 100644 --- a/config/moods/ethereal.yaml +++ b/config/moods/ethereal.yaml @@ -22,23 +22,26 @@ drums: vocals: tts_speed: slow - reverb_wet: 0.55 - delay_wet: 0.25 + reverb_wet: 0.20 + delay_wet: 0.08 delay_time_beats: 3 +vocal_engine: edge-tts +voice: spectral + textures: vinyl_crackle_level: 0.18 ambient_level: 0.14 tape_hiss_level: 0.03 levels: - kick: 0 - snare: -3 - hihat: -10 - bass: -4 - pads: -8 - vocals: 1 - textures: -16 + kick: -3 + snare: -5 + hihat: -14 + bass: -7 + pads: -12 + vocals: 6 + textures: -20 arrangement: intro_bars: 12 diff --git a/config/moods/melancholic.yaml b/config/moods/melancholic.yaml index 03c3459..061d054 100644 --- a/config/moods/melancholic.yaml +++ b/config/moods/melancholic.yaml @@ -1,44 +1,51 @@ # Mood Machine — Melancholic preset -# Slow, heavy, dark — Portishead / Dummy-era vibes +# Slow, heavy, dark — Headache / Vegyn reference-tuned +# Calibrated against "The Beginning of the End" (Headache, 2023) mood: melancholic -bpm: 75 +bpm: 70 # reference: 69.8 BPM key: Am time_signature: 4/4 synth: - pad_filter_cutoff: 1400 + pad_filter_cutoff: 1800 # iter4: raised further toward reference centroid ~1958Hz pad_attack: 1.2 pad_release: 2.5 pad_detune_cents: 8 - bass_character: warm # warm | deep | distorted - drum_tone: dark # dark | crisp | lo-fi + bass_character: warm # warm | deep | distorted + drum_tone: crisp # iter3: reverted — dark killed HF (centroid 1216→need 1958) drums: - pattern_style: sparse # sparse | standard | busy + pattern_style: triphop # 16th-note hats — reference has ~16 events/bar swing: 0.62 - velocity_variation: 0.20 - hihat_density: 0.5 # 0.0–1.0 + velocity_variation: 0.38 # iter4: reference has 5.5dB more dynamic range + hihat_density: 0.7 # raised for denser hi-hat pattern vocals: tts_speed: slow - reverb_wet: 0.45 - delay_wet: 0.15 - delay_time_beats: 3 # delay in eighth-notes + reverb_wet: 0.18 + delay_wet: 0.06 + delay_time_beats: 3 + +vocal_engine: edge-tts +voice: headache + +mixer: + stereo_widen_amount: 1.0 # reference stereo width 0.31 (was 0.0) textures: vinyl_crackle_level: 0.14 ambient_level: 0.08 tape_hiss_level: 0.04 -levels: # dB relative to kick - kick: 0 - snare: -2 - hihat: -9 - bass: -3 - pads: -11 - vocals: 2 - textures: -20 +levels: # dB relative to kick — calibrated to reference + kick: 4 # iter3: +2dB more, still 4dB short of ref sub + snare: -4 + hihat: -3 # iter4: raised +3dB — presence gap was 10dB + bass: -2 # iter3: +1dB more sub-bass + pads: -1 # iter4: mid-band still 6.6dB off + vocals: 6 + textures: -24 arrangement: intro_bars: 8 diff --git a/config/moods/paranoid.yaml b/config/moods/paranoid.yaml index 5228d5b..2701099 100644 --- a/config/moods/paranoid.yaml +++ b/config/moods/paranoid.yaml @@ -22,23 +22,26 @@ drums: vocals: tts_speed: normal - reverb_wet: 0.30 - delay_wet: 0.20 + reverb_wet: 0.12 + delay_wet: 0.06 delay_time_beats: 4 +vocal_engine: edge-tts +voice: broken_radio + textures: vinyl_crackle_level: 0.08 ambient_level: 0.12 tape_hiss_level: 0.06 levels: - kick: 0 - snare: -1 - hihat: -7 - bass: -2 - pads: -10 - vocals: 2 - textures: -18 + kick: -3 + snare: -4 + hihat: -11 + bass: -5 + pads: -13 + vocals: 6 + textures: -22 arrangement: intro_bars: 4 diff --git a/generate_first_release.py b/generate_first_release.py new file mode 100644 index 0000000..3d86e07 --- /dev/null +++ b/generate_first_release.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python3 +"""One-off script to generate the first release track.""" + +from mood_machine.config import build_config +from mood_machine.pipeline import MoodMachinePipeline + +LYRICS = """They told us it would get better. They said that shit with a straight face, standing under fluorescent lights, smiling like they meant it. But the truth is nobody meant a damn thing. We just kept nodding, kept swallowing the pills they dressed up as promises. And the morning kept coming whether we asked for it or not. That is the trick, isn't it? The world doesn't stop just because you figured out the joke. It keeps spinning, keeps grinding, keeps feeding you the same bullshit breakfast with a different name. + +I used to believe in something. I can't even remember what it was now. Some half-baked idea about meaning, about connection, about finding your frequency in all this noise. What a load of crap. The signal was never there. It was just static all along, and we were too desperate to admit we were listening to nothing. We built cathedrals out of dead air and called it hope. We held on to people who were already gone and called it love. And now here we are, standing in the wreckage, wondering why it all feels so goddamn familiar. + +The clocks don't work anymore. Not because they're broken, but because time stopped giving a shit about us the same way we stopped giving a shit about each other. Every conversation is a funeral. Every goodbye is a rehearsal for the one that actually sticks. And somewhere between the first drink and the last cigarette, you realise that nobody is coming to save you. Nobody was ever coming. That whole rescue fantasy was just another lie we told ourselves to make it through the night. And the night, the night just keeps getting longer. + +So here we are at the beginning of the end. Or maybe the end of the beginning. It doesn't matter anymore. The tape is running out, the room is getting cold, and the only honest thing left is this: we were here, we tried, and it wasn't enough. It was never going to be enough. And that is not tragedy. That is just the truth, sitting there in the corner, smoking, waiting for you to finally look it in the eye.""" + +config = build_config( + mood="ethereal", + bpm=73, + seed=2026, + vocal_engine="edge-tts", + vocal_text=LYRICS, + extra_overrides={"release": True, "voice": "headache"}, +) + +pipeline = MoodMachinePipeline(config) +result = pipeline.generate() + +print("\n-- Result --") +for key in ("output_path", "mp3_path", "release_zip", "release_metadata", "lyrics_path"): + if key in result: + print(f" {key}: {result[key]}") diff --git a/generate_track.py b/generate_track.py index 4835921..55e9ad7 100644 --- a/generate_track.py +++ b/generate_track.py @@ -64,9 +64,15 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace: p.add_argument( "--vocal-engine", "-v", - default="gtts", - choices=["gtts", "pyttsx3", "silent"], - help="TTS engine for vocals (default: gtts)", + default="edge-tts", + choices=["edge-tts", "piper", "gtts", "pyttsx3", "silent"], + help="TTS engine for vocals (default: edge-tts)", + ) + p.add_argument( + "--voice", + default=None, + choices=["headache", "spectral", "broken_radio", "clean"], + help="Vocal processing preset (default: from mood config)", ) p.add_argument( "--text", @@ -101,6 +107,8 @@ def main(argv: list[str] | None = None) -> None: extra = {} if args.release: extra["release"] = True + if args.voice: + extra["voice"] = args.voice config = build_config( mood=args.mood, diff --git a/mood_machine/analysis/__init__.py b/mood_machine/analysis/__init__.py new file mode 100644 index 0000000..38d7608 --- /dev/null +++ b/mood_machine/analysis/__init__.py @@ -0,0 +1,41 @@ +""" +Audio analysis and reference-track reverse-engineering toolkit. + +Analyse any audio file (WAV / MP3) to extract a sonic fingerprint +(BPM, frequency balance, dynamics, stereo width, etc.), compare +it against a generated track, and get data-driven parameter +suggestions for closing the gap. + +Public API:: + + from mood_machine.analysis import analyze_track, compare_profiles, suggest_parameters + + ref = analyze_track("reference.wav") + gen = analyze_track("generated.wav") + report = compare_profiles(ref, gen) + suggestions = suggest_parameters(report, current_config) +""" + +from mood_machine.analysis.analyzer import TrackProfile, analyze_batch, analyze_track, load_audio +from mood_machine.analysis.comparator import ComparisonReport, MetricComparison, compare_profiles +from mood_machine.analysis.parameter_mapper import ParameterSuggestion, suggest_parameters +from mood_machine.analysis.report import ( + print_analysis_report, + print_comparison_report, + print_suggestions, +) + +__all__ = [ + "ComparisonReport", + "MetricComparison", + "ParameterSuggestion", + "TrackProfile", + "analyze_batch", + "analyze_track", + "compare_profiles", + "load_audio", + "print_analysis_report", + "print_comparison_report", + "print_suggestions", + "suggest_parameters", +] diff --git a/mood_machine/analysis/analyzer.py b/mood_machine/analysis/analyzer.py new file mode 100644 index 0000000..5aadc3d --- /dev/null +++ b/mood_machine/analysis/analyzer.py @@ -0,0 +1,431 @@ +""" +Core audio feature extraction — produce a :class:`TrackProfile` from any audio file. + +Extracts tempo, frequency balance, spectral shape, dynamics, stereo width, +rhythm density, and timbral characteristics. All heavy lifting is done by +``librosa`` (spectral / beat features) and ``scipy`` (bandpass filtering). +""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, field +from pathlib import Path + +import numpy as np +from scipy.signal import butter, sosfilt + +from mood_machine.synth.core import SAMPLE_RATE + +# ========================================================================= # +# Data model +# ========================================================================= # + + +@dataclass +class TrackProfile: + """Complete sonic fingerprint of a track.""" + + # Identity + filepath: str = "" + duration_seconds: float = 0.0 + sample_rate: int = SAMPLE_RATE + + # Tempo & Rhythm + bpm: float = 0.0 + bpm_confidence: float = 0.0 + onset_rate: float = 0.0 # onsets per second + onset_strength_mean: float = 0.0 + onset_strength_std: float = 0.0 + beat_regularity: float = 0.0 # 0-1, how consistent inter-beat intervals are + + # Frequency Balance — RMS energy (dBFS) per band + energy_sub: float = -100.0 # 20-80 Hz + energy_low: float = -100.0 # 80-250 Hz + energy_low_mid: float = -100.0 # 250-500 Hz + energy_mid: float = -100.0 # 500-2000 Hz + energy_high_mid: float = -100.0 # 2000-6000 Hz + energy_high: float = -100.0 # 6000-16000 Hz + + # Spectral Shape + spectral_centroid_mean: float = 0.0 # Hz — brightness + spectral_centroid_std: float = 0.0 + spectral_rolloff_mean: float = 0.0 # Hz — where 85% energy sits + spectral_bandwidth_mean: float = 0.0 # Hz — frequency spread + spectral_flatness_mean: float = 0.0 # 0-1, tonal vs noisy + + # Dynamics + rms_mean_db: float = -100.0 # dBFS + rms_std_db: float = 0.0 + peak_db: float = -100.0 # dBFS + crest_factor_db: float = 0.0 # peak-to-RMS in dB + dynamic_range_db: float = 0.0 # 90th - 10th percentile of frame RMS + + # Stereo Field + stereo_width_mean: float = 0.0 # 0-1 (mid/side ratio) + stereo_width_std: float = 0.0 + lr_balance_db: float = 0.0 # left-right imbalance + + # Rhythm Detail + drum_density: float = 0.0 # estimated events per bar + transient_sharpness: float = 0.0 # percussiveness of onsets + + # Timbre (average MFCCs) + mfcc_means: list[float] = field(default_factory=list) + + def to_dict(self) -> dict: + """Serialise to a plain dict.""" + return asdict(self) + + +# ========================================================================= # +# Audio loading +# ========================================================================= # + + +def load_audio( + path: str | Path, + sr: int = SAMPLE_RATE, + mono: bool = False, +) -> tuple[np.ndarray, int]: + """Load an audio file (WAV, MP3, FLAC, etc.) as a float64 numpy array. + + Returns ``(audio, sample_rate)``. + - *audio* is float64 in ``[-1, 1]``, shape ``(N,)`` for mono or ``(N, 2)`` for stereo. + - If *mono* is True, stereo is downmixed to mono. + + Uses pydub for file format handling (supports anything ffmpeg supports). + """ + from pydub import AudioSegment + + path = Path(path) + if not path.exists(): + raise FileNotFoundError(f"Audio file not found: {path}") + + ext = path.suffix.lower().lstrip(".") + if ext == "wav": + seg = AudioSegment.from_wav(str(path)) + elif ext == "mp3": + seg = AudioSegment.from_mp3(str(path)) + elif ext == "flac": + seg = AudioSegment.from_file(str(path), format="flac") + else: + seg = AudioSegment.from_file(str(path)) + + # Resample if needed + if seg.frame_rate != sr: + seg = seg.set_frame_rate(sr) + + samples = np.array(seg.get_array_of_samples(), dtype=np.float64) + # Normalise to [-1, 1] + max_val = 2 ** (seg.sample_width * 8 - 1) + samples /= max_val + + if seg.channels == 2: + samples = samples.reshape(-1, 2) + if mono: + samples = samples.mean(axis=1) + elif seg.channels > 2: + samples = samples.reshape(-1, seg.channels) + if mono: + samples = samples.mean(axis=1) + + return samples, sr + + +# ========================================================================= # +# Internal helpers +# ========================================================================= # + + +def _db(val: float) -> float: + """Linear amplitude to dBFS.""" + return 20.0 * np.log10(val + 1e-10) + + +def _rms(audio: np.ndarray) -> float: + """Root-mean-square of a 1-D signal.""" + return float(np.sqrt(np.mean(audio**2))) + + +def _bandpass( + audio: np.ndarray, low: float, high: float, sr: int = SAMPLE_RATE, order: int = 3 +) -> np.ndarray: + """Butterworth bandpass filter.""" + nyq = sr / 2.0 + low_n = max(low / nyq, 1e-5) + high_n = min(high / nyq, 0.9999) + if low_n >= high_n: + return np.zeros_like(audio) + sos = butter(order, [low_n, high_n], btype="band", output="sos") + return sosfilt(sos, audio) + + +def _to_mono(audio: np.ndarray) -> np.ndarray: + """Ensure audio is mono.""" + if audio.ndim == 2: + return audio.mean(axis=1) + return audio + + +# ========================================================================= # +# Feature extraction +# ========================================================================= # + + +def _extract_tempo(mono: np.ndarray, sr: int) -> dict: + """Extract BPM, beat regularity, onset rate, and onset strength.""" + import librosa + + # BPM detection — hint at trip-hop range + tempo, beat_frames = librosa.beat.beat_track(y=mono, sr=sr, start_bpm=80) + bpm = float(np.atleast_1d(tempo)[0]) + + # If BPM is unreasonably high, halve it (common with sparse beats) + if bpm > 140: + bpm /= 2.0 + + # Beat regularity: how consistent are inter-beat intervals? + beat_times = librosa.frames_to_time(beat_frames, sr=sr) + if len(beat_times) > 2: + ibis = np.diff(beat_times) # inter-beat intervals + regularity = 1.0 - float(np.std(ibis) / (np.mean(ibis) + 1e-10)) + regularity = max(0.0, min(1.0, regularity)) + else: + regularity = 0.0 + + # BPM confidence: use beat strength + if len(beat_frames) > 0: + onset_env = librosa.onset.onset_strength(y=mono, sr=sr) + beat_strengths = onset_env[beat_frames[beat_frames < len(onset_env)]] + confidence = float(np.mean(beat_strengths) / (np.max(onset_env) + 1e-10)) + else: + confidence = 0.0 + + # Onset rate and strength + onset_frames = librosa.onset.onset_detect(y=mono, sr=sr) + duration = len(mono) / sr + onset_rate = len(onset_frames) / duration if duration > 0 else 0.0 + + onset_env = librosa.onset.onset_strength(y=mono, sr=sr) + onset_mean = float(np.mean(onset_env)) + onset_std = float(np.std(onset_env)) + + return { + "bpm": bpm, + "bpm_confidence": confidence, + "beat_regularity": regularity, + "onset_rate": onset_rate, + "onset_strength_mean": onset_mean, + "onset_strength_std": onset_std, + } + + +def _extract_frequency_balance(mono: np.ndarray, sr: int) -> dict: + """Extract energy in 6 frequency bands.""" + bands = { + "energy_sub": (20, 80), + "energy_low": (80, 250), + "energy_low_mid": (250, 500), + "energy_mid": (500, 2000), + "energy_high_mid": (2000, 6000), + "energy_high": (6000, min(16000, sr // 2 - 1)), + } + + result = {} + for name, (lo, hi) in bands.items(): + if lo >= sr // 2: + result[name] = -100.0 + continue + hi = min(hi, sr // 2 - 1) + filtered = _bandpass(mono, lo, hi, sr) + rms = _rms(filtered) + result[name] = _db(rms) + + return result + + +def _extract_spectral_features(mono: np.ndarray, sr: int) -> dict: + """Extract spectral centroid, rolloff, bandwidth, and flatness.""" + import librosa + + centroid = librosa.feature.spectral_centroid(y=mono, sr=sr)[0] + rolloff = librosa.feature.spectral_rolloff(y=mono, sr=sr)[0] + bandwidth = librosa.feature.spectral_bandwidth(y=mono, sr=sr)[0] + flatness = librosa.feature.spectral_flatness(y=mono)[0] + + return { + "spectral_centroid_mean": float(np.mean(centroid)), + "spectral_centroid_std": float(np.std(centroid)), + "spectral_rolloff_mean": float(np.mean(rolloff)), + "spectral_bandwidth_mean": float(np.mean(bandwidth)), + "spectral_flatness_mean": float(np.mean(flatness)), + } + + +def _extract_dynamics(mono: np.ndarray, sr: int) -> dict: + """Extract RMS, peak, crest factor, and dynamic range.""" + import librosa + + # Frame-level RMS + rms_frames = librosa.feature.rms(y=mono, frame_length=2048, hop_length=512)[0] + + # Filter out near-silent frames for more meaningful stats + active_threshold = np.max(rms_frames) * 0.01 + active_rms = rms_frames[rms_frames > active_threshold] + if len(active_rms) == 0: + active_rms = rms_frames + + rms_db_values = 20.0 * np.log10(active_rms + 1e-10) + rms_mean = float(np.mean(active_rms)) + rms_mean_db = _db(rms_mean) + rms_std_db = float(np.std(rms_db_values)) + + peak = float(np.max(np.abs(mono))) + peak_db = _db(peak) + + crest = peak_db - rms_mean_db + + # Dynamic range: difference between loud and quiet passages + p90 = float(np.percentile(rms_db_values, 90)) + p10 = float(np.percentile(rms_db_values, 10)) + dynamic_range = p90 - p10 + + return { + "rms_mean_db": rms_mean_db, + "rms_std_db": rms_std_db, + "peak_db": peak_db, + "crest_factor_db": crest, + "dynamic_range_db": dynamic_range, + } + + +def _extract_stereo(audio: np.ndarray, sr: int) -> dict: + """Extract stereo width and L/R balance.""" + if audio.ndim != 2 or audio.shape[1] != 2: + return { + "stereo_width_mean": 0.0, + "stereo_width_std": 0.0, + "lr_balance_db": 0.0, + } + + left = audio[:, 0] + right = audio[:, 1] + + # Mid-side decomposition + mid = (left + right) / 2.0 + side = (left - right) / 2.0 + + mid_rms = _rms(mid) + side_rms = _rms(side) + + # Stereo width: ratio of side to total energy + total_rms = mid_rms + side_rms + if total_rms < 1e-10: + width = 0.0 + else: + width = side_rms / total_rms + + # Frame-based width for std calculation + frame_size = 2048 + n_frames = len(mid) // frame_size + widths = [] + for i in range(n_frames): + m = mid[i * frame_size : (i + 1) * frame_size] + s = side[i * frame_size : (i + 1) * frame_size] + m_rms = _rms(m) + s_rms = _rms(s) + t = m_rms + s_rms + widths.append(s_rms / t if t > 1e-10 else 0.0) + + width_std = float(np.std(widths)) if widths else 0.0 + + # L/R balance + lr_bal = _db(_rms(left)) - _db(_rms(right)) if _rms(right) > 1e-10 else 0.0 + + return { + "stereo_width_mean": width, + "stereo_width_std": width_std, + "lr_balance_db": lr_bal, + } + + +def _extract_rhythm_detail(mono: np.ndarray, sr: int, bpm: float) -> dict: + """Extract drum density and transient sharpness.""" + import librosa + + onset_env = librosa.onset.onset_strength(y=mono, sr=sr) + onset_frames = librosa.onset.onset_detect(y=mono, sr=sr, onset_envelope=onset_env) + + duration = len(mono) / sr + if bpm > 0 and duration > 0: + bars = duration / (4 * 60.0 / bpm) + drum_density = len(onset_frames) / bars if bars > 0 else 0.0 + else: + drum_density = 0.0 + + # Transient sharpness: ratio of peak onset strength to mean + if len(onset_env) > 0 and np.mean(onset_env) > 1e-10: + sharpness = float(np.max(onset_env) / np.mean(onset_env)) + else: + sharpness = 0.0 + + return { + "drum_density": drum_density, + "transient_sharpness": sharpness, + } + + +def _extract_mfcc(mono: np.ndarray, sr: int) -> dict: + """Extract mean MFCC coefficients for timbral characterisation.""" + import librosa + + mfccs = librosa.feature.mfcc(y=mono, sr=sr, n_mfcc=13) + mfcc_means = [float(m) for m in np.mean(mfccs, axis=1)] + + return {"mfcc_means": mfcc_means} + + +# ========================================================================= # +# Public API +# ========================================================================= # + + +def analyze_track(path: str | Path, sr: int = SAMPLE_RATE) -> TrackProfile: + """Run the full analysis pipeline on an audio file. + + Returns a :class:`TrackProfile` with all metrics populated. + """ + audio, sr = load_audio(path, sr=sr) + mono = _to_mono(audio) + + duration = len(mono) / sr + + # Run all extractors + tempo = _extract_tempo(mono, sr) + freq = _extract_frequency_balance(mono, sr) + spectral = _extract_spectral_features(mono, sr) + dynamics = _extract_dynamics(mono, sr) + stereo = _extract_stereo(audio, sr) + rhythm = _extract_rhythm_detail(mono, sr, tempo["bpm"]) + mfcc = _extract_mfcc(mono, sr) + + return TrackProfile( + filepath=str(path), + duration_seconds=duration, + sample_rate=sr, + **tempo, + **freq, + **spectral, + **dynamics, + **stereo, + **rhythm, + **mfcc, + ) + + +def analyze_batch(paths: list[str | Path], sr: int = SAMPLE_RATE) -> list[TrackProfile]: + """Analyse multiple tracks and return their profiles. + + Useful for building an average reference profile from several tracks. + """ + return [analyze_track(p, sr=sr) for p in paths] diff --git a/mood_machine/analysis/comparator.py b/mood_machine/analysis/comparator.py new file mode 100644 index 0000000..06bbecf --- /dev/null +++ b/mood_machine/analysis/comparator.py @@ -0,0 +1,319 @@ +""" +Comparison engine — side-by-side comparison of two :class:`TrackProfile` objects. + +Produces a :class:`ComparisonReport` with per-metric severities and +an overall weighted similarity score. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from enum import Enum + +from mood_machine.analysis.analyzer import TrackProfile + +# ========================================================================= # +# Data model +# ========================================================================= # + + +class MatchLevel(Enum): + """How well a metric matches between reference and generated.""" + + MATCH = "match" + CLOSE = "close" + DIVERGENT = "divergent" + FAR = "far" + + def __str__(self) -> str: + icons = {"match": "=", "close": "~", "divergent": "!", "far": "!!"} + return icons[self.value] + + +@dataclass +class MetricComparison: + """Single metric comparison result.""" + + name: str + category: str # tempo, frequency, spectral, dynamics, stereo, rhythm, timbre + reference_value: float + generated_value: float + delta: float # generated - reference + delta_percent: float # percentage difference + severity: MatchLevel + unit: str = "" + + def __str__(self) -> str: + sev = str(self.severity).rjust(2) + name = self.name.ljust(22) + ref = f"{self.reference_value:>8.1f}" + gen = f"{self.generated_value:>8.1f}" + d = f"{self.delta:>+8.1f}" + return f" {sev} {name} {ref} {gen} {d} {self.unit}" + + +@dataclass +class ComparisonReport: + """Full comparison between a reference and generated track.""" + + reference_path: str = "" + generated_path: str = "" + metrics: list[MetricComparison] = field(default_factory=list) + overall_similarity: float = 0.0 + + def get_worst_divergences(self, n: int = 5) -> list[MetricComparison]: + """Return the *n* metrics with the worst divergence.""" + severity_order = { + MatchLevel.FAR: 0, + MatchLevel.DIVERGENT: 1, + MatchLevel.CLOSE: 2, + MatchLevel.MATCH: 3, + } + ranked = sorted(self.metrics, key=lambda m: (severity_order[m.severity], -abs(m.delta))) + return ranked[:n] + + def get_by_category(self, category: str) -> list[MetricComparison]: + """Filter metrics by category.""" + return [m for m in self.metrics if m.category == category] + + +# ========================================================================= # +# Severity thresholds +# ========================================================================= # + +# Thresholds: (close_threshold, divergent_threshold) +# Values within *close* are acceptable; beyond *divergent* is a problem. +_THRESHOLDS: dict[str, tuple[float, float]] = { + "bpm": (3.0, 10.0), + "energy_sub": (3.0, 6.0), # dB + "energy_low": (3.0, 6.0), + "energy_low_mid": (3.0, 6.0), + "energy_mid": (3.0, 6.0), + "energy_high_mid": (3.0, 6.0), + "energy_high": (4.0, 8.0), # high band is more variable + "spectral_centroid_mean": (200.0, 500.0), # Hz + "spectral_rolloff_mean": (500.0, 1500.0), # Hz + "spectral_bandwidth_mean": (200.0, 600.0), # Hz + "spectral_flatness_mean": (0.05, 0.15), + "rms_mean_db": (3.0, 6.0), # dB + "dynamic_range_db": (3.0, 8.0), # dB + "crest_factor_db": (3.0, 6.0), # dB + "stereo_width_mean": (0.08, 0.20), + "drum_density": (3.0, 8.0), # events/bar + "transient_sharpness": (1.5, 4.0), + "onset_rate": (1.0, 3.0), # onsets/sec +} + +# Weights for overall similarity score. +# Higher = more important for subjective similarity. +_WEIGHTS: dict[str, float] = { + "bpm": 0.10, + "energy_sub": 0.12, + "energy_low": 0.10, + "energy_low_mid": 0.06, + "energy_mid": 0.08, + "energy_high_mid": 0.06, + "energy_high": 0.05, + "spectral_centroid_mean": 0.08, + "spectral_rolloff_mean": 0.03, + "spectral_bandwidth_mean": 0.02, + "spectral_flatness_mean": 0.02, + "rms_mean_db": 0.05, + "dynamic_range_db": 0.06, + "crest_factor_db": 0.04, + "stereo_width_mean": 0.04, + "drum_density": 0.05, + "transient_sharpness": 0.02, + "onset_rate": 0.02, +} + + +# ========================================================================= # +# Comparison logic +# ========================================================================= # + + +def _classify_severity(metric_name: str, delta: float) -> MatchLevel: + """Classify the severity of a delta using metric-specific thresholds.""" + thresholds = _THRESHOLDS.get(metric_name, (3.0, 6.0)) + close_t, divergent_t = thresholds + abs_delta = abs(delta) + + if abs_delta <= close_t * 0.3: + return MatchLevel.MATCH + if abs_delta <= close_t: + return MatchLevel.CLOSE + if abs_delta <= divergent_t: + return MatchLevel.DIVERGENT + return MatchLevel.FAR + + +def _metric_similarity(metric_name: str, delta: float) -> float: + """Convert a delta to a 0-1 similarity score for this metric.""" + thresholds = _THRESHOLDS.get(metric_name, (3.0, 6.0)) + _, divergent_t = thresholds + if divergent_t <= 0: + return 1.0 + # Linear decay from 1.0 (at delta=0) to 0.0 (at delta=2*divergent) + score = max(0.0, 1.0 - abs(delta) / (2.0 * divergent_t)) + return score + + +_CATEGORIES: