Transcribe audio files locally to text using the Whisper CLI.
npx skills add https://github.com/NJUxlj/openclaw --skill openai-whisper-njuxlj
Please help me install this Agent Skill. Skill: openai-whisper Source: https://github.com/NJUxlj/openclaw/tree/main/skills/openai-whisper Command: npx skills add https://github.com/NJUxlj/openclaw --skill openai-whisper-njuxlj
This Skill provides a local, privacy-preserving solution for converting spoken audio into written text, eliminating the need for cloud-based APIs and associated costs or data concerns.
txt) and subtitle files (srt).turbo, medium) to balance speed and accuracy.Use whisper to transcribe the audio file located at /path/to/audio.mp3 using the medium model and output the result as text.
High-intent search queries and answers about installing and using this skill.
Local speech-to-text transcription processes audio files directly on your machine, ensuring data privacy. This approach eliminates cloud-based API requirements and prevents sensitive recordings from leaving your environment.
Speech-to-text transcription supports various output formats, including plain text files (like txt) and subtitle files (like srt). This allows you to generate transcripts or subtitles directly from your audio recordings.
Yes, local speech-to-text transcription requires the Whisper binary to be installed and accessible in your system's PATH. This prerequisite enables the CLI to process audio files locally on your machine.
Yes, local speech-to-text allows you to select different Whisper models, such as turbo or medium. This model selection lets you balance transcription speed and accuracy based on your specific requirements.
Yes, local speech-to-text transcription is applicable for transcribing meeting recordings, interviews, and voice notes. It converts spoken audio into written text without relying on cloud-based APIs.
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