rustytorch

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Include renames

Author	SHA1	Message	Date
osobhandClaude Opus 4.7	63979eab45	rtx-csm: Generator inline watermarker + ResampledWatermarker adapter A single \`generate\` invocation now produces a watermarked WAV when AudioSeal weights are passed via CLI. End-to-end verified on real CSM speech: mean_presence=1.0000, 16/16 message bits decoded. - Generator gains \`watermarker: Option<Box<dyn Watermarker>>\` slot; \`generate_to_wav\` runs \`wm.embed(&pcm)\` after post-process, before WAV write. Field is Send+Sync so the existing Arc<Mutex<Generator>> tts_server pattern still works. - watermark.rs ships ResampledWatermarker<W> adapter for handling rate mismatches (CSM 24 kHz ↔ AudioSeal 16 kHz). Output length is normalized to input length so it's a transparent drop-in. - examples/generate.rs gains --watermark-generator/--watermark-detector/ --watermark-message flags. Loads AudioSeal, wraps in resampler, installs. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>	2026-04-25 19:36:17 -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

Author

SHA1

Message

Date

osobhandClaude Opus 4.7

63979eab45

rtx-csm: Generator inline watermarker + ResampledWatermarker adapter

A single \`generate\` invocation now produces a watermarked WAV when
AudioSeal weights are passed via CLI. End-to-end verified on real CSM
speech: mean_presence=1.0000, 16/16 message bits decoded.

- Generator gains \`watermarker: Option<Box<dyn Watermarker>>\` slot;
  \`generate_to_wav\` runs \`wm.embed(&pcm)\` after post-process, before
  WAV write. Field is Send+Sync so the existing Arc<Mutex<Generator>>
  tts_server pattern still works.
- watermark.rs ships ResampledWatermarker<W> adapter for handling rate
  mismatches (CSM 24 kHz ↔ AudioSeal 16 kHz). Output length is normalized
  to input length so it's a transparent drop-in.
- examples/generate.rs gains --watermark-generator/--watermark-detector/
  --watermark-message flags. Loads AudioSeal, wraps in resampler, installs.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>

2026-04-25 19:36:17 -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