[FEATURE] WaveNet: Model head, layer array head variable kernel size#249
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Implement PostStackHead matching Python Head export: weight order is layer arrays, post-stack conv weights, then head_scale. Parse head JSON in parse_config_json; output channels come from head.out_channels when present. Reject post-stack head on SlimmableWavenet. Add unit tests for RF and smoke process. Made-with: Cursor
Match neural-amp-modeler: head export omits in_channels; derive from last layer head_size. Optional legacy in_channels is validated when present. Made-with: Cursor
Apply each head layer's activation to that layer's actual input buffer, and add a regression test for two-layer heads so multi-layer kernel stacks remain numerically correct. Made-with: Cursor
- LayerArrayParams gains head_kernel_size; head rechannel uses Conv1D; RF includes head kernel. - parse_config_json: nested head or legacy head_size/head_bias; slimming rejects k!=1. - Tests: LayerArrayParams call sites, test_layer_head_config legacy vs new schema. Made-with: Cursor
Add test_process_with_post_stack_head_realtime_safe (two-layer head, several buffer sizes) and register it in run_tests. Includes formatting from format.sh on related sources. Made-with: Cursor
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rechannel_heads are no longerConv1x1s but can havekernel_size > 1(generalConv1D).