8-gen bench (4 emotions × 2 corpora) at seed=42 against firdhokk
Whisper-LV3:
target RAVDESS CREMA-D
happy happy (0.999) ✓ happy (0.999) ✓
angry neutral (0.92) sad (0.99)
fearful happy (0.998) fearful (0.984) ✓
sad angry (0.99) fearful (0.99)
CREMA-D 2/4 vs RAVDESS 1/4. Larger / more naturalistic corpus
produces more class-pure fearful direction. Neither corpus solves
angry or sad — recipe shifts into 'vague expressivity' rather than
class-specific corners.
Practical: prefer CREMA-D when available; A/B both per emotion if
class precision matters.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Single CLI ties together every capability shipped this session:
text -> CSM-1B (with optional LoRA) -> post-process (HPF/declick/LUFS)
-> AudioSeal watermark embed -> AudioSeal detect verify -> WavLM-SV
speaker embedding + optional reference scoring.
Verified on Metal: 4s speech generated + watermarked + detected
(mean_presence=0.9999, 16/16 bits decoded) + 512-d speaker embedding
extracted in ~30s.
Cross-content same-speaker cosine sits around 0.49 vs 0.998 for
same-content same-speaker — suggests the WavLM-SV port may leak content
into the speaker embedding more than the HF reference. Phase 5d numerical
parity work (Python sidecar comparison) would tighten this.
This is the canonical usage example for downstream callers
(clawsample-csm etc.) — copy the structure.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
