| name | whisper-transcribe |
| description | Transcribes audio and video files to text using OpenAI's Whisper CLI with contextual grounding.
Converts audio/video to text, transcribes recordings, and creates transcripts from media files.
Use when asked to "whisper transcribe", "transcribe audio", "convert recording to text", or
"speech to text". Uses markdown files in the same directory as context to improve transcription
accuracy for technical terms, proper nouns, and domain-specific vocabulary.
|
| version | 1.0.0 |
| category | media-processing |
| triggers | ["whisper","transcribe","transcription","audio to text","video to text","speech to text","convert recording","meeting transcript",".mp3",".wav",".m4a",".mp4",".webm"] |
| author | Claude Code |
| license | MIT |
| tags | ["whisper","transcription","audio","video","speech-to-text","context-grounding"] |
Whisper Transcribe Skill
Transcribe audio and video files to text using OpenAI's Whisper with contextual grounding from markdown files.
Purpose
Intelligent audio/video transcription that:
- Converts media files to accurate text transcripts
- Uses markdown context files to correct technical terms, names, and jargon
- Handles various audio/video formats (mp3, wav, m4a, mp4, webm, etc.)
When to Use
- User asks to transcribe an audio or video file
- User wants to convert a recording to text
- User mentions "whisper" in context of transcription
- User needs meeting notes or interview transcripts
- User has media files with domain-specific terminology
Installation
macOS (Recommended for MacBook Pro)
brew install ffmpeg openai-whisper
whisper --version
Linux/pip Installation
sudo apt install ffmpeg
pip install openai-whisper
Verify Installation
whisper --version
ffmpeg -version
Transcription Workflow
Step 1: Identify Media File and Context
- Locate the audio/video file to transcribe
- Check for markdown files in the same directory (context files)
- If no context files exist, optionally create one using
assets/context-template.md
Step 2: Run Whisper Transcription
Basic transcription:
whisper "/path/to/audio.mp3" --output_dir "/path/to/output"
With model selection (trade-off: speed vs accuracy):
whisper "audio.mp3" --model tiny
whisper "audio.mp3" --model base
whisper "audio.mp3" --model small
whisper --model medium
whisper --model large
