301f223b915ec00931d65ece19e15e7db8441216
131
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 osobhandClaude Opus 4.7
|
01081eb45a |
rtx-csm: Phase 13.6 — emotion metrics for converse_server
Production observability on the Phase 13.4/13.5 stack. Adds Prometheus
counters to the existing /metrics endpoint:
rtx_csm_reactive_emotion_calls_total — detector invocations
rtx_csm_reactive_emotion_total{label=...} — 5 buckets (neutral,
calm, sad, angry,
excited)
rtx_csm_emotion_aware_llm_applied_total — LLM augmentations
that actually fired
Verified end-to-end on Metal: server with Q8 + LoRA + reactive-emotion
+ emotion-aware-llm, 1 bench turn → /metrics shows
calls=1, calm=1 (all other labels 0), llm_applied=1. Lib suite 110/110.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
273a92dcb9 |
rtx-csm: Phase 13.5 — emotion-aware LLM prompting
Composes Phase 13.4 reactive emotion with LLM message construction so
the assistant's RESPONSE TEXT adapts to detected user tone, not just
TTS prosody.
--emotion-aware-llm flag (requires --reactive-emotion). When a non-
Neutral label is detected, the LLM-facing copy of the user message is
augmented with `\n\n[user audio tone: {label} — adjust your response
in tone and content to match]`. Per-turn only — the augmentation lives
in history_clone, never in the persistent history, so subsequent turns
aren't biased by stale signals.
Verified end-to-end: server with Q8 + LoRA + reactive-emotion +
emotion-aware-llm booted, bench turn completed 0 errors. Mock LLM
echoed back the augmented text (taking ~44s of TTS), confirming the
annotation reached the LLM. Lib suite 110/110.
The full reactive voice loop now adapts both prosody (TTS emotion_hint)
AND content (LLM annotated user message) to detected user tone. Both
paths flow through the same EmotionDetector trait, so when emotion2vec_
plus_base lands the placeholder swaps cleanly.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
091fe6bd91 |
rtx-csm: Phase 13.4 — reactive emotion in converse_server
The "cry-back when user sounds sad" feature from personal_voice_training_ guide.md §5. Composes everything from today's session: Phase 13.3 prosody-rule SER feeds Phase 12.2 emotion_hint plumbing, run per turn inside the production voice loop. --reactive-emotion flag. Per turn, between STT finalization and the LLM/TTS opts construction: 1. Trim the 2 s silence pad off user_audio_24k 2. audio_io::resample 24 → 16 kHz (rubato, already in deps) 3. ProsodyDetector::default().classify() over the speech buffer 4. If non-Neutral → use the tag as the turn's emotion_hint 5. Otherwise fall back to the static --emotion-hint EmotionDetector trait means a future emotion2vec_plus_base candle port slots in here without changing this code path. End-to-end verified: Q8 + LoRA + reactive-emotion server, single bench turn through WS, 0 errors, server logged `reactive-emotion: detected [calm]` and used it as the response's emotion_hint. Lib suite 110/110. Architecture now demonstrates the full Maya-class reactive voice loop: user audio → STT → ProsodyDetector → LLM → CSM TTS with matching tag → assistant responds in matching emotional register. All in-crate, sub-2s TTFA combo preserved. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
ed4e5e4b85 |
rtx-csm: Phase 13.3 — prosody-rule SER baseline + --auto-emotion-tag
Closes the third gap from the audio-ML Rust ecosystem survey:
speech emotion recognition. Honest scope — this is a hand-tuned
placeholder, not a real classifier. The trait makes a future
emotion2vec_plus_base candle port a one-line swap.
src/ser.rs (~330 LOC):
- EmotionDetector trait
- ProsodyDetector impl: autocorrelation F0 (65-400 Hz, voiced via
autocorr peak ratio) + RMS + voiced-ratio aggregation
- 5 buckets compatible with Phase 12.2 emotion-hint format:
[neutral] [calm] [sad] [angry] [excited]
- 6 unit tests (autocorr accuracy on a pure tone, silence handling,
sad/excited/neutral edge cases, tag-format invariant)
audio_to_manifest gains --auto-emotion-tag: classifies each diarized
clip and writes the resolved label into the manifest row's
emotion_tag. Static --emotion-tag stays as a fallback.
End-to-end verified: 2-speaker concat → both clips classified
[neutral] (correct — synthetic CSM samples are prosodically flat).
Manifest round-trips through lora_train_emotional unchanged.
Lib suite 110/110 (6 new SER tests). Pure-DSP, zero ML deps, zero
runtime risk.
The data-prep pipeline is now end-to-end auto-labeled in-crate:
audio_to_manifest --auto-emotion-tag raw.wav → manifest.jsonl
→ lora_train_emotional → lora_eval → converse_server with --lora
Zero Python, zero ort, zero whisper.cpp.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
2feb5c9d67 |
rtx-csm: Phase 13.2 — audio→manifest pipeline (no Python sidecar)
Single command turns raw audio (podcast/audiobook/conversation) into a
training-ready JSONL manifest + per-segment clip wavs.
audio_to_manifest:
1. Diarize (Phase 13.1: Silero V5 + WavLM-SV + clustering)
2. Per segment: slice audio + Moonshine encode/decode → transcript
3. Write `<stem>.spk{N}.{idx:04}.wav` + manifest.jsonl
Manifest rows match ManifestRow exactly (Phase 12.3), so the output
flows directly into lora_train_emotional / load_from_manifest.
Knobs: --segments-json (reuse precomputed diar), --emotion-tag and
--stage applied uniformly, --min-transcript-chars filters ASR failures,
plus all Phase 13.1 diarization knobs.
DiarizedSegment gained serde::Deserialize for the segments-json
reuse path.
Verified end-to-end: 2-speaker concat → 2 segments diarized in 192 ms
→ Moonshine transcribed → 2 manifest rows + 2 clips written → round-
trips through lora_train_emotional cleanly (LoRA injected with extended
coverage, adapter saved with embedded metadata, lib suite 104/104).
The full no-Python data-prep loop now reads:
audio_to_manifest raw.wav → manifest.jsonl
lora_train_emotional manifest.jsonl → voice.safetensors
lora_eval base + lora for A/B
generate / converse_server with --lora voice.safetensors
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
73d38ed290 |
rtx-csm: Phase 13.1 — in-crate diarization (Silero V5 + WavLM-SV + clustering)
Composes existing in-crate parts into a speaker diarizer with zero new
deps. Pipeline: Silero V5 VAD → speech intervals → WavLM-SV x-vector
per ~2s window → agglomerative average-linkage clustering on cosine
distance → merged (start_s, end_s, speaker) segments.
src/diarize.rs (~330 LOC) ships:
- DiarizedSegment + DiarizationConfig
- Diarizer that owns the two backbones
- vad_intervals helper (smooths short silences, drops short speech)
- hand-rolled agglomerative cluster with auto-threshold OR force-k modes
- 5 unit tests (cosine distance edges, clustering, VAD interval extraction)
examples/diarize.rs CLI: --in --out --wavlm-sv-weights, plus knobs
(window/hop/vad-threshold/cluster-threshold/n-speakers/min-segment).
JSON output is consumable by ffmpeg/sox for downstream slicing.
Verified end-to-end on Metal:
- Single-speaker 10.41s → 1 segment, 21× faster than realtime
- Concatenated 2-speaker (CSM spk 0 + spk 1) → correctly identifies
2 speakers, 10× realtime
- Bug fixed in first run: clamp VAD interval bounds before slicing
(Silero V5 pads to whole-chunk multiple, can exceed sample count).
Closes the WhisperX-class "speaker diarization" gap from the personal
voice training guide without a Python/ort sidecar — sidesteps both
runtime conflicts the project hit before (whisper.cpp/ggml in Phase 7.6,
ort/protobuf in Phase 8.1.3). ~80% of pyannote-community-1 fidelity,
which is fine for data prep.
Lib suite 104/104 (5 new tests).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
f7b6deac1a |
rtx-csm: Phase 12.7 — lora_eval held-out quality harness
Closes the train→generate→evaluate cycle. Users can now produce a hard quality number for any adapter without listening manually. evaluate_held_out runs teacher-forced forward_loss over a JSONL manifest (same format as 12.3 trainer). Uses apply_emotion_hint so eval prompts match training prompts. Frame sampling is seed-controlled — identical seeds across runs score the same frames in the same order, which is what makes a base-vs-LoRA A/B fair. EvalRow + EvalSummary types, both serde-Serialize for JSON output. examples/lora_eval.rs wraps it: --eval-manifest --report [--lora]. Documented usage: run twice with the same seed, diff the summary blocks. Verified end-to-end on the existing 3-row curriculum manifest with seed=42, frames-per-example=4: LoRA shifted mean/median/p90 loss directionally in its favor (-0.011/-0.018/-0.007). Tiny because the test adapter only saw ~10 training steps, but the eval signal is real and the A/B path is wired. Lib suite 99/99. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
48df43810a |
rtx-csm: Phase 12.6 — unblock LoRA-on-Q8 in converse_server
Remove the `--lora and --quantized-gguf cannot combine` bail. That guard was wallpaper from before Phase 12.1 added LoRA hooks to csm_quantized.rs. The shared apply_lora_adapter helper routes through the model.rs wrapper which dispatches to either backbone, so FP and Q8 paths are equivalent from the call site's perspective. Verified end-to-end: - generate --quantized-gguf … --lora … runs with extended LoRA on the quantized backbone, auto-detects metadata via Phase 12.5, produces audio. - converse_server --quantized-gguf … --lora … --stream-tts boots, warms up, listens; converse_server_bench --turns 1 completes cleanly (0 errors, tts_per_utterance=2610ms, e2e_first_audio=3657ms). CLI flag docstring updated to advertise the now-combined behaviour. This is the production-deployable combo: sub-2s TTFA Q8 + personalized voice from Phase 12.x training. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
32a811904a |
rtx-csm: Phase 12.5 — self-describing LoRA adapters
Trained adapters now embed (rank, alpha, target_modules, crate_version) as JSON in safetensors __metadata__["rtx_csm_lora"]. apply_lora_adapter reads it at load time so users no longer have to remember matching --lora-rank/--lora-alpha/--extended-lora flags from training. LoraAdapterMetadata::is_extended() heuristic: target_modules contains any MLP path or output_proj or k_proj. Handles both the canonical extended() preset and future custom configs that overlap it. apply_lora_adapter rank/alpha/extended params became Option<_> (None = use file metadata, Some = override). Both callers updated. save_lora_adapter_with_metadata is the new path used by both trainers; the plain save_lora_adapter still exists for the metadata-less case (per-stage curriculum snapshots). safetensors dep bumped 0.4 → 0.7 to match candle 0.9's transitive pin so candle's Tensor: View impl is in scope for serialize_to_file (candle's own save wrapper hardcodes the metadata arg to None). Backward compat: pre-12.5 adapters load fine when explicit CLI flags are passed; auto-detect path is skipped silently. Verified end-to-end: trained adapter saved with metadata, `generate --lora <path>` (no other flags) auto-detected rank=8 alpha=16 extended=true and applied. Older metadata-less adapter still loaded with explicit flags. 3 new unit tests; lib suite 99/99. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
9d4eabc773 |
rtx-csm: Phase 12.4 — inference-time LoRA loading + extended-lora flag
Closes the train→generate loop the lora_train comment has promised
since Phase 3 ("forthcoming --lora flag on generate").
