Wires emotion2vec into quality_eval so per-row metrics include
target_emotion_prob, top_emotion, top_emotion_prob. Pure inference,
optional via --emotion2vec / --target-emotion flags.
Critical empirical finding documented in code + user guide: the
emotion2vec_plus_base checkpoint classifies every input as
"Surprised" with prob ≥ 0.99, INCLUDING ground-truth RAVDESS clips
with explicit emotion labels. Real angry-RAVDESS → "Surprised"
(0.9999999). Real neutral-RAVDESS → "Surprised" (0.9999996).
The metric implementation is correct (matches the trait's
EmotionDetector::classify code path with same per-utterance zero-
mean unit-variance normalization); the underlying classifier
collapses to a dominant class on most input — likely the same
"9→5 fold collapse" the project already documented in the data-
labeling path.
Practical implication: target_emotion_prob is near-zero for almost
every (target, output) that isn't "surprised", so it can't be used
as a picker score. The emotion2vec metric still works as a
diagnostic ("did the model produce something that classifies as
audio at all?") but not as a generation-quality validator.
Doc'd in:
- examples/quality_eval.rs CLI doc (caveat block on --emotion2vec)
- docs/emotional_speech_guide.md (Known limitations section with
full sanity-check table)
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Sweep of 4 schedules × 4 prompts produced concrete data on schedule
sensitivity. step:3.0:1.5:12 (the Sprint 3 winner) catastrophically
fails dense prompts: lecture-style input → 0.08 s of audio (one
frame). step:2.0:1.0:8 produced the best single shot — near-verbatim
question rendering "Well, it had stem-wondered. Have you ever
wondered why we sometimes hear voices the way we do?" — but tanked
the lecture prompt (2.4 s "You").
linear:3.0:1.0:25 is the only schedule that's never the best AND
never the worst — graceful degradation across all four prompt
categories. Updates the recommended recipe in perf_history.md
(formerly step:3.0:1.5:12).
quality_eval: skip WavLM-SV scoring on clips shorter than 0.25 s
(emit -1 sentinel) — WavLM-SV's TDNN front-end requires a few
hundred samples and crashed mid-sweep on the 0.08 s clip. Now the
eval emits a row instead of bailing on the whole batch.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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]>
redclawsystems