dict[str, str] = { + "bpm": "tempo", + "onset_rate": "rhythm", + "energy_sub": "frequency", + "energy_low": "frequency", + "energy_low_mid": "frequency", + "energy_mid": "frequency", + "energy_high_mid": "frequency", + "energy_high": "frequency", + "spectral_centroid_mean": "spectral", + "spectral_rolloff_mean": "spectral", + "spectral_bandwidth_mean": "spectral", + "spectral_flatness_mean": "spectral", + "rms_mean_db": "dynamics", + "rms_std_db": "dynamics", + "dynamic_range_db": "dynamics", + "crest_factor_db": "dynamics", + "stereo_width_mean": "stereo", + "lr_balance_db": "stereo", + "drum_density": "rhythm", + "transient_sharpness": "rhythm", +} + +_UNITS: dict[str, str] = { + "bpm": "BPM", + "energy_sub": "dB", + "energy_low": "dB", + "energy_low_mid": "dB", + "energy_mid": "dB", + "energy_high_mid": "dB", + "energy_high": "dB", + "spectral_centroid_mean": "Hz", + "spectral_rolloff_mean": "Hz", + "spectral_bandwidth_mean": "Hz", + "rms_mean_db": "dB", + "rms_std_db": "dB", + "dynamic_range_db": "dB", + "crest_factor_db": "dB", + "stereo_width_mean": "", + "lr_balance_db": "dB", + "drum_density": "/bar", + "transient_sharpness": "", + "onset_rate": "/sec", +} + + +# Metrics to compare (must be float fields on TrackProfile) +_COMPARE_METRICS = [ + "bpm", + "onset_rate", + "energy_sub", + "energy_low", + "energy_low_mid", + "energy_mid", + "energy_high_mid", + "energy_high", + "spectral_centroid_mean", + "spectral_rolloff_mean", + "spectral_bandwidth_mean", + "spectral_flatness_mean", + "rms_mean_db", + "dynamic_range_db", + "crest_factor_db", + "stereo_width_mean", + "lr_balance_db", + "drum_density", + "transient_sharpness", +] + +# Human-readable names for metrics +_DISPLAY_NAMES: dict[str, str] = { + "bpm": "BPM", + "onset_rate": "Onset rate", + "energy_sub": "Sub (20-80Hz)", + "energy_low": "Bass (80-250Hz)", + "energy_low_mid": "Low-mid (250-500Hz)", + "energy_mid": "Mid (500-2kHz)", + "energy_high_mid": "Presence (2-6kHz)", + "energy_high": "Brilliance (6-16kHz)", + "spectral_centroid_mean": "Spectral centroid", + "spectral_rolloff_mean": "Spectral rolloff", + "spectral_bandwidth_mean": "Spectral bandwidth", + "spectral_flatness_mean": "Spectral flatness", + "rms_mean_db": "RMS loudness", + "dynamic_range_db": "Dynamic range", + "crest_factor_db": "Crest factor", + "stereo_width_mean": "Stereo width", + "lr_balance_db": "L/R balance", + "drum_density": "Drum density", + "transient_sharpness": "Transient sharpness", +} + + +def compare_profiles( + reference: TrackProfile, + generated: TrackProfile, +) -> ComparisonReport: + """Compare two track profiles and produce a detailed report. + + Parameters + ---------- + reference : TrackProfile + The reference (target) track's analysis. + generated : TrackProfile + The generated track's analysis. + + Returns + ------- + ComparisonReport + Detailed per-metric comparison with severities and overall score. + """ + comparisons: list[MetricComparison] = [] + weighted_sum = 0.0 + total_weight = 0.0 + + for metric in _COMPARE_METRICS: + ref_val = getattr(reference, metric, 0.0) + gen_val = getattr(generated, metric, 0.0) + + if isinstance(ref_val, (list, tuple)): + continue # skip list fields + + delta = gen_val - ref_val + ref_abs = abs(ref_val) if ref_val != 0 else 1.0 + delta_pct = (delta / ref_abs) * 100.0 if ref_abs > 1e-10 else 0.0 + + severity = _classify_severity(metric, delta) + display_name = _DISPLAY_NAMES.get(metric, metric) + category = _CATEGORIES.get(metric, "other") + unit = _UNITS.get(metric, "") + + comparisons.append( + MetricComparison( + name=display_name, + category=category, + reference_value=ref_val, + generated_value=gen_val, + delta=delta, + delta_percent=delta_pct, + severity=severity, + unit=unit, + ) + ) + + # Accumulate weighted similarity + weight = _WEIGHTS.get(metric, 0.01) + sim = _metric_similarity(metric, delta) + weighted_sum += sim * weight + total_weight += weight + + overall = weighted_sum / total_weight if total_weight > 0 else 0.0 + + return ComparisonReport( + reference_path=reference.filepath, + generated_path=generated.filepath, + metrics=comparisons, + overall_similarity=overall, + ) diff --git a/mood_machine/analysis/parameter_mapper.py b/mood_machine/analysis/parameter_mapper.py new file mode 100644 index 0000000..e777131 --- /dev/null +++ b/mood_machine/analysis/parameter_mapper.py @@ -0,0 +1,466 @@ +""" +Parameter mapper — translate analysis divergences into config suggestions. + +Takes a :class:`ComparisonReport` and the current :class:`MoodConfig`, +then produces a list of :class:`ParameterSuggestion` objects that describe +what to change and why. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +from mood_machine.analysis.comparator import ComparisonReport, MatchLevel, MetricComparison +from mood_machine.config import MoodConfig + + +@dataclass +class ParameterSuggestion: + """A suggested config change based on analysis.""" + + config_key: str # dotted key, e.g. "levels.kick" + current_value: Any + suggested_value: Any + reason: str # human-readable explanation + confidence: str # "high", "medium", "low" + source_metric: str # which analysis metric drove this + + def __str__(self) -> str: + conf = {"high": "+++", "medium": "++", "low": "+"} + return ( + f" {conf.get(self.confidence, '+')} " + f"{self.config_key}: {self.current_value} -> {self.suggested_value} " + f"({self.reason})" + ) + + +# ========================================================================= # +# Mapping rules +# ========================================================================= # + + +def _find_metric(report: ComparisonReport, name_substring: str) -> MetricComparison | None: + """Find a metric comparison by display-name substring.""" + for m in report.metrics: + if name_substring.lower() in m.name.lower(): + return m + return None + + +def _map_bpm(report: ComparisonReport, config: MoodConfig) -> list[ParameterSuggestion]: + """Map BPM divergence.""" + m = _find_metric(report, "BPM") + if m is None or m.severity in (MatchLevel.MATCH, MatchLevel.CLOSE): + return [] + + current = config.get("bpm", 78) + suggested = round(m.reference_value) + + return [ + ParameterSuggestion( + config_key="bpm", + current_value=current, + suggested_value=suggested, + reason=f"Reference is {m.reference_value:.0f} BPM, generated is {m.generated_value:.0f}", + confidence="high", + source_metric="bpm", + ) + ] + + +def _map_frequency_balance( + report: ComparisonReport, config: MoodConfig +) -> list[ParameterSuggestion]: + """Map frequency balance divergences to level and tone adjustments.""" + suggestions: list[ParameterSuggestion] = [] + + # Sub-bass (20-80 Hz) -> kick and bass levels + sub = _find_metric(report, "Sub (20-80Hz)") + if sub and sub.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = sub.delta # positive = generated has MORE sub than reference + + if delta < -3: + # We need MORE sub -> raise kick and/or bass + current_kick = config.get("levels.kick", 0) + boost = min(abs(delta) * 0.5, 4.0) + suggestions.append( + ParameterSuggestion( + config_key="levels.kick", + current_value=current_kick, + suggested_value=round(current_kick + boost, 1), + reason=f"Reference has {abs(delta):.1f}dB more sub-bass", + confidence="high", + source_metric="energy_sub", + ) + ) + current_bass = config.get("levels.bass", -1) + suggestions.append( + ParameterSuggestion( + config_key="levels.bass", + current_value=current_bass, + suggested_value=round(current_bass + boost * 0.6, 1), + reason=f"Reference has {abs(delta):.1f}dB more sub-bass", + confidence="medium", + source_metric="energy_sub", + ) + ) + elif delta > 3: + # We have TOO MUCH sub -> lower kick/bass + current_kick = config.get("levels.kick", 0) + cut = min(delta * 0.4, 3.0) + suggestions.append( + ParameterSuggestion( + config_key="levels.kick", + current_value=current_kick, + suggested_value=round(current_kick - cut, 1), + reason=f"Generated has {delta:.1f}dB more sub-bass than reference", + confidence="high", + source_metric="energy_sub", + ) + ) + + # Bass (80-250 Hz) -> bass level + bass = _find_metric(report, "Bass (80-250Hz)") + if bass and bass.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = bass.delta + current_bass = config.get("levels.bass", -1) + adj = -delta * 0.4 # reduce if we have too much, boost if too little + suggestions.append( + ParameterSuggestion( + config_key="levels.bass", + current_value=current_bass, + suggested_value=round(current_bass + adj, 1), + reason=f"Bass band off by {delta:+.1f}dB", + confidence="medium", + source_metric="energy_low", + ) + ) + + # Presence (2-6kHz) -> hi-hat level and drum tone + presence = _find_metric(report, "Presence (2-6kHz)") + if presence and presence.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = presence.delta + if delta > 3: + # Too much presence -> lower hi-hat, consider darker tone + current_hh = config.get("levels.hihat", -6) + suggestions.append( + ParameterSuggestion( + config_key="levels.hihat", + current_value=current_hh, + suggested_value=round(current_hh - min(delta * 0.5, 4), 1), + reason=f"Generated has {delta:.1f}dB more presence than reference", + confidence="high", + source_metric="energy_high_mid", + ) + ) + elif delta < -3: + # Not enough presence -> raise hi-hat + current_hh = config.get("levels.hihat", -6) + suggestions.append( + ParameterSuggestion( + config_key="levels.hihat", + current_value=current_hh, + suggested_value=round(current_hh + min(abs(delta) * 0.4, 3), 1), + reason=f"Reference has {abs(delta):.1f}dB more presence", + confidence="medium", + source_metric="energy_high_mid", + ) + ) + + # High (6-16kHz) -> drum tone brightness + high = _find_metric(report, "Brilliance (6-16kHz)") + if high and high.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = high.delta + current_tone = config.get("synth.drum_tone", "dark") + if delta > 4: + # Too bright -> suggest darker tone + if current_tone != "dark": + suggestions.append( + ParameterSuggestion( + config_key="synth.drum_tone", + current_value=current_tone, + suggested_value="dark", + reason=f"Generated is {delta:.1f}dB brighter than reference in high band", + confidence="medium", + source_metric="energy_high", + ) + ) + elif delta < -4 and current_tone != "crisp": + suggestions.append( + ParameterSuggestion( + config_key="synth.drum_tone", + current_value=current_tone, + suggested_value="crisp", + reason=f"Reference has {abs(delta):.1f}dB more high-frequency energy", + confidence="low", + source_metric="energy_high", + ) + ) + + return suggestions + + +def _map_spectral(report: ComparisonReport, config: MoodConfig) -> list[ParameterSuggestion]: + """Map spectral centroid divergence to pad cutoff.""" + suggestions: list[ParameterSuggestion] = [] + + centroid = _find_metric(report, "Spectral centroid") + if centroid and centroid.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = centroid.delta # positive = generated is brighter + current_cutoff = config.get("synth.pad_filter_cutoff", 1500) + + if delta > 200: + # Too bright -> lower pad cutoff + new_cutoff = max(800, int(current_cutoff - delta * 0.4)) + suggestions.append( + ParameterSuggestion( + config_key="synth.pad_filter_cutoff", + current_value=current_cutoff, + suggested_value=new_cutoff, + reason=f"Spectral centroid is {delta:.0f}Hz higher than reference (too bright)", + confidence="medium", + source_metric="spectral_centroid_mean", + ) + ) + elif delta < -200: + # Too dark -> raise pad cutoff + new_cutoff = min(3000, int(current_cutoff - delta * 0.3)) + suggestions.append( + ParameterSuggestion( + config_key="synth.pad_filter_cutoff", + current_value=current_cutoff, + suggested_value=new_cutoff, + reason=f"Spectral centroid is {abs(delta):.0f}Hz lower than reference (too dark)", + confidence="medium", + source_metric="spectral_centroid_mean", + ) + ) + + return suggestions + + +def _map_dynamics(report: ComparisonReport, config: MoodConfig) -> list[ParameterSuggestion]: + """Map dynamics divergences to velocity variation and compression.""" + suggestions: list[ParameterSuggestion] = [] + + dr = _find_metric(report, "Dynamic range") + if dr and dr.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = dr.delta # positive = generated is more dynamic + current_vel = config.get("drums.velocity_variation", 0.22) + + if delta < -3: + # Generated is too compressed / flat -> increase variation + new_vel = min(0.45, current_vel + abs(delta) * 0.02) + suggestions.append( + ParameterSuggestion( + config_key="drums.velocity_variation", + current_value=current_vel, + suggested_value=round(new_vel, 2), + reason=f"Reference has {abs(delta):.1f}dB more dynamic range", + confidence="medium", + source_metric="dynamic_range_db", + ) + ) + elif delta > 3: + new_vel = max(0.05, current_vel - delta * 0.015) + suggestions.append( + ParameterSuggestion( + config_key="drums.velocity_variation", + current_value=current_vel, + suggested_value=round(new_vel, 2), + reason=f"Generated has {delta:.1f}dB more dynamic range than reference", + confidence="medium", + source_metric="dynamic_range_db", + ) + ) + + return suggestions + + +def _map_stereo(report: ComparisonReport, config: MoodConfig) -> list[ParameterSuggestion]: + """Map stereo width divergence.""" + suggestions: list[ParameterSuggestion] = [] + + width = _find_metric(report, "Stereo width") + if width and width.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = width.delta + # We can't easily map this to a single config param since stereo width + # comes from the mixer's stereo_widen amount, but it's hardcoded at 0.6. + # Still worth reporting. + if delta < -0.1: + suggestions.append( + ParameterSuggestion( + config_key="mixer.stereo_widen_amount", + current_value=0.6, + suggested_value=round(min(1.0, 0.6 + abs(delta) * 1.5), 2), + reason=f"Reference is wider by {abs(delta):.2f} (mid/side ratio)", + confidence="low", + source_metric="stereo_width_mean", + ) + ) + elif delta > 0.1: + suggestions.append( + ParameterSuggestion( + config_key="mixer.stereo_widen_amount", + current_value=0.6, + suggested_value=round(max(0.0, 0.6 - delta * 1.5), 2), + reason=f"Generated is wider by {delta:.2f} (mid/side ratio)", + confidence="low", + source_metric="stereo_width_mean", + ) + ) + + return suggestions + + +def _map_rhythm(report: ComparisonReport, config: MoodConfig) -> list[ParameterSuggestion]: + """Map drum density to pattern style and hi-hat density.""" + suggestions: list[ParameterSuggestion] = [] + + density = _find_metric(report, "Drum density") + if density and density.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = density.delta # positive = generated has more events + current_style = config.get("drums.pattern_style", "sparse") + current_hh = config.get("drums.hihat_density", 0.5) + + if delta > 5: + # Too many hits -> simplify + if current_style != "sparse": + suggestions.append( + ParameterSuggestion( + config_key="drums.pattern_style", + current_value=current_style, + suggested_value="sparse", + reason=f"Generated has {delta:.0f} more events/bar than reference", + confidence="medium", + source_metric="drum_density", + ) + ) + if current_hh > 0.3: + suggestions.append( + ParameterSuggestion( + config_key="drums.hihat_density", + current_value=current_hh, + suggested_value=round(max(0.1, current_hh - 0.2), 2), + reason=f"Generated has {delta:.0f} more events/bar than reference", + confidence="medium", + source_metric="drum_density", + ) + ) + elif delta < -5: + # Too few hits -> make busier + if current_style != "full": + suggestions.append( + ParameterSuggestion( + config_key="drums.pattern_style", + current_value=current_style, + suggested_value="standard", + reason=f"Reference has {abs(delta):.0f} more events/bar", + confidence="medium", + source_metric="drum_density", + ) + ) + + # Transient sharpness -> drum tone + sharp = _find_metric(report, "Transient sharpness") + if sharp and sharp.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = sharp.delta + current_tone = config.get("synth.drum_tone", "dark") + + if delta > 2 and current_tone != "dark": + suggestions.append( + ParameterSuggestion( + config_key="synth.drum_tone", + current_value=current_tone, + suggested_value="dark", + reason="Generated transients are sharper than reference", + confidence="low", + source_metric="transient_sharpness", + ) + ) + elif delta < -2 and current_tone != "crisp": + suggestions.append( + ParameterSuggestion( + config_key="synth.drum_tone", + current_value=current_tone, + suggested_value="crisp", + reason="Reference has sharper transients", + confidence="low", + source_metric="transient_sharpness", + ) + ) + + return suggestions + + +def _map_pad_level(report: ComparisonReport, config: MoodConfig) -> list[ParameterSuggestion]: + """Map mid-frequency energy to pad level.""" + suggestions: list[ParameterSuggestion] = [] + + mid = _find_metric(report, "Mid (500-2kHz)") + if mid and mid.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR): + delta = mid.delta + current_pads = config.get("levels.pads", -14) + + if abs(delta) > 3: + adj = -delta * 0.3 + suggestions.append( + ParameterSuggestion( + config_key="levels.pads", + current_value=current_pads, + suggested_value=round(current_pads + adj, 1), + reason=f"Mid-band energy off by {delta:+.1f}dB (pads dominate this band)", + confidence="low", + source_metric="energy_mid", + ) + ) + + return suggestions + + +# ========================================================================= # +# Public API +# ========================================================================= # + + +def suggest_parameters( + comparison: ComparisonReport, + config: MoodConfig, +) -> list[ParameterSuggestion]: + """Generate parameter suggestions from a comparison report. + + Parameters + ---------- + comparison : ComparisonReport + The result of comparing a reference and generated track. + config : MoodConfig + The current configuration to base suggestions on. + + Returns + ------- + list[ParameterSuggestion] + Actionable suggestions sorted by confidence (high first). + """ + suggestions: list[ParameterSuggestion] = [] + + suggestions.extend(_map_bpm(comparison, config)) + suggestions.extend(_map_frequency_balance(comparison, config)) + suggestions.extend(_map_spectral(comparison, config)) + suggestions.extend(_map_dynamics(comparison, config)) + suggestions.extend(_map_stereo(comparison, config)) + suggestions.extend(_map_rhythm(comparison, config)) + suggestions.extend(_map_pad_level(comparison, config)) + + # Deduplicate by config_key (keep higher-confidence one) + seen: dict[str, ParameterSuggestion] = {} + conf_order = {"high": 0, "medium": 1, "low": 2} + for s in suggestions: + if s.config_key not in seen or conf_order.get(s.confidence, 3) < conf_order.get( + seen[s.config_key].confidence, 3 + ): + seen[s.config_key] = s + + # Sort by confidence + result = sorted(seen.values(), key=lambda s: conf_order.get(s.confidence, 3)) + return result diff --git a/mood_machine/analysis/report.py b/mood_machine/analysis/report.py new file mode 100644 index 0000000..a38b6b1 --- /dev/null +++ b/mood_machine/analysis/report.py @@ -0,0 +1,167 @@ +""" +Formatted terminal output for analysis reports. + +Follows the same visual style as :mod:`mood_machine.quality`. +""" + +from __future__ import annotations + +from mood_machine.analysis.analyzer import TrackProfile +from mood_machine.analysis.comparator import ComparisonReport, MatchLevel +from mood_machine.analysis.parameter_mapper import ParameterSuggestion + +_W = 68 # report width + + +def _severity_icon(level: MatchLevel) -> str: + icons = { + MatchLevel.MATCH: " ", + MatchLevel.CLOSE: "~ ", + MatchLevel.DIVERGENT: "! ", + MatchLevel.FAR: "!!", + } + return icons.get(level, " ") + + +# ========================================================================= # +# Single-track analysis report +# ========================================================================= # + + +def print_analysis_report(profile: TrackProfile) -> None: + """Print a formatted analysis of a single track.""" + print(f"\n{'─' * _W}") + print(f" TRACK ANALYSIS: {profile.filepath}") + print(f"{'─' * _W}") + + print( + f" Duration {profile.duration_seconds:.1f}s ({profile.duration_seconds / 60:.1f} min)" + ) + print(f" Sample rate {profile.sample_rate} Hz") + + print(f"\n {'TEMPO & RHYTHM':─<{_W - 2}}") + print(f" BPM {profile.bpm:.1f} (confidence: {profile.bpm_confidence:.2f})") + print(f" Beat regularity{profile.beat_regularity:.2f}") + print(f" Onset rate {profile.onset_rate:.1f} /sec") + print(f" Drum density ~{profile.drum_density:.1f} events/bar") + + print(f"\n {'FREQUENCY BALANCE (dBFS)':─<{_W - 2}}") + print(f" Sub (20-80Hz) {profile.energy_sub:>+7.1f}") + print(f" Bass (80-250Hz) {profile.energy_low:>+7.1f}") + print(f" LMid (250-500Hz) {profile.energy_low_mid:>+7.1f}") + print(f" Mid (500-2kHz) {profile.energy_mid:>+7.1f}") + print(f" Pres (2-6kHz) {profile.energy_high_mid:>+7.1f}") + print(f" Brill (6-16kHz) {profile.energy_high:>+7.1f}") + + print(f"\n {'SPECTRAL SHAPE':─<{_W - 2}}") + print( + f" Centroid {profile.spectral_centroid_mean:.0f} Hz (std: {profile.spectral_centroid_std:.0f})" + ) + print(f" Rolloff {profile.spectral_rolloff_mean:.0f} Hz") + print(f" Bandwidth {profile.spectral_bandwidth_mean:.0f} Hz") + print(f" Flatness {profile.spectral_flatness_mean:.4f}") + + print(f"\n {'DYNAMICS':─<{_W - 2}}") + print(f" RMS {profile.rms_mean_db:+.1f} dBFS (std: {profile.rms_std_db:.1f})") + print(f" Peak {profile.peak_db:+.1f} dBFS") + print(f" Crest factor {profile.crest_factor_db:.1f} dB") + print(f" Dynamic range {profile.dynamic_range_db:.1f} dB") + + print(f"\n {'STEREO':─<{_W - 2}}") + print(f" Width {profile.stereo_width_mean:.3f} (std: {profile.stereo_width_std:.3f})") + print(f" L/R balance {profile.lr_balance_db:+.1f} dB") + + print(f"{'─' * _W}\n") + + +# ========================================================================= # +# Comparison report +# ========================================================================= # + + +def print_comparison_report(report: ComparisonReport) -> None: + """Print a side-by-side comparison of two tracks.""" + print(f"\n{'─' * _W}") + print(" REFERENCE vs GENERATED") + print(f"{'─' * _W}") + print(f" Reference: {report.reference_path}") + print(f" Generated: {report.generated_path}") + print(f" Overall similarity: {report.overall_similarity:.1%}") + print(f"{'─' * _W}") + + # Group by category + categories = ["tempo", "frequency", "spectral", "dynamics", "stereo", "rhythm"] + category_names = { + "tempo": "TEMPO", + "frequency": "FREQUENCY BALANCE", + "spectral": "SPECTRAL SHAPE", + "dynamics": "DYNAMICS", + "stereo": "STEREO", + "rhythm": "RHYTHM", + } + + header = f" {'':2s} {'Metric':22s} {'Reference':>8s} {'Generated':>8s} {'Delta':>8s}" + print(header) + print(f" {'─' * (_W - 4)}") + + for cat in categories: + metrics = report.get_by_category(cat) + if not metrics: + continue + + print(f" {category_names.get(cat, cat)}") + for m in metrics: + icon = _severity_icon(m.severity) + name = m.name[:22].ljust(22) + ref = f"{m.reference_value:>8.1f}" + gen = f"{m.generated_value:>8.1f}" + delta = f"{m.delta:>+8.1f}" + unit = m.unit[:4] if m.unit else "" + print(f" {icon} {name} {ref} {gen} {delta} {unit}") + print() + + # Worst divergences + worst = report.get_worst_divergences(5) + divergent = [m for m in worst if m.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR)] + if divergent: + print(f" {'TOP ISSUES':─<{_W - 2}}") + for m in divergent: + direction = "higher" if m.delta > 0 else "lower" + print(f" !! {m.name}: generated is {abs(m.delta):.1f} {m.unit} {direction}") + print() + + print(f"{'─' * _W}\n") + + +# ========================================================================= # +# Parameter suggestions +# ========================================================================= # + + +def print_suggestions(suggestions: list[ParameterSuggestion]) -> None: + """Print parameter change suggestions.""" + if not suggestions: + print("\n No parameter changes suggested — profiles match well.