diarize-media
Run local speaker diarization for acquired audio/video with WhisperX and pyannote, preserving anonymous speaker clusters in canonical transcript sidecars
- License 许可
- MIT
- 所属仓库
- jmagly/aiwg
- 最近更新
- 2026-11-14
- Stars
- ★ 229
- Forks
- 36
01 前言 Front Matter
- name
- diarize-media
- script
- { "cwd": "project-root", "runtime": "node", "argsHint": "run --media <file> --output <sidecar.json> --credential-provider env:HF_TOKEN [--min-speakers N] [--max-speakers N]", "entrypoint": "scripts/diarize_media.mjs" }
- category
- media-curator
- triggers
- [ "speaker diarization audio", "diarize media", "diarize audio speakers", "diarize video speakers", "transcribe and diarize media speakers", "speaker clustering media" ]
- namespace
- aiwg
- platforms
- [ "all" ]
- description
- Run local speaker diarization for acquired audio/video with WhisperX and pyannote, preserving anonymous speaker clusters in canonical transcript sidecars
02 资源文件 1
- 脚本scripts/diarize_media.mjs228 B
03 SKILL.md 正文
Diarize Media
Run local speaker diarization for acquired audio/video and write a canonical
aiwg.media.transcript.v1 sidecar. This is a dedicated routing skill for the
diarization part of the media-curator transcript workflow.
What Diarization Means
Speaker diarization is anonymous clustering: it answers "which portions sound
like the same speaker?" with labels such as SPEAKER_00 and SPEAKER_01.
Speaker identification is different: it maps an anonymous cluster to a verified person. Do not replace anonymous labels with names unless acquisition metadata, a human transcript, or explicit review evidence supports that mapping.
Executable Adapter
The bundled adapter uses WhisperX plus pyannote Community-1:
HF_TOKEN="$TOKEN_FROM_OPERATOR_OR_VAULT" \
aiwg run skill diarize-media -- run \
--media /path/to/interview.wav \
--output .aiwg/media/transcripts/interview.transcript.json \
--credential-provider env:HF_TOKEN \
--min-speakers 2 \
--max-speakers 2
The credential provider is a reference, not a token value. The adapter checks the referenced environment variable before transcription starts, injects it into the local WhisperX process environment, and never writes token values to argv, disk, logs, state, provenance, or manifests.
Preflight
aiwg run skill diarize-media -- preflight \
--media /path/to/interview.wav \
--output .aiwg/media/transcripts/interview.transcript.json \
--credential-provider env:HF_TOKEN \
--min-speakers 2 \
--max-speakers 2 \
--format json
Preflight checks:
- media readability and source SHA-256
- output directory writability
whisperxavailabilityffmpegavailability warning- CPU/GPU capability reporting
- gated-model authorization through the named credential provider
Missing authorization emits aiwg.media.transcript-plan.v1 with
status: blocked-credential-missing before the expensive STT stage.
Output And Degraded Mode
Successful runs produce the same canonical sidecar as transcribe-media, with
stable segment IDs, source and transcript hashes, tool provenance, anonymous
speaker labels, and quality limitations.
If WhisperX writes transcription/alignment JSON and then exits non-zero during
diarization, the adapter preserves the completed output as a valid sidecar with
transcript.quality.status: degraded-diarization-failed. If no native JSON is
available, it writes an actionable transcript plan instead of fabricating text.
References
- @$AIWG_ROOT/agentic/code/frameworks/media-curator/skills/transcribe-media/SKILL.md — Shared transcript sidecar schema and WhisperX adapter
- @$AIWG_ROOT/agentic/code/frameworks/media-curator/skills/provenance-tracking/SKILL.md — Provenance model for derived media artifacts
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