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]>
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]>
