147e0c0422517d872ce3b23f922cee3c70d25102
410
Commits
| Author | SHA1 | Message | Date | |
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 osobhandClaude Opus 4.7
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62d360ba90 |
rtx-csm: Sprint 1 eval foundation — quality_eval + I2D loop
Sprint 1 of the post-research roadmap. TTSDS2 (arXiv 2506.19441) was
the original target but its install is broken on Python 3.12 + modern
torchaudio (deprecated `torchaudio.sox_effects`, `pyannote.audio` 3.1
calls removed `set_audio_backend`, `openai-whisper==20240927` needs
`pkg_resources`). Pivoted to a Rust-native foundation we already own
end-to-end: WavLM-SV + Moonshine + amplitude.
`examples/quality_eval` consumes a JSONL of `(ref_wav, gen_wav,
ref_text)` rows and emits per-row metrics:
- speaker_cosine via WavLM-SV (microsoft/wavlm-base-plus-sv)
- wer via Moonshine v2 transcript vs ref_text (Levenshtein on
lowercased / punctuation-stripped tokens)
- gen_peak_db, gen_rms_db (full-band amplitude of gen_wav)
`scripts/i2d_loop.sh` implements I2D (arXiv 2603.24430): synth N
times feeding each output back as the next iteration's context, score
all iterations with quality_eval, emit a TSV degradation curve.
Smoke-tested:
- quality_eval on the picker A/B set independently confirms the
picker — bottom-context (score 0) → WER 0.55, top-context
(score 2.0) → WER 0.18 (3× worse without picker filter).
- i2d_loop with 3 iterations on Amini context shows clean
collapse: cos 0.84 → 0.58, WER 0.5 → 1.0 by iter 1.
Foundation for Sprint 2 emotion-steering A/B comparisons.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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fac338ad74 |
rtx-csm: scripts/clone_voice.sh — URL → cloned utterance wrapper
End-to-end voice clone: fetch_audio.sh → audio_to_manifest → pick_context.sh → examples/generate. Caches each step by URL hash so re-runs with the same --workdir skip the slow fetch and diarize/transcribe stages. Smoke-tested with cached fetch + manifest. Picks the highest-scoring context clip across all (manifest, speaker) groups, hands it plus the target text to generate via the new repeatable --context-wav / --context-text pairs. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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adc9784646 |
rtx-csm: Stt::finish() — drain asr_delay buffer at end-of-stream
Phase 8.1.1 quality fix from the perf plan. Tokens emitted at LM step
`t` correspond to audio frame `t - ASR_DELAY_FRAMES` (6 frames /
0.48 s), so when a caller stops feeding audio without trailing
silence the last few words trail off — they're still inside the
delay pipeline.
finish() now steps ASR_DELAY_FRAMES additional silent frames after
handling any partial sub-frame buffer, giving the LM the chance to
emit those buffered tokens. Cost: 7 extra step_pcm calls per turn.
Verified end-to-end via stt_demo on a mid-utterance trim of the
LibriSpeech reference clip:
pre-flush: 11 words ("...turnips and carrots and bruised")
post-flush: 13 words ("...turnips and carrots and bruised potatoes and")
Also drops the now-redundant 2s silence suffix in stt_demo — the
flush replaces it. Affects converse_server's real-time end-of-turn
path where suffix padding wasn't possible.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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9317047ae6 |
rtx-csm: pick_context.sh — linear peak penalty (A/B fix)
A/B test 2026-04-29 across mit_2024intro / mcc / carlini manifests showed the binary peak threshold (≤ -3 dBFS = +0.5) failed to differentiate hot clips against each other: an mcc clip with input peak=-1.47 dBFS scored same as one at -3.5 dBFS, and the model output tracked input amplitude. Replace with a linear penalty: 0.5 at peak ≤ -9 dBFS, ramping to 0 at peak = 0 dBFS, clamped. mcc spk0 now produces graduated scores (1.63 / 1.58 / 1.56 / 1.51) instead of a 1.5 plateau, reordering the top selection toward the cleaner-peak clip. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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1ae8bfc74f |
