Transcription (/v1/audio/transcriptions):
- Route multipart uploads by peeked model (front _peek_model) so they land on
  the model's assigned engine instead of scattering by capability.
- New pluggable STT backends (codai/api/stt_backends.py): wav2vec2 (HF), vosk
  (CPU dirs), NVIDIA NeMo-Canary (isolated venv worker) + faster-whisper/whisper.
- Per-model language selection + validation; Canary source->target translation.
- OpenAI timestamp_granularities[]=word: top-level words[] in verbose_json
  (vosk/wav2vec2/faster-whisper/canary).
- VRAM-eviction tracking (acquire_stt_backend) + keep_resident co-residency for
  the small set; /v1/models surfaces languages + backends.

Diarization (/v1/audio/diarization + diarize=true):
- pyannote.audio isolated venv worker (codai/api/pyannote_worker.py,
  tools/pyannote_service.py) on the 3090; speaker-labeled segments; graceful
  degradation. Pinned torch/pyannote/hf_hub/matplotlib in requirements-pyannote.txt.

Speaker embeddings + recognition:
- /v1/audio/speaker-embeddings (ecapa via speechbrain, pyannote/wespeaker).
- Enroll/list/delete + identify/verify (codai/api/speaker_registry.py); persistent
  voiceprint store; diarization identify=true relabels turns with enrolled names.

New isolated-venv requirements: requirements-nemo.txt, requirements-pyannote.txt
(+ vosk in requirements.txt).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R6iugoNeEgt9PkajKrFyq4