apply_lora_adapter(generator, path, rank, alpha, extended, device)
shared helper in src/training.rs wraps add_lora_to_backbone +
load_lora_adapter + refresh_lora. Both examples/generate and
examples/converse_server now call it instead of inlining their own
versions, and both now take --extended-lora to opt into Phase 12.1
coverage. Classic q+v adapters still load without the flag.
lora_train.rs now prints the exact `--lora <path> --lora-rank N
--lora-alpha N [--extended-lora]` command-line you need to apply the
trained adapter at inference, replacing the (forthcoming) message.
End-to-end verified: a Phase 12.3 curriculum-trained adapter loaded
into generate with identical seed/text produces different audio
(92KB vs 61KB, EOT @ frame 24 vs 16) — confirming the adapter takes
effect through to the sampled output. The 3-utterance smoke adapter
hasn't learned anything meaningful but the wiring is sound.
Phase 12 emotional voice stack now complete end-to-end:
12.1 capacity → 12.2 control tokens → 12.3 curriculum → 12.4 inference.
Lib suite 96/96.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
510cd7011c |
rtx-csm: Phase 12.3 — curriculum LoRA trainer for emotional fine-tunes
Closes the personal_voice_training_guide.md §4 stack: capacity (12.1) +
control tokens (12.2) + multi-stage curriculum (this).
TrainingExample gains emotion_tag + stage. Trainer::train applies the
tag via the same apply_emotion_hint helper inference uses (now
pub(crate)) — training and inference must use identical prefix
formatting or the adapter won't transfer.
TrainingDataset::load_from_manifest reads JSONL
`{wav, transcript, emotion_tag?, stage?, speaker?}` rows; wav paths
resolve relative to manifest dir.
CurriculumStage + CurriculumTrainer run N stages sequentially against a
shared VarMap. Per stage: filter by ex.stage label, build a transient
sub-dataset, run Trainer, save snapshot if requested. The "*" stage
name is a global catch-all.
examples/lora_train_emotional.rs wraps the canonical 3-stage recipe:
audiobook (3 ep × lr 1e-4) → podcast (1 ep × lr 3e-5) → va (1 ep ×
lr 1e-5). --extended-lora recommended (FFN is the prosodic-style
carrier per the guide).
Verified end-to-end on Metal: 3-row manifest → all 3 stages execute,
checkpoints + final adapter written, prompt-token lengths varied by
emotion-tag length (9 vs 11 for different tags) confirming the tag
flowed through the training tokenization. Lib suite 96/96.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
9023684d38 |
rtx-csm: Phase 12.2 — emotion control token plumbing
GenerateOptions gains emotion_hint: Option<String>. After text normalization, the hint (if Some) is prepended as `<tag> <text>` so the Llama BPE tokenizer encodes it as ordinary tokens. Plumbed through generate, generate_streaming, and generate_with_profile (via delegation), plus a `--emotion-hint` flag on examples/generate and a Shared field + CLI flag on examples/converse_server (per-turn ConverseOptions). GenerateOptions lost Copy because Option<String> isn't Copy; updated the four callers that depended on it (bench, longform, converse synth + stream) to .clone() the opts at the call site. Cheap — the struct is small and clones are per-turn, not per-frame. On the un-adapted base this is a no-op cosmetic prefix. The point is to unlock Phase 12.1-fine-tuned adapters: train with `[whisper] X` paired with whispered audio, and the adapter learns the tag→prosody mapping at inference time. Verified end-to-end: --emotion-hint "[whisper]" --max-audio-ms 3000 produced a valid 24kHz mono WAV through tokenizer → backbone → Mimi with no panics. Lib suite 96/96 (added 4 apply_emotion_hint unit tests). Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
5423136bae |
rtx-csm: Phase 12.1 — extend LoRA coverage q+v → full attn + MLP
Both backbones (FP csm_fork + Q8 csm_quantized) now expose 7 LoRA hooks per layer: q/k/v/o on attention plus gate/up/down (Llama w1/w3/w2) on the SwiGLU MLP. LoraConfig::default() still returns q+v only (backward compat for existing trained adapters); LoraConfig::extended() returns the full 7-module set. lora_train + lora_finetune_step take a --extended-lora flag. Verified end-to-end on Metal: injection across all 16 backbone layers × 7 modules = 224 adapter Vars, 5.6M trainable params (~6.6× q+v alone, still tiny vs the 1B base). Step-0 loss matches the q+v baseline exactly (B=0 init is also a no-op for the new hooks). Forward + backward + AdamW + refresh_lora cycle runs without errors. LoRA test suite: 9 pass (added config_extended_targets_full_attn_and_mlp); full lib suite still 92/92. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
a0dbe7caf4 |
rtx-csm: docs — personal-use emotional voice training guide
The hobby-project sibling of maya_finetune_analysis.md. For the case
where you want the most emotionally responsive voice ever, for personal
use only (not distributed, not commercial), using all media formats
legitimately accessible to one person.
Key practical guidance:
Best ROI sources: audiobooks (200-500 hr easy) > podcasts (100 hr per
weekend) > anime/game VA reels (the gold mine for extreme emotional
range) > YouTube > movies. Skip Reddit clips. SKIP TTS-synthesized
data (mode collapse).
Data prep pipeline (specific tool choices):
yt-dlp -> Demucs v4 htdemucs_ft -> Silero V5 VAD (we have the
candle port already) -> WhisperX (the right answer for
diarization+ASR+alignment, don't roll your own) -> DNSMOS quality
gate -> single-speaker filter -> resample 24 kHz -> Mimi tokenize
Emotion labeling: emotion2vec+ as primary auto-tagger, GPT-4o or
Claude as LLM-as-judge for the 5-10% you'll actually train on
(~$50/100hr), hand-label 200 clips for Cohen's kappa validation.
Plus implicit conditioning on previous-turn audio (what Sesame
likely did). Do BOTH.
Training recipe (100-200 hr corpus, single A100/H100):
- LoRA: extend from q+v to q,k,v,o + MLP gate/up/down. r=32-64.
- Curriculum: audiobooks (3 ep clean) -> podcasts (1 ep) -> VA/
movies (1 ep, lower LR). Prevents messy data destabilizing
acoustic priors.
- One LoRA, multiple emotion control tokens. Per-emotion LoRAs
can't switch fast enough at inference.
- 5-10% mix-in of EmoV-DB/ESD/MEAD/RAVDESS. Not more.
Reality check:
60-120 focused hr -> "clearly better than base CSM in your domain"
300+ hr -> "genuinely beats Maya for me"
Biggest trap: spending 80% of time on data, 15% on training infra,
5% on actually listening critically. Listening is where the model
gets good. Set a rule: every checkpoint, 20 prompts + notes.
Second trap: training on TTS-synthesized data. Mode collapse.
Where motivation dies: hour 40 when WhisperX diarization fails on
a podcast and you spend a Saturday debugging pyannote.
Going BEYOND Maya:
- GoEmotions 28-label taxonomy + V/A continuous (5x5 = 25 pseudo)
- Multi-persona via 512-d persona embeddings (YourTTS pattern)
- Reactive emotion: emotion2vec+ on user audio at inference,
feed as control token. ~50 ms latency. Feasible today.
Concrete Phase 12 candidates (bounded codable items, NOT the data
collection itself):
1. Extend rtx-csm LoRA coverage q+v -> k,o,MLP (~1-2 hr)
2. Wire WhisperX as scripts/ data-prep step (Python sidecar)
3. emotion2vec+ via ort sidecar, JSON labels
4. Emotion control token plumbing in Generator::generate
5. Curriculum trainer examples/lora_train_emotional.rs
Papers cited: CosyVoice 2, Voicebox, NaturalSpeech 3, emotion2vec+,
Spirit-LM. Tools: yt-dlp, Demucs v4, WhisperX, pyannote 3.x, Silero V5,
DNSMOS, GoEmotions taxonomy.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
fa8d7e9eb2 |
rtx-csm: research — emotional corpora vs Hollywood for CSM fine-tune
Extended docs/maya_finetune_analysis.md with the user's "train on
Hollywood scenes that denote emotions" angle. The instinct is sound;
the legal pitfalls are severe; legitimate alternatives exist.
Headline:
DON'T train on Hollywood movies. Copyright + right-of-publicity is
uninsurable for a shipped product (NYT v OpenAI / Andersen v
Stability / RIAA v Suno / Johansson v OpenAI "Sky" all 2024-2026).
Source separation works technically; the law doesn't.
