Individual skill
evo-speaker-diarization-subtitles
End-to-end speaker diarization and subtitle generation pipeline for video/audio files. Extracts audio via ffmpeg, performs VAD (silero-vad), extracts ECAPA-TDNN speaker embeddings (speechbrain), clusters speakers (agglomerative clustering with cosine distance), transcribes with OpenAI Whisper, aligns transcripts to speakers via IoU overlap, and outputs RTTM, ASS subtitles, and a JSON report.
tasks-evolved/speaker-diarization-subtitles/environment/skills/evo-speaker-diarization-subtitles/SKILL.md
Source commit 84ca182da055fafb6453dcec4a6defdbbfd987c1
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- Last checked
- Nov 6, 2026
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- Sep 10, 2026
- License
- Apache-2.0
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- agent-skills Repository topic
- ai-agents Repository topic
- llm-agents Repository topic
- open-world Repository topic
- self-evolving-agents Repository topic
- engineering Derived category
- testing Derived category
- testing-browser Derived category
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# evo-speaker-diarization-subtitles
End-to-end speaker diarization and subtitle generation pipeline for video/audio files.
## Pipeline
1. **Audio extraction**: ffmpeg → 16kHz mono 16-bit PCM WAV
2. **Voice Activity Detection**: silero-vad (with fallback to torch.hub if direct import unavailable)
3. **Speaker embedding extraction**: SpeechBrain ECAPA-TDNN (`spkrec-ecapa-voxceleb`)
4. **Speaker clustering**: Agglomerative clustering with cosine distance + silhouette-based speaker count selection
5. **Transcription**: OpenAI Whisper with word-level timestamps
6. **Alignment**: Word-level IoU overlap mapping from Whisper words to diarized speaker segments
7. **Output generation**: RTTM (pyannote.core or manual), ASS subtitles, JSON report
## Usage