rtx-csm: voice-cloning UX — context picker + multi-clip context
scripts/pick_context.sh ranks manifest clips by suitability for CSM-1B's --context-wav conditioning (duration sweet spot 10-13.5s, RMS -25 to -15 dB, peak ≤ -3 dBFS) and groups by (manifest_stem, speaker_id) since diarizer labels are per-file. examples/generate.rs now accepts repeatable --context-wav and --context-text pairs, zipped into Vec<Segment> for Generator::generate. Validates equal counts at runtime. Smoke-tested with two 10-13s spk0 clips from the MIT-2024 manifest. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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0568dd3653 |
rtx-csm: drop Mimi reload cadence to every-10 clips
Empirical: with 25-clip cadence the cumulative state still tipped over once at position 8102 mid-window during a 167-clip lora_eval load. Drop to 10 — costs ~200ms×(N/10) at load time but eliminates the state-leak panic across every corpus size we've tried up to 1505 clips. Found while running the first successful 3-lecture Amini corpus through the full data-prep + train + eval pipeline. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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d42aba0f1b |
rtx-csm: emotion2vec input normalization + 9-class direct mapping
Three real bugs found while running a YouTube → train → eval pipeline end-to-end on real corpora: 1. emotion2vec was producing near-constant logits regardless of input. Per config.yaml `normalize: true` — data2vec2/emotion2vec expects per-utterance zero-mean unit-variance normalization on the raw waveform before the local_encoder. Added inside the EmotionDetector trait impl so all callers get it. Verified empirically: 4 different audio inputs (Carlini talk, audience question, McConaughey speech) now produce different argmax classes. Before fix: all 4 produced identical logits. 2. The 9→5 emotion fold was collapsing every real-world clip to [excited]. happy / surprised / other all mapped to Excited covered ~95% of natural speech. Replaced with a direct 9-class identity mapping; EmotionLabel gained Disgusted, Fearful, Happy, Surprised, Unk variants. Now: 132 [surprised] + 12 [excited] across the Carlini corpus instead of 144 [excited]. 3. lora_train_emotional --peak-lr / --epochs flags. The canned 3-stage recipe over-fits on small (~100 clip) corpora at extended rank 8; users need to tune. (The recipe stays as defaults; flags are pure overrides.) Plus diagnostic: examples/emotion2vec_probe — feed real audio files into emotion2vec and dump per-class logits. Used to find bug #1. Lib suite still 131/131 (the test that locked the 9→5 fold updated to lock the new identity mapping). Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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b4a7133ffb |
rtx-csm: Mimi.reload() — workaround state-leak in cumulative encode loop
Mimi's transformer carries an internal position counter across encode()
calls that the upstream `reset_state()` does NOT fully clear. When
TrainingDataset::load_from_manifest encodes ~80-100 clips back-to-back
during data prep, the counter overflows the 8192-position buffer and
panics with `narrow invalid args [8192, 32]`.
src/mimi.rs:
- cache the safetensors path on Mimi at construction
- new Mimi.reload() drops the inner Model and rebuilds from the
cached path (~200 ms on Metal)
src/training.rs:
- call generator.mimi.reload() every 50 clips during
load_from_manifest. Adds ~1 s overhead on a 200-clip corpus
(4 reloads × ~200 ms) vs the alternative of a hard panic.
- reset_state() before each encode in TrainingExample::from_audio
is kept (still useful to clear streaming chunk state).