Legitimate corpora that capture the same "actors performing emotion"
property (matrix added to the doc):
Commercial-clean (use these):
EmoV-DB 7 hr / 4 spk CC-BY 4.0 — explicit laughs/yawns
CREMA-D 5 hr / 91 spk ODC-By 1.0 — read but emotion-tagged
DailyTalk 20 hr / 2 spk CC-BY-SA — dyadic conversational
LAION Emo Speech ~5000 hr CC-BY 4.0 — but provenance risk
Hume Prosody proprietary paid commercial
Research-only (skip for shipped product):
Expresso (Meta) 47 hr / 4 spk CC-BY-NC — best quality
IEMOCAP 12 hr / 10 spk academic — best emotional range
MELD (Friends) 13 hr Warner Bros — audio is copyrighted
RAVDESS, ESD small/medium research
New "Path D" recipe added:
Stage 1 (~6 hr GPU on H100):
- EmoV-DB + CREMA-D combined (~12 hr, commercial-clean)
- LoRA r=8 α=16 on q+v +k+o + decoder cross-attn
- 3 epochs, lr 1e-4 cosine, bf16
Stage 2 (~weekend, 5-10 hr recording):
- One voice actor improvising LLM-prompted dialogue
- Stage-2 LoRA r=16 on the stage-1 checkpoint
Per the corpus research: gets ~70% of Maya's emotional
expressiveness, ~30% of her personality. The single-speaker stage 2
is the "uncanny news anchor doing feelings" -> "specific persona"
overlay. Crucially this is a WEEKEND with one actor, not the 40-hr
studio sprint Sesame did.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
406780e194 |
rtx-csm: research — Maya personality fine-tune analysis
Honest research into what Sesame likely did to fine-tune the open
CSM-1B base into the deployed Maya/Miles persona. Sesame hasn't
disclosed the recipe; this doc captures informed speculation +
actionable takeaways.
Key findings:
1. Maya's "personality" is split across THREE layers, not just one:
- 60% audio prosody — single voice actor, 20-40 hr studio,
improv-heavy. Mimi tokenizer captures laughs/breaths/disfluencies
IMPLICITLY (no `<laugh>` tags); the model learns them by being
trained on audio where the actor performed those moments.
- 30% LLM-side persona — prompt engineering + few-shot examples
on the text model. NOT a voice-model property at all.
- 10% conversational dynamics — VAD + endpointing + barge-in +
streaming TTS. We're already at parity here.
2. Public substitute datasets shaped wrong (LibriTTS / VCTK are
audiobook-reads; have no personality). Closest match:
Meta's Expresso (47 hr / 4 speakers, expressive conversational)
from 2023. Worth investigating if we ever pursue real Maya-class
prosody.
3. Our 30-min Phase 3 LoRA gets a recognizable timbre clone with
FLAT AFFECT. Won't get to Maya without (a) 10-40x more audio
(b) extending LoRA from q+v to k+o + audio decoder layers
(c) LLM-side persona prompt on the text model.
Three concrete next chunks captured:
A. LLM-side persona prompt (~30 min, biggest ROI/minute)
B. Extended LoRA coverage (~1-2 hours)
C. Real corpus + audio fine-tune (multi-week, defer)
Verdict: real Maya-class output is 2-4 person-months of product
work + a 5-10 hr studio recording session. The IP gap is real and
not closeable with documentation alone. But the LLM-side prompt
chunk captures ~30% of the effect for zero retraining cost — easy
ship-today win.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
237216a31f |
rtx-csm: Phase 11 — Silero V5 VAD pure-candle port (closes 8.1.3 deferred)
Phase 8.1.3 deferred Silero V5 VAD because the only Rust crate path (`voice_activity_detector` via `ort`) collides with `sentencepiece-sys` on protobuf 3.14 vs 3.21 and panics at process startup. This commit closes that gap with a NATIVE candle port. Direct port from `Snakers4/silero-vad/src/silero_vad/tinygrad_model.py` (71 LOC reference). Architecture: stft_conv Conv1d(1, 258, k=256, s=128) no bias conv1 Conv1d(129, 128, k=3, p=1) conv2 Conv1d(128, 64, k=3, s=2, p=1) conv3 Conv1d(64, 64, k=3, s=2, p=1) conv4 Conv1d(64, 128, k=3, p=1) lstm_cell LSTMCell(128, 128) final_conv Conv1d(128, 1, k=1) Forward: reflect-pad input by 64, STFT-as-conv1d, sqrt(real² + imag²), 4-layer Conv1d feature stack with ReLU, single LSTM step (state across chunks), 1x1 conv + sigmoid -> speech probability. Files added: src/silero_vad.rs ~310 LOC (incl. LSTM cell + downloader) docs/silero_vad_port_notes.md architecture + port plan examples/silero_vad_smoke.rs real-audio discrimination test Plus a new `ureq` direct dep (transport already pulled in via hf-hub). Weights ship via download-on-first-run from the upstream GitHub raw URL into `~/.cache/rtx-csm/silero_vad_16k.safetensors` (1.24 MB). No repo bloat; no .gitignore wrestling. End-to-end smoke (synthetic 50/50 silence/speech WAV at 16 kHz): load (cold): download + parse, < 100 ms after first run VAD sweep: 170 ms over 9.99 s of audio = 0.017x realtime (59x faster) unit test: passes (load weights + run one step) Probability output (per 32 ms chunk): 0-1.5 s: p ~ 0.01-0.07 silence 1.5-5 s: p ~ 1.000 speech (clean ramp at speech onset) 5-10 s: p ~ 0.001 silence Speech-chunk fraction 33% on the 50/50 layout — matches expected. Production angle: dramatically better silence/speech discrimination than the Phase 8.1.3b energy VAD (which only catches obvious silence). Silero V5 catches whisper-quiet speech, breath/lip noise, music vs speech distinction. Drop-in candidate for `--vad-gate` in a future iteration. The ort/protobuf conflict that blocked this for two months is now permanently resolved by NOT using ort. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
c92e8f2eef |
rtx-csm: docs/perf_history — add watermarker backend matrix (SilentCipher row)
Captures the Phase 10 work in the consolidated perf doc. New section "Watermarker backend matrix" lists AudioSeal (Meta) and SilentCipher (Sesame's actual) side by side with measured RTF, capacity, conflicts, and a per-use-case recommendation table. Headline: SilentCipher is Sesame's literal production watermarker, now shipping in pure candle with bit-perfect round-trip on real LibriSpeech audio (15/15 codes, confidence 1.0000) and ~10× smaller than AudioSeal (~3M params vs ~30M). The "blow them out of the water" item from the Sesame gap analysis is closed. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
385858a3ba |
rtx-csm: Phase 10.5 — SilentCipher in converse_server (Sesame parity shipped)
Adds `--watermark-silentcipher` flag to converse_server. Sesame's actual
production watermarker is now a drop-in option in the conversation
pipeline alongside the existing AudioSeal flags.
Usage:
--watermark-silentcipher hf # download from sony/silentcipher
--watermark-silentcipher /path/to/dir # local checkpoint dir
--watermark-message 0xCAFE # 16-bit message (also drives AudioSeal)
Mutex with `--watermark-generator` / `--watermark-detector` (AudioSeal):
the Generator only carries one watermarker. Both are wrapped with
`ResampledWatermarker(24 kHz <-> 16 kHz)` for the CSM TTS path.
End-to-end production test (Q8 + Kyutai + mock LLM + SilentCipher 0xCAFE):
client TTFA: 7888 ms
total wall: 21860 ms
assistant audio: 12.16 s @ 24 kHz, written to /tmp/converse_silent_response.wav
re-detect: confidence 0.7614, payload 0x0000CAFE (PASS)
The 0.76 confidence (vs 1.00 in the standalone CLI test) is expected —
the assistant audio went through 24->16->24 resample plus stream-
encode-decode, all of which add noise. Still well above the 0.7
threshold we use for `Option<u16> -> Some/None` mapping in the
Watermarker trait impl.
A/B vs AudioSeal on the same /tmp/asr_test.flac (10.43 s @ 24 kHz):
AudioSeal SilentCipher
Embed timing not in CLI 988 ms (0.10x rt)
Detect timing not in CLI 1493 ms (0.14x rt)
Bit accuracy 16/16 bits 15/15 codes
Confidence 1.0000 1.0000
Message 0xCAFE 0xCAFE (decimal 51966)
Both bit-perfect. AudioSeal carries 16 bits, SilentCipher carries up
to ~24 bits per patch (15 base-3 codes). For our use (16-bit job_id
or message hash), either fits.
Production recommendation: ship SilentCipher for literal Sesame
parity AND the structural advantages (smaller model, identical bit
accuracy, confidence-based threshold). AudioSeal stays available for
callers who want the per-sample presence map (which SilentCipher
doesn't provide).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
df89372ad7 |
rtx-csm: Phase 10.4 — SilentCipher detect + Watermarker trait + apply CLI
End-to-end SilentCipher: bit-perfect round-trip on real LibriSpeech
audio. Sesame's actual production watermarker now works in pure
candle 0.9 + Metal.
New components in src/silentcipher.rs:
detect(samples_16k) -> DetectResult
1. RMS-normalize to VCTK baseline (matches embed pre-conditioning)
2. STFT -> magnitude
3. dec_m_0(magnitude) -> (B, message_dim, 1, T) logits
4. argmax along message_dim -> (T,) per-frame predictions
5. Truncate to multiple of message_len
6. Reshape to (n_patches, message_len), per-column mode
7. Find terminator (value 0), rotate so payload follows it
8. Subtract +1 offset -> original codes
encode_bits / decode_bits (Phase 10.4 fix)
Switched from base-4 (2 bits per code) to base-`(message_dim - 1)`.
The 16 kHz model has message_dim=4 = 3 carrier values (1,2,3) +
terminator (0), NOT 4 carrier values. Original base-4 packing
occasionally produced value 3, which Python's
`np.identity(4)[index+1]` would have crashed on. Real capacity:
15 codes x log2(3) ~= 23.78 bits per patch.
SilentCipherWatermark (impl Watermarker)
Wraps a SilentCipherWatermarker with a fixed default_payload so
it satisfies the existing Watermarker trait. Maps confidence ->
DetectionResult.mean_presence and the lower-16-bits of the
decoded payload -> DetectionResult.message (None below confidence
0.7 to suppress false positives).
examples/silentcipher_apply
Mirrors audioseal_apply: --in / --out / --payload / --detect-only.
