| name | extract-speaker-samples |
| description | Cluster unique voices in an audio recording and extract a short sample of each to a file, then prompt the user to label them. Feeds diarization in downstream transcription. Use when the user asks to identify speakers, extract voice samples, prep for diarization, or label voices. |
Extract speaker samples
Cluster distinct voices in a recording, save a representative clip of each, and ask the user to put names to voices. The labeled samples feed diarized transcription.
Backend
pyannote.audio (best quality, requires HuggingFace token for pyannote/speaker-diarization-3.1)
uv run --with pyannote.audio --with torch --with torchaudio python <<'PY'
from pyannote.audio import Pipeline
import os, torchaudio
pipe = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1",
use_auth_token=os.environ["HF_TOKEN"])
diar = pipe("INPUT.wav")
PY
resemblyzer (lighter, no auth) — fallback if no HF token.
Workflow
- Load source audio.
- Run diarization → list of (speaker_id, start, end) turns.
- For each unique speaker_id, pick the first turn ≥ 5s.
- Extract that 5s slice to
speakers/<source_stem>_speaker_<id>.wav via ffmpeg.
- Write
speakers/<source_stem>_speakers.json with {"speaker_1": {"sample": "...", "label": null}, ...}.
- Present the list of sample files to the user, asking them to listen and label.
After labeling
User updates the JSON or renames the sample files. Downstream transcription skills (e.g. transcribe-assemblyai) consume _speakers.json for speaker name mapping.
Output convention
Directory: speakers/ next to the source audio.
Files: <stem>_speaker_1.wav, <stem>_speaker_2.wav, ..., <stem>_speakers.json.