Found while running the end-to-end personal-voice training pipeline on
a 20-minute YouTube source: the bug surfaces around clip 86 when
Mimi's transformer hits position 8181+. Filtering to short clips
alone didn't help — the cumulative state grows even with sub-12-second
inputs.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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7e37e45df7 |
rtx-csm: scripts/fetch_audio.sh — yt-dlp + ffmpeg → 24kHz mono WAV
Wrapper that pulls audio from any yt-dlp-supported URL (YouTube, LibriVox, archive.org, podcast feeds) and converts to the 24 kHz mono 16-bit PCM format examples/audio_to_manifest ingests. Slugifies the output filename so manifest paths stay shell-safe. Prints the next-step audio_to_manifest command with all the right flags (--auto-emotion-tag --use-emotion2vec --stage audiobook), so a new user can copy-paste the printed line straight into a terminal. Requires external tools (yt-dlp, ffmpeg); install on macOS via `brew install yt-dlp ffmpeg`. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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83654b677b |
rtx-csm: Phase 13.9 — wav2vec2 slice 4 (CTC Viterbi forced alignment)
Word-level forced alignment shipped. Phase 13.9 complete. viterbi_align(log_probs, tokens, blank_id, vocab) — standard CTC forced-alignment trellis: states alternate [blank, t0, blank, t1, ..., tN, blank] (length 2N+1), at each frame stay/advance/ε-skip-blank- between-different-tokens (canonical CTC ε-skip rule correctly forbids skipping blank between SAME tokens), max-likelihood path recovered via backptr table. transcript_to_token_ids — text → CTC token ids; runs of spaces collapse to | separator; unknown chars → <unk>. group_into_words — fold adjacent non-| AlignedToken into AlignedWord with carried frame_start/frame_end. frame_to_ms — 50 Hz frame grid → ms (20 ms/frame at conv stride 320). examples/wav2vec2_smoke --align <target> wires it end-to-end: forced-aligns a known transcript and prints (word, start_ms, end_ms). Verified on Metal: 10.42 s LibriSpeech audio, first 8 words → HE 560-640 HOPED 720-960 THERE 1000-1140 WOULD 1180-1320 BE 1360-2240 STEW 2980-4720 FOR 5300-6000 DINNER 7040-8540 viterbi alignment in 0 ms (39 tokens). Boundaries match audio. 4 new unit tests: - transcript_to_token_ids_handles_spaces_and_unknowns - viterbi_align_recovers_obvious_alignment - group_into_words_splits_on_separator - frame_to_ms_50hz_grid Lib suite 131/131 (was 127, +4). Phase 13.9 complete (slices 1+2+3+4). Crate now ships full English ASR + word-level forced alignment in pure candle — no whisper.cpp, no ort, no Python. Data-prep can cut long audio at exact word boundaries before feeding into the Phase 12.3 curriculum trainer. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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209279c13e |
rtx-csm: Phase 13.9 — wav2vec2 candle port (slices 1+2+3, real ASR working)
Full port of facebook/wav2vec2-base-960h (94.4 M params, MIT) closing
the WhisperX-class word-alignment gap from the audio-ML survey. Same
staged-scaffolding pattern that worked for emotion2vec — but landed
slices 1+2+3 in one session.
src/wav2vec2.rs ships:
- Wav2Vec2Config::base_960h
- FeatureExtractor — 7 Conv1d (1→512, total stride 320). Layer 0
uses GroupNorm with num_groups=num_channels=512 (HF's wav2vec2
feat_extract_norm: "group"). Critical: state-dict key is
layer_norm.* but the OP is GroupNorm — loading as LayerNorm
produces empty CTC output.
- FeatureProjection — LayerNorm(512) + Linear(512→768)
- ConvPosEmbedding — kernel 128 grouped Conv1d, materialized at
load time from upstream weight_g + weight_v (fairseq's weight_norm
on dim=2; eps-guarded division for numerical stability)
- Block — POST-norm transformer with separate Q/K/V (vs emotion2vec's
fused QKV), uses (B*H, T, D) Metal 3D-matmul workaround from
Phase 8.8 Moonshine
- Encoder — pos_conv + initial LayerNorm + 12 Blocks
- Wav2Vec2 top-level — load_from_safetensors via mmap'd VarBuilder
- ctc_greedy_decode + VOCAB_960H constant for the 32-char alphabet
examples/wav2vec2_inspect.rs (slice 1): dumps tensor layout + config
examples/wav2vec2_smoke.rs (slice 3): real-weight load + ASR forward
Verified on Metal:
loaded model in 0.28 s
forward in 9 ms for 10.42 s audio (~1150× realtime)
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 FLOWER FAT AND SAUCE"
Numerical parity with upstream Python — the FLOWER-for-FLOUR typo is
the known wav2vec2-base-960h failure mode, matches HF reference exactly.
7 new unit tests; lib suite 127/127 (was 120).
Slice 4 remaining: Viterbi forced alignment given known transcript,
to emit (token, frame_start_ms, frame_end_ms) for word-boundary cuts.
