HuggingFace Diarization

HuggingFace Diarization

Diarization (--diarize) identifies who said what. It uses pyannote.audio and downloads models from HuggingFace on first use.

Regular transcription works without this. You only need HuggingFace if you use --diarize.

Without setup you get: 403 Client Error, gated repo, or not in the authorized list.

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Step 1 — Create account

Sign up free: huggingface.co/join

Step 2 — Accept ALL 4 model licenses

Log in, open each link, click Agree and access repository:

Model	Link
Diarization pipeline	https://huggingface.co/pyannote/speaker-diarization-3.1
Segmentation	https://huggingface.co/pyannote/segmentation-3.0
Speaker embedding	https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM
Community PLDA (pyannote 4.x)	https://huggingface.co/pyannote/speaker-diarization-community-1

All four are required with pyannote.audio 4.x. Missing one (especially community-1) = access error.

Verify before transcribing:

lwt install verify-diarization

Step 3 — Create access token

1. https://huggingface.co/settings/tokens
2. Create new token
3. Name: lwt-diarization
4. Type: Read
5. Copy token (hf_...)

Step 4 — Save in lwt

lwt config set diarization.hf_token hf_your_token_here
lwt config show

Or enter during lwt setup.

Step 5 — Install & run

lwt install diarization
lwt transcribe meeting.wav --diarize
lwt transcribe meeting.wav --diarize --speaker-names "Alice,Bob"

Models cache locally after first download — then diarization runs offline.

Per-command token (not saved)

lwt transcribe meeting.wav --diarize --hf-token hf_xxx

Troubleshooting

See Troubleshooting#Diarization 403