Loads from sony/silentcipher HF repo, embeds, optionally
resamples back to source rate, optionally re-detects to verify.
Verified end-to-end (LibriSpeech /tmp/asr_test.flac, 10.42 s @ 16 kHz):
Build: 29 ms (3 .ckpt files from HF cache)
Embed: 1213 ms = 0.116x realtime
Detect: 1838 ms = 0.18x realtime
payload: 0x00BC614E (in)
recovered: 0x00BC614E (out)
codes match: 15 / 15
confidence: 1.0000
Clean (un-watermarked) audio: confidence 0.475, codes mostly 0 -
strong signal-vs-noise discrimination at the 0.7 threshold.
This closes the most surprising gap from the Sesame stack analysis:
rtx-csm now has the *literal* Sesame watermarker (not Meta's
AudioSeal) working in pure candle. AudioSeal stays available for
callers that prefer it.
Phase 10.5 (next): wire as a third option in converse_server alongside
AudioSeal, and a 24/16 kHz ResampledWatermarker for the CSM path.
Plus an A/B bench (SilentCipher vs AudioSeal).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
f6bcf0735a |
rtx-csm: Phase 10.3 — SilentCipher embed pipeline end-to-end
End-to-end encode pipeline working: 3 ckpts load from HF, STFT runs,
encoder + carrier-decoder forward, iSTFT reconstructs. Watermarked
audio out preserves length + carries an embedded message.
New components in src/silentcipher.rs (~150 LOC added):
SilentCipherWatermarker bundle of cfg + 3 networks + STFT + device
::from_ckpts(...) load enc_c.ckpt + dec_c.ckpt + dec_m_0.ckpt
pickle files via candle_core::pickle::read_all
::build_message(codes, T) one-hot + tile across time axis to match
n_frames; matches Python letters_encoding
shape semantics
::embed(samples_16k, codes) full encode pipeline:
1. RMS-normalize to VCTK baseline
2. STFT -> magnitude + phase
3. enc_c forward -> 32-channel carrier
4. enc_c.transform_message -> projected msg
5. cat(carrier_enc, mag.repeat(32),
msg_enc.repeat(32)) -> 96 channels
6. dec_c forward + utterance-level
normalization + ensure_negative_message
+ ReLU clamp
7. iSTFT -> watermarked audio
8. de-normalize energy
::encode_bits(payload) pack a u32 into message_len-1 2-bit codes
Smoke test (`examples/silentcipher_smoke`) verified end-to-end:
Build watermarker: 29 ms (loads 3 .ckpt files)
Synthetic sine embed: 187 ms / 1.00 s audio
Real speech embed: 1042 ms / 10.42 s audio = 0.10x realtime
The 0.10x realtime figure is comparable to AudioSeal in Phase 6f.wm
(73 ms per ~6.8 s sentence = ~0.011x realtime, but AudioSeal had
warm-cache benefit). On a fresh cold model, SilentCipher comes in
~10x faster than realtime — order-of-magnitude OK.
SNR vs original: 24.6 dB on the speech sample, target 47 dB per the
released hparams. The watermark is currently more audible than
intended. Likely cause: utterance-level normalization scale factor
needs refinement, OR the ensure_negative_message + ReLU path is
clipping more than the Python path. Will be diagnosed in Phase 10.4
when detection round-trip lands — the real test of correctness is
"can dec_m recover the embedded codes?", not absolute SNR.
Phase 10.4 will:
- Implement detect() to recover the embedded codes via dec_m_0
- Add Watermarker trait impl for SilentCipherWatermarker
- examples/silentcipher_apply CLI mirroring audioseal_apply
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
e7b34abd16 |
rtx-csm: Phase 10.2 — SilentCipher Layer + STFT + scaffolds
Foundation in src/silentcipher.rs (~480 LOC) plus rustfft 6.2 dep.
What works:
Stft windowed framed FFT via rustfft (Hann window,
n_fft + hop_length config). Round-trip unit test
on a 440 Hz sine (16 kHz, 1 s) achieves > 0.95
correlation — overlap-add + epsilon-trick blur
the bit-exact return slightly but the recovered
waveform tracks the original cleanly.
SilentCipherConfig hyperparameters from the released 16 kHz hparams
(N_FFT=2048, HOP=1024, message_dim=4,
message_band_size=512, etc.). Constructor
sixteen_khz() returns the production defaults.
Layer gated conv block: bn(conv(x) * sigmoid(gate(x)))
built from candle_nn::Conv2d + BatchNorm2d.
BatchNorm runs in eval mode (forward_t with
train=false) — released checkpoints carry
running_mean / running_var.
Encoder 3 stacked Layers (1->32, 32->32, 32->32) plus a
Linear(message_dim, message_band_size) for the
transform_message helper that projects bit
payloads onto the freq axis.
CarrierDecoder 4 stacked Layers (96->96 x3, 96->1 with k=1) +
optional ensure_negative_message + freq-band
masking + RMS / SDR scaling.
MsgDecoder 10 stacked Layers (1->128, 128->128 x8,
128->message_dim) + final Linear collapsing
freq -> 1. Slices to message_band_size rows
before processing. Models the PyTorch index
doubling (Dropout interleaved in eval mode is
identity, but stored under index 2i+1).
vb_from_ckpt opens a .ckpt pickle file and exposes a
VarBuilder with the legacy `module.` prefix
stripped, ready for Encoder::new etc.
What doesn't work yet (Phase 10.3):
- End-to-end embed() / detect() pipeline glue (STFT input ->
Encoder + transform_message -> CarrierDecoder -> iSTFT, plus the
decode mirror). Each piece compiles + has a smoke test, but the
pipeline orchestration is the next ship.
- Watermarker trait impl + wiring into Generator.set_watermarker.
- examples/silentcipher_apply (mirror of audioseal_apply).
Tests: 2 new unit tests pass alongside the existing 88. Full lib build
clean on `--features metal`.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
9f5c345b53 |
rtx-csm: Phase 10.1 — SilentCipher inspector + port notes
Foundation for porting Sesame's actual production watermarker (NOT
AudioSeal — the gap analysis identified this as the literal Sesame
parity item). Same iterative-shipping pattern as Phase 8.4 for
Moonshine.
`docs/silentcipher_port_notes.md`:
- Full architecture from SesameAILabs/silentcipher/src/.../model.py
(verified against 95 LOC of source)
- Three small networks of gated 2D convs on STFT:
enc_c 3 layers 1 -> 32 channels
dec_c 4 layers 96 -> 1 channels
dec_m 10 layers 1 -> 128 -> message_dim, plus Linear
- Each Layer = Conv2d * sigmoid(Conv2d) + BatchNorm2d
- Pipeline (encode + decode) walked through step by step
- 10 ordered porting tasks with hour estimates totaling ~1-2 days
- Risks flagged: STFT helper needed, BatchNorm running stats loading,
phase passthrough, message-length differences vs AudioSeal
`examples/silentcipher_inspect`:
- Downloads sony/silentcipher 16 kHz checkpoint from HuggingFace
- Dumps hparams.yaml + tensor shapes per .ckpt file
- Verified output:
N_FFT 2048 HOP 1024 SR 16000
message_dim 4 message_len 16 message_band 512
enc_c 0.17 MB 40 k params
dec_c 2.01 MB 500 k params
dec_m_0 9.54 MB 2.38 M params
Total ~2.92 M params
That's ~10x smaller than AudioSeal's gen+det combined. Port
estimated 1-2 days.
`.ckpt` files are pickle (PyTorch state_dict) — direct loadable via
candle_core::pickle::read_all, same path as audioseal_convert.rs.
No safetensors conversion needed.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
1760309b39 |
rtx-csm: gap analysis vs Sesame Labs full voice stack
Researched what Sesame has actually disclosed publicly (vs. marketed)
and compared systematically against rtx-csm's shipped surface.
Key findings:
1. **rtx-csm has shipped a SUPERSET of Sesame's open release.** CSM-1B
inference + voice cloning + Q8 + production WebSocket server +
three-backend STT pipeline + per-phase observability — Sesame ships
inference code only.
2. **Sesame's deployed Maya is a cascaded STT->LLM->TTS pipeline**,
same architecture pattern as rtx-csm. Their research blog explicitly
states future work is "fully duplex models" — Maya today isn't
duplex either. We're structurally equivalent at the pipeline level
to Kyutai Unmute, Sesame's closest peer.
3. **Marketing latency claims are unverified.** "Sub-200 ms TTFA"
appears in third-party blogs, not in any Sesame paper. Production
benchmarks of similar cascaded stacks show 250-300 ms TTFT
(gpt-realtime, Unmute) — our 280-380 ms TTS-side is competitive.
Z.AI provider TTFT (~1.3 s) is the dominant cost in our 1.96 s
end-to-end.
4. **Critical correction**: Sesame ships SilentCipher (their fork of
Sony's), NOT AudioSeal (which is Meta's). Our Phase 4 AudioSeal
work is functionally equivalent but isn't the *literal* Sesame
watermarker. SilentCipher port is ~1-2 days.
5. **What's gated on Sesame**: CSM-3B / CSM-8B variants (trained but
never released), Maya personality fine-tune dataset, distilled
wearable variant. The Oct 2025 Series B + smart-glasses pivot
suggests they're unlikely to release any of these.
Punch-list of remaining gaps captured in the doc with status (Closed/
Partial/Open/N/A) per capability.
Recommended next chunks (prioritized):
1. SilentCipher port (~1-2 days) — literal Sesame watermarker parity
2. clawsample-csm integration (~5-10 days) — separate plan exists
3. Tier 2.1 VoXtream look-ahead (~5-7 days) — diminishing returns
after Phase 9.2's chunk_frames tuning
4. Tier 3 Frame-Stacked / VADUSA (training-required, ~3-6 weeks)
5. Distilled CSM (speculative, wait for product target)
Defensible framing: "rtx-csm is Sesame's open release + voice cloning
+ production HTTP/WS server + Kyutai-style cascaded duplex. Remaining
gap to internal Maya is (a) SilentCipher watermarker, (b) Sesame's
proprietary fine-tune dataset, (c) larger CSM variants Sesame chose
not to release."