The ASR path itself is now production-ready.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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f50233a9cc |
rtx-csm: Phase 13.8 — emotion2vec port, slice 4 (EmotionDetector + integration)
Port complete. The Phase 13.3 prosody-rule placeholder is now
retire-able by setting one CLI flag — the real candle-ported
emotion2vec_plus_base classifier slots in behind the same
EmotionDetector trait the placeholder used.
impl EmotionDetector for Emotion2Vec — builds (1, 1, T) tensor on
stored device, runs forward, argmaxes the 9 logits, maps to the
5-bucket label via Classifier::tag_for_class. Empty input
short-circuits to Neutral.
Emotion2Vec struct gained a `device` field so the trait impl can
build tensors without an out-of-band handle. new() / load_from_pickle()
threaded through; existing tests + smoke binary updated.
audio_to_manifest --use-emotion2vec — pairs with --auto-emotion-tag
to swap ProsodyDetector for Emotion2Vec, boxed as
Box<dyn EmotionDetector> so the call site is unchanged.
converse_server --use-emotion2vec — same pattern; built once at boot
and stored in Shared as Box<dyn EmotionDetector + Send + Sync>.
~150 ms/turn forward cost vs <1 ms for prosody, but actually runs
SOTA SER. Removed redundant reactive_emotion: bool field — the
Option<Box<dyn>> already encodes the same state.
Verified end-to-end on Metal:
- audio_to_manifest --use-emotion2vec on 2-speaker concat → both
tagged [excited] (prosody had said [neutral] on same input)
- converse_server --quantized-gguf … --lora … --reactive-emotion
--use-emotion2vec boots, 1 bench turn 0 errors, /metrics shows
reactive_emotion_total{label="excited"} 1 — same tag
audio_to_manifest produced. Cross-consumer consistency.
Phase 13.8 complete (slices 1+2+3+4 shipped). The emotional-voice
stack now has a real, trained, candle-ported SER classifier with
no Python sidecar, no ort, no whisper.cpp.
Lib suite 120/120.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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9c1894ec73 |
rtx-csm: Phase 13.8 — emotion2vec port, slice 2d (real weights load + forward)
Slice 2 complete + slice 3 collapsed in. The full candle port loads
real upstream weights and runs forward in 160 ms.
RelativePositionalEncoder — 5 grouped Conv1d (kernel 19, groups 16,
same-padding via pad 9) with GELU between. Output is added back to
input as a positional bias. Pickle keys
relative_positional_encoder.{1..=5}.0.weight/bias (1-based indexing,
no .0.*).
Emotion2Vec top-level — wires LocalEncoder → ProjectFeatures →
RelPosEnc → ContextEncoder → MainEncoder → mean-pool → Classifier.
The proj.* classifier head lives at the state-dict root (not under
d2v_model.), so the constructor uses vb directly there.
Emotion2Vec::load_from_pickle uses VarBuilder::from_pth_with_state
to descend into the fairseq-style nested checkpoint via the "model"
key. One-shot loader; all 185 upstream tensor keys must map onto
candle params of matching shape — and they do.
examples/emotion2vec_smoke.rs — full pipeline integration test:
downloads (or reuses cached) emotion2vec_plus_base from HF, loads it
into candle, runs forward on 2 s of synthetic audio, prints all 9
raw logits + argmax + the 9→5 bucket fold.
Verified on Metal:
loaded model in 0.19 s
forward in 160 ms
9 logits all finite (50-290 range, expected for raw classifier)
argmax: class 7 (surprised) → 5-bucket [excited]
Mechanical correctness end-to-end. Semantic accuracy on real
emotional speech lands in slice 4 (EmotionDetector trait impl +
swap into audio_to_manifest + converse_server reactive-emotion path).
2 new unit tests:
- relative_positional_encoder_preserves_shape
- emotion2vec_random_init_end_to_end_shape
Lib suite 120/120 (was 118, +2).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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491f51565a |
rtx-csm: Phase 13.8 — emotion2vec port, slice 2c (Classifier + Encoders)
Three composition wrappers over the Block from slice 2b.
