Transcribe audio files to text via a local whisper.cpp server.
npx skills add https://github.com/lawless-m/Gwen --skill whisper-transcription
Please help me install this Agent Skill. Skill: Whisper-Transcription Source: https://github.com/lawless-m/Gwen/tree/main/.claude/skills/Whisper-Transcription Command: npx skills add https://github.com/lawless-m/Gwen --skill whisper-transcription
This Skill enables local speech-to-text transcription using whisper.cpp with GPU acceleration, avoiding cloud services and preserving privacy.
Install whisper.cpp, run the whisper-server with a large model, and POST audio files to /inference on the local server.
High-intent search queries and answers about installing and using this skill.
Local audio transcription using whisper.cpp with GPU acceleration preserves privacy by running speech-to-text on your machine. Set up a whisper-server on port 5555 with the ggml-large-v3 model, then POST audio files to the /inference endpoint to receive JSON responses with transcribed text.
Yes, whisper.cpp transcription supports WAV, MP3, and other common audio formats with CUDA GPU acceleration. Optimize input as 16kHz mono WAV for best performance, POST to the local HTTP server, and receive text output in JSON format.
Prerequisites include a CUDA-enabled GPU, whisper.cpp installed, the ggml-large-v3 model downloaded, and a running whisper-server listening on port 5555. Audio input should be prepared as 16kHz mono WAV files for optimal transcription results.
The local whisper-server exposes an HTTP API at /inference accepting POST requests, enabling integration with Python, shell utilities, and other applications. Send audio files to the endpoint and parse the returned JSON object containing the transcribed text field.
Local transcription requires upfront GPU hardware investment and model setup but eliminates cloud dependency, latency, and privacy concerns. Performance depends on GPU capability; the ggml-large-v3 model provides accuracy at the cost of resource consumption.
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