Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Fixed --skip-existing in multi-format mode to check ALL format outputs before skipping - Fixed --no-timestamps conflict check missing lrc, ass, ttml formats - Fixed --speaker-names silently doing nothing without --diarize; now prints a warning - Batch summary now shows skipped file count when --skip-existing is active
What This Skill Does
Runs OpenAI Whisper locally via a CTranslate2 backend, achieving 4-6x faster transcription with identical accuracy. GPU acceleration pushes this to ~20x realtime. Outputs SRT, VTT, ASS, LRC, TTML, CSV, and plain text; supports speaker diarization, YouTube/URL input, podcast RSS feeds, transcript search, and chapter detection.
Runs entirely offline after the initial model download, eliminating API costs, while matching OpenAI Whisper accuracy at 4-6x the speed.
When to use it
- Transcribing a recorded meeting with speaker labels
- Generating SRT subtitles for a video file
- Fetching and transcribing all episodes from a podcast RSS feed
- Searching a long audio file for when a specific phrase was mentioned
- Translating a foreign-language interview to English text
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: transcribe this meeting recording and identify who is speaking
- 1Runs ./scripts/transcribe meeting.wav --diarize
- 2Assigns speaker labels (SPEAKER_1, SPEAKER_2) to each turn using pyannote.audio
- 3Outputs a labeled transcript with timestamps for each speaker segment
Plain text transcript with per-turn speaker labels and timestamps
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.