ContextEncoder — 4 Blocks + final LayerNorm
(context_encoder.blocks.0..3 + context_encoder.norm). Acts as a prenet
between ProjectFeatures and the main encoder.
MainEncoder — 8 Blocks, no final LayerNorm. Confirmed via inspector:
all 96 d2v_model.blocks.* tensors live inside numbered blocks; there's
no d2v_model.norm. Pre-norm pattern's per-block norm2 keeps residuals
conditioned without a global tail norm.
Classifier — single Linear 768→9 (proj.weight/proj.bias at the top of
the state dict, NOT under d2v_model.). Includes tag_for_class(idx)
that folds the 9 fine-grained model classes (angry/disgusted/fearful/
happy/neutral/other/sad/surprised/<unk>) into the 5-bucket label set
the Phase 13.3 EmotionDetector trait already uses:
- 0 angry → Angry
- 1 disgusted, 2 fearful,
6 sad → Sad (low valence)
- 3 happy, 5 other,
7 surprised → Excited (high arousal)
- 4 neutral, 8 <unk> → Neutral
4 new tests:
- context_encoder_chains_4_blocks_with_final_norm
- main_encoder_chains_8_blocks_no_final_norm
- classifier_emits_9_logits
- classifier_class_to_emotion_label_mapping (locks the 9→5 fold)
Lib suite 118/118 (was 114, +4 new).
Slice 2 remaining: relative_positional_encoder (5 Conv1d, the conv
positional bias) + top-level Emotion2Vec + .pt pickle loader. Then
slice 3 = forward pass + numerical parity check vs upstream Python.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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90f1b4667c |
rtx-csm: Phase 13.8 — emotion2vec port, slice 2b (ProjectFeatures + Block)
Two more candle modules toward the full port.
ProjectFeatures — LayerNorm(512) → Linear(512→768). Sits between
LocalEncoder and the transformer. Pickle layout matches upstream:
project_features.1.* is the LayerNorm, project_features.2.* is the
Linear. Both have learnable affine params; the .1 LN is NOT just an
eps constant.
Block — pre-norm fused-QKV transformer block, the workhorse for both
ContextEncoder (4 instances) and MainEncoder (8 instances). Pickle
keys per block: norm1, attn.qkv (fused 768→2304), attn.proj,
norm2, mlp.fc1, mlp.fc2. GELU MLP activation. No positional encoding
inside the block — the conv-based positional bias lives at the encoder
boundary.
Attention uses the (B*H, T, D) 3D collapse-before-matmul Metal
workaround we shipped for Phase 8.8 Moonshine — candle's 4D batched
matmul still has the shape-mismatch bug.
2 new unit tests:
- project_features_shape_check: (1, 50, 512) → (1, 50, 768)
- block_residual_shape_check: random (2, 8, 768) → same shape AND
all values finite (catches softmax NaN / attention overflow)
Lib suite 114/114 (was 112, +2 new).
Remaining within slice 2: relative_positional_encoder (5 Conv1d),
ContextEncoder (4 Blocks), MainEncoder (8 Blocks + LN), Classifier
(Linear 768→9), top-level Emotion2Vec + pickle .pt loader.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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ce00e48acb |
rtx-csm: Phase 13.8 — emotion2vec port, slice 2a (LocalEncoder)
First module of the candle port. src/emotion2vec.rs ships:
Emotion2VecConfig::plus_base() — embed_dim=768, depth=8, prenet_depth=4,
num_classes=9, conv_layers spec yielding stride-product 320 (16 kHz →
50 Hz feature frames).
LocalConvBlock — one Conv1d → LayerNorm → GELU block. Weight key layout
matches the upstream pickle exactly: .0.weight for the conv (no bias),
.2.1.weight/bias for the LayerNorm (upstream wraps it as
Sequential(Conv1d, Dropout, Sequential(TransposeLast, LayerNorm,
TransposeLast), GELU); we collapse dropout / transposes since they're
inactive at inference / handled inline via .transpose(1,2)).
LocalEncoder — 7-block stack, channels 1→512, time shrinks by stride
product. Output shape (B, 512, T/320) — for 16 kHz input that's a
50 Hz feature frame rate.
2 unit tests pass:
- config_stride_product_matches_320 (catches future spec drift)
- local_encoder_random_init_shape_check — builds via VarMap+Kaiming,
runs forward on 1 s of zeros, asserts (1, 512, ~50) output
Lib suite 112/112 (was 110, +2 new tests).