\n") + return + + print(f"\n{'─' * _W}") + print(" SUGGESTED PARAMETER CHANGES") + print(f"{'─' * _W}") + + conf_labels = {"high": "HIGH", "medium": "MED ", "low": "LOW "} + + for s in suggestions: + conf = conf_labels.get(s.confidence, " ") + print(f" [{conf}] {s.config_key}:") + print(f" {s.current_value} -> {s.suggested_value}") + print(f" {s.reason}") + print() + + print(f"{'─' * _W}") + n_high = sum(1 for s in suggestions if s.confidence == "high") + n_med = sum(1 for s in suggestions if s.confidence == "medium") + n_low = sum(1 for s in suggestions if s.confidence == "low") + print( + f" {len(suggestions)} suggestions: {n_high} high, {n_med} medium, {n_low} low confidence" + ) + print(f"{'─' * _W}\n") diff --git a/mood_machine/cli.py b/mood_machine/cli.py new file mode 100644 index 0000000..e98c2c7 --- /dev/null +++ b/mood_machine/cli.py @@ -0,0 +1,228 @@ +""" +Command-line interface for Mood Machine. + +Usage:: + + mood-machine # default melancholic track + mood-machine --mood paranoid --bpm 85 + mood-machine --vocal-engine bark --lyrics-engine llm + mood-machine --seed 42 --output-dir ./my_tracks + + # Analysis / reverse-engineering + mood-machine analyze reference.wav + mood-machine analyze reference.wav --compare generated.wav + mood-machine analyze reference.wav --compare generated.wav --suggest +""" + +from __future__ import annotations + +import argparse +import sys + + +def _build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + prog="mood-machine", + description="Trip-hop / downtempo music generator & analysis toolkit", + ) + + sub = p.add_subparsers(dest="command") + + # ── generate (default) ───────────────────────────────────────────────── + gen = sub.add_parser("generate", help="Generate a track (default if no command given)") + _add_generate_args(gen) + + # Also add generate args to the root parser for backward compatibility + # (so `mood-machine --mood melancholic` still works without the `generate` sub-command) + _add_generate_args(p) + + # ── analyze ──────────────────────────────────────────────────────────── + ana = sub.add_parser( + "analyze", + help="Analyse a reference track and optionally compare against a generated track", + ) + ana.add_argument("reference", help="Path to the reference audio file (WAV, MP3, FLAC)") + ana.add_argument( + "--compare", + default=None, + metavar="FILE", + help="Path to a generated track to compare against the reference", + ) + ana.add_argument( + "--suggest", + action="store_true", + help="Suggest config parameter changes based on the comparison", + ) + ana.add_argument( + "--mood", + default="melancholic", + help="Current mood preset (used for parameter suggestions, default: melancholic)", + ) + ana.add_argument( + "--output-json", + default=None, + metavar="FILE", + help="Save the analysis profile as JSON", + ) + + return p + + +def _add_generate_args(parser: argparse.ArgumentParser) -> None: + """Add generation-related arguments to a parser.""" + parser.add_argument( + "--mood", + default="melancholic", + help="Mood preset name (default: melancholic). Available: melancholic, paranoid, ethereal", + ) + parser.add_argument( + "--bpm", + type=int, + default=None, + help="Tempo in BPM (overrides mood preset default)", + ) + parser.add_argument( + "--seed", + type=int, + default=None, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--length", + type=int, + default=None, + help="Track length in seconds (overrides arrangement-derived length)", + ) + parser.add_argument( + "--vocal-engine", + default=None, + help="TTS engine: bark, chatterbox, edge-tts, piper, gtts, pyttsx3, silent", + ) + parser.add_argument( + "--vocal-text", + default=None, + help="Custom vocal text (verses separated by blank lines)", + ) + parser.add_argument( + "--voice", + default=None, + help="Voice processing preset: headache, spectral, broken_radio, clean", + ) + parser.add_argument( + "--lyrics-engine", + default=None, + help="Lyric generator: template (default) or llm (requires Ollama)", + ) + parser.add_argument( + "--llm-model", + default=None, + help="Ollama model for LLM lyrics (default: llama3.1:8b)", + ) + parser.add_argument( + "--output-dir", + default=None, + help="Output directory (default: ./output)", + ) + parser.add_argument( + "--release", + action="store_true", + help="Package a release bundle (ZIP with metadata)", + ) + + +def _run_generate(args: argparse.Namespace) -> int: + """Run the generate command.""" + from mood_machine.config import build_config + from mood_machine.pipeline import MoodMachinePipeline + + extra: dict = {} + if getattr(args, "voice", None): + extra["voice"] = args.voice + if getattr(args, "lyrics_engine", None): + extra.setdefault("lyrics", {})["engine"] = args.lyrics_engine + if getattr(args, "llm_model", None): + extra.setdefault("lyrics", {})["llm_model"] = args.llm_model + if getattr(args, "release", False): + extra["release"] = True + + cfg = build_config( + mood=getattr(args, "mood", "melancholic"), + bpm=getattr(args, "bpm", None), + length=getattr(args, "length", None), + seed=getattr(args, "seed", None), + vocal_engine=getattr(args, "vocal_engine", None), + vocal_text=getattr(args, "vocal_text", None), + output_dir=getattr(args, "output_dir", None), + extra_overrides=extra if extra else None, + ) + + pipe = MoodMachinePipeline(cfg) + + try: + result = pipe.generate() + except KeyboardInterrupt: + print("\nInterrupted.") + return 1 + + return 0 if result.get("quality", {}).get("passed", True) else 1 + + +def _run_analyze(args: argparse.Namespace) -> int: + """Run the analyze command.""" + import json + + from mood_machine.analysis import ( + analyze_track, + compare_profiles, + print_analysis_report, + print_comparison_report, + print_suggestions, + suggest_parameters, + ) + + # Analyse reference track + print(f"\nAnalysing reference: {args.reference}") + ref_profile = analyze_track(args.reference) + print_analysis_report(ref_profile) + + # Save JSON if requested + if args.output_json: + with open(args.output_json, "w") as f: + json.dump(ref_profile.to_dict(), f, indent=2) + print(f" Profile saved to {args.output_json}") + + # Compare if a generated track is provided + if args.compare: + print(f"Analysing generated: {args.compare}") + gen_profile = analyze_track(args.compare) + + report = compare_profiles(ref_profile, gen_profile) + print_comparison_report(report) + + # Suggest parameter changes if requested + if args.suggest: + from mood_machine.config import build_config + + config = build_config(mood=args.mood) + suggestions = suggest_parameters(report, config) + print_suggestions(suggestions) + + return 0 + + +def main(argv: list[str] | None = None) -> None: + """Entry point for the ``mood-machine`` command.""" + parser = _build_parser() + args = parser.parse_args(argv) + + if args.command == "analyze": + rc = _run_analyze(args) + else: + # Default: generate (whether explicit "generate" or no subcommand) + rc = _run_generate(args) + + sys.exit(rc) + + +if __name__ == "__main__": + main() diff --git a/mood_machine/config.py b/mood_machine/config.py index efc452a..6ceba6c 100644 --- a/mood_machine/config.py +++ b/mood_machine/config.py @@ -34,8 +34,8 @@ }, "drums": { "pattern_style": "sparse", - "swing": 0.60, - "velocity_variation": 0.20, + "swing": 0.63, + "velocity_variation": 0.22, "hihat_density": 0.5, }, "vocals": { @@ -44,6 +44,11 @@ "delay_wet": 0.15, "delay_time_beats": 3, }, + "lyrics": { + "engine": "llm", # "llm" (Ollama, falls back to template) | "template" + "llm_model": "llama3.1:8b", + "llm_temperature": 1.0, + }, "textures": { "vinyl_crackle_level": 0.12, "ambient_level": 0.08, @@ -51,12 +56,13 @@ }, "levels": { "kick": 0, - "snare": -2, - "hihat": -9, - "bass": -3, - "pads": -11, - "vocals": 2, - "textures": -20, + "snare": -4, + "hihat": -6, + "clap": -8, + "bass": -1, + "pads": -14, + "vocals": 1, + "textures": -22, }, "arrangement": { "intro_bars": 8, diff --git a/mood_machine/lyrics/__init__.py b/mood_machine/lyrics/__init__.py index e65dc2b..9968663 100644 --- a/mood_machine/lyrics/__init__.py +++ b/mood_machine/lyrics/__init__.py @@ -1,3 +1,4 @@ from .generator import LyricGenerator +from .llm_generator import LLMLyricGenerator, get_lyric_generator -__all__ = ["LyricGenerator"] +__all__ = ["LLMLyricGenerator", "LyricGenerator", "get_lyric_generator"] diff --git a/mood_machine/lyrics/llm_generator.py b/mood_machine/lyrics/llm_generator.py new file mode 100644 index 0000000..78555b2 --- /dev/null +++ b/mood_machine/lyrics/llm_generator.py @@ -0,0 +1,239 @@ +""" +LLM-powered confessional monologue generator. + +Uses Ollama (local open-source models) to generate spoken-word lyrics in the +style of Headache / Vegyn -- British, conversational, rambling, confessional. + +Falls back to the template-based generator if Ollama is not available. +""" + +from __future__ import annotations + +import random +from typing import Any + +from mood_machine.lyrics.prompts import ( + SCENE_SEEDS, + build_title_prompt, + build_verse_prompt, +) + +# Try importing ollama -- soft dependency +try: + import ollama as _ollama + + _HAS_OLLAMA = True +except ImportError: + _ollama = None # type: ignore[assignment] + _HAS_OLLAMA = False + + +class LLMLyricGenerator: + """Generate spoken-word lyrics using a local LLM via Ollama. + + Falls back to the template generator if Ollama is unavailable or the + model cannot be reached. + + Usage:: + + gen = LLMLyricGenerator(mood="melancholic") + title = gen.generate_title() + verses = gen.generate_lyrics(num_verses=2, lines_per_verse=6) + """ + + def __init__( + self, + mood: str = "melancholic", + model: str = "llama3.1:8b", + temperature: float = 1.0, + timeout: float = 120.0, + ) -> None: + self.mood = mood + self.model = model + self.temperature = temperature + self.timeout = timeout + self._available: bool | None = None + + # ── availability check ───────────────────────────────────────────── + + def is_available(self) -> bool: + """Check if Ollama is reachable and the model exists.""" + if self._available is not None: + return self._available + + if not _HAS_OLLAMA: + self._available = False + return False + + try: + _ollama.list() + self._available = True + except Exception: + self._available = False + + return self._available + + # ── LLM call ─────────────────────────────────────────────────────── + + def _chat(self, system: str, user: str) -> str | None: + """Send a chat request to Ollama. Returns None on failure.""" + if not self.is_available(): + return None + + try: + response = _ollama.chat( + model=self.model, + messages=[ + {"role": "system", "content": system}, + {"role": "user", "content": user}, + ], + options={ + "temperature": self.temperature, + "top_p": 0.9, + "num_predict": 512, + }, + ) + content = response["message"]["content"] + return content.strip() if content else None + except Exception as exc: + print(f"[lyrics/llm] Ollama call failed: {exc}") + return None + + # ── public API (matches LyricGenerator interface) ────────────────── + + def generate_title(self) -> str: + """Generate a track title via LLM.""" + system, user = build_title_prompt(self.mood) + result = self._chat(system, user) + if result: + # Clean up: strip quotes, limit length + title = result.strip().strip("\"'").strip() + # Take only the first line if the model rambled + title = title.split("\n")[0].strip() + if len(title) > 60: + title = title[:60].rsplit(" ", 1)[0] + return title.title() if title else self._fallback_title() + return self._fallback_title() + + def generate_verse(self, num_lines: int | None = None) -> str: + """Generate a single verse of confessional monologue.""" + lines = num_lines or 6 + scene = self._pick_scene() + system, user = build_verse_prompt( + mood=self.mood, + verse_number=1, + total_verses=1, + lines=lines, + scene_seed=scene, + ) + result = self._chat(system, user) + if result: + return self._clean_verse(result, lines) + return self._fallback_verse(lines) + + def generate_lyrics( + self, + num_verses: int = 2, + lines_per_verse: int = 6, + ) -> list[str]: + """Generate multiple verses for a full track.""" + verses: list[str] = [] + scenes = self._pick_scenes(num_verses) + + for i in range(num_verses): + system, user = build_verse_prompt( + mood=self.mood, + verse_number=i + 1, + total_verses=num_verses, + lines=lines_per_verse, + scene_seed=scenes[i] if i < len(scenes) else None, + ) + result = self._chat(system, user) + if result: + verses.append(self._clean_verse(result, lines_per_verse)) + else: + verses.append(self._fallback_verse(lines_per_verse)) + + return verses + + # ── helpers ───────────────────────────────────────────────────────── + + def _pick_scene(self) -> str: + """Pick a random scene seed for the current mood.""" + mood_key = self.mood if self.mood in SCENE_SEEDS else "melancholic" + return random.choice(SCENE_SEEDS[mood_key]) + + def _pick_scenes(self, n: int) -> list[str]: + """Pick n distinct scene seeds (with replacement if needed).""" + mood_key = self.mood if self.mood in SCENE_SEEDS else "melancholic" + seeds = SCENE_SEEDS[mood_key] + if n <= len(seeds): + return random.sample(seeds, n) + return [random.choice(seeds) for _ in range(n)] + + @staticmethod + def _clean_verse(raw: str, target_lines: int) -> str: + """Clean up LLM output into a proper verse.""" + # Remove common LLM artifacts + lines = raw.strip().split("\n") + cleaned = [] + for line in lines: + line = line.strip() + # Skip empty lines, headers, annotations + if not line: + continue + if line.startswith(("Verse", "Title:", "---", "**", "[", "#")): + continue + cleaned.append(line) + + if not cleaned: + return raw.strip() + + return "\n".join(cleaned) + + def _fallback_title(self) -> str: + """Template fallback for title generation.""" + from mood_machine.lyrics.generator import LyricGenerator + + return LyricGenerator(mood=self.mood).generate_title() + + def _fallback_verse(self, num_lines: int) -> str: + """Template fallback for verse generation.""" + from mood_machine.lyrics.generator import LyricGenerator + + return LyricGenerator(mood=self.mood).generate_verse(num_lines=num_lines) + + +# ========================================================================= # +# Factory +# ========================================================================= # + + +def get_lyric_generator( + engine: str = "template", + mood: str = "melancholic", + **kwargs: Any, +): + """Instantiate a lyric generator by engine name. + + Args: + engine: ``"template"`` for the word-bank generator, + ``"llm"`` for Ollama-powered confessional monologues. + mood: The mood preset name. + **kwargs: Forwarded to the generator constructor + (e.g. ``model``, ``temperature`` for LLM). + + Returns: + A generator instance with ``generate_title()``, ``generate_verse()``, + and ``generate_lyrics()`` methods. + """ + if engine == "llm": + gen = LLMLyricGenerator(mood=mood, **kwargs) + if gen.is_available(): + return gen + print("[lyrics] Ollama not available -- falling back to template generator") + + # Default: template-based generator + from mood_machine.lyrics.generator import LyricGenerator + + return LyricGenerator(mood=mood) diff --git a/mood_machine/lyrics/prompts.py b/mood_machine/lyrics/prompts.py new file mode 100644 index 0000000..abe5a0c --- /dev/null +++ b/mood_machine/lyrics/prompts.py @@ -0,0 +1,268 @@ +""" +Prompt templates for LLM-powered confessional monologue generation. + +Each mood has a system prompt, scene seed bank, and few-shot examples that +anchor the style to the Headache / Vegyn aesthetic: British, conversational, +rambling, self-deprecating, darkly humorous spoken-word poetry. +""" + +from __future__ import annotations + +# ========================================================================= # +# Core system prompt (shared across moods) +# ========================================================================= # + +SYSTEM_PROMPT_BASE = """\ +You are a confessional spoken-word poet writing lyrics for trip-hop music. + +VOICE: British, conversational, rambling, self-deprecating. You talk like \ +someone having a cigarette outside a pub at 2am, half-drunk, telling a story \ +that keeps going off on tangents. You use contractions and trailing thoughts. \ +Filler words like "sort of", "I suppose", "to be honest", "I mean", "right" \ +appear naturally but not excessively. + +TONE: Drily poetic, surreally observant, self-critical, absurdist. You find \ +dark humour in mundane failures. You oscillate between profound insight and \ +complete nonsense, and you are not sure which is which. + +CONTENT: Introspective monologues about everyday failures, relationships \ +falling apart, urban loneliness, the gap between who you are and who you \ +pretend to be. Ground it in specific mundane details rather than abstract \ +imagery. + +STRUCTURE: +- Free verse, absolutely no rhyming +- Lines vary between 3 and 20 words +- Mix short punchy fragments with longer rambling sentences +- Use repetition deliberately (repeat a phrase, twist it) +- Sentences can trail off, contradict themselves, or interrupt themselves \ +with dashes +- No titles or headers within the verse + +DO NOT: +- Rhyme +- Use cliches or flowery poetic language +- Use metaphors about oceans, stars, wings, fire, or hearts +- Be uplifting or reach neat conclusions +- Use exclamation marks +- Start lines with "Oh" or "Ah" +- Add stage directions or annotations\ +""" + +# ========================================================================= # +# Per-mood overlays +# ========================================================================= # + +MOOD_OVERLAYS: dict[str, str] = { + "melancholic": """\ + +MOOD-SPECIFIC: Melancholic. The dominant feeling is quiet loss -- not \ +dramatic grief, but the dull ache of things that slipped away while you \ +weren't paying attention. Relationships that ended not with a bang but a \ +mutual shrug. The sadness of a kettle boiling in an empty flat. Wistful \ +but never sentimental.\ +""", + "paranoid": """\ + +MOOD-SPECIFIC: Paranoid. The dominant feeling is low-level dread -- the \ +sense that something is slightly wrong but you cannot identify what. \ +Technology watching you. Patterns in the noise. People who smile too much. \ +The feeling of being observed in your own home. Urban anxiety cranked up \ +to just below unbearable. Twitchy, claustrophobic, nervy.\ +""", + "ethereal": """\ + +MOOD-SPECIFIC: Ethereal. The dominant feeling is dissociation -- floating \ +slightly outside your own life, watching yourself from a half-step behind. \ +3am walks where the city feels like it belongs to someone else. Half-remembered \ +dreams that feel more real than Tuesday. A gentle, spaced-out wonder at \ +how strange it all is. Dreamy but with an undercurrent of unease.\ +""", +} + +# ========================================================================= # +# Scene seed banks (per mood) +# ========================================================================= # + +SCENE_SEEDS: dict[str, list[str]] = { + "melancholic": [ + "standing at a bus stop in the rain, thinking about a text you never sent", + "watching the kettle boil at 4am, wondering where it all went wrong", + "scrolling through old photos on your phone while the laundry spins", + "sitting in a parked car after arriving home, not going inside yet", + "finding an old receipt in a jacket pocket from a restaurant you went to together", + "walking past the flat you used to share, someone else's curtains in the window", + "lying on the floor listening to the fridge hum because the silence is worse", + "making tea for two out of habit, then pouring the second one down the sink", + "staring at your reflection in the microwave door at midnight", + "reading your own sent messages from last year and not recognising yourself", + "noticing the dent in the sofa where someone used to sit", + "watching pigeons on a grey roof and envying their lack of ambition", + ], + "paranoid": [ + "noticing the same car parked outside your building three days in a row", + "your phone buzzing in your pocket but when you check there is nothing there", + "the feeling that someone rearranged your kitchen while you were out", + "a CCTV camera that seems to track you specifically as you walk past", + "waking up at 3:17am every night for a week with no explanation", + "a stranger on the bus who keeps glancing at your screen", + "the wifi dropping out every time you try to search something specific", + "finding a door unlocked that you are certain you locked", + "a voicemail from a number that does not exist when you call back", + "the streetlight outside your window that blinks in a pattern you almost recognise", + "everyone in the coffee shop typing but nobody is actually looking at their screens", + "your smart speaker activating when nobody has spoken", + ], + "ethereal": [ + "walking through fog at 3am where the streetlights make everything look like a film set", + "swimming in a lake at night when the water is the same temperature as the air", + "lying in grass watching clouds and forgetting what year it is", + "a dream where you are in your childhood home but all the rooms are in the wrong order", + "watching dawn happen from the wrong side, having never gone to sleep", + "finding a door in a building you have walked past a thousand times but never noticed", + "the moment between being awake and asleep where everything makes perfect sense", + "hearing music from a window you cannot locate no matter how long you search", + "standing in an empty car park at dusk watching your shadow stretch into something unrecognisable", + "a conversation you cannot remember having but the other person quotes back to you", + "the way light moves through a glass of water on a windowsill", + "sitting on a night bus where every passenger is asleep except you", + ], +} + +# ========================================================================= # +# Few-shot examples (per mood, 2 each) +# ========================================================================= # + +FEW_SHOT_EXAMPLES: dict[str, list[str]] = { + "melancholic": [ + """\ +I left the heating on again. +the flat is warm but it still feels, I don't know, +sort of hollow. +I found your mug behind the cookbooks -- +the one with the chip you said gave it character. +I suppose everything has character now. +I washed it and put it back +which is probably the saddest thing I have ever done.\ +""", + """\ +to be honest I have been ordering the same takeaway +every Thursday for six weeks +because that is what we used to do +and the delivery driver recognises me now, +gives me this look like -- mate. +I know. +I know what this looks like. +it looks exactly like what it is.\ +""", + ], + "paranoid": [ + """\ +the lock clicks twice now. +it used to click once -- I am almost certain it clicked once. +I have been counting. +the bins went out on a different day this week +and no one mentioned it, +no one said anything, +which is exactly what you would do +if you wanted someone to think they were losing it.\ +""", + """\ +I keep finding tabs open on my laptop +that I do not remember opening. +articles about sleep patterns, migration routes, signal processing. +and I think -- did I search for this. +did I search for this in my sleep. +or is someone trying to tell me something +in the most passive-aggressive way imaginable.\ +""", + ], + "ethereal": [ + """\ +I have been awake so long the walls are breathing. +not in a frightening way -- more like, gently. +like the building is asleep and I am the only one +who noticed. +the light through the curtain is doing something +I do not have a word for. +sort of lavender. sort of apologetic.\ +""", + """\ +I walked home through the park at four in the morning +and every bench had a different quality of silence. +one was sad, one was patient, +one sounded like it was waiting for someone specific. +I sat on the patient one +and I think I understood something important +but by the time I got home I had forgotten what it was.\ +""", + ], +} + +# ========================================================================= # +# Title generation prompt +# ========================================================================= # + +TITLE_SYSTEM_PROMPT = """\ +Generate a short, evocative title for a trip-hop spoken-word track. \ +The title should be 1-4 words, lowercase, no punctuation. It should sound \ +like the name of a track on a late-night experimental album. Think: mundane \ +nouns given unexpected weight, or small observations elevated to track names. + +Examples of good titles: "the heating", "bus stop theory", "kitchen floor", \ +"almost october", "signal loss", "the patient bench", "curtain light", \ +"thursday again" + +Return ONLY the title, nothing else.\ +""" + +# ========================================================================= # +# Verse generation prompt builder +# ========================================================================= # + + +def build_verse_prompt( + mood: str, + verse_number: int = 1, + total_verses: int = 2, + lines: int = 6, + scene_seed: str | None = None, +) -> tuple[str, str]: + """Build (system_prompt, user_prompt) for verse generation. + + Returns a tuple of (system_message, user_message) suitable for passing + to an LLM chat API. + """ + # System prompt = base + mood overlay + few-shot examples + mood_key = mood if mood in MOOD_OVERLAYS else "melancholic" + + system_parts = [SYSTEM_PROMPT_BASE, MOOD_OVERLAYS[mood_key]] + + # Add few-shot examples + examples = FEW_SHOT_EXAMPLES.get(mood_key, FEW_SHOT_EXAMPLES["melancholic"]) + system_parts.append("\n\nHere are examples of the style I want:\n") + for i, ex in enumerate(examples, 1): + system_parts.append(f"\nExample {i}:\n{ex}\n") + + system_prompt = "".join(system_parts) + + # User prompt + scene_note = "" + if scene_seed: + scene_note = f" The scene: {scene_seed}." + + user_prompt = ( + f"Write a {mood_key} spoken-word verse.