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]>