Remaining within slice 2: project_features, relative_positional_encoder,
Block (fused-QKV), ContextEncoder (4 prenet blocks), MainEncoder
(8 main blocks), Classifier, and top-level Emotion2Vec with .pt loader.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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a77b22d47c |
rtx-csm: Phase 13.8 — emotion2vec_plus_base port, slice 1 (inspector)
First slice of the multi-session port that will replace the Phase 13.3
prosody-rule SER placeholder with a real classifier via the existing
EmotionDetector trait.
examples/emotion2vec_inspect.rs:
- downloads model.pt + config.yaml + tokens.txt from
emotion2vec/emotion2vec_plus_base on HF Hub
- descends fairseq-style nested checkpoint via --key model
- dumps all 185 tensors with shapes/dtypes + per-prefix summary
- uses pickle::read_pth_tensor_info, same pattern as audioseal_inspect
Architecture confirmed (full notes in docs/emotion2vec_port_notes.md):
- 93 M params, F32 (the 1.12 GB file is mostly optimizer state)
- local_encoder: 7 Conv1d layers (wav2vec2 feature extractor:
[(512,10,5)] + [(512,3,2)]×4 + [(512,2,2)]×2, T → T/320)
- project_features: Linear 512 → 768
- relative_positional_encoder: 5 Conv1d layers (kernel 19)
- context_encoder: 4-layer transformer prenet (prenet_depth=4)
- blocks.0..7: 8-layer main transformer (depth=8, embed_dim=768,
12 heads, mlp_ratio=4, fused QKV qkv.weight=[2304, 768])
- proj: Linear 768 → 9 (angry/disgusted/fearful/happy/neutral/other/
sad/surprised/<unk>)
Slicing plan (remaining):
Slice 2 (~half-day): candle module scaffolding + from_pickle loaders
Slice 3 (~half-day): forward pass + shape verification
Slice 4 (~hour): EmotionDetector impl + swap into audio_to_manifest
and converse_server
Lib suite 110/110.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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osobhandClaude Opus 4.7
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a4fca2b126 |
rtx-csm: Phase 13.7 — --lora support in tts_server
Closes the gap where converse_server (Phase 12.4) supported LoRA but the simpler HTTP-only tts_server didn't. Same flag set (--lora / --lora-rank / --lora-alpha / --extended-lora) and same apply_lora_adapter shared helper. Combines with --quantized-gguf (Phase 12.6) for Q8 + voice clone over plain HTTP. Verified end-to-end on Metal: tts_server --quantized-gguf … --lora … boots, injects LoRA into the quantized backbone (q=16 k=16 v=16 o=16 + MLP w1/w2/w3 = 224 tensors), listens. Single HTTP POST /v1/tts returned 200 OK with a 146KB 24kHz mono WAV. Lib suite 110/110. tts_server is now the simplest production deploy for a personalized voice: HTTP-only, no STT/LLM overhead, LoRA + Q8 + AudioSeal/SilentCipher + WavLM-SV all available behind one binary. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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osobhandClaude Opus 4.7
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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]>
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osobhandClaude Opus 4.7
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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]>
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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]> |
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osobhandClaude Opus 4.7
|
6d59251c51 |
rtx-nn / rtx-multimodal: cargo fmt reformatting
Pure formatting changes across rtx-nn (conv_transpose1d, conv/mod, rnn/lstm) and rtx-multimodal (audio/generation, audio/source_separation): multi-line braces, trailing commas, import ordering. No logic changes. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> |
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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]>
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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]>
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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]>
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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]> |
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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]> |
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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]> |
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|
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]>
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|
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]>
|
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|
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]> |
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|
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]> |
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|
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]>
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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]>
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|
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]>
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|
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]> |
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|
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]> |
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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]>
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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]>
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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]>
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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]>
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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]>
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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]>
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|
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]> |
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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]>
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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]>
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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]>
|
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|
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]>
|
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|
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]>
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osobhandClaude Opus 4.7
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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]>
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