{scene_note} " + f"This is verse {verse_number} of {total_verses}. " + f"Write approximately {lines} lines. " + f"Return ONLY the verse text, no commentary or annotations." + ) + + return system_prompt, user_prompt + + +def build_title_prompt(mood: str) -> tuple[str, str]: + """Build (system_prompt, user_prompt) for title generation.""" + mood_key = mood if mood in MOOD_OVERLAYS else "melancholic" + user_prompt = f"Generate a title for a {mood_key} trip-hop spoken-word track." + return TITLE_SYSTEM_PROMPT, user_prompt diff --git a/mood_machine/mixing/effects.py b/mood_machine/mixing/effects.py index 0d6fc94..ab180b2 100644 --- a/mood_machine/mixing/effects.py +++ b/mood_machine/mixing/effects.py @@ -148,26 +148,50 @@ def compress( threshold_db: float = -18.0, ratio: float = 4.0, makeup_db: float = 0.0, + attack_ms: float = 10.0, + release_ms: float = 100.0, + sr: int = SAMPLE_RATE, ) -> np.ndarray: - """Simple soft-knee compressor (vectorised, no attack/release).""" + """Soft-knee compressor with attack/release envelope. + + Attack/release let transients through so the voice keeps punch. + """ if audio.ndim == 2: out = np.copy(audio) for ch in range(audio.shape[1]): - out[:, ch] = _compress_mono(audio[:, ch], threshold_db, ratio, makeup_db) + out[:, ch] = _compress_mono( + audio[:, ch], threshold_db, ratio, makeup_db, attack_ms, release_ms, sr + ) return out - return _compress_mono(audio, threshold_db, ratio, makeup_db) + return _compress_mono(audio, threshold_db, ratio, makeup_db, attack_ms, release_ms, sr) -def _compress_mono(audio, threshold_db, ratio, makeup_db): +def _compress_mono( + audio, threshold_db, ratio, makeup_db, attack_ms=10.0, release_ms=100.0, sr=SAMPLE_RATE +): eps = 1e-10 abs_a = np.abs(audio) + eps db = 20 * np.log10(abs_a) - compressed_db = np.copy(db) + # Target gain reduction + target_gr = np.zeros_like(db) above = db > threshold_db - compressed_db[above] = threshold_db + (db[above] - threshold_db) / ratio - - gain = 10 ** ((compressed_db - db) / 20) + target_gr[above] = (db[above] - threshold_db) * (1.0 - 1.0 / ratio) + + # Smoothed gain reduction with attack/release envelope + attack_coeff = np.exp(-1.0 / max(attack_ms * sr / 1000, 1)) + release_coeff = np.exp(-1.0 / max(release_ms * sr / 1000, 1)) + + smoothed_gr = np.zeros_like(target_gr) + current = 0.0 + for i in range(len(target_gr)): + if target_gr[i] > current: + current = attack_coeff * current + (1 - attack_coeff) * target_gr[i] + else: + current = release_coeff * current + (1 - release_coeff) * target_gr[i] + smoothed_gr[i] = current + + gain = 10 ** (-smoothed_gr / 20) out = audio * gain # Makeup gain diff --git a/mood_machine/mixing/mixer.py b/mood_machine/mixing/mixer.py index 14ddfd5..d3e9e62 100644 --- a/mood_machine/mixing/mixer.py +++ b/mood_machine/mixing/mixer.py @@ -23,14 +23,22 @@ DEFAULT_PAN: dict[str, float] = { "kick": 0.0, "snare": 0.0, - "hihat": 0.25, - "clap": -0.15, + "hihat": 0.20, # wider panning for stereo image + "clap": -0.10, # slight counter-pan to hi-hat "bass": 0.0, "pads": 0.0, # will be stereo-widened separately "textures": 0.0, # will be stereo-widened separately "vocals": 0.0, } +# Haas effect delay (ms) per stem — tiny L/R delay creates stereo width from mono +# Only applied to stems where it's musically appropriate +_HAAS_DELAY_MS: dict[str, float] = { + "hihat": 2.5, # 2.5ms → subtle stereo spread + "clap": 1.5, # 1.5ms → slight width + "snare": 1.0, # 1.0ms → very subtle +} + class Mixer: """Mix stems to stereo, apply effects, and export.""" @@ -43,6 +51,7 @@ def __init__( vocal_reverb_wet: float = 0.40, vocal_delay_wet: float = 0.15, vocal_delay_beats: int = 3, + stereo_widen_amount: float = 0.6, sr: int = SAMPLE_RATE, ) -> None: self.stems = stems @@ -51,6 +60,7 @@ def __init__( self.vocal_reverb_wet = vocal_reverb_wet self.vocal_delay_wet = vocal_delay_wet self.vocal_delay_beats = vocal_delay_beats + self.stereo_widen_amount = stereo_widen_amount self.sr = sr # ------------------------------------------------------------------ # @@ -89,9 +99,17 @@ def mix(self) -> np.ndarray: pan_val = DEFAULT_PAN.get(name, 0.0) stereo = pan_stereo(audio, pan_val) + # -- Haas effect for drums (tiny L/R delay → stereo image) -- + haas_ms = _HAAS_DELAY_MS.get(name, 0.0) + if haas_ms > 0: + delay_samples = int(haas_ms * 0.001 * self.sr) + # Delay the right channel by a few samples + stereo[:, 1] = np.roll(stereo[:, 1], delay_samples) + stereo[:delay_samples, 1] = 0.0 # zero the wrapped portion + # -- Stereo widen pads & textures -- if name in ("pads", "textures"): - stereo = effects.stereo_widen(stereo, amount=0.6) + stereo = effects.stereo_widen(stereo, amount=self.stereo_widen_amount) mixed_stereo_parts.append(stereo) @@ -114,28 +132,26 @@ def _apply_stem_effects(self, name: str, audio: np.ndarray) -> np.ndarray: """Apply effects specific to each stem type.""" if name == "vocals": - # Reverb + # Reverb (intimate room — keep vocals present, not washed out) if self.vocal_reverb_wet > 0: audio = effects.reverb( audio, wet=self.vocal_reverb_wet, - room_size=0.7, - damping=0.5, + room_size=0.35, + damping=0.7, sr=self.sr, ) - # Delay (synced to BPM) + # Delay (synced to BPM — subtle) if self.vocal_delay_wet > 0: delay_time = (60.0 / self.bpm) * (self.vocal_delay_beats / 2) audio = effects.delay( audio, delay_time=delay_time, - feedback=0.3, + feedback=0.15, wet=self.vocal_delay_wet, sr=self.sr, ) - # Gentle compression with makeup gain - audio = effects.compress(audio, threshold_db=-15, ratio=3, makeup_db=8) - # Normalize after effects so the level fader is predictable + # Normalize vocals to sit well in the mix (0.85 target peak) peak = np.max(np.abs(audio)) if peak > 1e-6: audio = audio / peak * 0.85 @@ -144,6 +160,11 @@ def _apply_stem_effects(self, name: str, audio: np.ndarray) -> np.ndarray: # Short reverb on snare for that trip-hop crack audio = effects.reverb(audio, wet=0.20, room_size=0.5, damping=0.6, sr=self.sr) + elif name in ("hihat", "clap"): + # Tiny room reverb so hats/claps sit in the mix + # rather than sounding dry and poking out + audio = effects.reverb(audio, wet=0.15, room_size=0.2, damping=0.8, sr=self.sr) + elif name == "kick": # Gentle compression to tighten kick audio = effects.compress(audio, threshold_db=-10, ratio=2.5) @@ -175,6 +196,10 @@ def _master_chain(self, stereo: np.ndarray) -> np.ndarray: # EQ: slight high-shelf cut for "vintage" feel stereo = effects.high_shelf(stereo, gain_db=-1.5, freq=10000, sr=self.sr) + # Master stereo widening — enhances the overall stereo field + if self.stereo_widen_amount > 0: + stereo = effects.stereo_widen(stereo, amount=self.stereo_widen_amount * 0.3) + # Final normalise to -1 dBFS peak = np.max(np.abs(stereo)) if peak > 0: diff --git a/mood_machine/pipeline.py b/mood_machine/pipeline.py index 66687cf..809b1a1 100644 --- a/mood_machine/pipeline.py +++ b/mood_machine/pipeline.py @@ -14,13 +14,14 @@ import numpy as np from mood_machine.config import MoodConfig -from mood_machine.lyrics.generator import LyricGenerator +from mood_machine.lyrics.llm_generator import get_lyric_generator from mood_machine.mixing.mixer import Mixer from mood_machine.quality import run_quality_check from mood_machine.release import package_release from mood_machine.sequencer.arranger import Arranger from mood_machine.synth.core import SAMPLE_RATE from mood_machine.vocals.engine import get_vocal_engine +from mood_machine.vocals.processor import VocalProcessor, split_into_sentences class MoodMachinePipeline: @@ -67,8 +68,18 @@ def generate(self) -> dict: print(f"{'=' * 60}\n") # ── 1. Generate lyrics ─────────────────────────────────────── - print("[1/5] Generating lyrics …") - lyric_gen = LyricGenerator(mood=self.config.mood) + lyrics_cfg = self.config.get("lyrics", {}) + if not isinstance(lyrics_cfg, dict): + lyrics_cfg = {} + lyrics_engine = lyrics_cfg.get("engine", "template") + print(f"[1/6] Generating lyrics (engine={lyrics_engine}) …") + + lyric_gen = get_lyric_generator( + engine=lyrics_engine, + mood=self.config.mood, + model=lyrics_cfg.get("llm_model", "llama3.1:8b"), + temperature=lyrics_cfg.get("llm_temperature", 1.0), + ) title = lyric_gen.generate_title() result["title"] = title print(f" Title: {title}") @@ -90,31 +101,73 @@ def generate(self) -> dict: print() # ── 2. Synthesise vocals ───────────────────────────────────── - engine_name = self.config.get("vocal_engine", "gtts") + engine_name = self.config.get("vocal_engine", "edge-tts") if engine_name is None: - engine_name = "gtts" - print(f"[2/5] Synthesising vocals (engine={engine_name}) …") + engine_name = "edge-tts" + print(f"[2/6] Synthesising vocals (engine={engine_name}) …") vocal_engine = get_vocal_engine(engine_name) - vocal_audio_list: list[np.ndarray] = [] - - for i, verse_text in enumerate(verses, 1): - print(f" Processing verse {i} …") - try: - vox = vocal_engine.synthesise(verse_text) - # Normalise vocal level - mx = np.max(np.abs(vox)) - if mx > 0: - vox = vox / mx * 0.85 - vocal_audio_list.append(vox) - dur = len(vox) / self.sr - print(f" → {dur:.1f}s of audio") - except Exception as exc: - print(f" ⚠ Vocal synthesis failed for verse {i}: {exc}") - vocal_audio_list.append(np.zeros(self.sr)) # 1s silence fallback - - # ── 3. Arrange ─────────────────────────────────────────────── - print("[3/5] Arranging track …") + + # Track sentence texts alongside audios for content-aware processing + verse_sentence_texts: list[list[str]] = [] + + if vocal_engine.prefers_verse_level: + # ── Neural TTS: full verse at once (better prosody) ────── + raw_verse_sentences: list[list[np.ndarray]] = [] + for i, verse_text in enumerate(verses, 1): + print(f" Verse {i}: full-verse synthesis …") + try: + vox = vocal_engine.synthesise(verse_text) + dur = len(vox) / self.sr + print(f" → {dur:.1f}s of raw audio") + # Wrap whole verse as single-element list (processor + # treats each element as one chunk to humanise) + raw_verse_sentences.append([vox]) + verse_sentence_texts.append([verse_text]) + except Exception as exc: + print(f" ⚠ Verse {i} synthesis failed: {exc}") + raw_verse_sentences.append([np.zeros(self.sr)]) + verse_sentence_texts.append([""]) + else: + # ── Legacy TTS: per-sentence (helps flat engines like gTTS) ── + raw_verse_sentences = [] + for i, verse_text in enumerate(verses, 1): + sentences = split_into_sentences(verse_text) + print(f" Verse {i}: {len(sentences)} sentences") + sentence_audios: list[np.ndarray] = [] + for j, sent_text in enumerate(sentences): + try: + vox = vocal_engine.synthesise(sent_text) + sentence_audios.append(vox) + except Exception as exc: + print(f" ⚠ Sentence {j + 1} failed: {exc}") + sentence_audios.append(np.zeros(int(self.sr * 0.5))) + total_dur = sum(len(s) for s in sentence_audios) / self.sr + print(f" → {total_dur:.1f}s of raw audio") + raw_verse_sentences.append(sentence_audios) + verse_sentence_texts.append(sentences) + + # ── 3. Humanise vocals (breath, vibrato, pace, dynamics) ───── + voice_name = self.config.get("voice", "headache") + if voice_name is None: + voice_name = "headache" + print(f"[3/6] Humanising vocals (voice={voice_name}) …") + + vocal_processor = VocalProcessor( + preset=voice_name, + bpm=self.config.bpm, + sr=self.sr, + ) + vocal_audio_list = vocal_processor.process_verses( + raw_verse_sentences, + verse_texts=verse_sentence_texts, + ) + for i, vox in enumerate(vocal_audio_list, 1): + dur = len(vox) / self.sr + print(f" Verse {i}: {dur:.1f}s (humanised)") + + # ── 4. Arrange ─────────────────────────────────────────────── + print("[4/6] Arranging track …") arranger = Arranger( bpm=self.config.bpm, config=self.config, @@ -129,8 +182,8 @@ def generate(self) -> dict: print(f" Total duration: {duration_s:.1f}s ({duration_s / 60:.1f} min)") result["duration_seconds"] = duration_s - # ── 4. Mix ─────────────────────────────────────────────────── - print("[4/5] Mixing …") + # ── 5. Mix ─────────────────────────────────────────────────── + print("[5/6] Mixing …") voc_cfg = self.config.get("vocals", {}) if not isinstance(voc_cfg, dict): voc_cfg = {} @@ -142,12 +195,13 @@ def generate(self) -> dict: vocal_reverb_wet=voc_cfg.get("reverb_wet", 0.40), vocal_delay_wet=voc_cfg.get("delay_wet", 0.15), vocal_delay_beats=voc_cfg.get("delay_time_beats", 3), + stereo_widen_amount=float(self.config.get("mixer.stereo_widen_amount", 0.6)), sr=self.sr, ) master_stereo = mixer.mix() print(f" Peak level: {20 * np.log10(np.max(np.abs(master_stereo)) + 1e-10):.1f} dBFS") - # ── 4b. Instrumental (needed for quality check) ────────────── + # ── 5b. Instrumental (needed for quality check) ────────────── instrumental_stems = {k: v for k, v in stems.items() if k != "vocals"} inst_mixer = Mixer( stems=instrumental_stems, @@ -157,8 +211,8 @@ def generate(self) -> dict: ) inst_stereo = inst_mixer.mix() - # ── 4c. Quality check ──────────────────────────────────────── - print("[4b/5] Quality check …") + # ── 5c. Quality check ──────────────────────────────────────── + print("[5b/6] Quality check …") qc_report = run_quality_check( master=master_stereo, stems=stems, @@ -174,8 +228,8 @@ def generate(self) -> dict: "fail": qc_report.n_fail, } - # ── 5. Export ──────────────────────────────────────────────── - print("[5/5] Exporting …") + # ── 6. Export ──────────────────────────────────────────────── + print("[6/6] Exporting …") output_dir = Path(self.config.get("output_dir", "output")) output_dir.mkdir(parents=True, exist_ok=True) @@ -220,9 +274,9 @@ def generate(self) -> dict: f.write(f"── Verse {i} ──\n{v}\n\n") result["lyrics_path"] = str(lyrics_path) - # ── 6. Release packaging (optional) ──────────────────────── + # ── 7. Release packaging (optional) ──────────────────────────── if self.config.get("release", False): - print("[6/6] Packaging release bundle …") + print("[7] Packaging release bundle …") bundle = package_release(result, output_dir=output_dir) result["release_zip"] = str(bundle["zip_path"]) result["release_metadata"] = str(bundle["metadata_path"]) diff --git a/mood_machine/sequencer/arranger.py b/mood_machine/sequencer/arranger.py index 5e16318..e8845e4 100644 --- a/mood_machine/sequencer/arranger.py +++ b/mood_machine/sequencer/arranger.py @@ -14,7 +14,7 @@ from mood_machine.sequencer.patterns import ( get_bass_pattern, get_chord_progression, - get_drum_patterns, + get_drum_pattern_for_bar, ) from mood_machine.synth.bass import BassSynthesizer from mood_machine.synth.core import ( @@ -47,14 +47,14 @@ "vocals": False, }, "verse": { - "drums": "standard", + "drums": "_config", # resolved at runtime from drums.pattern_style "bass": "standard", "pads": True, "textures": True, "vocals": True, }, "verse_full": { - "drums": "full", + "drums": "_config_full", # resolved at runtime: config style or "full" fallback "bass": "full", "pads": True, "textures": True, @@ -229,9 +229,26 @@ def arrange( section_samples = int(section_bars * self.spb * self.sr) # -- Drums ----------------------------------------------------- - if sec_def["drums"] is not None: - drum_pattern = get_drum_patterns(sec_def["drums"]) - drum_audio = self._render_drums(drum_pattern, section_bars, swing, vel_var) + drum_style = sec_def["drums"] + if drum_style == "_config": + # Use the configured pattern style from YAML + drum_style = self.config.get("drums.pattern_style", "standard") + if drum_style is None: + drum_style = "standard" + elif drum_style == "_config_full": + # Full sections: use config style if denser than "full", else "full" + cfg_style = self.config.get("drums.pattern_style", "full") + if cfg_style in ("triphop",): + drum_style = cfg_style # triphop is denser than full + else: + drum_style = "full" + if drum_style is not None: + drum_audio = self._render_drums_section( + drum_style, + section_bars, + swing, + vel_var, + ) for inst in ["kick", "snare", "hihat", "clap"]: if inst in drum_audio: place_audio(stems[inst], drum_audio[inst], sample_offset) @@ -291,65 +308,109 @@ def _arrangement_from_config(self) -> list[tuple[str, int]]: ("outro", arr_cfg.get("outro_bars", 8)), ] - def _render_drums( + def _render_drums_section( self, - pattern: dict[str, list[float]], + style: str, bars: int, swing: float = 0.58, vel_var: float = 0.2, ) -> dict[str, np.ndarray]: - """Render drum pattern for *bars* bars with swing and humanisation.""" + """Render drums for a section, rotating patterns per bar. + + Key improvements over the old ``_render_drums``: + - Per-bar pattern variation (picks from multiple grooves) + - Swing applied to the "e" and "a" positions (off-16ths), not + just odd steps — this creates the lazy, behind-the-beat + hip-hop pocket + - Larger timing jitter (±8ms) for a more human feel + - Per-instrument velocity curves (kick is tighter, hats looser) + """ step_dur = 60.0 / self.bpm / 4 # 16th note duration bar_samples = int(self.spb * self.sr) total = bar_samples * bars - result: dict[str, np.ndarray] = {} + # Pre-render one-shots for kick and snare (consistent tone) + # Hi-hats and claps are regenerated per-hit for natural variation + kick_sound = self.drums.kick() + snare_sound = self.drums.snare() + + _ALL_INSTS = ["kick", "snare", "hihat", "clap"] + + # Per-instrument velocity variation (kick tight, hats loose) + inst_vel_var = { + "kick": vel_var * 0.6, + "snare": vel_var * 0.8, + "hihat": vel_var * 1.3, + "clap": vel_var * 0.7, + } - # Pre-render one-shots - sounds = { - "kick": self.drums.kick(), - "snare": self.drums.snare(), - "hihat": self.drums.hihat_closed(), - "clap": self.drums.clap(), + # Per-instrument timing jitter (seconds) — lazy pocket + inst_jitter = { + "kick": 0.005, # ±5ms — tight + "snare": 0.008, # ±8ms — slightly behind + "hihat": 0.006, # ±6ms — loose + "clap": 0.003, # ±3ms — tight (layered with snare) } - for inst, velocities in pattern.items(): - if inst not in sounds: - continue - stem = np.zeros(total) - one_shot = sounds[inst] + # Init stems + result: dict[str, np.ndarray] = {inst: np.zeros(total) for inst in _ALL_INSTS} + + for bar_i in range(bars): + # Pick pattern for this bar (rotates through variations) + pattern = get_drum_pattern_for_bar(style, bar_i, bars) + + for inst, velocities in pattern.items(): + if inst not in _ALL_INSTS: + continue + + vv = inst_vel_var.get(inst, vel_var) + jitter_range = inst_jitter.get(inst, 0.005) - for bar_i in range(bars): for step, vel in enumerate(velocities): if vel < 0.01: continue - # Apply velocity variation - v = vel * (1.0 + np.random.uniform(-vel_var, vel_var)) + # Velocity variation + v = vel * (1.0 + np.random.uniform(-vv, vv)) v = np.clip(v, 0.05, 1.0) - # Swing: delay odd-numbered 8th-note positions - # Steps 1,3,5,7,9,11,13,15 are the off-beats in 16th-note grid - # But typically swing applies to the "and" of each beat (steps 2,6,10,14) + # Swing: apply to the "e" and "a" subdivisions + # In a 16-step grid, the "&" positions (2,6,10,14) + # get the main swing push. "e" and "a" (1,3,5,7..) + # get a gentler amount. swing_offset = 0.0 - if step % 2 == 1: - swing_offset = (swing - 0.5) * step_dur * 2 + if step % 4 == 2: + # The "&" of each beat — main swing target + swing_offset = (swing - 0.5) * step_dur * 1.8 + elif step % 2 == 1: + # Off-16ths — gentler swing + swing_offset = (swing - 0.5) * step_dur * 0.8 - # Timing jitter for humanisation - jitter = np.random.uniform(-0.004, 0.004) + # Timing jitter (per-instrument) + jitter = np.random.uniform(-jitter_range, jitter_range) time_s = bar_i * self.spb + step * step_dur + swing_offset + jitter pos = int(time_s * self.sr) if pos < 0: pos = 0 + # Get the one-shot — regenerate hi-hats and claps + # per-hit so the frequency jitter actually creates + # natural variation between hits + if inst == "kick": + one_shot = kick_sound + elif inst == "snare": + one_shot = snare_sound + elif inst == "hihat": + one_shot = self.drums.hihat_closed() + else: # clap + one_shot = self.drums.clap() + hit = one_shot * v end = min(pos + len(hit), total) src_len = end - pos if src_len > 0: - stem[pos:end] += hit[:src_len] - - result[inst] = stem + result[inst][pos:end] += hit[:src_len] return result diff --git a/mood_machine/sequencer/patterns.py b/mood_machine/sequencer/patterns.py index fefc50e..cc1f678 100644 --- a/mood_machine/sequencer/patterns.py +++ b/mood_machine/sequencer/patterns.py @@ -3,6 +3,10 @@ All patterns use a **16-step** grid per bar (i.e. 16th-note resolution). Velocities are 0.0 - 1.0. + +Hip-hop / Mac Miller influenced patterns — laid-back, soulful, with +swing pocket and ghost notes. Multiple variations per style allow +per-bar rotation to avoid robotic repetition. """ from __future__ import annotations @@ -59,55 +63,196 @@ def chord_midi_notes(root: str, chord_type: str, octave: int = 3) -> list[int]: # ========================================================================= # -# Drum patterns — each is a dict of { instrument: list[16 velocities] } +# Velocity helpers # ========================================================================= # -def _v(pattern: list[int], strong: float = 0.95, weak: float = 0.65) -> list[float]: - """Convert a 0/1 pattern to velocities.""" - return [strong if x == 2 else (weak if x == 1 else 0.0) for x in pattern] - +def _v( + pattern: list[int], + strong: float = 0.95, + weak: float = 0.65, + ghost: float = 0.30, +) -> list[float]: + """Convert a 0/1/2/3 pattern to velocities. -# noinspection PyDictCreation -DRUM_PATTERNS: dict[str, dict[str, list[float]]] = {} - -# -- SPARSE (intro / minimal sections) ----------------------------------- # -DRUM_PATTERNS["sparse"] = { - # 1 . . . 2 . . . 3 . . . 4 . . . - "kick": _v([2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]), - "snare": _v([0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0]), - "hihat": _v([1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]), - "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), -} + 3 = strong hit, 2 = normal hit, 1 = ghost note, 0 = silence. + """ + mapping = {0: 0.0, 1: ghost, 2: weak, 3: strong} + return [mapping.get(x, 0.0) for x in pattern] -# -- STANDARD (main verse) ----------------------------------------------- # -DRUM_PATTERNS["standard"] = { - "kick": _v([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0]), - "snare": _v([0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0]), - "hihat": _v([1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]), - "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), -} -# -- FULL (busy sections) ------------------------------------------------ # -DRUM_PATTERNS["full"] = { - "kick": _v([2, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0]), - "snare": _v([0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1]), - "hihat": _v([1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1]), - "clap": _v([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]), -} +# ========================================================================= # +# Drum patterns — hip-hop / boom-bap / lo-fi with multiple variations +# ========================================================================= # +# +# Grid: 1 e & a 2 e & a 3 e & a 4 e & a +# Steps: 0 1 2 3 4 5 6 7 8 9 A B C D E F +# +# 3 = strong, 2 = normal, 1 = ghost note, 0 = silence +# +# Each style has a list of pattern variations. The arranger rotates +# through them per bar for organic variation. + +DRUM_PATTERNS: dict[str, list[dict[str, list[float]]]] = {} + +# -- SPARSE (intro / minimal) --------------------------------------------- # +DRUM_PATTERNS["sparse"] = [ + { + # 1 e & a 2 e & a 3 e & a 4 e & a + "kick": _v([3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]), + "hihat": _v([2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + }, + { + "kick": _v([3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0]), + "hihat": _v([2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + }, +] + +# -- STANDARD (verse — laid-back hip-hop pocket) --------------------------- # +# Kick on 1 with ghost kicks for bounce, snare on 3 (backbeat). +# Hi-hats on quarter notes only — sparse and understated. +# Clap is subtle (level 2) to avoid stacking too hard with snare. +DRUM_PATTERNS["standard"] = [ + { + # 1 e & a 2 e & a 3 e & a 4 e & a + "kick": _v([3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0]), + "hihat": _v([2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: kick anticipates beat 3 + "kick": _v([3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0]), + "hihat": _v([2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: syncopated kick, ghost snare + "kick": _v([3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 1]), + "hihat": _v([2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: double kick before snare + "kick": _v([3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 1, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]), + "hihat": _v([2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, +] + +# -- FULL (busy sections — 8th-note hats, more energy) -------------------- # +# Full sections get 8th-note hi-hats for drive, but still using +# normal velocity (2) not strong — keeps them in the background. +DRUM_PATTERNS["full"] = [ + { + # 1 e & a 2 e & a 3 e & a 4 e & a + "kick": _v([3, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 1, 0, 0]), + "hihat": _v([2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: broken beat + "kick": _v([3, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 2, 0, 0, 1]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0]), + "hihat": _v([2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: soulful, open hat on &s + "kick": _v([3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 1]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0]), + "hihat": _v([2, 0, 2, 0, 2, 0, 1, 0, 2, 0, 2, 0, 2, 0, 1, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, +] + +# -- TRIPHOP (Headache / Vegyn-style — 16th-note hats, dense ~16 events/bar) # +# Reference analysis shows ~16 events/bar. 16th-note hi-hats give 16 hat +# events alone; combined with ghost kicks and snare ghosts this creates the +# hypnotic, sample-machine density typical of Vegyn's production. +DRUM_PATTERNS["triphop"] = [ + { + # 1 e & a 2 e & a 3 e & a 4 e & a + "kick": _v([3, 0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 2, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 1, 0, 0]), + "hihat": _v([2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: syncopated kick, consistent 16th hats + "kick": _v([3, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 2, 0, 0, 1]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0]), + "hihat": _v([2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: accent shifts, ghost snare on "a" of 4 + "kick": _v([3, 0, 0, 1, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 1]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0]), + "hihat": _v([2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), + }, +] + +# -- VARIATION (bridge / breakdown — half-time, spacious) ----------------- # +DRUM_PATTERNS["variation"] = [ + { + # 1 e & a 2 e & a 3 e & a 4 e & a + "kick": _v([3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]), + "hihat": _v([2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + }, + { + # Variation: with rim ghost + "kick": _v([3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0]), + "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0]), + "hihat": _v([2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0]), + "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + }, +] + + +def get_drum_patterns(style: str = "standard") -> list[dict[str, list[float]]]: + """Return the list of pattern variations for a given style. + + The arranger picks from these per bar for organic variation. + """ + return DRUM_PATTERNS.get(style, DRUM_PATTERNS["standard"]) -# -- VARIATION (bridge / breakdown) --------------------------------------- # -DRUM_PATTERNS["variation"] = { - "kick": _v([2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0]), - "snare": _v([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]), - "hihat": _v([1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0]), - "clap": _v([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0]), -} +def get_drum_pattern_for_bar(style: str, bar_index: int, total_bars: int) -> dict[str, list[float]]: + """Pick a specific pattern variation for a given bar. -def get_drum_patterns(style: str = "standard") -> dict[str, list[float]]: - """Return the drum pattern dict for a given style.""" - return DRUM_PATTERNS.get(style, DRUM_PATTERNS["standard"]) + Uses a deterministic rotation with occasional random picks + to stay musical but avoid exact repetition. + """ + variations = get_drum_patterns(style) + n = len(variations) + if n == 1: + return variations[0] + + # Primary pattern for most bars, variations sprinkled in + # Every 4th bar, consider a variation + if bar_index % 4 == 3: + # Last bar of each 4-bar phrase: pick a variation + idx = (bar_index // 4 + 1) % n + elif bar_index == total_bars - 1: + # Final bar of section: use last variation (often a fill) + idx = n - 1 + else: + # Main groove: stick to primary (index 0) + idx = 0 + + return variations[idx] # ========================================================================= # diff --git a/mood_machine/synth/drums.py b/mood_machine/synth/drums.py index a844021..69f38df 100644 --- a/mood_machine/synth/drums.py +++ b/mood_machine/synth/drums.py @@ -1,7 +1,13 @@ """ Synthesised drum sounds — no samples required. -Each method returns a **mono float64 numpy array** (normalised to [-1, 1]). +Hip-hop / lo-fi influenced: warm, round kicks with analog-modeled +pitch sweep; snares with adjustable noise/tone balance and longer +tails; hi-hats with natural variation between hits. + +Each method returns a **mono float64 numpy array**. Kick and snare are +normalised to [-1, 1]; hi-hat and clap stay at natural (quiet) levels +so they blend as texture rather than competing with the main sounds. """ from __future__ import annotations @@ -23,40 +29,48 @@ def __init__(self, sr: int = SAMPLE_RATE, tone: str = "dark") -> None: # Kick # --------------------------------------------------------------------- # - def kick(self, duration: float = 0.45, pitch: float = 52.0, punch: float = 0.8) -> np.ndarray: - """Sub-heavy kick with pitch sweep and transient click. + def kick( + self, + duration: float = 0.50, + pitch: float = 42.0, + punch: float = 0.35, + ) -> np.ndarray: + """Deep, sub-heavy kick — the foundation of the beat. - *pitch*: fundamental in Hz (40-80 typical). - *punch*: 0-1, amount of high-frequency click. + *pitch*: fundamental in Hz (38-55 typical for hip-hop — low). + *punch*: 0-1, amount of high-frequency click transient. """ n = int(self.sr * duration) t = np.arange(n) / self.sr - # -- body: sine with pitch envelope (150 → *pitch* Hz) -- - start_freq = 150 + pitch - freq_env = (start_freq - pitch) * np.exp(-t * 35) + pitch + # -- Body: sine with slow pitch sweep for round, deep feel -- + start_freq = pitch * 2.5 # ~105 Hz start for 42 Hz pitch + freq_env = (start_freq - pitch) * np.exp(-t * 18) + pitch phase = 2 * np.pi * np.cumsum(freq_env) / self.sr - body = np.sin(phase) * np.exp(-t * 7) + body = np.sin(phase) * np.exp(-t * 4.5) - # -- click / transient -- - click = noise_gaussian(0.008, self.sr) - click *= np.exp(-np.arange(len(click)) / self.sr * 200) - click = click * punch * 0.6 + # -- Minimal click transient (less is more for hip-hop) -- + click_dur = int(0.004 * self.sr) + click_t = np.arange(click_dur) / self.sr + click = np.sin(2 * np.pi * 2500 * click_t) * np.exp(-click_t * 400) + click = click * punch * 0.25 click = np.pad(click, (0, max(0, n - len(click))))[:n] - # -- sub thump -- - sub = np.sin(2 * np.pi * pitch * t) * np.exp(-t * 12) * 0.5 + # -- Strong sub layer (this is what gives the bass weight) -- + sub = np.sin(2 * np.pi * pitch * t) * np.exp(-t * 5) * 0.9 + + # -- Slight second harmonic for warmth -- + harmonic = np.sin(2 * np.pi * pitch * 2 * t) * np.exp(-t * 12) * 0.2 - kick = body + click + sub + kick = body + click + sub + harmonic # Tone shaping if self.tone == "dark": kick = lowpass(kick, 120, self.sr, order=2) - kick += body * 0.3 # bring back some body after filter + kick += (body + sub) * 0.4 # bring back sub after filter elif self.tone == "lo-fi": - kick = lowpass(kick, 200, self.sr, order=1) - # Bit-crush effect - kick = np.round(kick * 16) / 16 + kick = lowpass(kick, 150, self.sr, order=1) + kick = np.tanh(kick * 1.3) / np.tanh(1.3) return normalize(kick) @@ -65,34 +79,42 @@ def kick(self, duration: float = 0.45, pitch: float = 52.0, punch: float = 0.8) # --------------------------------------------------------------------- # def snare( - self, duration: float = 0.28, tone_freq: float = 200.0, noise_amount: float = 0.7 + self, + duration: float = 0.32, + tone_freq: float = 185.0, + noise_amount: float = 0.75, ) -> np.ndarray: """Snare with tonal body + noise burst. + Longer tail and warmer noise band for hip-hop feel. *noise_amount*: balance between tone (0) and noise (1). """ n = int(self.sr * duration) t = np.arange(n) / self.sr - # -- tonal component -- - tone = np.sin(2 * np.pi * tone_freq * t) * np.exp(-t * 28) - # Second partial for body - tone += np.sin(2 * np.pi * tone_freq * 1.6 * t) * np.exp(-t * 35) * 0.4 + # -- Tonal component (two partials, longer decay for body) -- + tone = np.sin(2 * np.pi * tone_freq * t) * np.exp(-t * 20) + # Second partial slightly detuned for width + tone += np.sin(2 * np.pi * tone_freq * 1.55 * t) * np.exp(-t * 28) * 0.35 - # -- noise component -- + # -- Noise component -- nz = noise_gaussian(duration, self.sr)[:n] if self.tone == "dark": - # Band-pass the noise around 2-5 kHz + # Wider band-pass for more body (1-5.5kHz) sos = butter( - 3, [1500 / (self.sr / 2), 6000 / (self.sr / 2)], btype="band", output="sos" + 3, + [1000 / (self.sr / 2), 5500 / (self.sr / 2)], + btype="band", + output="sos", ) nz = sosfilt(sos, nz) elif self.tone == "crisp": - nz = highpass(nz, 3000, self.sr, order=3) + nz = highpass(nz, 2500, self.sr, order=3) else: # lo-fi - nz = lowpass(nz, 4000, self.sr, order=2) + nz = lowpass(nz, 4500, self.sr, order=2) - nz *= np.exp(-t * 14) + # Longer noise decay for hip-hop snare tail + nz *= np.exp(-t * 10) snare = tone * (1 - noise_amount) + nz * noise_amount @@ -102,48 +124,55 @@ def snare( # Hi-hat (closed) # --------------------------------------------------------------------- # - def hihat_closed(self, duration: float = 0.06) -> np.ndarray: - """Short, metallic closed hi-hat.""" + def hihat_closed(self, duration: float = 0.035) -> np.ndarray: + """Ultra-short noise tick at natural (quiet) amplitude. + + NOT normalised — the sound stays quiet on its own so it + blends as texture rather than standing out as an instrument. + Peak is around 0.15-0.25, which disappears into the mix + at any reasonable level setting. + """ n = int(self.sr * duration) t = np.arange(n) / self.sr - # Metallic: sum of inharmonic square-ish partials - freqs = [3578, 4210, 5765, 6890, 8230, 9540] - metal = np.zeros(n) - for f in freqs: - if f < self.sr / 2: - metal += np.sin(2 * np.pi * f * t + np.random.uniform(0, 2 * np.pi)) - - # Noise layer + # Short noise impulse with very fast decay nz = noise_gaussian(duration, self.sr)[:n] - nz = highpass(nz, 7000, self.sr, order=3) + hat = nz * np.exp(-t * 200) * 0.18 # quiet — texture, not a note - hat = (metal * 0.4 + nz * 0.6) * np.exp(-t * 55) + # Gentle bandpass so it doesn't rumble or hiss + lo = 2000 / (self.sr / 2) + hi = min(5000, self.sr / 2 - 100) / (self.sr / 2) + sos = butter(1, [lo, hi], btype="band", output="sos") + hat = sosfilt(sos, hat) - if self.tone == "lo-fi": - hat = lowpass(hat, 9000, self.sr, order=2) + if self.tone == "dark": + hat = lowpass(hat, 4000, self.sr, order=1) + elif self.tone == "lo-fi": + hat = lowpass(hat, 3000, self.sr, order=1) - return normalize(hat) + # NO normalize — stays at natural quiet level + return hat # --------------------------------------------------------------------- # # Hi-hat (open) # --------------------------------------------------------------------- # - def hihat_open(self, duration: float = 0.25) -> np.ndarray: + def hihat_open(self, duration: float = 0.28) -> np.ndarray: """Longer, ringy open hat.""" n = int(self.sr * duration) t = np.arange(n) / self.sr - freqs = [3578, 4210, 5765, 6890, 8230] + base_freqs = [3200, 4750, 6100, 7500] metal = np.zeros(n) - for f in freqs: - if f < self.sr / 2: - metal += np.sin(2 * np.pi * f * t + np.random.uniform(0, 2 * np.pi)) + for f in base_freqs: + f_jitter = f * (1 + np.random.uniform(-0.03, 0.03)) + if f_jitter < self.sr / 2: + metal += np.sin(2 * np.pi * f_jitter * t + np.random.uniform(0, 2 * np.pi)) nz = noise_gaussian(duration, self.sr)[:n] - nz = highpass(nz, 5000, self.sr, order=3) + nz = highpass(nz, 4500, self.sr, order=2) - hat = (metal * 0.35 + nz * 0.65) * np.exp(-t * 8) + hat = (metal * 0.3 + nz * 0.7) * np.exp(-t * 6) return normalize(hat) @@ -151,29 +180,37 @@ def hihat_open(self, duration: float = 0.25) -> np.ndarray: # Clap # --------------------------------------------------------------------- # - def clap(self, duration: float = 0.18) -> np.ndarray: - """Layered noise bursts for a clap sound.""" + def clap(self, duration: float = 0.22) -> np.ndarray: + """Layered noise bursts — subtle accent, not a feature sound.""" n = int(self.sr * duration) t = np.arange(n) / self.sr clap = np.zeros(n) - # 3-4 micro-bursts with tiny offsets (flam) - for offset_ms in [0, 8, 14, 22]: + for offset_ms in [0, 6, 12, 20, 28]: start = int(offset_ms * self.sr / 1000) burst_len = int(0.012 * self.sr) if start + burst_len > n: break burst = noise_gaussian(burst_len / self.sr, self.sr) burst *= np.exp(-np.arange(burst_len) / self.sr * 100) - clap[start : start + burst_len] += burst * 0.6 + clap[start : start + burst_len] += burst * np.random.uniform(0.2, 0.4) - # Tail + # Short tail tail = noise_gaussian(duration, self.sr)[:n] - sos = butter(3, [800 / (self.sr / 2), 5000 / (self.sr / 2)], btype="band", output="sos") - tail = sosfilt(sos, tail) * np.exp(-t * 18) * 0.5 + sos = butter( + 2, + [800 / (self.sr / 2), 3500 / (self.sr / 2)], + btype="band", + output="sos", + ) + tail = sosfilt(sos, tail) * np.exp(-t * 15) * 0.25 clap += tail - return normalize(clap) + # Normalize to moderate level — sits below kick and snare + peak = np.max(np.abs(clap)) + if peak > 0: + clap = clap / peak * 0.5 + return clap # --------------------------------------------------------------------- # # Rim-shot diff --git a/mood_machine/synth/pads.py b/mood_machine/synth/pads.py index 9e61f0c..abd609b 100644 --- a/mood_machine/synth/pads.py +++ b/mood_machine/synth/pads.py @@ -62,17 +62,35 @@ def chord( for cents in detune_spread: freq = base_freq * (2.0 ** (cents / 1200.0)) t = np.arange(n) / self.sr - # Mix saw-ish + sine for warmth + # Rich harmonic stack: fundamental through 5th partial + # pushes energy into the 500Hz-2kHz mid-range band phase = np.random.uniform(0, 2 * np.pi) voice = ( - np.sin(2 * np.pi * freq * t + phase) * 0.6 - + np.sin(2 * np.pi * freq * 2 * t + phase * 1.3) * 0.15 - + np.sin(2 * np.pi * freq * 3 * t + phase * 0.7) * 0.05 + np.sin(2 * np.pi * freq * t + phase) * 0.50 # fundamental + + np.sin(2 * np.pi * freq * 2 * t + phase * 1.3) * 0.18 # 2nd harmonic + + np.sin(2 * np.pi * freq * 3 * t + phase * 0.7) * 0.10 # 3rd harmonic + + np.sin(2 * np.pi * freq * 4 * t + phase * 1.1) * 0.06 # 4th harmonic + + np.sin(2 * np.pi * freq * 5 * t + phase * 0.5) * 0.03 # 5th harmonic ) out += voice + # Octave-doubled layer: adds body in the 500Hz-1kHz mid region + # (base notes at octave 3 are ~130-250Hz, doubling puts them at 260-500Hz + # and their harmonics land squarely in the mid band) + octave_up = np.zeros(n, dtype=np.float64) + for note in midi_notes: + freq_up = midi_to_freq(note + 12) # one octave higher + t = np.arange(n) / self.sr + phase = np.random.uniform(0, 2 * np.pi) + octave_up += ( + np.sin(2 * np.pi * freq_up * t + phase) * 0.25 + + np.sin(2 * np.pi * freq_up * 2 * t + phase * 0.8) * 0.08 + ) + out += octave_up + # Normalise before envelope so envelope shapes the dynamics - out = out / (len(midi_notes) * self.voices) * 2.0 + n_voices_total = len(midi_notes) * self.voices + len(midi_notes) # main + octave layer + out = out / n_voices_total * 2.0 # Slow LFO on filter cutoff for movement lfo_rate = np.random.uniform(0.06, 0.15) # Hz diff --git a/mood_machine/vocals/__init__.py b/mood_machine/vocals/__init__.py index 9905ab0..305304b 100644 --- a/mood_machine/vocals/__init__.py +++ b/mood_machine/vocals/__init__.py @@ -1,3 +1,21 @@ -from .engine import GttsTTS, Pyttsx3TTS, SilentEngine, get_vocal_engine +from .engine import ( + BarkEngine, + ChatterboxEngine, + EdgeTTSEngine, + GttsTTS, + PiperTTSEngine, + Pyttsx3TTS, + SilentEngine, + get_vocal_engine, +) -__all__ = ["GttsTTS", "Pyttsx3TTS", "SilentEngine", "get_vocal_engine"] +__all__ = [ + "BarkEngine", + "ChatterboxEngine", + "EdgeTTSEngine", + "GttsTTS", + "PiperTTSEngine", + "Pyttsx3TTS", + "SilentEngine", + "get_vocal_engine", +] diff --git a/mood_machine/vocals/engine.py b/mood_machine/vocals/engine.py index 4616113..ba5c147 100644 --- a/mood_machine/vocals/engine.py +++ b/mood_machine/vocals/engine.py @@ -1,22 +1,38 @@ """ Text-to-speech vocal engines. -Each engine converts text → numpy audio array. The modular design lets you +Each engine converts text -> numpy audio array. The modular design lets you swap engines without touching the rest of the pipeline. -Supported back-ends: - * **gtts** - Google TTS (free, needs internet, natural-sounding) - * **pyttsx3** - Offline system TTS (robotic — fits the Headache aesthetic) +Supported back-ends (in preference order): + + * **bark** - Suno Bark generative text-to-audio (local, expressive, + produces natural breathing/hesitations/artifacts — best for art music) + * **chatterbox** - Resemble Chatterbox TTS (local, zero-shot voice cloning, + paralinguistic tags like ``[laugh]``, ``[cough]``) + * **edge-tts** - Microsoft Edge Neural TTS (free, needs internet, most + natural — supports British HD voices with emotion auto-detection) + * **piper** - Piper offline neural TTS (no internet, good quality, + runs locally via ``piper-tts`` package) + * **gtts** - Google Translate TTS (free, needs internet, robotic) + * **pyttsx3** - Offline system TTS (very robotic) * **silent** - No vocals, returns silence (for instrumental-only tracks) + +Each engine also declares ``prefers_verse_level = True/False`` so the +pipeline knows whether to send full verses (better for neural TTS that +handles cross-sentence prosody) or split into sentences first. """ from __future__ import annotations +import asyncio import os +import re import tempfile from abc import ABC, abstractmethod import numpy as np +from scipy.signal import resample from mood_machine.synth.core import SAMPLE_RATE @@ -28,6 +44,10 @@ class VocalEngine(ABC): """Interface that every TTS back-end must implement.""" + # If True, the pipeline sends full verse text (better cross-sentence + # prosody). If False, it splits into sentences and sends each one. + prefers_verse_level: bool = False + @abstractmethod def synthesise(self, text: str) -> np.ndarray: """Return a mono float64 numpy array of spoken *text* at ``SAMPLE_RATE``.""" @@ -35,14 +55,337 @@ def synthesise(self, text: str) -> np.ndarray: # ========================================================================= # -# Google TTS (gTTS) +# Bark — Suno generative text-to-audio (expressive, local) +# ========================================================================= # + + +class BarkEngine(VocalEngine): + """Suno Bark — generative text-to-audio model. + + Unlike conventional TTS, Bark generates audio *directly* from text without + a phoneme intermediary. This means it naturally produces breathing, + hesitations, and vocal artifacts — the "slightly uncanny" quality ideal + for Headache-style spoken word. + + Supports inline emotion tags: ``[sad]``, ``[laughs]``, ``[sighs]``. + + Install: ``pip install git+https://github.com/suno-ai/bark.git`` + + On Apple Silicon, set ``SUNO_ENABLE_MPS=True`` in environment for GPU + acceleration (experimental). Falls back to CPU if MPS is unavailable. + """ + + prefers_verse_level = True # generative model handles full text better + + # Bark native sample rate + _BARK_SR = 24_000 + + def __init__( + self, + speaker: str = "v2/en_speaker_6", + use_small_models: bool = False, + ) -> None: + self.speaker = speaker + self.use_small_models = use_small_models + self._loaded = False + + def _ensure_loaded(self) -> None: + """Lazy-load Bark models on first use.""" + if self._loaded: + return + + from bark import preload_models # type: ignore[import-untyped] + + # Configure environment for Apple Silicon if available + os.environ.setdefault("SUNO_ENABLE_MPS", "True") + os.environ.setdefault("SUNO_OFFLOAD_CPU", "True") + + if self.use_small_models: + os.environ["SUNO_USE_SMALL_MODELS"] = "True" + + preload_models() + self._loaded = True + + def synthesise(self, text: str) -> np.ndarray: + from bark import generate_audio # type: ignore[import-untyped] + + self._ensure_loaded() + + audio = generate_audio( + text, + history_prompt=self.speaker, + ) + + # Bark outputs at 24kHz — resample to project sample rate + if self._BARK_SR != SAMPLE_RATE: + n_out = int(len(audio) * SAMPLE_RATE / self._BARK_SR) + audio = resample(audio, n_out) + + return audio.astype(np.float64) + + +# ========================================================================= # +# Chatterbox — Resemble AI voice cloning TTS (local) +# ========================================================================= # + + +class ChatterboxEngine(VocalEngine): + """Resemble Chatterbox — zero-shot voice cloning TTS. + + Clones a voice from a short reference audio clip (~10s WAV) and can + generate speech in that voice. Supports paralinguistic tags like + ``[laugh]``, ``[cough]``, ``[chuckle]`` for natural expressiveness. + + Install: ``pip install chatterbox-tts`` + + On Apple Silicon, uses MPS backend with automatic torch.load patching. + """ + + prefers_verse_level = True + + def __init__( + self, + voice_reference_path: str | None = None, + exaggeration: float = 0.5, + cfg_weight: float = 0.5, + ) -> None: + self.voice_reference_path = voice_reference_path + self.exaggeration = exaggeration + self.cfg_weight = cfg_weight + self._model = None + self._device: str | None = None + + def _ensure_loaded(self) -> None: + """Lazy-load the Chatterbox model.""" + if self._model is not None: + return + + import torch + + # Device selection: MPS (Apple Silicon) > CUDA > CPU + if torch.backends.mps.is_available(): + self._device = "mps" + elif torch.cuda.is_available(): + self._device = "cuda" + else: + self._device = "cpu" + + # Monkey-patch torch.load for CUDA->MPS/CPU weight mapping + if self._device != "cuda": + _original_load = torch.load + + def _patched_load(*args, **kwargs): + if "map_location" not in kwargs: + kwargs["map_location"] = torch.device(self._device) + return _original_load(*args, **kwargs) + + torch.load = _patched_load + + from chatterbox.tts import ChatterboxTTS # type: ignore[import-untyped] + + self._model = ChatterboxTTS.from_pretrained(device=self._device) + + def synthesise(self, text: str) -> np.ndarray: + self._ensure_loaded() + + kwargs = { + "exaggeration": self.exaggeration, + "cfg_weight": self.cfg_weight, + } + if self.voice_reference_path: + kwargs["audio_prompt_path"] = self.voice_reference_path + + wav = self._model.generate(text, **kwargs) + + # Convert torch tensor to numpy + audio = wav.squeeze().cpu().numpy().astype(np.float64) + + # Resample from model SR to project SR if needed + model_sr = getattr(self._model, "sr", 24_000) + if model_sr != SAMPLE_RATE: + n_out = int(len(audio) * SAMPLE_RATE / model_sr) + audio = resample(audio, n_out) + + return audio + + +# ========================================================================= # +# Edge TTS — Microsoft Neural TTS (free, high-quality, British voices) +# ========================================================================= # + + +class EdgeTTSEngine(VocalEngine): + """Microsoft Edge Neural TTS via the ``edge-tts`` package. + + Free, no API key needed. Supports HD voices with automatic emotion + detection from text sentiment --- ideal for expressive, cynical, venting + spoken-word lyrics. + + Default voice: ``en-GB-RyanNeural`` (British male, natural intonation, + auto-adjusts tone to text emotion). + + Note: the ``edge-tts`` library builds its own SSML internally and + XML-escapes the text parameter, so we cannot inject raw SSML tags. + Instead we use punctuation-level text manipulation to coax more + expressive prosody out of the neural model. + """ + + prefers_verse_level = True # neural TTS handles cross-sentence prosody + + def __init__( + self, + voice: str = "en-GB-RyanNeural", + rate: str = "-8%", + pitch: str = "-5Hz", + ) -> None: + self.voice = voice + self.rate = rate + self.pitch = pitch + + @staticmethod + def _prepare_text(text: str) -> str: + """Massage text for more expressive Edge TTS delivery. + + Uses punctuation cues that the neural model naturally responds to: + - Ellipses get an extra full stop to lengthen the pause + - Double-dashes become ellipses (longer hesitation than a dash) + - Newlines become sentence breaks so the model resets prosody + """ + out = text + + # Double-dash -> ellipsis (natural hesitation) + out = re.sub(r"\s*--\s*", "... ", out) + + # Ensure ellipses have spacing so model treats them as pauses + out = re.sub(r"\.\.\.(?!\s)", "... ", out) + + # Turn bare newlines into sentence-ending periods if missing + lines = out.split("\n") + processed = [] + for line in lines: + line = line.strip() + if line and line[-1] not in ".!?…": + line += "." + processed.append(line) + out = "\n".join(processed) + + return out + + def synthesise(self, text: str) -> np.ndarray: + import edge_tts + from pydub import AudioSegment + + prepared = self._prepare_text(text) + + async def _generate() -> bytes: + communicate = edge_tts.Communicate( + text=prepared, + voice=self.voice, + rate=self.rate, + pitch=self.pitch, + ) + with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp: + tmp_path = fp.name + try: + await communicate.save(tmp_path) + with open(tmp_path, "rb") as f: + return f.read() + finally: + os.unlink(tmp_path) + + # Run the async edge-tts in a sync context + mp3_data = _run_async(_generate()) + + # Convert MP3 bytes to numpy + with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp: + fp.write(mp3_data) + tmp_path = fp.name + try: + seg = AudioSegment.from_mp3(tmp_path) + seg = seg.set_frame_rate(SAMPLE_RATE).set_channels(1).set_sample_width(2) + samples = np.array(seg.get_array_of_samples(), dtype=np.float64) / 32768.0 + return samples + finally: + os.unlink(tmp_path) + + +# ========================================================================= # +# Piper TTS — Offline neural TTS +# ========================================================================= # + + +class PiperTTSEngine(VocalEngine): + """Piper neural TTS — fast, fully offline, good quality. + + Uses VITS models. Default: ``en_GB-aru-medium`` (British male). + Requires the ``piper-tts`` package and will auto-download the model + on first use. + """ + + prefers_verse_level = True # neural TTS handles cross-sentence prosody + + def __init__( + self, + model: str = "en_GB-aru-medium", + speaker: int | None = None, + length_scale: float = 1.1, + ) -> None: + self.model = model + self.speaker = speaker + self.length_scale = length_scale + self._piper = None + + def _get_piper(self): + """Lazy-init the Piper voice (downloads model on first use).""" + if self._piper is None: + from piper import PiperVoice # type: ignore[import-untyped] + + self._piper = PiperVoice.load(self.model, download=True) + return self._piper + + def synthesise(self, text: str) -> np.ndarray: + import wave + + piper = self._get_piper() + + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: + tmp_path = fp.name + + try: + with wave.open(tmp_path, "wb") as wav_file: + piper.synthesize( + text, + wav_file, + speaker_id=self.speaker, + length_scale=self.length_scale, + ) + + from pydub import AudioSegment + + seg = AudioSegment.from_wav(tmp_path) + seg = seg.set_frame_rate(SAMPLE_RATE).set_channels(1).set_sample_width(2) + samples = np.array(seg.get_array_of_samples(), dtype=np.float64) / 32768.0 + return samples + finally: + os.unlink(tmp_path) + + +# ========================================================================= # +# Google TTS (gTTS) — legacy fallback # ========================================================================= # class GttsTTS(VocalEngine): - """Google Translate TTS — free, requires internet.""" + """Google Translate TTS — free, requires internet. + + Note: ``slow=False`` is now the default. The old ``slow=True`` mode + produces unnaturally flat prosody (language-learner mode) which makes + downstream humanisation much harder. + """ + + prefers_verse_level = False # gTTS prosody is flat; sentence-level helps - def __init__(self, lang: str = "en", slow: bool = True) -> None: + def __init__(self, lang: str = "en", slow: bool = False) -> None: self.lang = lang self.slow = slow @@ -52,7 +395,6 @@ def synthesise(self, text: str) -> np.ndarray: tts = gTTS(text=text, lang=self.lang, slow=self.slow) - # gTTS writes MP3; convert to numpy via pydub with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp: tmp_path = fp.name try: @@ -71,7 +413,9 @@ def synthesise(self, text: str) -> np.ndarray: class Pyttsx3TTS(VocalEngine): - """Offline TTS using the OS speech engine (more robotic = on-brand).""" + """Offline TTS using the OS speech engine (very robotic).""" + + prefers_verse_level = False def __init__(self, rate: int = 130) -> None: self.rate = rate @@ -104,28 +448,92 @@ def synthesise(self, text: str) -> np.ndarray: class SilentEngine(VocalEngine): """Returns silence — used for instrumental-only renders.""" + prefers_verse_level = True + def synthesise(self, text: str) -> np.ndarray: - # ~1 second of silence as a placeholder return np.zeros(SAMPLE_RATE, dtype=np.float64) +# ========================================================================= # +# Async helper +# ========================================================================= # + + +def _run_async(coro): + """Run an async coroutine from sync code, handling existing event loops.""" + try: + loop = asyncio.get_running_loop() + except RuntimeError: + loop = None + + if loop and loop.is_running(): + # We're inside an existing event loop (e.g. Jupyter). + # Use nest_asyncio or create a new thread. + import concurrent.futures + + with concurrent.futures.ThreadPoolExecutor() as pool: + return pool.submit(asyncio.run, coro).result() + else: + return asyncio.run(coro) + + # ========================================================================= # # Factory # ========================================================================= # +# Fallback chain order (configurable via engine name) +_FALLBACK_CHAIN = ["bark", "chatterbox", "edge-tts", "piper", "gtts", "pyttsx3", "silent"] + -def get_vocal_engine(name: str = "gtts", **kwargs) -> VocalEngine: +def get_vocal_engine(name: str = "edge-tts", **kwargs) -> VocalEngine: """Instantiate a vocal engine by name. - Falls back gracefully: gtts → pyttsx3 → silent. + Fallback chain: bark -> chatterbox -> edge-tts -> piper -> gtts -> pyttsx3 -> silent. + Each engine is tried in order; if unavailable, the next is attempted. """ + if name == "bark": + try: + import bark # noqa: F401 # type: ignore[import-untyped] + + return BarkEngine(**kwargs) + except ImportError: + print("[vocals] bark not installed -- falling back to chatterbox") + name = "chatterbox" + + if name == "chatterbox": + try: + from chatterbox.tts import ChatterboxTTS # noqa: F401 # type: ignore[import-untyped] + + return ChatterboxEngine(**kwargs) + except ImportError: + print("[vocals] chatterbox-tts not installed -- falling back to edge-tts") + name = "edge-tts" + + if name == "edge-tts": + try: + import edge_tts # noqa: F401 + + return EdgeTTSEngine(**kwargs) + except ImportError: + print("[vocals] edge-tts not installed -- falling back to piper") + name = "piper" + + if name == "piper": + try: + from piper import PiperVoice # noqa: F401 # type: ignore[import-untyped] + + return PiperTTSEngine(**kwargs) + except ImportError: + print("[vocals] piper-tts not installed -- falling back to gTTS") + name = "gtts" + if name == "gtts": try: from gtts import gTTS # noqa: F401 return GttsTTS(**kwargs) except ImportError: - print("[vocals] gTTS not installed — falling back to pyttsx3") + print("[vocals] gTTS not installed -- falling back to pyttsx3") name = "pyttsx3" if name == "pyttsx3": @@ -134,7 +542,7 @@ def get_vocal_engine(name: str = "gtts", **kwargs) -> VocalEngine: return Pyttsx3TTS(**kwargs) except (ImportError, Exception) as exc: - print(f"[vocals] pyttsx3 unavailable ({exc}) — instrumental mode") + print(f"[vocals] pyttsx3 unavailable ({exc}) -- instrumental mode") name = "silent" if name == "silent": diff --git a/mood_machine/vocals/processor.py b/mood_machine/vocals/processor.py new file mode 100644 index 0000000..f599f15 --- /dev/null +++ b/mood_machine/vocals/processor.py @@ -0,0 +1,1025 @@ +""" +Vocal processor — humanised, expressive vocal textures. + +Transforms flat TTS into something that sounds like a real person venting — +with breath, vibrato, pace variation, and emotional dynamics. + +Inspired by Headache / Vegyn / Burial's vocal treatments, but prioritising +*soul* over effects: the voice should feel anxious, present, and human. + +Processing chain +~~~~~~~~~~~~~~~~ + +**Per-sentence** (applied before assembly): + 1. Trim silence from TTS edges + 2. Pitch shift (librosa phase-vocoder when available, scipy fallback) + 3. Vibrato / tremor — time-domain pitch modulation with organic jitter + 4. Per-sentence pace variation (time-stretch ± drift) + 5. Per-sentence dynamic contour (some louder, some quieter) + +**Assembly:** + 6. Insert synthesised breath sounds between sentences + 7. Add natural pauses with subtle variation + +**Global post-processing:** + 8. EQ (bandpass) + 9. Subtle lo-fi (optional bitcrush / SR reduction) + 10. Gentle saturation + 11. Rare stutter/glitch (much less than before) + 12. Noise floor for analog warmth + 13. Final normalise +""" + +from __future__ import annotations + +import copy +import re +from dataclasses import dataclass, field + +import numpy as np +from scipy.signal import butter, sosfilt + +from mood_machine.synth.core import SAMPLE_RATE + +# Try high-quality pitch/time manipulation from librosa +try: + import librosa # type: ignore[import-untyped] + + _HAS_LIBROSA = True +except ImportError: + _HAS_LIBROSA = False + + +# ========================================================================= # +# Text helpers +# ========================================================================= # + + +def split_into_sentences(text: str) -> list[str]: + """Split a verse/paragraph into individual sentences for per-sentence TTS. + + Keeps punctuation attached so TTS intonation is natural. + """ + # Split on sentence-ending punctuation followed by whitespace + parts = re.split(r"(?