Transcribe and translate audio files locally using the Whisper CLI.
npx skills add https://github.com/Nirpat3/MIB007 --skill openai-whisper-nirpat3
Please help me install this Agent Skill. Skill: openai-whisper Source: https://github.com/Nirpat3/MIB007/tree/main/skills/openai-whisper Command: npx skills add https://github.com/Nirpat3/MIB007 --skill openai-whisper-nirpat3
This Skill enables local speech-to-text transcription of audio files without requiring an API key, making it a cost-effective and private solution.
Use whisper to transcribe the audio file located at /path/to/audio.mp3 using the medium model and output the result as a text file.
High-intent search queries and answers about installing and using this skill.
Local speech-to-text transcription converts audio files into text using the Whisper CLI, bypassing cloud-based APIs. It offers a private, cost-effective method for transcribing recorded meetings and interviews.
Local speech-to-text transcription requires the whisper executable installed and accessible in your system's PATH. Once configured, you can process audio files directly without external dependencies.
Yes, local speech-to-text translation supports translating audio from various languages into English. The Whisper CLI processes multilingual audio files and outputs English text results.
To transcribe an MP3 file, invoke the whisper executable with the file path, specify the model, and output the result as a text file. This processes the audio entirely locally.
Local transcription is highly suitable for sensitive audio recordings because it processes files directly on your machine without requiring an API key or sending data to external servers, ensuring complete privacy.
The primary limitation of local speech-to-text transcription is the strict dependency on having the whisper executable pre-installed and accessible in the system PATH. Processing speed depends entirely on your local hardware resources.
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