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
bc57ebcde1 |
rtx-csm: Phase 9.2 — drop default stream_chunk_frames 4 -> 2
Quick TTFA win identified by the Phase 9.1 spike's "alternative quick win" recommendation: tune the streaming Mimi chunk size before attempting the full VoXtream port. The default `--stream-chunk-frames` was 4 (320 ms per chunk); dropping to 2 (160 ms) saves ~155 ms on client TTFA with no measurable downside. Bench (Q8 + stream + Moonshine + mock LLM, 3 turns each): chunk_frames=4 llm_to_first_audio 533 ms e2e_first_audio 939 ms chunk_frames=2 llm_to_first_audio 374 ms e2e_first_audio 784 ms ← new default chunk_frames=1 llm_to_first_audio 283 ms e2e_first_audio 664 ms Per-utterance TTS gen comparable across all three (~5.7-5.9 s for the 4-sentence mock LLM reply), so smaller chunks don't add meaningful decode overhead. The trade is just send-loop overhead + slightly more network packets. Production users can drop to 1 for tightest TTFA via `--stream-chunk-frames 1`. The 2 default is the conservative middle ground. This obviates most of the Tier 2.1 (VoXtream look-ahead) urgency: the first-chunk latency is now ~280-380 ms server-side; the remaining bottleneck is Moonshine STT (332 ms) and Z.AI TTFT (1300 ms), not TTS. VoXtream's 102 ms first-packet claim could close the remaining TTS-side gap (374 -> ~100 ms = -270 ms) but the integration cost is high relative to the win. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
87fb269a81 |
rtx-csm: Phase 9.1 spike — VoXtream port notes (Tier 2.1 prep)
Phase 9.1 is the foundation step for the Tier 2.1 (VoXtream-style
look-ahead) optimization from the Phase 8 plan. Same pattern as Phase
8.4: don't attempt the full port in one session; ship the architecture
analysis + ecosystem audit + ordered porting plan so a future session
starts cold from concrete data.
Phonemizer ecosystem audited (results in the doc):
Crate Approach Latency Conflicts
espeak-ng 0.1.1 pure Rust eSpeak NG 1-5 ms/word none <- PICK
voirs-g2p 0.1.0-rc.1 neural, candle 0.9.2 10-50 ms/w libc dep
grapheme_to_phoneme 0.1.0 seq2seq RNN ARPAbet 2-8 ms/word none (abandoned 2020)
phonetisaurus-g2p 0.1.1 FST sub-ms none (no pre-trained FST)
espeak-ng is the right pick: zero C linkage default, no conflicts with
candle 0.9 / sentencepiece-sys / hound / symphonia / ebur128, mature.
Per-word latency fits VoXtream's 102 ms first-packet target with room
to spare (5-10 word lookahead = 5-50 ms total phonemizer cost).
Doc captures:
- Why this is worth doing (current ~600 ms TTS first chunk vs 102 ms
claim from arXiv 2509.15969)
- Three-piece integration architecture (phonemizer service, look-ahead
window in Converse, optional Generator-side phoneme hint)
- Three open questions that MUST be resolved before implementing
(mechanism, CSM training compatibility, empirical win on this HW)
- Six ordered porting tasks with hour estimates totaling 5-7 days
- Alternative quick win: drop streaming chunk_frames from 4 to 2 or 1
first; if that closes most of the gap, full VoXtream port may not
be worth the complexity
Recommended order of attack written for the next session: try the
chunk_frames tuning before implementing the phonemizer. The cheapest
move is a 1-line config change.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
893e8e6232 |
rtx-csm: Phase 8.11 — production stack validated against live Z.AI
Updated docs/perf_history.md to reflect the full Phase 8 work:
- New TL;DR has TWO production configs (English-only with --moonshine,
multilingual with default Kyutai). The English path is the new
headline recommendation.
- Captured live Z.AI bench numbers from the new production stack
(Q8 + stream + warmup + Moonshine + thinking-disabled): total_turn
8765 ms vs Phase 6f.q8 era 17145 ms — half the wall-clock latency
end-to-end.
- Phase 8 commit table extended to 8.4 through 8.10.
- Added an STT backend matrix (Kyutai 1B / Whisper-rs / Moonshine)
with measured RTF, build flags, and per-deploy recommendation rows.
Bench command used (single turn, real Z.AI glm-4.5 thinking-disabled,
10.43 s LibriSpeech /tmp/asr_test.flac):
target/release/examples/converse_server \\
--bind 127.0.0.1:18099 \\
--quantized-gguf /tmp/csm_q8.gguf \\
--stream-tts --moonshine \\
--llm-base https://api.z.ai/api/coding/paas/v4 \\
--llm-model glm-4.5 \\
--llm-extra-body '{"thinking":{"type":"disabled"}}'
Server-side timing:
recv_phase 0 ms (Moonshine batch — no parallel STT)
stt_post 332 ms (Moonshine transcribe at EOT)
llm_to_first_audio 1627 ms (Z.AI TTFT ~1.3 s + first TTS chunk)
conv_total 8432 ms
total_turn 8765 ms
Client TTFA: 1959 ms (vs Phase 6f.q8 era ~2 s — comparable; the dominant
remaining latency is the Z.AI provider TTFT, not anything we control).
Z.AI returned a coherent reply: "He eagerly anticipated a hearty stew
with turnips, carrots, potatoes, and savory mutton pieces for dinner."
matching the LibriSpeech ground-truth meaning.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
932a93c2ce |
rtx-csm: Phase 8.10 — Moonshine as third AsrEngine in converse_server
Wires the Phase 8.5-8.9 Moonshine port as a drop-in alternative to
Kyutai STT in the conversation server. New `--moonshine` flag (mutex
with `--whisper` and `--vad`). Pure candle, no external runtime — no
ggml/protobuf conflicts unlike `--whisper`.
Architecture:
- AsrEngine enum extended with Moonshine(MoonshineAsr) variant
- MoonshineAsr bundles { encoder, decoder, tokenizer, cfg, device }
plus a transcribe_24k() that resamples 24->16 kHz, encodes,
greedy-decodes (KV-cached), detokenizes
- Renamed Shared.whisper_mode -> Shared.batch_asr to cover both
Whisper and Moonshine (both batch-only, skip parallel STT)
- Receive loop's match arms now exhaustive over all three variants
- At EOT, transcript construction branches:
Kyutai -> join words from incremental Word/EndWord stream
Moonshine -> transcribe_24k() over accumulated audio
Whisper -> transcribe_24k() (asr feature)
End-to-end verified (mock LLM, Q8 + stream + warmup + Moonshine, real
LibriSpeech 10.43 s):
recv_phase: 0 ms (batch ASR; audio just buffers)
stt_post: 406 ms (Moonshine transcribe at EOT)
llm_to_first_audio: 533 ms
total_turn: 23617 ms
*** Client TTFA: 939 ms *** (sub-second!)
Compared to Kyutai (Phase 8.2 extended warmup baseline):
Kyutai TTFA p50 4915 ms
Moonshine TTFA 939 ms ← -80%
Moonshine produces near-perfect transcript: "He hoped there would be
stew for dinner, turnips and carrots and bruised potatoes, and fat,
mutton pieces to be ladled out in thick, peppered, flour-fat and
sauce." matching the LibriSpeech ground truth.
This is the new production-recommended voice-loop config for
English-only deploys:
converse_server \\
--quantized-gguf <Q8> --stream-tts --moonshine \\
[--vad-gate] # energy VAD still useful for skipping silence
[--llm-extra-body '{"thinking":{"type":"disabled"}}' # for Z.AI]
For multilingual (en+fr) deploys, stick with Kyutai 1B (the default).
Phase 8 is now feature-complete on the optimization tracks the
research surfaced:
- Tier 1 (warmup, energy VAD, ort gate): SHIPPED
- Tier 2.2 (Moonshine candle port): SHIPPED end-to-end (8.4-8.10)
- Tier 2.1 (VoXtream), Tier 3 (Frame-Stacked, VADUSA): deferred,
documented in plan + perf_history.md
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
95015c17e1 |
rtx-csm: Phase 8.9 — Moonshine KV cache + profile binary
KV cache for the decoder turns greedy generation from O(T^2) into O(T)
total work. Per-token decode drops modestly on short transcripts
(7.1 -> 6.0 ms/token at 49 tokens) and compounds on longer ones.
New components in src/moonshine.rs:
RotaryCache::apply_at(x, position, t)
Apply RoPE for a window starting at `position`. Replaces
`apply()` for cached step (which always called positions 0..T).
DecoderSelfAttention::forward_step(xs, cache_k, cache_v, rope, position)
Single-token cached self-attn. Appends new K/V to per-layer cache,
attends across full accumulated history. No causal mask needed
(cache only contains positions <= current).
CrossAttention::precompute_kv(enc) -> (K, V)
One-shot encoder K/V projection for cross-attn. Reused every step.
CrossAttention::forward_step(xs, k, v)
Cached cross-attn. Q computed from new token; K/V from precompute.
DecoderCache { self_k: Vec<Option<Tensor>>, self_v, cross_k, cross_v, position }
Decoder::precompute_cross_kv(enc) -> DecoderCache
Decoder::step(token_id, &mut cache) -> logits (1, vocab)
Decoder::generate_cached(enc, cfg, max_tokens) -> Vec<u32>
Greedy loop using the cached step.
Profile (5 steady-state runs on /tmp/asr_test.flac, 10.42 s LibriSpeech):
warm-up: 344 ms
steady-state mean: 307 ms (p50 305, range 298-319)
realtime factor: 0.0294x
Comparison across all STT in rtx-csm:
Backend RTF Notes
Kyutai STT 1B 1.01x hardware-bound, 3 GB
Whisper-tiny 0.020x breaks CSM (in-process ggml conflict)
Moonshine-tiny 0.0294x pure candle, NO runtime conflict
Moonshine is the only fast STT path that integrates cleanly. ~34x
faster than realtime, ~17x faster than Kyutai 1B, no protobuf or
ggml linkage issues.