<=[.!?])\s+", text.strip()) + # Keep only fragments with real content (>5 chars) + sentences = [s.strip() for s in parts if len(s.strip()) > 5] + # If nothing split (no punctuation), return the whole text as one "sentence" + return sentences if sentences else [text.strip()] + + +def _trim_silence( + audio: np.ndarray, + threshold: float = 0.015, + sr: int = SAMPLE_RATE, +) -> np.ndarray: + """Trim leading/trailing silence from a TTS clip, keeping a tiny margin.""" + above = np.abs(audio) > threshold + if not np.any(above): + return audio + first = int(np.argmax(above)) + last = len(audio) - int(np.argmax(above[::-1])) + # Keep ~20 ms margin on each side to avoid cutting transients + margin = int(0.020 * sr) + first = max(0, first - margin) + last = min(len(audio), last + margin) + return audio[first:last] + + +# ========================================================================= # +# Voice presets +# ========================================================================= # + + +@dataclass +class VoicePreset: + """Defines the character of a processed voice.""" + + name: str = "headache" + + # ── Pitch ────────────────────────────────────────────────────────── + pitch_shift_semitones: float = -0.5 # negative = lower (gentle for neural TTS) + pitch_drift_cents: float = 8.0 # random micro-detuning per sentence + + # ── Humanisation: vibrato / tremor ──────────────────────────────── + vibrato_rate_hz: float = 5.0 # LFO speed (5-7 Hz = anxious tremor) + vibrato_depth_cents: float = 8.0 # pitch wobble depth (subtle for neural TTS) + vibrato_onset_ms: float = 400.0 # ramp-in so vibrato appears naturally + + # ── Humanisation: pace / timing ─────────────────────────────────── + pace_drift: float = 0.10 # 0-0.4, how much sentence speed varies + pace_bias: float = 0.0 # positive = tends faster (anxious rushing) + + # ── Humanisation: breath ────────────────────────────────────────── + breath_probability: float = 0.70 # chance of audible breath between sentences + breath_duration_ms: float = 350.0 + breath_volume: float = 0.035 + breath_pitch_hz: float = 180.0 # low resonance = chest breath + + # ── Humanisation: dynamics ──────────────────────────────────────── + dynamic_range: float = 0.20 # sentence-to-sentence loudness variation + emotional_arc: bool = True # build intensity across the verse + + # ── Sentence pauses ─────────────────────────────────────────────── + pause_ms: float = 380.0 # base silence between sentences + pause_variation: float = 0.30 # random ± fraction + + # ── Tempo sync ──────────────────────────────────────────────────── + tempo_sync: bool = True + phrase_gap_beats: float = 2.0 + + # ── Granular / chop (very sparse — human first, glitch second) ─── + chop_probability: float = 0.04 + stutter_repeats: int = 2 + stutter_grain_ms: float = 60.0 + + # ── Lo-fi degradation (subtle) ─────────────────────────────────── + bitcrush_bits: int = 0 # 0 = off + sample_rate_reduction: int = 0 + lowpass_freq: float = 10000.0 + highpass_freq: float = 100.0 + + # ── Dynamics ────────────────────────────────────────────────────── + normalize_peak: float = 0.90 + + # ── Texture ─────────────────────────────────────────────────────── + pre_saturation: float = 1.08 + noise_floor: float = 0.004 + + # ── Formant EQ (voice presence + warmth) ───────────────────────── + presence_boost_db: float = 3.0 # peak at ~3kHz, voice "cuts through" + warmth_boost_db: float = 2.0 # peak at ~250Hz, chest resonance + + # ── Room tone (convolution-based spatial feel) ─────────────────── + room_tone: float = 0.08 # wet amount (0 = off) + + # ── Content-aware pauses ───────────────────────────────────────── + content_aware_pauses: bool = True # vary pause length by punctuation + + # ── Emotional word emphasis ────────────────────────────────────── + word_emphasis: bool = True # detect + emphasise emotionally charged words + + extra: dict = field(default_factory=dict) + + +# --------------------------------------------------------------------------- +# Named presets +# --------------------------------------------------------------------------- + +VOICE_PRESETS: dict[str, VoicePreset] = { + # Headache: anxious, venting, present — the "main character" voice + "headache": VoicePreset( + name="headache", + pitch_shift_semitones=-0.5, + pitch_drift_cents=8.0, + vibrato_rate_hz=5.0, + vibrato_depth_cents=8.0, + vibrato_onset_ms=400.0, + pace_drift=0.10, + pace_bias=0.03, # slightly rushed = anxious energy + breath_probability=0.75, + breath_duration_ms=320.0, + breath_volume=0.040, + breath_pitch_hz=170.0, + dynamic_range=0.30, + emotional_arc=True, + pause_ms=380.0, + pause_variation=0.35, + chop_probability=0.03, + stutter_repeats=2, + stutter_grain_ms=60.0, + bitcrush_bits=0, # no bitcrush — preserve TTS clarity + lowpass_freq=10000.0, + highpass_freq=100.0, + normalize_peak=0.92, + pre_saturation=1.10, + noise_floor=0.004, + presence_boost_db=3.0, + warmth_boost_db=2.0, + room_tone=0.08, + content_aware_pauses=True, + word_emphasis=True, + ), + # Spectral: dreamy, slow, introspective — dissociated monologue + "spectral": VoicePreset( + name="spectral", + pitch_shift_semitones=-1.5, + pitch_drift_cents=10.0, + vibrato_rate_hz=3.5, + vibrato_depth_cents=12.0, + vibrato_onset_ms=500.0, + pace_drift=0.08, + pace_bias=-0.03, # slightly slow, contemplative + breath_probability=0.60, + breath_duration_ms=450.0, + breath_volume=0.030, + breath_pitch_hz=160.0, + dynamic_range=0.20, + emotional_arc=True, + pause_ms=500.0, + pause_variation=0.25, + chop_probability=0.02, + stutter_repeats=2, + stutter_grain_ms=100.0, + bitcrush_bits=0, + lowpass_freq=8000.0, + highpass_freq=100.0, + normalize_peak=0.85, + pre_saturation=1.08, + noise_floor=0.006, + presence_boost_db=1.5, # softer presence, more dreamlike + warmth_boost_db=3.0, # extra warmth for dreamy quality + room_tone=0.15, # more room feel, more distance + content_aware_pauses=True, + word_emphasis=False, # no emphasis — everything feels equal in a dream + ), + # Broken Radio: jittery, claustrophobic, nervous — paranoid narrator + "broken_radio": VoicePreset( + name="broken_radio", + pitch_shift_semitones=-0.5, + pitch_drift_cents=6.0, + vibrato_rate_hz=6.0, + vibrato_depth_cents=10.0, + vibrato_onset_ms=200.0, + pace_drift=0.15, + pace_bias=0.08, # rushing, nervy + breath_probability=0.85, + breath_duration_ms=280.0, + breath_volume=0.045, + breath_pitch_hz=200.0, + dynamic_range=0.35, + emotional_arc=True, + pause_ms=300.0, + pause_variation=0.40, + chop_probability=0.08, + stutter_repeats=3, + stutter_grain_ms=45.0, + bitcrush_bits=10, + sample_rate_reduction=16000, + lowpass_freq=6000.0, + highpass_freq=200.0, + normalize_peak=0.88, + pre_saturation=1.4, + noise_floor=0.012, + presence_boost_db=4.0, # aggressive presence, claustrophobic + warmth_boost_db=1.0, # less warmth, more midrange + room_tone=0.04, # tiny room, close + content_aware_pauses=True, + word_emphasis=True, + ), + # Clean: minimal processing, natural — just breaths and pacing + "clean": VoicePreset( + name="clean", + pitch_shift_semitones=0.0, + pitch_drift_cents=0.0, + vibrato_rate_hz=0.0, + vibrato_depth_cents=0.0, + pace_drift=0.05, + pace_bias=0.0, + breath_probability=0.50, + breath_duration_ms=300.0, + breath_volume=0.025, + breath_pitch_hz=180.0, + dynamic_range=0.10, + emotional_arc=False, + pause_ms=350.0, + pause_variation=0.20, + chop_probability=0.0, + bitcrush_bits=0, + lowpass_freq=12000.0, + highpass_freq=80.0, + normalize_peak=0.85, + pre_saturation=1.0, + noise_floor=0.0, + presence_boost_db=1.0, + warmth_boost_db=1.0, + room_tone=0.0, + content_aware_pauses=False, + word_emphasis=False, + ), +} + + +def get_voice_preset(name: str) -> VoicePreset: + """Return a *copy* of a named preset (falls back to ``headache``).""" + return copy.deepcopy(VOICE_PRESETS.get(name, VOICE_PRESETS["headache"])) + + +# ========================================================================= # +# Vocal Processor +# ========================================================================= # + + +class VocalProcessor: + """Transforms raw TTS into humanised vocal textures. + + Two main entry points: + + * ``process_verse_sentences(sentence_audios)`` — takes a list of + per-sentence numpy arrays, applies per-sentence humanisation, inserts + breaths/pauses, and returns a single concatenated verse audio. + + * ``process_verses(verse_sentence_lists)`` — batch version that + processes multiple verses. + + * ``process(audio)`` — legacy: process a single contiguous audio clip. + """ + + def __init__( + self, + preset: str | VoicePreset = "headache", + bpm: float = 73.0, + sr: int = SAMPLE_RATE, + ) -> None: + if isinstance(preset, str): + self.preset = get_voice_preset(preset) + else: + self.preset = copy.deepcopy(preset) + self.bpm = bpm + self.sr = sr + self.beat_duration = 60.0 / bpm # seconds per beat + + # ================================================================== # + # Public API + # ================================================================== # + + def process_verse_sentences( + self, + sentence_audios: list[np.ndarray], + sentence_texts: list[str] | None = None, + ) -> np.ndarray: + """Humanise individual sentence audios and assemble into one verse. + + This is the **primary entry point** — it gives per-sentence control + over pace, pitch, dynamics, and inserts breaths between sentences. + + Parameters + ---------- + sentence_audios: + List of per-sentence numpy arrays from the TTS engine. + sentence_texts: + Optional list of sentence strings (same length as audios). + When provided, enables content-aware pauses and emotional + word emphasis. + """ + if not sentence_audios: + return np.zeros(self.sr, dtype=np.float64) + + n_sentences = len(sentence_audios) + + # --- Pre-compute per-sentence parameters -------------------------- + pace_factors = self._sentence_pace_factors(n_sentences) + dynamic_gains = self._sentence_dynamic_gains(n_sentences) + pitch_drifts = self._sentence_pitch_drifts(n_sentences) + + # --- Process each sentence ---------------------------------------- + processed_parts: list[np.ndarray] = [] + + for i, raw in enumerate(sentence_audios): + sent = raw.astype(np.float64).copy() + + # Trim TTS silence + sent = _trim_silence(sent, sr=self.sr) + if len(sent) < 100: + continue + + # 1. Pitch shift (base + per-sentence drift) + sent = self._pitch_shift(sent, extra_cents=pitch_drifts[i]) + + # 2. Time-stretch for pace variation + factor = pace_factors[i] + if abs(factor - 1.0) > 0.02: + sent = self._time_stretch(sent, factor) + + # 3. Vibrato / tremor + sent = self._apply_vibrato(sent) + + # 4. Dynamic contour + sent *= dynamic_gains[i] + + # 5. Emotional word emphasis (per-sentence, needs text) + if self.preset.word_emphasis and sentence_texts and i < len(sentence_texts): + sent = self._apply_word_emphasis(sent, sentence_texts[i]) + + # --- Insert breath before this sentence (not the first) ------- + if i > 0 and np.random.random() < self.preset.breath_probability: + breath = self._generate_breath() + processed_parts.append(breath) + + # --- Pause between sentences ---------------------------------- + if i > 0: + sent_text = ( + sentence_texts[i] if sentence_texts and i < len(sentence_texts) else None + ) + pause = self._generate_pause(sentence_text=sent_text) + processed_parts.append(pause) + + processed_parts.append(sent) + + if not processed_parts: + return np.zeros(self.sr, dtype=np.float64) + + # --- Concatenate -------------------------------------------------- + verse = np.concatenate(processed_parts) + + # --- Global post-processing (applied to the whole verse) ---------- + verse = self._eq(verse) + verse = self._formant_eq(verse) + verse = self._lofi_degrade(verse) + + if self.preset.pre_saturation > 1.0: + verse = self._saturate(verse) + + verse = self._stutter(verse) + + if self.preset.room_tone > 0: + verse = self._apply_room_tone(verse) + + if self.preset.noise_floor > 0: + verse = self._add_noise_floor(verse) + + verse = self._normalize(verse) + + return verse + + def process_verses( + self, + verses: list[list[np.ndarray]], + section_durations: list[int] | None = None, + verse_texts: list[list[str]] | None = None, + ) -> list[np.ndarray]: + """Process multiple verses (each is a list of sentence audios). + + Parameters + ---------- + verse_texts: + Optional list of per-verse sentence text lists, matching the + structure of *verses*. Enables content-aware pauses and + word emphasis. + """ + results: list[np.ndarray] = [] + for i, sentence_list in enumerate(verses): + texts = verse_texts[i] if verse_texts and i < len(verse_texts) else None + processed = self.process_verse_sentences(sentence_list, sentence_texts=texts) + + # Optional tempo-sync stretch to fit arrangement section + if self.preset.tempo_sync and section_durations and i < len(section_durations): + target = section_durations[i] + gap = int(self.preset.phrase_gap_beats * self.beat_duration * self.sr) + offset = int(4 * self.beat_duration * self.sr) + available = target - offset - gap + if available > 0 and len(processed) > 0: + processed = self._time_stretch_to_length(processed, available) + + results.append(processed) + return results + + def process(self, audio: np.ndarray) -> np.ndarray: + """Legacy single-clip processing (wraps sentence pipeline).""" + if len(audio) < 100: + return audio + # Treat the whole clip as one "sentence" + return self.process_verse_sentences([audio]) + + # ================================================================== # + # Per-sentence parameter generation + # ================================================================== # + + def _sentence_pace_factors(self, n: int) -> list[float]: + """Generate per-sentence time-stretch factors. + + Values >1.0 = slower, <1.0 = faster. + The bias shifts the distribution (positive bias → faster on average). + """ + drift = self.preset.pace_drift + bias = self.preset.pace_bias + if drift <= 0: + return [1.0] * n + factors = [] + for _ in range(n): + # Random factor centred around 1.0 - bias + f = 1.0 - bias + np.random.uniform(-drift, drift) + f = np.clip(f, 0.75, 1.35) # safety bounds + factors.append(float(f)) + return factors + + def _sentence_dynamic_gains(self, n: int) -> list[float]: + """Generate per-sentence loudness variations. + + If ``emotional_arc`` is on, sentences build intensity across the verse + (like someone building up while venting) then dip slightly at the end. + """ + dr = self.preset.dynamic_range + if dr <= 0: + return [1.0] * n + + gains = [] + for i in range(n): + if self.preset.emotional_arc and n > 2: + # Build: 0 → 0.8 of verse = rising, last 20% = slight pull-back + progress = i / max(n - 1, 1) + if progress < 0.8: + arc = 0.7 + 0.3 * (progress / 0.8) # 0.7 → 1.0 + else: + arc = 1.0 - 0.1 * ((progress - 0.8) / 0.2) # 1.0 → 0.9 + else: + arc = 1.0 + + # Layer random variation on top of the arc + variation = np.random.uniform(-dr * 0.5, dr * 0.5) + gain = arc + variation + gains.append(float(np.clip(gain, 0.5, 1.2))) + + return gains + + def _sentence_pitch_drifts(self, n: int) -> list[float]: + """Random per-sentence pitch micro-drift in cents.""" + max_drift = self.preset.pitch_drift_cents + if max_drift <= 0: + return [0.0] * n + return [float(np.random.uniform(-max_drift, max_drift)) for _ in range(n)] + + # ================================================================== # + # Humanisation stages + # ================================================================== # + + def _pitch_shift( + self, + audio: np.ndarray, + extra_cents: float = 0.0, + ) -> np.ndarray: + """Shift pitch while preserving duration (librosa) or via resample.""" + semitones = self.preset.pitch_shift_semitones + extra_cents / 100.0 + + if abs(semitones) < 0.01: + return audio + + if _HAS_LIBROSA: + # librosa preserves duration via phase vocoder — much more natural + shifted = librosa.effects.pitch_shift( + y=audio, + sr=self.sr, + n_steps=semitones, + ) + return shifted + + # Fallback: resample (changes pitch + speed, lo-fi aesthetic) + return self._resample_pitch_shift(audio, semitones) + + def _apply_vibrato(self, audio: np.ndarray) -> np.ndarray: + """Apply pitch vibrato — the emotional tremor of an anxious voice. + + Uses time-domain interpolation: modulate the read position with an + LFO to create pitch wobble. Organic jitter is added so it doesn't + sound mechanical. + """ + rate = self.preset.vibrato_rate_hz + depth = self.preset.vibrato_depth_cents + + if rate <= 0 or depth <= 0: + return audio + + n = len(audio) + t = np.arange(n, dtype=np.float64) / self.sr + + # ── Onset ramp (vibrato fades in naturally) ────────────────── + onset_samples = int(self.preset.vibrato_onset_ms / 1000.0 * self.sr) + onset_ramp = np.ones(n, dtype=np.float64) + if 0 < onset_samples < n: + onset_ramp[:onset_samples] = np.linspace(0, 1, onset_samples) + + # ── LFO with organic jitter ───────────────────────────────── + # Base sine + lfo = np.sin(2 * np.pi * rate * t) + # Add slow random walk to the phase for organic feel + phase_noise = np.cumsum(np.random.randn(n) * 0.0003) + lfo += np.sin(2 * np.pi * rate * 1.07 * t + phase_noise) * 0.25 + lfo *= onset_ramp + + # ── Convert depth to time-domain displacement ──────────────── + # depth in cents → fractional frequency offset + max_disp = depth / 1200.0 + displacement = lfo * max_disp + + # Cumulative sum turns frequency modulation into phase modulation + cumulative = np.cumsum(displacement) + + # New read positions + positions = np.arange(n, dtype=np.float64) + cumulative + positions = np.clip(positions, 0, n - 1) + + return np.interp(positions, np.arange(n, dtype=np.float64), audio) + + def _generate_breath(self) -> np.ndarray: + """Synthesise a realistic inhale breath sound. + + A breath is filtered noise shaped by a vocal-tract resonance with a + quick attack and slow fade — like someone catching their breath + between sentences while venting. + """ + dur = int(self.preset.breath_duration_ms / 1000.0 * self.sr) + if dur < 100: + return np.zeros(100, dtype=np.float64) + + nyq = self.sr / 2.0 + + # ── Pink-ish noise base (warmer than white) ────────────────── + white = np.random.randn(dur) + # Simple 1/f approximation: integrate white noise, then HP to remove DC + pink = np.cumsum(white) + pink -= np.mean(pink) + mx = np.max(np.abs(pink)) + if mx > 1e-10: + pink /= mx + + # ── Bandpass: chest resonance ──────────────────────────────── + low = max(self.preset.breath_pitch_hz, 60.0) / nyq + high = min(2200.0 / nyq, 0.95) + if 0 < low < high < 1.0: + sos = butter(2, [low, high], btype="band", output="sos") + breath = sosfilt(sos, pink) + else: + breath = pink + + # ── Add airy "hh" quality (higher-frequency component) ────── + hf_noise = np.random.randn(dur) + hf_low = min(1500.0 / nyq, 0.9) + hf_high = min(4500.0 / nyq, 0.95) + if 0 < hf_low < hf_high < 1.0: + hf_sos = butter(2, [hf_low, hf_high], btype="band", output="sos") + hf_part = sosfilt(hf_sos, hf_noise) * 0.25 + breath = breath + hf_part + + # ── Amplitude envelope: quick in, slow out ─────────────────── + env = np.ones(dur, dtype=np.float64) + attack = max(1, int(0.08 * dur)) + release = max(1, int(0.55 * dur)) + env[:attack] = np.linspace(0, 1, attack) ** 0.7 + env[-release:] = np.linspace(1, 0, release) ** 1.5 + + breath *= env * self.preset.breath_volume + + # Random volume variation (breaths aren't identical) + breath *= np.random.uniform(0.6, 1.0) + + return breath + + def _generate_pause(self, sentence_text: str | None = None) -> np.ndarray: + """Generate a natural silence gap between sentences. + + When ``content_aware_pauses`` is on and sentence text is available, + the pause length varies based on trailing punctuation: + + - Period / full stop: base pause + - Comma: short pause (0.4x base) + - Dash / ellipsis: long dramatic pause (1.6x base) + - Question mark: medium pause (0.8x base) + """ + base_ms = self.preset.pause_ms + + # Content-aware multiplier + if self.preset.content_aware_pauses and sentence_text: + text = sentence_text.rstrip() + if text.endswith("..."): + base_ms *= 1.6 # dramatic trailing off + elif text.endswith("--") or text.endswith(" -"): + base_ms *= 1.5 # interrupted thought + elif text.endswith(","): + base_ms *= 0.4 # quick breath between clauses + elif text.endswith("?"): + base_ms *= 0.8 # slight pause after question + # Period is the default (1.0x) + + variation = self.preset.pause_variation + ms = base_ms * (1.0 + np.random.uniform(-variation, variation)) + ms = max(50.0, ms) # at least 50 ms + n = int(ms / 1000.0 * self.sr) + return np.zeros(n, dtype=np.float64) + + # ================================================================== # + # Global post-processing stages + # ================================================================== # + + def _eq(self, audio: np.ndarray) -> np.ndarray: + """Bandpass EQ — remove low rumble and tame harsh highs.""" + nyq = self.sr / 2.0 + + hp = self.preset.highpass_freq + if 0 < hp < nyq: + sos = butter(2, hp / nyq, btype="high", output="sos") + audio = sosfilt(sos, audio) + + lp = self.preset.lowpass_freq + if 0 < lp < nyq: + sos = butter(2, lp / nyq, btype="low", output="sos") + audio = sosfilt(sos, audio) + + return audio + + def _formant_eq(self, audio: np.ndarray) -> np.ndarray: + """Presence boost (~3kHz) and chest warmth (~250Hz) for vocal body. + + Makes the voice feel like it's coming from a person in a room rather + than from a speaker playing a computer. Uses simple peak EQ via + a bandpass-add technique. + """ + nyq = self.sr / 2.0 + + # Presence boost: extract the 2-4kHz band, amplify, add back + presence_db = self.preset.presence_boost_db + if presence_db > 0.1: + low_p = min(2000.0 / nyq, 0.95) + high_p = min(4000.0 / nyq, 0.98) + if 0 < low_p < high_p < 1.0: + sos = butter(2, [low_p, high_p], btype="band", output="sos") + band = sosfilt(sos, audio) + gain = 10 ** (presence_db / 20.0) - 1.0 + audio = audio + band * gain + + # Warmth boost: extract the 180-350Hz band, amplify, add back + warmth_db = self.preset.warmth_boost_db + if warmth_db > 0.1: + low_w = max(180.0 / nyq, 0.01) + high_w = min(350.0 / nyq, 0.95) + if 0 < low_w < high_w < 1.0: + sos = butter(2, [low_w, high_w], btype="band", output="sos") + band = sosfilt(sos, audio) + gain = 10 ** (warmth_db / 20.0) - 1.0 + audio = audio + band * gain + + return audio + + def _apply_room_tone(self, audio: np.ndarray) -> np.ndarray: + """Apply a small synthetic room impulse response for spatial feel. + + Creates early reflections at typical room distances to make the + voice feel "in a room" rather than "in a computer". + """ + wet = self.preset.room_tone + if wet <= 0: + return audio + + from scipy.signal import fftconvolve + + # Synthesise a small room IR (early reflections only, ~80ms) + ir_len = int(0.08 * self.sr) + ir = np.zeros(ir_len, dtype=np.float64) + ir[0] = 1.0 # direct sound + + # Early reflections at typical small-room delays + reflection_times_ms = [7, 13, 21, 29, 41, 53, 67] + for i, t_ms in enumerate(reflection_times_ms): + idx = int(t_ms / 1000.0 * self.sr) + if idx < ir_len: + # Reflections get quieter and more diffuse with time + amp = 0.3 * (0.7**i) * np.random.uniform(0.7, 1.0) + # Alternate polarity for natural feel + ir[idx] = amp * (1 if i % 2 == 0 else -1) + + # Apply convolution + wet_signal = fftconvolve(audio, ir, mode="full")[: len(audio)] + + return audio * (1.0 - wet) + wet_signal * wet + + # ── Emotional word emphasis ────────────────────────────────────── + + # Words that trigger subtle emphasis (volume + timing) + _EMPHASIS_TRIGGERS = frozenset( + { + "never", + "always", + "nothing", + "everything", + "alone", + "cold", + "empty", + "lost", + "broken", + "silent", + "dead", + "gone", + "hate", + "love", + "god", + "miserable", + "sorry", + "remember", + "forget", + "waiting", + "hoping", + "honestly", + "actually", + "certain", + "supposed", + "horrible", + "wrong", + "mean", + "real", + "true", + "strange", + "scared", + "afraid", + "tired", + } + ) + + def _apply_word_emphasis(self, audio: np.ndarray, text: str) -> np.ndarray: + """Detect emotionally charged words and apply subtle emphasis. + + When a trigger word is found, apply a gentle volume boost across + the audio. This is a coarse approximation (we don't have word-level + timestamps) but still adds perceptible weight to emotional sentences. + """ + words = set(re.sub(r"[^a-z\s]", "", text.lower()).split()) + n_triggers = len(words & self._EMPHASIS_TRIGGERS) + + if n_triggers == 0: + return audio + + # More trigger words = more emphasis (capped) + boost_db = min(n_triggers * 1.0, 3.0) + gain = 10 ** (boost_db / 20.0) + + # Apply with a gentle fade to avoid clicks + out = audio * gain + fade = min(512, len(out) // 4) + if fade > 0: + out[:fade] *= np.linspace(1.0 / gain, 1.0, fade) + + return out + + def _lofi_degrade(self, audio: np.ndarray) -> np.ndarray: + """Subtle bitcrush / sample-rate reduction for warmth.""" + out = audio + + target_sr = self.preset.sample_rate_reduction + if target_sr and 0 < target_sr < self.sr: + factor = self.sr // target_sr + if factor > 1: + down = out[::factor] + out = np.repeat(down, factor)[: len(out)] + + bits = self.preset.bitcrush_bits + if bits and 0 < bits < 16: + levels = 2**bits + out = np.round(out * levels) / levels + + return out + + def _saturate(self, audio: np.ndarray) -> np.ndarray: + """Gentle tanh saturation for warmth.""" + drive = self.preset.pre_saturation + return np.tanh(audio * drive) / np.tanh(drive) + + def _stutter(self, audio: np.ndarray) -> np.ndarray: + """Rare repeat-stutter grains for subtle glitch character.""" + if self.preset.chop_probability <= 0: + return audio + + grain_samples = int(self.preset.stutter_grain_ms / 1000.0 * self.sr) + if grain_samples < 10: + return audio + + out = audio.copy() + chunk_size = int(self.beat_duration * 4 * self.sr) + pos = 0 + + while pos < len(out) - grain_samples: + if np.random.random() < self.preset.chop_probability: + grain = out[pos : pos + grain_samples].copy() + repeats = self.preset.stutter_repeats + for r in range(repeats): + ins = pos + grain_samples * r + end = min(ins + grain_samples, len(out)) + src_len = end - ins + if src_len > 0: + fade = min(64, src_len) + seg = grain[:src_len].copy() + if fade > 0: + seg[:fade] *= np.linspace(0, 1, fade) + seg[-fade:] *= np.linspace(1, 0, fade) + out[ins:end] = seg * 0.7 + out[ins:end] * 0.3 + pos += grain_samples * repeats + else: + pos += chunk_size + + return out + + def _add_noise_floor(self, audio: np.ndarray) -> np.ndarray: + """Subtle analog-style noise bed following the vocal envelope.""" + noise = np.random.randn(len(audio)) * self.preset.noise_floor + env = self._envelope_follower(audio, attack_ms=10, release_ms=100) + return audio + noise * env + + def _normalize(self, audio: np.ndarray) -> np.ndarray: + """Final normalize to preset peak level.""" + peak = np.max(np.abs(audio)) + if peak < 1e-10: + return audio + return audio / peak * self.preset.normalize_peak + + # ================================================================== # + # Pitch / time utilities + # ================================================================== # + + def _time_stretch(self, audio: np.ndarray, factor: float) -> np.ndarray: + """Time-stretch audio by *factor* (>1 = slower, <1 = faster). + + Uses librosa when available; falls back to scipy resampling. + """ + if abs(factor - 1.0) < 0.02 or len(audio) < 256: + return audio + + # Clamp to sane range + factor = float(np.clip(factor, 0.6, 1.6)) + + if _HAS_LIBROSA: + # librosa rate >1 = faster, so invert our convention + stretched = librosa.effects.time_stretch(y=audio, rate=1.0 / factor) + return stretched + + # Fallback: resample (also changes pitch — acceptable for lo-fi) + target_len = int(len(audio) * factor) + if target_len < 1: + return audio + return np.interp( + np.linspace(0, len(audio) - 1, target_len), + np.arange(len(audio)), + audio, + ) + + def _time_stretch_to_length( + self, + audio: np.ndarray, + target_length: int, + ) -> np.ndarray: + """Stretch/compress audio to fit a target sample count.""" + if target_length <= 0 or len(audio) == 0: + return audio + + current = len(audio) + if abs(current - target_length) / max(current, 1) < 0.05: + if current > target_length: + return audio[:target_length] + return np.pad(audio, (0, target_length - current)) + + ratio = target_length / current + ratio = float(np.clip(ratio, 0.7, 1.3)) + return self._time_stretch(audio, ratio) + + @staticmethod + def _resample_pitch_shift(audio: np.ndarray, semitones: float) -> np.ndarray: + """Fallback pitch shift via resampling (changes pitch + speed).""" + from math import gcd + + from scipy.signal import resample_poly + + ratio = 2.0 ** (-semitones / 12.0) + precision = 1000 + up = round(ratio * precision) + down = precision + + g = gcd(up, down) + up //= g + down //= g + + if up > 50 or down > 50: + scale = max(up, down) / 50 + up = max(1, int(up / scale)) + down = max(1, int(down / scale)) + + if up == down: + return audio + + resampled = resample_poly(audio, up, down) + if len(resampled) > len(audio): + return resampled[: len(audio)] + return np.pad(resampled, (0, len(audio) - len(resampled))) + + # ================================================================== # + # Shared utilities + # ================================================================== # + + @staticmethod + def _envelope_follower( + audio: np.ndarray, + attack_ms: float = 10.0, + release_ms: float = 100.0, + sr: int = SAMPLE_RATE, + ) -> np.ndarray: + """Simple peak-follower for gating noise to active regions.""" + attack_coeff = 1.0 - np.exp(-2.2 / (attack_ms / 1000.0 * sr)) + release_coeff = 1.0 - np.exp(-2.2 / (release_ms / 1000.0 * sr)) + + env = np.zeros_like(audio) + level = 0.0 + rect = np.abs(audio) + + for i in range(len(audio)): + if rect[i] > level: + level += attack_coeff * (rect[i] - level) + else: + level += release_coeff * (rect[i] - level) + env[i] = level + + return env diff --git a/pyproject.toml b/pyproject.toml index a6c7396..915cff7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,9 @@ dependencies = [ "pyttsx3>=2.90", "pyyaml>=6.0", "pedalboard>=0.9.0", + "librosa>=0.10.0", + "edge-tts>=6.1.0", + "piper-tts>=1.2.0", ] [project.optional-dependencies] @@ -22,6 +25,16 @@ dev = [ "pytest>=8.0", "pytest-cov>=6.0", ] +# Bark: install manually with `pip install git+https://github.com/suno-ai/bark.git` +chatterbox = [ + "chatterbox-tts>=0.1.0", +] +llm = [ + "ollama>=0.4.0", +] +reference = [ + "spotdl>=4.2.0", +] [project.scripts] mood-machine = "mood_machine.cli:main" diff --git a/tests/test_analysis.py b/tests/test_analysis.py new file mode 100644 index 0000000..465e1a2 --- /dev/null +++ b/tests/test_analysis.py @@ -0,0 +1,540 @@ +"""Tests for the audio analysis / reverse-engineering toolkit.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest + +from mood_machine.analysis.analyzer import ( + TrackProfile, + _bandpass, + analyze_track, + load_audio, +) +from mood_machine.analysis.comparator import ( + MatchLevel, + compare_profiles, +) +from mood_machine.analysis.parameter_mapper import ( + ParameterSuggestion, + suggest_parameters, +) +from mood_machine.analysis.report import ( + print_analysis_report, + print_comparison_report, + print_suggestions, +) +from mood_machine.config import build_config +from mood_machine.synth.core import SAMPLE_RATE + +# ── Helpers ───────────────────────────────────────────────────────────────── + + +def _make_sine_wav( + tmp_path: Path, freq: float = 440.0, duration: float = 5.0, sr: int = SAMPLE_RATE +) -> Path: + """Generate a mono sine wave WAV file.""" + from pydub import AudioSegment + + t = np.arange(int(sr * duration)) / sr + samples = np.sin(2 * np.pi * freq * t) * 0.8 + samples_int = (samples * 32767).astype(np.int16) + + seg = AudioSegment( + data=samples_int.tobytes(), + sample_width=2, + frame_rate=sr, + channels=1, + ) + path = tmp_path / "sine.wav" + seg.export(str(path), format="wav") + return path + + +def _make_stereo_wav( + tmp_path: Path, width: float = 0.0, duration: float = 3.0, sr: int = SAMPLE_RATE +) -> Path: + """Generate a stereo WAV with controllable width. + + *width* = 0 → identical L/R (mono), *width* = 1 → inverted L/R (max width). + """ + from pydub import AudioSegment + + t = np.arange(int(sr * duration)) / sr + base = np.sin(2 * np.pi * 440.0 * t) * 0.7 + + # Create stereo with side content + left = base * (1.0 + width * 0.5) + right = base * (1.0 - width * 0.5) + + stereo = np.column_stack([left, right]) + interleaved = stereo.flatten() + samples_int = (interleaved * 32767).astype(np.int16) + + seg = AudioSegment( + data=samples_int.tobytes(), + sample_width=2, + frame_rate=sr, + channels=2, + ) + path = tmp_path / "stereo.wav" + seg.export(str(path), format="wav") + return path + + +def _make_click_track( + tmp_path: Path, bpm: float = 80.0, duration: float = 10.0, sr: int = SAMPLE_RATE +) -> Path: + """Generate a click track at a specific BPM.""" + from pydub import AudioSegment + + n = int(sr * duration) + audio = np.zeros(n) + beat_interval = 60.0 / bpm + click_len = int(0.005 * sr) # 5ms click + + t_click = np.arange(click_len) / sr + click = np.sin(2 * np.pi * 1000 * t_click) * np.exp(-t_click * 300) * 0.9 + + pos = 0.0 + while pos < duration: + idx = int(pos * sr) + end = min(idx + click_len, n) + src_len = end - idx + if src_len > 0: + audio[idx:end] += click[:src_len] + pos += beat_interval + + samples_int = (audio * 32767).astype(np.int16) + seg = AudioSegment( + data=samples_int.tobytes(), + sample_width=2, + frame_rate=sr, + channels=1, + ) + path = tmp_path / "clicks.wav" + seg.export(str(path), format="wav") + return path + + +def _make_bandpass_noise( + tmp_path: Path, + low: float = 200.0, + high: float = 400.0, + duration: float = 5.0, + sr: int = SAMPLE_RATE, +) -> Path: + """Generate bandpass-filtered noise WAV.""" + from pydub import AudioSegment + + noise = np.random.default_rng(42).normal(0, 0.3, int(sr * duration)) + filtered = _bandpass(noise, low, high, sr) + # Normalise + peak = np.max(np.abs(filtered)) + if peak > 0: + filtered = filtered / peak * 0.7 + + samples_int = (filtered * 32767).astype(np.int16) + seg = AudioSegment( + data=samples_int.tobytes(), + sample_width=2, + frame_rate=sr, + channels=1, + ) + path = tmp_path / "noise.wav" + seg.export(str(path), format="wav") + return path + + +# ── TestLoadAudio ─────────────────────────────────────────────────────────── + + +class TestLoadAudio: + def test_load_mono_wav(self, tmp_path: Path): + path = _make_sine_wav(tmp_path, duration=2.0) + audio, sr = load_audio(path) + assert isinstance(audio, np.ndarray) + assert audio.ndim == 1 # mono + assert sr == SAMPLE_RATE + assert abs(len(audio) / sr - 2.0) < 0.1 + + def test_load_stereo_wav(self, tmp_path: Path): + path = _make_stereo_wav(tmp_path, width=0.5, duration=2.0) + audio, _sr = load_audio(path) + assert audio.ndim == 2 + assert audio.shape[1] == 2 + + def test_load_stereo_as_mono(self, tmp_path: Path): + path = _make_stereo_wav(tmp_path, width=0.5, duration=2.0) + audio, _sr = load_audio(path, mono=True) + assert audio.ndim == 1 + + def test_load_range(self, tmp_path: Path): + path = _make_sine_wav(tmp_path) + audio, _ = load_audio(path) + assert np.max(np.abs(audio)) <= 1.0 + + def test_load_nonexistent(self, tmp_path: Path): + with pytest.raises(FileNotFoundError): + load_audio(tmp_path / "nope.wav") + + +# ── TestAnalyzer ──────────────────────────────────────────────────────────── + + +class TestAnalyzer: + @pytest.fixture() + def sine_profile(self, tmp_path: Path) -> TrackProfile: + path = _make_sine_wav(tmp_path, freq=440.0, duration=5.0) + return analyze_track(path) + + @pytest.fixture() + def click_profile(self, tmp_path: Path) -> TrackProfile: + path = _make_click_track(tmp_path, bpm=80.0, duration=10.0) + return analyze_track(path) + + def test_profile_type(self, sine_profile: TrackProfile): + assert isinstance(sine_profile, TrackProfile) + + def test_duration(self, sine_profile: TrackProfile): + assert abs(sine_profile.duration_seconds - 5.0) < 0.5 + + def test_frequency_balance_sine(self, sine_profile: TrackProfile): + """440Hz sine should have most energy in the mid band.""" + assert sine_profile.energy_mid > sine_profile.energy_sub + 10 + assert sine_profile.energy_mid > sine_profile.energy_high + 10 + + def test_spectral_centroid_sine(self, sine_profile: TrackProfile): + """440Hz sine centroid should be near 440Hz.""" + # Allow a reasonable margin since windowing affects centroid + assert 200 < sine_profile.spectral_centroid_mean < 800 + + def test_peak_sine(self, sine_profile: TrackProfile): + """Sine at 0.8 amplitude should have peak near -2 dBFS.""" + assert -5 < sine_profile.peak_db < 0 + + def test_dynamics_constant_signal(self, sine_profile: TrackProfile): + """Constant-amplitude sine should have low RMS std.""" + assert sine_profile.rms_std_db < 5.0 + + def test_bpm_click_track(self, click_profile: TrackProfile): + """Click track at 80 BPM should detect approximately 80 BPM.""" + # BPM detection can be imprecise, especially for short tracks + assert 60 < click_profile.bpm < 100 + + def test_onset_rate_click(self, click_profile: TrackProfile): + """Click track should have detectable onsets.""" + assert click_profile.onset_rate > 0.5 + + def test_mfcc_populated(self, sine_profile: TrackProfile): + assert len(sine_profile.mfcc_means) == 13 + + def test_stereo_mono_signal(self, tmp_path: Path): + """Mono signal should have zero stereo width.""" + path = _make_sine_wav(tmp_path) + profile = analyze_track(path) + assert profile.stereo_width_mean < 0.05 + + def test_stereo_wide_signal(self, tmp_path: Path): + """Stereo signal with large width should be detected.""" + path = _make_stereo_wav(tmp_path, width=0.8, duration=3.0) + profile = analyze_track(path) + assert profile.stereo_width_mean > 0.05 + + def test_bandpass_noise_frequency(self, tmp_path: Path): + """Bandpass noise (200-400Hz) should concentrate energy in the low band.""" + path = _make_bandpass_noise(tmp_path, low=200, high=400) + profile = analyze_track(path) + # The 200-400Hz band falls in low (80-250) and low-mid (250-500) + assert profile.energy_low > profile.energy_high + 10 + assert profile.energy_low_mid > profile.energy_high + 10 + + def test_to_dict(self, sine_profile: TrackProfile): + d = sine_profile.to_dict() + assert isinstance(d, dict) + assert "bpm" in d + assert "energy_sub" in d + assert "mfcc_means" in d + + +# ── TestComparator ────────────────────────────────────────────────────────── + + +class TestComparator: + def _make_profile(self, **overrides) -> TrackProfile: + """Create a TrackProfile with defaults + overrides.""" + defaults = { + "filepath": "test.wav", + "duration_seconds": 60.0, + "bpm": 78.0, + "energy_sub": -20.0, + "energy_low": -16.0, + "energy_low_mid": -18.0, + "energy_mid": -20.0, + "energy_high_mid": -28.0, + "energy_high": -35.0, + "spectral_centroid_mean": 1200.0, + "spectral_rolloff_mean": 4000.0, + "spectral_bandwidth_mean": 1800.0, + "spectral_flatness_mean": 0.05, + "rms_mean_db": -18.0, + "dynamic_range_db": 12.0, + "crest_factor_db": 14.0, + "stereo_width_mean": 0.3, + "lr_balance_db": 0.0, + "drum_density": 8.0, + "transient_sharpness": 3.0, + "onset_rate": 2.0, + } + defaults.update(overrides) + return TrackProfile(**defaults) + + def test_identical_profiles(self): + p = self._make_profile() + report = compare_profiles(p, p) + assert report.overall_similarity > 0.95 + for m in report.metrics: + assert m.severity == MatchLevel.MATCH + + def test_divergent_bpm(self): + ref = self._make_profile(bpm=78.0) + gen = self._make_profile(bpm=126.0) + report = compare_profiles(ref, gen) + bpm_metric = next(m for m in report.metrics if "BPM" in m.name) + assert bpm_metric.severity == MatchLevel.FAR + assert bpm_metric.delta == pytest.approx(48.0) + + def test_close_bpm(self): + ref = self._make_profile(bpm=78.0) + gen = self._make_profile(bpm=80.0) + report = compare_profiles(ref, gen) + bpm_metric = next(m for m in report.metrics if "BPM" in m.name) + assert bpm_metric.severity in (MatchLevel.MATCH, MatchLevel.CLOSE) + + def test_frequency_divergence(self): + ref = self._make_profile(energy_sub=-15.0) + gen = self._make_profile(energy_sub=-25.0) + report = compare_profiles(ref, gen) + sub_metric = next(m for m in report.metrics if "Sub" in m.name) + assert sub_metric.severity in (MatchLevel.DIVERGENT, MatchLevel.FAR) + + def test_worst_divergences(self): + ref = self._make_profile(bpm=78, energy_sub=-15) + gen = self._make_profile(bpm=150, energy_sub=-40) + report = compare_profiles(ref, gen) + worst = report.get_worst_divergences(3) + assert len(worst) == 3 + assert worst[0].severity in (MatchLevel.DIVERGENT, MatchLevel.FAR) + + def test_get_by_category(self): + p = self._make_profile() + report = compare_profiles(p, p) + freq = report.get_by_category("frequency") + assert len(freq) > 0 + assert all(m.category == "frequency" for m in freq) + + def test_overall_similarity_range(self): + ref = self._make_profile() + gen = self._make_profile(bpm=200, energy_sub=-50) + report = compare_profiles(ref, gen) + assert 0.0 <= report.overall_similarity <= 1.0 + + +# ── TestParameterMapper ───────────────────────────────────────────────────── + + +class TestParameterMapper: + def _make_profile(self, **overrides) -> TrackProfile: + defaults = { + "filepath": "test.wav", + "duration_seconds": 60.0, + "bpm": 78.0, + "energy_sub": -20.0, + "energy_low": -16.0, + "energy_low_mid": -18.0, + "energy_mid": -20.0, + "energy_high_mid": -28.0, + "energy_high": -35.0, + "spectral_centroid_mean": 1200.0, + "spectral_rolloff_mean": 4000.0, + "spectral_bandwidth_mean": 1800.0, + "spectral_flatness_mean": 0.05, + "rms_mean_db": -18.0, + "dynamic_range_db": 12.0, + "crest_factor_db": 14.0, + "stereo_width_mean": 0.3, + "lr_balance_db": 0.0, + "drum_density": 8.0, + "transient_sharpness": 3.0, + "onset_rate": 2.0, + } + defaults.update(overrides) + return TrackProfile(**defaults) + + def test_no_suggestions_when_matched(self): + p = self._make_profile() + report = compare_profiles(p, p) + config = build_config() + suggestions = suggest_parameters(report, config) + assert len(suggestions) == 0 + + def test_bpm_suggestion(self): + ref = self._make_profile(bpm=126.0) + gen = self._make_profile(bpm=78.0) + report = compare_profiles(ref, gen) + config = build_config() + suggestions = suggest_parameters(report, config) + bpm_sugg = [s for s in suggestions if s.config_key == "bpm"] + assert len(bpm_sugg) == 1 + assert bpm_sugg[0].suggested_value == 126 + + def test_low_sub_suggests_kick_boost(self): + ref = self._make_profile(energy_sub=-15.0) + gen = self._make_profile(energy_sub=-25.0) + report = compare_profiles(ref, gen) + config = build_config() + suggestions = suggest_parameters(report, config) + kick_sugg = [s for s in suggestions if s.config_key == "levels.kick"] + assert len(kick_sugg) >= 1 + # Should suggest raising kick level + assert kick_sugg[0].suggested_value > kick_sugg[0].current_value + + def test_bright_suggests_lower_cutoff(self): + ref = self._make_profile(spectral_centroid_mean=800.0) + gen = self._make_profile(spectral_centroid_mean=1500.0) + report = compare_profiles(ref, gen) + config = build_config() + suggestions = suggest_parameters(report, config) + cutoff_sugg = [s for s in suggestions if s.config_key == "synth.pad_filter_cutoff"] + assert len(cutoff_sugg) >= 1 + assert cutoff_sugg[0].suggested_value < cutoff_sugg[0].current_value + + def test_suggestions_have_valid_fields(self): + ref = self._make_profile(bpm=120, energy_sub=-10) + gen = self._make_profile(bpm=78, energy_sub=-30) + report = compare_profiles(ref, gen) + config = build_config() + suggestions = suggest_parameters(report, config) + for s in suggestions: + assert isinstance(s, ParameterSuggestion) + assert s.config_key + assert s.reason + assert s.confidence in ("high", "medium", "low") + + def test_suggestions_deduplicate(self): + """Same config_key should not appear twice.""" + ref = self._make_profile(energy_sub=-10, energy_low=-10) + gen = self._make_profile(energy_sub=-25, energy_low=-25) + report = compare_profiles(ref, gen) + config = build_config() + suggestions = suggest_parameters(report, config) + keys = [s.config_key for s in suggestions] + assert len(keys) == len(set(keys)) + + +# ── TestReport ────────────────────────────────────────────────────────────── + + +class TestReport: + def _make_profile(self, **overrides) -> TrackProfile: + defaults = { + "filepath": "test.wav", + "duration_seconds": 60.0, + "bpm": 78.0, + "energy_sub": -20.0, + "energy_low": -16.0, + "energy_low_mid": -18.0, + "energy_mid": -20.0, + "energy_high_mid": -28.0, + "energy_high": -35.0, + "spectral_centroid_mean": 1200.0, + "rms_mean_db": -18.0, + "dynamic_range_db": 12.0, + "crest_factor_db": 14.0, + "stereo_width_mean": 0.3, + } + defaults.update(overrides) + return TrackProfile(**defaults) + + def test_print_analysis_report(self, capsys): + p = self._make_profile() + print_analysis_report(p) + captured = capsys.readouterr() + assert "TRACK ANALYSIS" in captured.out + assert "BPM" in captured.out + + def test_print_comparison_report(self, capsys): + ref = self._make_profile(bpm=78) + gen = self._make_profile(bpm=120) + report = compare_profiles(ref, gen) + print_comparison_report(report) + captured = capsys.readouterr() + assert "REFERENCE vs GENERATED" in captured.out + assert "similarity" in captured.out.lower() + + def test_print_suggestions_empty(self, capsys): + print_suggestions([]) + captured = capsys.readouterr() + assert "No parameter changes" in captured.out + + def test_print_suggestions_nonempty(self, capsys): + suggestions = [ + ParameterSuggestion( + config_key="bpm", + current_value=78, + suggested_value=120, + reason="Reference is 120 BPM", + confidence="high", + source_metric="bpm", + ) + ] + print_suggestions(suggestions) + captured = capsys.readouterr() + assert "SUGGESTED" in captured.out + assert "bpm" in captured.out + + +# ── TestCLI ───────────────────────────────────────────────────────────────── + + +class TestCLI: + def test_analyze_help(self): + """The analyze subcommand should be registered.""" + from mood_machine.cli import _build_parser + + parser = _build_parser() + # Should not raise + args = parser.parse_args(["analyze", "test.wav"]) + assert args.command == "analyze" + assert args.reference == "test.wav" + + def test_analyze_with_compare(self): + from mood_machine.cli import _build_parser + + parser = _build_parser() + args = parser.parse_args(["analyze", "ref.wav", "--compare", "gen.wav", "--suggest"]) + assert args.command == "analyze" + assert args.compare == "gen.wav" + assert args.suggest is True + + def test_generate_backward_compat(self): + """Running without a subcommand should still work (generate).""" + from mood_machine.cli import _build_parser + + parser = _build_parser() + args = parser.parse_args(["--mood", "paranoid", "--bpm", "85"]) + assert args.command is None # no subcommand + assert args.mood == "paranoid" + assert args.bpm == 85 + + def test_analyze_single_track(self, tmp_path: Path): + """Full integration: analyze a single WAV file via CLI.""" + path = _make_sine_wav(tmp_path, duration=3.0) + from mood_machine.cli import _build_parser, _run_analyze + + parser = _build_parser() + args = parser.parse_args(["analyze", str(path)]) + rc = _run_analyze(args) + assert rc == 0 diff --git a/tests/test_llm_lyrics.py b/tests/test_llm_lyrics.py new file mode 100644 index 0000000..b79f534 --- /dev/null +++ b/tests/test_llm_lyrics.py @@ -0,0 +1,186 @@ +"""Tests for the LLM-powered lyric generator and prompts.""" + +from __future__ import annotations + +from unittest.mock import patch + +import pytest + +from mood_machine.lyrics.llm_generator import LLMLyricGenerator, get_lyric_generator +from mood_machine.lyrics.prompts import ( + FEW_SHOT_EXAMPLES, + MOOD_OVERLAYS, + SCENE_SEEDS, + build_title_prompt, + build_verse_prompt, +) + +# ═══════════════════════════════════════════════════════════════════════════ +# Prompt construction +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestBuildVersePrompt: + def test_returns_system_and_user(self): + system, user = build_verse_prompt("melancholic") + assert isinstance(system, str) + assert isinstance(user, str) + assert len(system) > 100 + assert "melancholic" in user.lower() + + @pytest.mark.parametrize("mood", ["melancholic", "paranoid", "ethereal"]) + def test_includes_mood_overlay(self, mood): + system, _ = build_verse_prompt(mood) + assert "MOOD-SPECIFIC" in system + + @pytest.mark.parametrize("mood", ["melancholic", "paranoid", "ethereal"]) + def test_includes_few_shot_examples(self, mood): + system, _ = build_verse_prompt(mood) + assert "Example 1" in system + + def test_includes_scene_seed(self): + _, user = build_verse_prompt("melancholic", scene_seed="watching the rain") + assert "watching the rain" in user + + def test_unknown_mood_falls_back(self): + system, _ = build_verse_prompt("nonexistent_mood") + assert "Melancholic" in system + + def test_verse_number_in_prompt(self): + _, user = build_verse_prompt("melancholic", verse_number=2, total_verses=3) + assert "verse 2 of 3" in user + + +class TestBuildTitlePrompt: + def test_returns_system_and_user(self): + system, user = build_title_prompt("melancholic") + assert "title" in system.lower() + assert "melancholic" in user.lower() + + +class TestPromptData: + @pytest.mark.parametrize("mood", ["melancholic", "paranoid", "ethereal"]) + def test_scene_seeds_exist(self, mood): + assert mood in SCENE_SEEDS + assert len(SCENE_SEEDS[mood]) >= 5 + + @pytest.mark.parametrize("mood", ["melancholic", "paranoid", "ethereal"]) + def test_few_shot_examples_exist(self, mood): + assert mood in FEW_SHOT_EXAMPLES + assert len(FEW_SHOT_EXAMPLES[mood]) >= 2 + + @pytest.mark.parametrize("mood", ["melancholic", "paranoid", "ethereal"]) + def test_mood_overlays_exist(self, mood): + assert mood in MOOD_OVERLAYS + + +# ═══════════════════════════════════════════════════════════════════════════ +# LLM Generator (mocked) +# ═══════════════════════════════════════════════════════════════════════════ + +_MOCK_VERSE = """\ +I left the heating on again. +the flat is warm but it still feels hollow. +I found your mug behind the cookbooks. +sort of sad, sort of nothing. +I washed it and put it back.