Whisper Transcription
Skill Verifiziert AktivTranscribe audio and video files to text using OpenAI Whisper. Use when: converting podcasts to blog posts; creating video subtitles; extracting quotes from interviews; repurposing video content to text; building searchable audio archives
To accurately convert spoken word from audio and video files into searchable text formats using advanced AI, enabling content repurposing and archival.
Funktionen
- Transcribe audio and video files
- Batch processing of multiple files
- Translate transcriptions to specified languages
- Extract timestamps with text segments
- Support for multiple output formats (txt, srt, vtt, json, tsv)
Anwendungsfälle
- Convert podcasts to blog posts
- Create video subtitles (SRT/VTT)
- Extract quotes from interviews
- Build searchable audio archives
Nicht-Ziele
- Replacing professional audio engineering
- Making subjective creative decisions
- Directly accessing or editing audio files
- Guaranteeing commercial success of content
Workflow
- Specify input file and desired command (transcribe, batch, translate, timestamps).
- Select model size, output format, and optionally language.
- Execute the command via Python script.
- Receive the transcribed text or formatted output file.
Voraussetzungen
- Python 3
- pip install openai-whisper torch ffmpeg-python click
- ffmpeg installed on system
Code Execution
- info:LoggingThe script provides informative output to stdout/stderr during execution, detailing model loading, transcription progress, and output file creation.
Installation
npx skills add guia-matthieu/clawfu-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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