# Optional Speaker Diarization

The direct-protocol MVP keeps speaker diarization disabled by default. Enable
anonymous Community-1 speaker labels with `--diarization auto`, `gpu`, or
`cpu`:

```bash
python3 scripts/run_mvp_meeting.py meeting.wav \
  --whisper-model /path/to/ggml-model.bin \
  --diarization auto
```

Native mode (the default runtime) requires a compatible local PyTorch and
`pyannote.audio==4.0.7`. For isolated ROCm/CUDA environments, select the
container runtime and provide its image and hardware arguments explicitly:

```bash
python3 scripts/run_mvp_meeting.py meeting.wav \
  --whisper-model /path/to/ggml-model.bin \
  --diarization gpu \
  --diarization-runtime container \
  --diarization-container-image IMAGE \
  --diarization-container-arg=--device=/dev/kfd \
  --diarization-container-arg=--device=/dev/dri \
  --diarization-container-arg=--group-add \
  --diarization-container-arg=video
```

The container receives `HF_TOKEN` by environment-variable name only. It mounts
the source audio and repository read-only and writes diarization artifacts into
the current run directory. Meeting Lab loads mono 16 kHz PCM16 WAV with
Python's `wave` module and sends an in-memory tensor to pyannote, avoiding its
torchcodec file decoder.

Anonymous `SPEAKER_XX` labels are aligned to Whisper segments by maximum
temporal overlap with Community-1 exclusive diarization. The original Whisper
transcript is preserved; the derived transcript under `diarization/` is used as
the unchanged direct-protocol generator's input.