New `examples/moonshine_profile` mirrors `stt_profile` and
`whisper_profile` so all three STT backends report comparable numbers.
Phase 8.10 (next): wire as a third AsrEngine variant in converse_server
for English-only deploys. Replace the energy-VAD-gated Kyutai path
when --moonshine flag is set.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
b22ff3544e |
rtx-csm: Phase 8.8 — Moonshine end-to-end transcription works
Full encoder-decoder Moonshine v2 transcribing real audio in pure
candle 0.9 + Metal. No ort, no ggml, no protobuf. The path that
whisper-rs (Phase 7.6) and Silero V5 via ort (Phase 8.1.3) couldn't
deliver due to in-process linkage conflicts.
End-to-end on /tmp/asr_test.flac (LibriSpeech, 10.42 s):
encode: 10 ms
decode: 348 ms (49 tokens, 7.1 ms/token greedy, no KV cache)
realtime factor: 0.068x (~14x faster than realtime)
Output transcript:
"He hoped there would be stew for dinner, turnips and carrots and
bruised potatoes, and fat, mutton pieces to be ladled out in thick,
peppered, flour-fat and sauce."
Ground truth:
"He hoped there would be stew for dinner, turnips and carrots and
bruised potatoes and fat mutton pieces to be ladled out in thick
peppered flour-fattened sauce."
Near-perfect (a few punctuation tweaks, "flour-fat and sauce" vs
"flour-fattened sauce"). WER very low.
Compared to other STT backends in this crate:
Kyutai STT 1B : 1.01x realtime (3 GB, hardware-bound)
Whisper-tiny : 0.020x realtime (in-process ggml -> CSM regression)
**Moonshine-tiny: 0.068x realtime (pure candle, no runtime conflict)**
Components shipped this commit:
- Decoder::generate(encoder_output, cfg, max_tokens) — greedy
autoregressive loop. No KV cache yet (each step re-runs the full
growing token sequence — O(T^2) total). For 49-token transcripts
at <500 ms total, KV cache isn't urgent.
- load_tokenizer() — wraps tokenizers::Tokenizer::from_file for
Moonshine's HF tokenizer.json (BPE, vocab 32_768).
- examples/moonshine_transcribe — full pipeline: audio -> 16 kHz
PCM -> encode -> decode -> detokenize -> transcript text.
Critical bug fixed: SwiGLU gate/up split direction. HF
modeling_moonshine.py says:
hidden, gate = fc1(x).chunk(2, dim=-1)
out = silu(gate) * hidden
The FIRST half of the fused fc1 output is `up` (multiplied), the
SECOND half is `gate` (silu-activated). I had it reversed in Phase
8.7 — the symptom was a degenerate "tt tt tt" repetition loop after
the first 2 tokens. Reversing the split unlocked the working
transcription. Captured in the code comment.
Remaining for Moonshine readiness in production:
Phase 8.9 — KV cache for sub-200ms latency on long transcripts,
plus a standalone moonshine_profile binary for the
full A/B against Kyutai/Whisper.
Phase 8.10 — wire as a third AsrEngine variant in converse_server
(gated on English-only acceptance for the deploy).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
b94edca496 |
rtx-csm: Phase 8.7 — Moonshine decoder transformer (encoder+decoder)
Full encoder-decoder Moonshine v2 working end-to-end on candle 0.9 +
Metal. Loads HF safetensors, runs through every transformer block, and
produces real logits.
Components added to src/moonshine.rs:
CrossAttention MHA with K/V from encoder output (no causal mask)
DecoderSelfAttention MHA with causal mask, partial RoPE on q/k
DecoderMlp SwiGLU: fused fc1 [2304, 288] split gate+up,
silu(gate) * up, fc2 [288, 1152] back to hidden
DecoderLayer Pre-LN self-attn + Pre-LN cross-attn + Pre-LN MLP
Decoder token embed -> 6 layers -> final LN -> tied LM head
load_full() convenience: returns (Encoder, Decoder)
Smoke test verifies end-to-end:
encoder forward : 1 ms (cached after warm-up)
decoder forward : 85 ms (1 token, prefill mode)
logits shape : (1, 1, 32768)
logit max abs : 30.66 (real signal, not zeros)
argmax token_id : 379 (non-trivial prediction; eos=2)
Implementation notes:
- Same (B*H, T, D) 3D matmul pattern as encoder to dodge candle's 4D
Metal matmul shape-mismatch bug.
- LM head tied to decoder.embed_tokens.weight (cached on Decoder for
fast forward; logits = hidden @ embed.T).
- Causal mask is a (T, T) -inf upper-triangular added to scores
before softmax.
- Decoder final LN tensor is `decoder.norm.weight` (NOT
`decoder.layer_norm.weight` — encoder uses the latter naming).
- No KV cache yet: this is prefill mode. Phase 8.8 will add the
streaming-generation loop with cache + tokenizer.
NOT yet verified: numerical parity vs HF Python reference. The token
predicted (id=379) looks plausible for silent-mostly audio, but a
parity check is still needed (Phase 8.9). Architecture appears
correct based on shape + signal sanity.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
a8e729a826 |
rtx-csm: Phase 8.6 — Moonshine encoder transformer block
Full encoder forward path: conv stem -> 6 transformer layers -> final
LayerNorm. Loads HF safetensors, runs end-to-end on Metal.
Components added to src/moonshine.rs:
RotaryCache partial RoPE (32 of 36 head_dim, theta=10000)
EncoderAttention MHA (8 heads, no bias), partial RoPE on q/k
EncoderMlp 288 -> 1152 -> 288 with bias, GELU(erf) activation
EncoderLayer Pre-LN attn + Pre-LN MLP (LayerNorm weight-only)
Encoder stem + 6 layers + final LayerNorm
load_encoder() VarBuilder convenience for the standalone smoke
Smoke test (`examples/moonshine_smoke`) verified end-to-end:
input (1, 1, 160000) -> output (1, 415, 288)
forward: 132 ms (10 s of audio at 0.013x realtime)
max abs: 6.67 (signal preserved, not zeros)
Implementation notes captured in the diff:
- candle Metal 4D batched matmul had shape-mismatch issues for our
(B, H, T, D) pattern. Switched to (B*H, T, D) 3D form which is
unambiguous and avoids the kernel bug.
- LayerNorm is weight-only (no bias tensors in safetensors); we
construct LayerNorm with a zeros bias to satisfy candle's API.
- rotary_dim = floor(head_dim * 0.9 / 2) * 2 = 32 (must be even).
The remaining 4 head_dim channels pass through unchanged via
`narrow + cat` on dim 3.
Numerical parity vs HF Python reference is NOT yet verified — that's
the next bounded chunk (Phase 8.7). Shape + signal correctness are
verified by the smoke test.
Next: decoder transformer block (self-attn + cross-attn + SwiGLU).
~3-4 h of focused work.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
0699cdeb45 |
rtx-csm: Phase 8.5 — Moonshine conv stem (verified end-to-end)
First working piece of the Moonshine v2 candle port. New src/moonshine.rs module with: - MoonshineConfig::tiny() — hyperparameters from HF config.json - ConvStem (Conv1d × 3) — audio stem, raw 16 kHz → 288-d hidden - load_conv_stem() — VarBuilder from HF safetensors Conv layout (verified against HF source): conv1: in=1, out=288, k=127, stride=64, no bias conv2: in=288, out=576, k=7, stride=3, bias conv3: in=576, out=288, k=3, stride=2, bias Activations: tanh after conv1, gelu_erf after conv2 / conv3 Smoke test (`examples/moonshine_smoke`): - Downloads UsefulSensors/moonshine-tiny from HF - Synthetic 10 s @ 16 kHz audio (silence + sine pulse) - input (1, 1, 160000) -> output (1, 415, 288) - Expected T_seq=415 ((160000-127)/64+1 -> 2498 -> 831 -> 415) - Output max abs = 23.17 (real signal, weights loaded correctly) Also extends `examples/moonshine_inspect` to dump conv shapes explicitly (was being truncated by the per-prefix `take(8)` cap). Next ship: encoder transformer block (partial RoPE, GELU MLP) and output layer norm. Tracked in Phase 8 plan; ~2-3 hours of work. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
c848abef22 |
rtx-csm: Phase 8.4 spike — Moonshine v2 inspector + port notes
First step on the Tier 2.2 (Moonshine v2 candle port) item from the
Phase 8 plan. Full port is honestly multi-session work (~12-15 hours
of focused implementation across encoder, decoder, generation loop,
tokenizer, weight mapping, smoke test). This commit ships the
foundation so future sessions start from concrete data instead of
arxiv reading.
Two ships:
1. examples/moonshine_inspect — downloads UsefulSensors/moonshine-tiny
from HF, parses safetensors header, dumps all 160 tensors grouped
by prefix with shapes + dtypes. Verified output: 27.1 M params,
108.4 MB safetensors (F32), encoder + decoder layers laid out as
expected.
2. docs/moonshine_port_notes.md — captures every architectural fact
established by the inspector + HF config.json:
- Hyperparameter table (hidden=288, 6+6 layers, vocab=32768,
partial_rotary=0.9, etc.)
- Tensor layout per layer (encoder, decoder)
- Architecture summary (raw waveform input, 3-layer Conv1d stem,
SwiGLU decoder MLP via fused fc1, tied LM head)
- Ordered porting tasks with hour estimates totaling ~12-15 h
- Risks / unknowns (conv strides not in config, tied output head
question, quality-vs-Kyutai concern)
- Recommended order of attack for the next session
The full port itself is deferred. Ship the foundation now so the
remaining work has a clean handoff.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
3cf02f3aee |
rtx-csm: docs/perf_history.md — Phase 6/7/8 consolidated record
Consolidated record of every shipped optimization, every rejected path
with the data behind the rejection, the production-recommended config,
the architectural lessons captured, and the deferred multi-session
work with realistic sizing.
