Commit Graph
6 Commits
Author SHA1 Message Date
osobhandClaude Opus 4.7 e61bd70e03 rtx-csm: WavLM-SV bug fixes — gelu_erf + gru_rel_pos_const loading
Two real bugs found via code inspection against HF source:

1. candle's .gelu() is the tanh approximation; PyTorch's default 'gelu'
   activation (used in WavLM via ACT2FN['gelu']) is the exact erf-based
   version. Switched all 3 sites (feature extractor convs, pos_conv,
   FFN) from .gelu() to .gelu_erf() to match the reference.

2. gru_rel_pos_const lookup used vb.pp("name").get(shape, "") which
   resolves to "<prefix>.name." (trailing dot) and fails to find the
   tensor. The .or_else(|_| zeros) silently swallowed the failure,
   leaving all 12 layers' gating constants at zero instead of the
   trained values. Fixed to attn.get(shape, "gru_rel_pos_const") which
   resolves correctly.

examples/wavlm_sv_inspect.rs: utility for sanity-checking specific
tensors inside converted safetensors (e.g. layer_weights).

Same-content same-speaker cosine: 0.9985 -> 0.9963 (≈unchanged).
Cross-content same-speaker cosine: 0.4882 -> 0.4118 (still drifting).
Phase 5d (Python reference comparison) remains the gate for
identifying the residual numerical drift.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
2026-04-25 22:00:59 -07:00
osobhandClaude Opus 4.7 f4aaee2f1b rtx-csm: end-to-end pipeline showcase demo
Single CLI ties together every capability shipped this session:
text -> CSM-1B (with optional LoRA) -> post-process (HPF/declick/LUFS)
-> AudioSeal watermark embed -> AudioSeal detect verify -> WavLM-SV
speaker embedding + optional reference scoring.

Verified on Metal: 4s speech generated + watermarked + detected
(mean_presence=0.9999, 16/16 bits decoded) + 512-d speaker embedding
extracted in ~30s.

Cross-content same-speaker cosine sits around 0.49 vs 0.998 for
same-content same-speaker — suggests the WavLM-SV port may leak content
into the speaker embedding more than the HF reference. Phase 5d numerical
parity work (Python sidecar comparison) would tighten this.

This is the canonical usage example for downstream callers
(clawsample-csm etc.) — copy the structure.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
2026-04-25 21:16:11 -07:00
osobhandClaude Opus 4.7 d1dba7a05c rtx-csm: WavLM-SV converter + end-to-end speaker similarity
Phase 5c — pure-Rust converter for microsoft/wavlm-base-plus-sv with
auto-detecting weight_norm merger; verified on real 100M-param weights:
load + embed + cosine-similarity round-trip works on Metal.

- wavlm_sv_convert.rs: candle_core::pickle reads pytorch_model.bin
  directly. merge_weight_norm_auto picks the kept dim from g's shape
  (dim=0 for AudioSeal SEANet, dim=2 for WavLM pos_conv_embed). Skips
  classifier.*/objective.* (train-only AMSoftmax head).
- examples/wavlm_sv_convert: HF download + convert CLI. Verified output:
  1 weight_norm pair merged + 261 passthrough + 3 skipped = 262 tensors.
- examples/wavlm_sv_demo: load + embed pair of WAVs + cosine similarity.
- examples/audioseal_inspect: gains --which wavlm-sv variant for key
  discovery.
- hub.rs: REPO_WAVLM_SV + resolve_wavlm_sv() helper.
- wavlm_sv::XVectorHead bug fix: layer_weights is top-level, not nested
  under prefix.

Verified end-to-end on Metal: cosine sim 0.9985 on same-speaker pair
(CSM vs CSM-watermarked, 10s @ 24 kHz resampled to 16 kHz). Numerical
parity vs HF reference is Phase 5d.

3 converter tests + 12 wavlm_sv tests; 78 lib tests total green.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
2026-04-25 19:57:52 -07:00
osobhandClaude Opus 4.7 9d28597687 rtx-csm: AudioSeal apply CLI for arbitrary-rate WAVs
End-to-end watermarker driver that handles any source sample rate by
resampling to AudioSeal's 16 kHz native, embedding, then resampling back.
Tested on 10s of real CSM 24 kHz speech: mean_presence=0.9988 detection,
12/16 message bits round-trip (4-bit erosion from double resample).

- examples/audioseal_apply.rs: --in/--out/--source-rate/--message; loads
  source via audio_io::load_mono_at_rate, calls AudioSealWatermarker
  through the public Watermarker trait, verifies via in-process detect.
- Fix bit-match counter overflow in audioseal_demo.rs and audioseal_apply.rs.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
2026-04-25 19:32:14 -07:00
osobhandClaude Opus 4.7 938b54a2b0 rtx-csm: AudioSeal watermark — Rust port end-to-end
SEANet generator + detector matching `facebook/audioseal` reference layout
(weight_norm-merged via pure-Rust pickle reader). Verified on real CSM
speech: mean_presence=0.9943, 16/16 message bits decoded.

- src/audioseal.rs: SeanetEncoder (4-stage strided downsample, 2-layer
  LSTM bottleneck at 512 channels, 128-dim projection), MsgProcessor
  (16-bit message via embedding sum + broadcast-add), SeanetDecoder,
  Generator (encoder+msg+decoder), Detector (encoder + single 320×
  reverse_convolution + 1×1 head). Padding mirrors audiocraft
  _get_extra_padding_for_conv1d exactly.
- src/audioseal_convert.rs: candle_core::pickle reads .pth directly;
  merge_weight_norm computes g*v/‖v‖ over all axes except 0; writes
  flat safetensors keyed identically to what Generator/Detector read.
- examples/audioseal_inspect.rs: dumps tensor keys + shapes.
- examples/audioseal_convert.rs: HF download + convert CLI.
- examples/audioseal_demo.rs: load + embed + detect on real WAV or
  synthetic burst, optionally writes watermarked WAV.
- audio_io.rs gains generic load_mono_at_rate, resample, write_wav_mono
  (16 kHz path needed for AudioSeal).

12 new unit tests + 2 converter tests; 63 lib tests total green.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
2026-04-25 19:26:13 -07:00
osobhandClaude Opus 4.7 15dd3575d4 Add rtx-csm: Rust-native port of Sesame CSM-1B with LoRA voice cloning
A new model crate at crates/models/rtx-csm implementing end-to-end
inference, quantization, and fine-tuning for Sesame's Conversational
Speech Model (CSM-1B). Built on candle 0.9 + Kyutai Mimi codec.

Key capabilities:
- Inference (FP F16 on Metal, F32 on CPU, BF16 on CUDA)
- Quantized inference (Q8_0 / Q4_K_M GGUF, ~3x speedup, ~50% memory)
- Streaming Mimi decode with proper StreamTensor state machine
- In-context voice cloning via SpeakerProfile
- Classifier-Free Guidance (Koel-TTS recipe)
- Long-form chunked generation with rolling context
- Audio post-processing (HPF + declick + EBU R128 LUFS)
- Text input normalization (brackets, times, unicode, length caps)
- Frame-level repetition guard (loop-escape)
- Top-k + top-p sampling
- LoRA fine-tuning end-to-end (training + inference, on FP and Q8 bases)
- In-process Whisper ASR via whisper-rs (under --features asr)
- Standalone TTS HTTP server (Axum)
- Bench harness with manifest export + per-prompt WER

Phases delivered: quantization, ASR/WER eval, LoRA voice cloning, HTTP
service. AudioSeal/WavLM/Unmute remain as documented future work.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
2026-04-25 18:33:57 -07:00