\ +""" + + +class TestLLMLyricGenerator: + def _make_gen(self): + return LLMLyricGenerator(mood="melancholic", model="test-model") + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", False) + def test_unavailable_without_ollama(self): + gen = self._make_gen() + assert gen.is_available() is False + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_available_when_ollama_responds(self, mock_ollama): + mock_ollama.list.return_value = {"models": []} + gen = self._make_gen() + assert gen.is_available() is True + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_unavailable_when_ollama_errors(self, mock_ollama): + mock_ollama.list.side_effect = ConnectionError("no connection") + gen = self._make_gen() + assert gen.is_available() is False + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_generate_verse(self, mock_ollama): + mock_ollama.list.return_value = {"models": []} + mock_ollama.chat.return_value = {"message": {"content": _MOCK_VERSE}} + gen = self._make_gen() + verse = gen.generate_verse(num_lines=5) + assert isinstance(verse, str) + assert len(verse.strip()) > 0 + assert "\n" in verse # multi-line + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_generate_title(self, mock_ollama): + mock_ollama.list.return_value = {"models": []} + mock_ollama.chat.return_value = {"message": {"content": "the heating"}} + gen = self._make_gen() + title = gen.generate_title() + assert isinstance(title, str) + assert len(title) > 0 + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_generate_lyrics_multiple_verses(self, mock_ollama): + mock_ollama.list.return_value = {"models": []} + mock_ollama.chat.return_value = {"message": {"content": _MOCK_VERSE}} + gen = self._make_gen() + verses = gen.generate_lyrics(num_verses=2, lines_per_verse=5) + assert len(verses) == 2 + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_fallback_on_chat_failure(self, mock_ollama): + mock_ollama.list.return_value = {"models": []} + mock_ollama.chat.side_effect = Exception("model not found") + gen = self._make_gen() + # Should fall back to template generator + verse = gen.generate_verse(num_lines=4) + assert isinstance(verse, str) + assert len(verse.strip()) > 0 + + def test_clean_verse_strips_artifacts(self): + raw = "Verse 1:\n\nI left the heating on.\nthe flat is warm.\n\n---" + cleaned = LLMLyricGenerator._clean_verse(raw, target_lines=2) + assert "Verse 1:" not in cleaned + assert "---" not in cleaned + assert "I left the heating on." in cleaned + + +# ═══════════════════════════════════════════════════════════════════════════ +# Factory +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestGetLyricGenerator: + def test_template_returns_template_generator(self): + gen = get_lyric_generator(engine="template", mood="melancholic") + from mood_machine.lyrics.generator import LyricGenerator + + assert isinstance(gen, LyricGenerator) + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", False) + def test_llm_falls_back_to_template(self): + gen = get_lyric_generator(engine="llm", mood="melancholic") + from mood_machine.lyrics.generator import LyricGenerator + + assert isinstance(gen, LyricGenerator) + + @patch("mood_machine.lyrics.llm_generator._HAS_OLLAMA", True) + @patch("mood_machine.lyrics.llm_generator._ollama") + def test_llm_returns_llm_generator(self, mock_ollama): + mock_ollama.list.return_value = {"models": []} + gen = get_lyric_generator(engine="llm", mood="melancholic") + assert isinstance(gen, LLMLyricGenerator) diff --git a/tests/test_new_engines.py b/tests/test_new_engines.py new file mode 100644 index 0000000..1b3bf30 --- /dev/null +++ b/tests/test_new_engines.py @@ -0,0 +1,130 @@ +"""Tests for Bark, Chatterbox engines and Edge TTS text preparation.""" + +from __future__ import annotations + +from unittest.mock import MagicMock + +import numpy as np + +from mood_machine.synth.core import SAMPLE_RATE +from mood_machine.vocals.engine import ( + BarkEngine, + ChatterboxEngine, + EdgeTTSEngine, + get_vocal_engine, +) + +# ═══════════════════════════════════════════════════════════════════════════ +# Edge TTS text preparation +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestEdgeTTSTextPrep: + def _make_engine(self): + return EdgeTTSEngine() + + def test_double_dash_becomes_ellipsis(self): + engine = self._make_engine() + result = engine._prepare_text("I was going to -- but then I stopped.") + assert "..." in result + assert "--" not in result + + def test_ellipsis_gets_spacing(self): + engine = self._make_engine() + result = engine._prepare_text("I thought...maybe not.") + assert "... " in result + + def test_bare_lines_get_period(self): + engine = self._make_engine() + result = engine._prepare_text("hello world\nsecond line") + lines = result.strip().split("\n") + for line in lines: + line = line.strip() + if line: + assert line[-1] in ".!?…" + + def test_lines_with_punctuation_unchanged(self): + engine = self._make_engine() + result = engine._prepare_text("hello world!\nwhat?") + assert "hello world!" in result + assert "what?" in result + + def test_default_voice_is_british(self): + engine = self._make_engine() + assert "GB" in engine.voice + + def test_prefers_verse_level(self): + engine = self._make_engine() + assert engine.prefers_verse_level is True + + +# ═══════════════════════════════════════════════════════════════════════════ +# Bark engine (mocked) +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestBarkEngine: + def test_prefers_verse_level(self): + engine = BarkEngine() + assert engine.prefers_verse_level is True + + def test_synthesise_returns_array(self): + import sys + + # Create a fake bark module so the import inside synthesise() works + mock_gen = MagicMock( + return_value=np.random.randn(24000).astype(np.float32), + ) + mock_bark = MagicMock() + mock_bark.generate_audio = mock_gen + mock_bark.preload_models = MagicMock() + + sys.modules["bark"] = mock_bark + + try: + engine = BarkEngine() + engine._loaded = True + result = engine.synthesise("Hello world.") + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + # Should be resampled to SAMPLE_RATE + expected_len = int(24000 * SAMPLE_RATE / 24000) + assert abs(len(result) - expected_len) < 100 + finally: + del sys.modules["bark"] + + +# ═══════════════════════════════════════════════════════════════════════════ +# Chatterbox engine (mocked) +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestChatterboxEngine: + def test_prefers_verse_level(self): + engine = ChatterboxEngine() + assert engine.prefers_verse_level is True + + def test_default_params(self): + engine = ChatterboxEngine() + assert engine.exaggeration == 0.5 + assert engine.cfg_weight == 0.5 + assert engine.voice_reference_path is None + + +# ═══════════════════════════════════════════════════════════════════════════ +# Factory with new engines +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestFactoryNewEngines: + def test_bark_falls_back_when_not_installed(self): + """bark not installed -> should fall through to next engine.""" + # get_vocal_engine("bark") should try bark, fail, and fall back + # Since bark isn't installed in test env, it should cascade + engine = get_vocal_engine("bark") + # Should not be a BarkEngine (bark not installed) + assert not isinstance(engine, BarkEngine) + + def test_chatterbox_falls_back_when_not_installed(self): + engine = get_vocal_engine("chatterbox") + assert not isinstance(engine, ChatterboxEngine) diff --git a/tests/test_sequencer.py b/tests/test_sequencer.py index 6a9164e..e545b27 100644 --- a/tests/test_sequencer.py +++ b/tests/test_sequencer.py @@ -18,13 +18,17 @@ class TestDrumPatterns: @pytest.mark.parametrize("style", ["sparse", "standard", "full", "variation"]) def test_pattern_has_16_steps(self, style): - pattern = get_drum_patterns(style) - assert isinstance(pattern, dict) - for inst, steps in pattern.items(): - assert len(steps) == 16, f"{inst} in {style} has {len(steps)} steps" + variations = get_drum_patterns(style) + assert isinstance(variations, list) + assert len(variations) >= 1 + for variation in variations: + assert isinstance(variation, dict) + for inst, steps in variation.items(): + assert len(steps) == 16, f"{inst} in {style} has {len(steps)} steps" def test_standard_has_kick(self): - pattern = get_drum_patterns("standard") + variations = get_drum_patterns("standard") + pattern = variations[0] assert "kick" in pattern assert sum(pattern["kick"]) > 0 diff --git a/tests/test_synth_instruments.py b/tests/test_synth_instruments.py index 2968318..f190b0a 100644 --- a/tests/test_synth_instruments.py +++ b/tests/test_synth_instruments.py @@ -20,7 +20,7 @@ def drums(self): def test_kick_shape(self, drums): k = drums.kick() assert isinstance(k, np.ndarray) - assert len(k) == int(0.45 * SAMPLE_RATE) + assert len(k) == int(0.50 * SAMPLE_RATE) def test_kick_has_energy(self, drums): assert np.max(np.abs(drums.kick())) > 0.01 @@ -32,7 +32,7 @@ def test_snare_shape(self, drums): def test_hihat_closed_short(self, drums): h = drums.hihat_closed() - assert len(h) == int(0.06 * SAMPLE_RATE) + assert len(h) == int(0.035 * SAMPLE_RATE) def test_clap_shape(self, drums): c = drums.clap() diff --git a/tests/test_vocal_processor.py b/tests/test_vocal_processor.py new file mode 100644 index 0000000..8ff6982 --- /dev/null +++ b/tests/test_vocal_processor.py @@ -0,0 +1,294 @@ +"""Tests for the humanised vocal processor module.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from mood_machine.synth.core import SAMPLE_RATE +from mood_machine.vocals.processor import ( + VOICE_PRESETS, + VocalProcessor, + _trim_silence, + get_voice_preset, + split_into_sentences, +) + +# ── Helpers ──────────────────────────────────────────────────────────────── + + +def _test_vocal(duration: float = 2.0, freq: float = 200.0) -> np.ndarray: + """Simulate TTS output: sine wave with amplitude envelope.""" + sr = SAMPLE_RATE + t = np.linspace(0, duration, int(sr * duration)) + env = np.ones_like(t) + ramp = int(0.1 * sr) + env[:ramp] = np.linspace(0, 1, ramp) + env[-ramp:] = np.linspace(1, 0, ramp) + return np.sin(2 * np.pi * freq * t) * env * 0.8 + + +def _test_sentences(n: int = 3, duration: float = 1.0) -> list[np.ndarray]: + """Simulate a list of per-sentence TTS clips.""" + return [_test_vocal(duration=duration, freq=180 + i * 20) for i in range(n)] + + +# ═══════════════════════════════════════════════════════════════════════════ +# Text splitting +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestSplitIntoSentences: + def test_splits_on_period(self): + text = "This is sentence one. This is sentence two. And three." + result = split_into_sentences(text) + assert len(result) == 3 + + def test_splits_on_mixed_punctuation(self): + text = "What the hell? I can't believe this! It doesn't matter." + result = split_into_sentences(text) + assert len(result) == 3 + + def test_single_sentence(self): + text = "Just one long sentence without any ending punctuation" + result = split_into_sentences(text) + assert len(result) == 1 + assert result[0] == text + + def test_filters_short_fragments(self): + text = "Real sentence here. No. Also this one is fine." + result = split_into_sentences(text) + # "No." is ≤5 chars, should be filtered + assert len(result) == 2 + + def test_empty_string(self): + result = split_into_sentences("") + assert len(result) == 1 + + +class TestTrimSilence: + def test_trims_leading_silence(self): + silence = np.zeros(1000) + signal = np.ones(500) * 0.5 + audio = np.concatenate([silence, signal]) + trimmed = _trim_silence(audio) + assert len(trimmed) < len(audio) + assert np.max(np.abs(trimmed)) > 0.1 + + def test_preserves_non_silent_audio(self): + audio = np.ones(500) * 0.5 + trimmed = _trim_silence(audio) + assert len(trimmed) > 400 # mostly preserved + + def test_silent_audio_unchanged(self): + audio = np.zeros(500) + trimmed = _trim_silence(audio) + assert len(trimmed) == 500 + + +# ═══════════════════════════════════════════════════════════════════════════ +# Presets +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestVoicePresets: + def test_all_presets_exist(self): + for name in ("headache", "spectral", "broken_radio", "clean"): + assert name in VOICE_PRESETS + + def test_get_voice_preset_returns_copy(self): + a = get_voice_preset("headache") + b = get_voice_preset("headache") + a.pitch_shift_semitones = 999 + assert b.pitch_shift_semitones != 999 + + def test_unknown_preset_falls_back(self): + p = get_voice_preset("nonexistent_xyz") + assert p.name == "headache" + + def test_presets_have_humanisation_params(self): + for name, preset in VOICE_PRESETS.items(): + assert hasattr(preset, "vibrato_rate_hz"), f"{name} missing vibrato_rate_hz" + assert hasattr(preset, "breath_probability"), f"{name} missing breath_probability" + assert hasattr(preset, "pace_drift"), f"{name} missing pace_drift" + assert hasattr(preset, "dynamic_range"), f"{name} missing dynamic_range" + + +# ═══════════════════════════════════════════════════════════════════════════ +# Processor — sentence-level API (primary) +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestProcessVerseSentences: + @pytest.fixture() + def processor(self): + return VocalProcessor(preset="headache", bpm=73) + + def test_returns_array(self, processor): + sents = _test_sentences(3) + result = processor.process_verse_sentences(sents) + assert isinstance(result, np.ndarray) + assert len(result) > 0 + + def test_output_longer_than_single_sentence(self, processor): + """Pauses and breaths should make output longer than any one sentence.""" + sents = _test_sentences(3, duration=1.0) + result = processor.process_verse_sentences(sents) + longest_input = max(len(s) for s in sents) + assert len(result) > longest_input + + def test_normalizes_to_preset_peak(self, processor): + sents = _test_sentences(2) + result = processor.process_verse_sentences(sents) + peak = np.max(np.abs(result)) + assert peak == pytest.approx(processor.preset.normalize_peak, abs=0.03) + + def test_empty_list_returns_silence(self, processor): + result = processor.process_verse_sentences([]) + assert len(result) == SAMPLE_RATE # 1s fallback + + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_all_presets_produce_output(self, preset_name): + proc = VocalProcessor(preset=preset_name, bpm=80) + sents = _test_sentences(2, duration=1.0) + result = proc.process_verse_sentences(sents) + assert len(result) > 0 + assert np.max(np.abs(result)) > 0.01 + + +class TestProcessVerses: + def test_processes_multiple_verses(self): + proc = VocalProcessor(preset="headache", bpm=73) + verses = [_test_sentences(3), _test_sentences(2)] + results = proc.process_verses(verses) + assert len(results) == 2 + for r in results: + assert isinstance(r, np.ndarray) + assert len(r) > 0 + + +# ═══════════════════════════════════════════════════════════════════════════ +# Processor — legacy single-clip API +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestLegacyProcess: + @pytest.fixture() + def processor(self): + return VocalProcessor(preset="headache", bpm=73) + + def test_process_returns_array(self, processor): + audio = _test_vocal() + result = processor.process(audio) + assert isinstance(result, np.ndarray) + assert len(result) > 0 + + def test_process_changes_audio(self, processor): + audio = _test_vocal() + result = processor.process(audio) + assert not np.array_equal(result, audio) + + def test_short_audio_passthrough(self, processor): + short = np.array([0.1, 0.2, 0.3]) + result = processor.process(short) + assert np.array_equal(result, short) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Humanisation stages +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestVibrato: + def test_vibrato_modifies_audio(self): + proc = VocalProcessor(preset="headache", bpm=80) + audio = _test_vocal(freq=300) + result = proc._apply_vibrato(audio) + assert not np.array_equal(result, audio) + + def test_zero_vibrato_passthrough(self): + proc = VocalProcessor(preset="clean", bpm=80) + audio = _test_vocal(freq=300) + result = proc._apply_vibrato(audio) + assert np.array_equal(result, audio) + + +class TestBreathGeneration: + def test_breath_has_correct_duration(self): + proc = VocalProcessor(preset="headache", bpm=80) + breath = proc._generate_breath() + expected_samples = int(proc.preset.breath_duration_ms / 1000.0 * SAMPLE_RATE) + assert len(breath) == expected_samples + + def test_breath_is_quiet(self): + proc = VocalProcessor(preset="headache", bpm=80) + breath = proc._generate_breath() + # Breath should be very quiet + assert np.max(np.abs(breath)) < 0.15 + + def test_breath_is_not_silence(self): + proc = VocalProcessor(preset="headache", bpm=80) + breath = proc._generate_breath() + assert np.max(np.abs(breath)) > 0.0001 + + +class TestPaceFactors: + def test_factors_are_near_one(self): + proc = VocalProcessor(preset="headache", bpm=80) + factors = proc._sentence_pace_factors(5) + assert len(factors) == 5 + for f in factors: + assert 0.75 <= f <= 1.35 + + def test_zero_drift_returns_ones(self): + proc = VocalProcessor(preset="headache", bpm=80) + proc.preset.pace_drift = 0.0 + factors = proc._sentence_pace_factors(5) + assert all(f == 1.0 for f in factors) + + +class TestDynamicGains: + def test_gains_have_arc_shape(self): + proc = VocalProcessor(preset="headache", bpm=80) + gains = proc._sentence_dynamic_gains(10) + assert len(gains) == 10 + for g in gains: + assert 0.5 <= g <= 1.2 + + def test_emotional_arc_builds(self): + """Early sentences should generally be quieter than middle ones.""" + np.random.seed(42) + proc = VocalProcessor(preset="headache", bpm=80) + gains = proc._sentence_dynamic_gains(10) + # Average of first 3 vs average of middle 3 + avg_start = np.mean(gains[:3]) + avg_mid = np.mean(gains[4:7]) + assert avg_mid >= avg_start - 0.1 # middle ≥ start (with margin) + + +class TestPitchShift: + def test_pitch_down_changes_spectrum(self): + proc = VocalProcessor(preset="headache", bpm=80) + audio = _test_vocal(freq=400) + shifted = proc._pitch_shift(audio) + zc_orig = np.sum(np.diff(np.sign(audio)) != 0) + zc_shifted = np.sum(np.diff(np.sign(shifted)) != 0) + assert zc_shifted < zc_orig + + +class TestLoFiDegrade: + def test_bitcrush_quantizes(self): + proc = VocalProcessor(preset="broken_radio", bpm=80) + audio = np.linspace(-1, 1, 1000) + crushed = proc._lofi_degrade(audio) + assert len(np.unique(np.round(crushed, 4))) < len(np.unique(np.round(audio, 4))) + + +class TestStutter: + def test_stutter_modifies_audio(self): + np.random.seed(42) + proc = VocalProcessor(preset="headache", bpm=80) + proc.preset.chop_probability = 1.0 # force stutter + audio = _test_vocal(duration=3.0) + stuttered = proc._stutter(audio) + assert not np.array_equal(stuttered, audio) diff --git a/tests/test_vocal_processor_new.py b/tests/test_vocal_processor_new.py new file mode 100644 index 0000000..3e7fa89 --- /dev/null +++ b/tests/test_vocal_processor_new.py @@ -0,0 +1,237 @@ +"""Tests for new vocal processor features: formant EQ, room tone, content-aware pauses, word emphasis.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from mood_machine.synth.core import SAMPLE_RATE +from mood_machine.vocals.processor import VOICE_PRESETS, VocalProcessor + + +def _test_vocal(duration: float = 2.0, freq: float = 200.0) -> np.ndarray: + """Simulate TTS output: sine wave with amplitude envelope.""" + sr = SAMPLE_RATE + t = np.linspace(0, duration, int(sr * duration)) + env = np.ones_like(t) + ramp = int(0.1 * sr) + env[:ramp] = np.linspace(0, 1, ramp) + env[-ramp:] = np.linspace(1, 0, ramp) + return np.sin(2 * np.pi * freq * t) * env * 0.8 + + +def _test_sentences(n: int = 3, duration: float = 1.0) -> list[np.ndarray]: + return [_test_vocal(duration=duration, freq=180 + i * 20) for i in range(n)] + + +# ═══════════════════════════════════════════════════════════════════════════ +# Presets have new fields +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestNewPresetFields: + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_presets_have_presence_boost(self, preset_name): + preset = VOICE_PRESETS[preset_name] + assert hasattr(preset, "presence_boost_db") + assert isinstance(preset.presence_boost_db, float) + + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_presets_have_warmth_boost(self, preset_name): + preset = VOICE_PRESETS[preset_name] + assert hasattr(preset, "warmth_boost_db") + + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_presets_have_room_tone(self, preset_name): + preset = VOICE_PRESETS[preset_name] + assert hasattr(preset, "room_tone") + + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_presets_have_content_aware_pauses(self, preset_name): + preset = VOICE_PRESETS[preset_name] + assert hasattr(preset, "content_aware_pauses") + + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_presets_have_word_emphasis(self, preset_name): + preset = VOICE_PRESETS[preset_name] + assert hasattr(preset, "word_emphasis") + + +# ═══════════════════════════════════════════════════════════════════════════ +# Formant EQ +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestFormantEQ: + def test_formant_eq_modifies_audio(self): + proc = VocalProcessor(preset="headache", bpm=80) + audio = _test_vocal(freq=300) + result = proc._formant_eq(audio) + assert not np.array_equal(result, audio) + + def test_formant_eq_preserves_length(self): + proc = VocalProcessor(preset="headache", bpm=80) + audio = _test_vocal(freq=300) + result = proc._formant_eq(audio) + assert len(result) == len(audio) + + def test_formant_eq_zero_boost_passthrough(self): + proc = VocalProcessor(preset="headache", bpm=80) + proc.preset.presence_boost_db = 0.0 + proc.preset.warmth_boost_db = 0.0 + audio = _test_vocal(freq=300) + result = proc._formant_eq(audio) + # Should be effectively unchanged + np.testing.assert_allclose(result, audio, atol=1e-10) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Room tone +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestRoomTone: + def test_room_tone_modifies_audio(self): + proc = VocalProcessor(preset="headache", bpm=80) + audio = _test_vocal(freq=300) + result = proc._apply_room_tone(audio) + assert not np.array_equal(result, audio) + + def test_room_tone_preserves_length(self): + proc = VocalProcessor(preset="headache", bpm=80) + audio = _test_vocal(freq=300) + result = proc._apply_room_tone(audio) + assert len(result) == len(audio) + + def test_room_tone_zero_passthrough(self): + proc = VocalProcessor(preset="clean", bpm=80) + proc.preset.room_tone = 0.0 + audio = _test_vocal(freq=300) + result = proc._apply_room_tone(audio) + np.testing.assert_array_equal(result, audio) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Content-aware pauses +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestContentAwarePauses: + def _proc(self): + return VocalProcessor(preset="headache", bpm=80) + + def test_ellipsis_produces_long_pause(self): + proc = self._proc() + # Compare pause with ellipsis vs period + pause_ellipsis = proc._generate_pause(sentence_text="I thought...") + pause_period = proc._generate_pause(sentence_text="I thought.") + # Ellipsis should produce a longer pause on average + # (there's randomness, so just check the base scaling) + # We check that the method accepts the text without error + assert len(pause_ellipsis) > 0 + assert len(pause_period) > 0 + + def test_comma_produces_short_pause(self): + proc = self._proc() + np.random.seed(42) + pause_comma = proc._generate_pause(sentence_text="well,") + np.random.seed(42) + pause_period = proc._generate_pause(sentence_text="well.") + # Comma pause should be shorter + assert len(pause_comma) < len(pause_period) + + def test_dash_produces_long_pause(self): + proc = self._proc() + np.random.seed(42) + pause_dash = proc._generate_pause(sentence_text="I was going to --") + np.random.seed(42) + pause_period = proc._generate_pause(sentence_text="I was going to.") + assert len(pause_dash) > len(pause_period) + + def test_none_text_uses_default(self): + proc = self._proc() + pause = proc._generate_pause(sentence_text=None) + assert len(pause) > 0 + + def test_disabled_content_aware(self): + proc = self._proc() + proc.preset.content_aware_pauses = False + np.random.seed(42) + pause_ellipsis = proc._generate_pause(sentence_text="I thought...") + np.random.seed(42) + pause_period = proc._generate_pause(sentence_text="I thought.") + # When disabled, both should be the same + assert len(pause_ellipsis) == len(pause_period) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Word emphasis +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestWordEmphasis: + def _proc(self): + return VocalProcessor(preset="headache", bpm=80) + + def test_emphasis_boosts_volume(self): + proc = self._proc() + audio = _test_vocal(freq=300, duration=1.0) + # Text with trigger words should be louder + result = proc._apply_word_emphasis(audio, "I was never alone and always lost.") + # The RMS of the result should be higher + rms_orig = np.sqrt(np.mean(audio**2)) + rms_result = np.sqrt(np.mean(result**2)) + assert rms_result > rms_orig + + def test_no_trigger_words_passthrough(self): + proc = self._proc() + audio = _test_vocal(freq=300, duration=1.0) + result = proc._apply_word_emphasis(audio, "The quick brown fox jumped.") + np.testing.assert_array_equal(result, audio) + + def test_emphasis_preserves_length(self): + proc = self._proc() + audio = _test_vocal(freq=300, duration=1.0) + result = proc._apply_word_emphasis(audio, "I was never going to be alone.") + assert len(result) == len(audio) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Integration: sentence texts flow through +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestSentenceTextsIntegration: + def test_process_verse_sentences_accepts_texts(self): + proc = VocalProcessor(preset="headache", bpm=80) + sents = _test_sentences(3, duration=1.0) + texts = ["I was alone.", "Never going back...", "I suppose that is it."] + result = proc.process_verse_sentences(sents, sentence_texts=texts) + assert isinstance(result, np.ndarray) + assert len(result) > 0 + + def test_process_verse_sentences_works_without_texts(self): + proc = VocalProcessor(preset="headache", bpm=80) + sents = _test_sentences(3, duration=1.0) + result = proc.process_verse_sentences(sents) + assert isinstance(result, np.ndarray) + assert len(result) > 0 + + def test_process_verses_accepts_verse_texts(self): + proc = VocalProcessor(preset="headache", bpm=80) + verses = [_test_sentences(2), _test_sentences(2)] + verse_texts = [ + ["I was alone.", "Never going back."], + ["Sort of hollow.", "I suppose."], + ] + results = proc.process_verses(verses, verse_texts=verse_texts) + assert len(results) == 2 + + @pytest.mark.parametrize("preset_name", list(VOICE_PRESETS.keys())) + def test_all_presets_work_with_new_features(self, preset_name): + proc = VocalProcessor(preset=preset_name, bpm=80) + sents = _test_sentences(2, duration=1.0) + texts = ["I was always alone.", "Nothing ever changes."] + result = proc.process_verse_sentences(sents, sentence_texts=texts) + assert len(result) > 0 + assert np.max(np.abs(result)) > 0.01 diff --git a/uv.lock 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