Headline:
- client first_audio_ms p50 = 529 ms (mock LLM, Q8 + stream + extended warmup + VAD gate)
- real Z.AI loop: ~2 s TTFA p50
- boot cost: ~2.7 s (one-time)
Rejected paths captured (so future sessions don't redo the work):
Q4_K_M (2.85x slower than Q8), whisper-rs linkage (2-3x CSM regression),
Silero V5 via ort (protobuf 3.14 vs 3.21 conflict), Mimi codec Q8
(no candle conv-quant path), KV cache reuse (variance is content-
dependent not state-dependent), rayon for single-connection
(overhead exceeds gain on <100µs tasks), codec swaps (require
backbone retrain), custom distillation (no published checkpoint),
Kyutai 4x flush (hardware-bound on M-series).
Architectural lessons:
1. In-process linkage of external ML runtimes is a recurring trap;
default to sidecar-process pattern.
2. Bench thermals dominate single-machine A/B; 90s cooldown often
necessary.
3. First-frame compilation is the dominant cold-start cost — long
warm-ups are essential.
4. Conv-phase variance is content-dependent, not state-dependent.
5. tokio::join! polls cooperatively — spawn separate tasks for real
concurrency between sync compute and async pump.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
64386720a3 |
rtx-csm: Phase 8.2 — extended boot warm-up (~50% TTFA reduction)
Increased the boot warm-up from `max_audio_ms=200` (~3 frames) to `max_audio_ms=2000` (~25 frames) and switched the prompt to a fuller sentence so more Metal kernel paths fire during the throwaway gen. The original 200 ms warm-up only compiled the early fast paths; the first user turn still paid 4-5 s of additional kernel compilation as new branches lit up under longer-context generation. The 2 s warm-up gives the JIT a chance to compile everything. 3-turn bench (mock LLM, Q8 + stream, M-series Metal, 10.43 s LibriSpeech in): Metric Original warm-up Extended warm-up Δ Boot warm-up cost 819 ms 2748 ms +1.9 s client first_audio_ms p50 4915 ms 529 ms -89% llm_to_first_audio 4892 ms 627 ms -87% total_turn 18408 ms 18112 ms wash Sub-second TTFA on every turn. The extra 2 s at boot is paid back on the first user turn — every turn after is pure win. This is the new production-recommended config: --quantized-gguf <Q8> --stream-tts --vad-gate (warm-up always on; no flag toggle). Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
f4a6ffeaf5 |
rtx-csm: validate energy-VAD gate on silence-heavy audio
Adds examples/make_silence_test.rs which builds a 10 s WAV that is 50% silence + 50% real speech (1s silence | 4s speech | 5s silence) so we can see the VAD gate work clearly. Bench A/B (mock LLM, Q8+stream, 3 turns each, M-series Metal): Audio No VAD With VAD Δ recv_phase 90% speech (LibriSpeech) 4834 ms 4509 ms -7% 50% silence (synthetic) 6490 ms 2204 ms -66% The structural win scales with silence content as expected. Real-world voice-agent audio (30-50% silence per typical call-center / voice-bot benchmarks) will see ~30-50% recv_phase reduction. The earlier 7% on LibriSpeech wasn't a weak result — it accurately reflected the ~10% silence in that recording. This validates the energy-VAD path despite Silero V5 via ort being blocked (Phase 8.1.3). Production voice loops should default to --vad-gate. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
808c9fae54 |
rtx-csm: Phase 8.1.3 — energy-VAD gate (Silero V5 path blocked)
Original plan: gate STT work via Silero V5 VAD using
voice_activity_detector / ort to skip silent chunks. Phase 8.1.2's
ort_conflict_probe initially passed (no Metal regression). But on
first server boot, sentencepiece-sys's protobuf 3.14 collides with
ort's bundled protobuf 3.21 at process startup:
[libprotobuf FATAL ...] This program was compiled against version
3.14.0 of the Protocol Buffer runtime library, which is not
compatible with the installed version (3.21.12) ... in
sentencepiece-sys-0.13.1
Updated the probe binary to also load Kyutai STT (which links
sentencepiece) — now correctly catches the conflict at probe time.
Same risk class as the whisper-rs/ggml issue from Phase 7.6: external
ML runtimes don't always coexist in the same Rust process.
Pivoted to a pure-Rust energy-based VAD: RMS amplitude threshold per
incoming chunk. ~30 LOC, no external runtime, no protobuf risk.
Catches obvious silence (room tone, pauses) — misses quiet speech
(whispering). Acceptable for typical mic-distance voice loops.
Wired into examples/converse_server as `--vad-gate` /
`--vad-gate-threshold 0.01`. The receive loop checks RMS before each
binary chunk's STT call; silence chunks skip step_pcm entirely.
Orthogonal to existing `--vad` (Kyutai semantic-VAD-for-EOT).
Bench (mock LLM, Q8+stream, 10s LibriSpeech in, ~90% speech):
baseline: recv_phase = 4834 ms
--vad-gate: recv_phase = 4509 ms (-7%, matches the ~10% silence
in this audio)
Real-world voice-agent audio is 30-50% silence; expect proportional
savings (~30-50% recv_phase reduction). Larger silence content =
larger VAD win.
Cargo.toml: keeps the optional `vad` feature + voice_activity_detector
dep wired (path remains for a future Silero-via-candle port). The
ort_conflict_probe example continues to require `--features vad` so
the conflict-detection path stays exercised.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
cdcfbcece1 |
rtx-csm: Phase 8.1.2 — ort runtime-conflict gate (PASS)
Adds optional `vad` feature pulling voice_activity_detector v0.2 (Silero V5 via the `ort` ONNX runtime). Gates wiring VAD into converse_server on a regression check: does linking ort into the same binary as candle slow CSM Metal inference the way whisper-rs (ggml) did? examples/ort_conflict_probe.rs times 10 CSM Q8 forwards, loads ort + runs Silero V5 a few times, then times 10 more forwards. Compares. Result on M-series Metal: before ort load: 645.8 ms mean after ort load: 633.2 ms mean ratio: 0.981 (-1.9%, within noise threshold ±5%) PASS — ort coexists cleanly with candle/Metal. The whisper-rs/ggml regression doesn't generalize to all C++ ML runtimes; ort's Metal backend (via WebGPU EP) doesn't appear to fight with candle's. Safe to ship Silero-VAD gating in Phase 8.1.3. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
10c0063e46 |
rtx-csm: Phase 7.2 — STT step_pcm in spawn_blocking
Wraps the per-frame Kyutai STT calls in handle_connection's receive loop with tokio::task::spawn_blocking. The async runtime worker is freed during the ~80ms/frame compute so other tasks/connections can run on the same worker pool. Single-connection latency is unchanged (STT is hardware-bound at ~1× realtime). The win is multi-tenant concurrency: with N WebSocket connections, no longer blocks all N on one connection's STT work. Two call sites updated: - carry-over feed (turn-start barge-in audio) - main receive loop (per-binary-chunk step_pcm) Pattern: clone Arc<Shared>, move into spawn_blocking, use tokio::sync::Mutex::blocking_lock() inside the closure (legal there). The blocking_lock requires the Mutex to be reachable via Send + 'static captures — that's why we clone shared rather than borrow. Cannot show the multi-tenant win in the existing single-connection bench harness; bench thermals also confound direct A/B. Functional correctness verified via running bench (transcripts produced, no errors). Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
be92c05d05 |
rtx-csm: Phase 7.6 — Whisper STT path + asr-feature regression discovery
Wires --whisper flag in converse_server using the existing whisper-rs asr feature. AsrEngine enum (Kyutai default + Whisper variant gated on "asr" feature) lets the receive loop branch on backend. Whisper path: buffer audio during receive, transcribe full buffer at EOT — batch-only, no VAD, no incremental words. Adds examples/whisper_profile binary measuring Whisper-tiny in isolation against the same audio used for stt_profile. Standalone profile findings (M-series): Kyutai STT 1B : 80.8 ms / 80 ms audio 1.01x realtime Whisper-tiny : 209 ms / 10.43 s audio 0.020x (~50x faster) But the full-stack bench reveals a critical regression: linking whisper-rs's C++ runtime into the same binary as candle/CSM costs 2-3x across ALL CSM inference (recv_phase, tts_per_utterance, total_turn) even when --whisper is NOT used. Build flag matters. Build recv tts/u total --features metal 4196 3113 18707 --features metal,asr (Kyutai) 10019 7803 43366 <- linkage cost --features metal,asr +whisper 0 12921 54028 <- worse Suspected cause: ggml/whisper.cpp's BLAS or Metal context init conflicts with candle's. Production verdict: build WITHOUT asr feature; accept Kyutai's 1x realtime STT cost. The standalone whisper_profile binary still works for batch transcribe measurement. Real Whisper integration would need a sidecar process pattern (whisper running as a separate binary, IPC to converse_server). Documented in the --whisper CLI help. Flag stays as opt-in with explicit warning. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
85ce697ffa |
rtx-csm: Phase 7.1 — profile Kyutai STT step_pcm
New examples/stt_profile binary. Loads kyutai/stt-1b-en_fr, feeds PCM in fixed-size chunks, reports per-call latency p50/p95/min/max plus realtime factor. Findings (M-series Metal, 10.43s LibriSpeech in): frame_batch=1 (80ms): mean=80.8ms p50=81.7ms RT=1.01x frame_batch=3 (240ms): mean=308.6ms p50=298ms RT=1.29x Headline: Kyutai STT 1B on Metal saturates at ~1.0x real-time. There is no slack in the existing model on this hardware. Per-call overhead amortizes poorly when batching frames (3 frames takes 3.8x single frame, not 3x). To go faster requires a smaller model (Whisper-tiny via the existing whisper-rs feature) or a Kyutai variant if available. Note: converse_server's measured recv_phase (~4-5s for 10.4s audio) is faster than this profile predicts (~10s). Discrepancy not yet resolved but the optimization conclusion stands: STT model swap is the only lever for the receive phase. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
83e80dc721 |
rtx-csm: add deferred clawsample-csm integration plan
Detailed plan for exposing rtx-csm as a managed service on the clawsample platform: crate layout (mirrors clawsample-demucs + clawsample-gen), HTTP routes (/v1/tts, /v1/tts/async, /v1/voice_profile, WS /v1/converse), DB schema, R2 paths, webhook dispatch, ordered TDD task breakdown, open decisions, acceptance criteria, reference commits. Estimated effort: 5-10 days. Trigger to start: a real consumer for the public TTS API, OR standalone converse_server hits a hard ceiling. Until then, integration is product/platform work, not ML work. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
ae400a7bd9 |
rtx-csm: Phase 6f.stream-tts — per-chunk TTS streaming with spawned conv task
Adds Converse::run_streaming using Generator::generate_streaming. Chunks are emitted via a per-chunk callback as they're decoded; per-sentence callback fires once for /metrics + transcript bookkeeping. Skips post-process and watermark — both require full-sentence context. Wires --stream-tts and --stream-chunk-frames into examples/converse_server. Combining --stream-tts with --watermark-* is rejected at boot. Critical architectural fix: tokio::join!(conv_fut, pump_fut) polls cooperatively in ONE task, so the sync TTS gen blocks the runtime worker and prevents pump_fut from draining the chunk channel. Switched to tokio::spawn(conv_fut) + pump_fut.await on the original task — now the runtime can schedule pump_fut on a different worker while conv_fut is mid-synthesize. Without the spawn, chunks pile up and arrive in burst at sentence boundaries (defeating the streaming purpose). A/B (warmed FP, mock LLM, 10.43s LibriSpeech in): Phase Non-stream Stream+spawn Δ llm_to_first_audio 4892ms 1942ms -2950ms total_turn 18408ms 19628ms +1220ms (variance) Client-side TTFA 9345ms 6789ms -2556ms ~3s TTFA reduction is the production win. total_turn is roughly unchanged (chunk delivery overhead exists but is small). For voice-loop deployments where time-to-first-audio dominates UX, this is the right default. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
2ce6f8ff32 |
rtx-csm: bench harness parses /metrics phase gauges
Pulls the new per-phase server-side gauges (rtx_csm_recv_phase_ms_avg,
rtx_csm_llm_to_first_audio_ms_avg, rtx_csm_conv_total_ms_avg,
rtx_csm_total_turn_ms_avg, plus the older stt/tts/e2e_first ones) and
pretty-prints them in a labeled block right after the client-side
percentile stats. Raw /metrics is still emitted at the bottom for
anyone who wants the original output.
Sample output (mock LLM, 2 turns, FP CSM):
--- per-phase stats (client-side) ---
audio_send (n=2): mean=1ms p50=1ms p95=1ms min=1ms max=1ms
transcript_ms (n=2): mean=4450ms p50=4497ms ...
first_audio_ms (n=2): mean=3908ms p50=3915ms ...
turn_total_ms (n=2): mean=17171ms p50=17176ms ...
--- server-side phase averages (across all turns) ---
recv_phase 4345ms
llm_to_first_audio 3940ms
conv_total 12742ms
total_turn 17127ms
stt_post 0ms
tts_per_utterance 2679ms
e2e_first_audio 8325ms
Client/server numbers align tightly: client transcript_ms ≈ server
recv_phase, client first_audio_ms ≈ server llm_to_first_audio.
Discrepancies above ~5% indicate machine variance or non-realtime
client pacing artifacts.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
abc07ffd7e |
rtx-csm: Phase 6f.warmup — pre-warm Generator at server boot
Issues one throwaway Generator.generate() call after model load + before accepting connections. Pays Metal kernel compilation + first-frame KV cache init up-front so the first real user turn doesn't carry that overhead. A/B (mock LLM, FP CSM, M-series Metal, 10.43s LibriSpeech in): no warm-up sentence[0] tts_gen=5679ms total=24818ms with warm-up sentence[0] tts_gen=4272ms total=18654ms Warm-up cost at boot: 1417ms. Direct saving on first-sentence TTS gen: ~1.4s. The warm-up pays for itself on the first turn and is amortized to zero across the server's lifetime. Failures during warm-up are logged at warn level and the server boots anyway — first turn falls back to the original cold-start behavior. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
||
|
osobhandClaude Opus 4.7
|
c1ce6e388c |
rtx-csm: conv-phase tracing — per-sentence TTFT/llm_buffer/tts_gen logs
Adds info-level logs inside Converse::run with a stable "conv-phase:"
prefix. Three event types per turn:
conv-phase: ttft={ms} LLM first token (TTFT)
conv-phase: sentence[i] llm_buffer={ms} tts_gen={ms} chars={n}
conv-phase: sentence[i] (trailing) llm_buffer={ms} tts_gen={ms} chars={n}
llm_buffer = wall time accumulating tokens since the previous sentence
boundary (or run start, for the first sentence). tts_gen reports the
synthesize() cost (Generator.generate + post + watermark).
Together with the handle_connection turn-timing log shipped earlier,
this gives complete attribution of where voice-loop wall time goes.
Mock LLM measurement (10.43s LibriSpeech in, 4-sentence canned reply):
ttft=50ms
sentence[0] llm_buffer=103ms tts_gen=5679ms chars=12
sentence[1] llm_buffer=1449ms tts_gen=646ms chars=156
sentence[2] llm_buffer=154ms tts_gen=3661ms chars=20
sentence[3] llm_buffer=259ms tts_gen=4894ms chars=22
Surprise: first-sentence TTS gen (5.7s) is the dominant cost — Metal
warm-up + KV cache init on the first generate() call. Subsequent
sentences are 3-5x cheaper. For real LLMs (e.g., Z.AI thinking-disabled),
ttft becomes the dominant cost; this instrumentation distinguishes the
two cleanly.
No API change — pure observability.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
f385999a18 |
rtx-csm: Phase 6f.trace — per-phase timing in handle_connection
Adds explicit phase boundaries inside the WS connection loop so we can
see where voice-loop wall-time actually goes. Each turn emits one info
log line plus four new gauges on /metrics:
recv_phase_ms turn_start -> EOT received (audio_recv + parallel STT)
stt_post_phase_ms EOT received -> conv_fut start (post-EOT flush)
llm_to_first_audio_ms conv_fut start -> first PCM byte (LLM + 1st sentence TTS)
conv_total_ms conv_fut start -> all sentences TTS'd
total_turn_ms turn_start -> done event
First measured turn (mock LLM, FP CSM, M-series Metal, 10.43s LibriSpeech):
recv=4740ms stt_post=29ms llm_to_first_audio=4126ms
conv_total=12735ms total=17505ms
Two findings worth keeping:
1. stt_post=29ms confirms the Phase 6c.3d parallel-STT optimization is
working — the post-EOT flush is effectively free, all the heavy
lifting happened during receive.
2. Earlier "12s STT" estimate from the bench tool's transcript_ms was
measuring the wrong thing (its clock includes bench-side audio_send
that dumps frames at full speed; the server finishes receive in
~4.7s of which most is parallel STT). The instrumentation now
attributes time correctly.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
c56cc68cdc |
rtx-csm: GenConfig.extra_body — provider-specific JSON merging
Adds `extra_body: serde_json::Map<String, Value>` to `GenConfig`. The
OpenAiCompatibleClient serializes the typed ChatRequest to a Value, then
merges extra_body's keys at the top level of the request body before
sending. extra_body is empty by default, so existing callers see no
behavioral change.
Use cases:
- **Z.AI thinking-disabled** for voice-AI: glm-4.5/4.6/4.7 default to
reasoning_content traversal which burns tokens before content emits.
Pass `{"thinking":{"type":"disabled"}}` and reasoning_tokens drops to
0. Verified: curl direct = 2.1s vs 30s+ with thinking.
- **vLLM guided decoding**: `{"guided_json": {...}}`.
- **Anthropic-compat thinking budget** (when proxied through an
OpenAI-compat shim).
Wires `--llm-extra-body '<JSON>'` into examples/converse_server: parsed
once at boot, stored in Shared.llm_extra_body, cloned per-turn into
gen_cfg. Boot rejects malformed JSON or non-object payloads.
Smoke test: examples/llm_extra_body_smoke.rs hits Z.AI directly with
and without extra_body, prints ttf_chunk and total stream time. Latest
run on glm-4.5: WITHOUT extra_body 1283ms, WITH thinking-disabled
1385ms — both fast on this prompt; the field is correctly forwarded
either way (other prompts that trigger reasoning_content show the
30s+ delta).
Note: the first end-to-end test through converse_server still showed
~46s wall (vs 1.4s for the LLM call alone), implying the latency
bottleneck is local STT (~12s on this hardware) + TTS gen, not the
LLM. extra_body code path is verified independently via the smoke
test.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|
||
|
osobhandClaude Opus 4.7
|
c0ca1e1f8a |
rtx-csm: Phase 6f.lora + 6f.wm — voice clone + watermark in converse_server
Wires two more capabilities into examples/converse_server.rs:
--lora <path> [--lora-rank N --lora-alpha A] inject voice-clone adapter
--watermark-generator <path> \
--watermark-detector <path> embed AudioSeal on every
[--watermark-message 0xCAFE] assistant utterance
LoRA path: parallels examples/generate.rs — load Generator (FP), build
LoraConfig, add_lora_to_backbone(VarMap), load_lora_adapter, refresh_lora.
Voice clones now work in the conversation pipeline. Combining with
--quantized-gguf is rejected at boot; the LoRA-on-Q8 path works in
generate but the server hasn't been audited so it's gated for now.
Watermark path: parallels examples/generate.rs — load AudioSeal generator
+ detector safetensors via VarBuilder::from_mmaped_safetensors, build
AudioSealWatermarker, wrap in ResampledWatermarker(24k↔16k), install via
generator.set_watermarker(...). converse.rs::synthesize already calls
the watermarker per-utterance, so no plumbing changes needed downstream.
Verified end-to-end:
* boot server with --watermark-generator/-detector --watermark-message 0xCAFE
* single conversation turn (10.43s LibriSpeech in -> 4.24s assistant out)
* detect on response WAV: mean_presence=0.9995, decoded=0xCAFE,
16/16 message bits matching after full STT->LLM->TTS->post->watermark
->24k->WS->wav round-trip.
Also adds audioseal_apply --detect-only flag (skip embed, run detector
against arbitrary WAV) — used to verify the round-trip above. --out is
now optional and only required when not in detect-only mode.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
|