Transcribe local audio files to text using the Whisper CLI.
npx skills add https://github.com/openclaw/openclaw --skill openai-whisper-openclaw
Please help me install this Agent Skill. Skill: openai-whisper Source: https://github.com/openclaw/openclaw/tree/main/skills/openai-whisper Command: npx skills add https://github.com/openclaw/openclaw --skill openai-whisper-openclaw
This Skill provides a local, privacy-preserving solution for converting spoken audio into written text, eliminating the need for cloud-based services and API keys.
Use whisper to transcribe the audio file located at /path/to/meeting.mp3 using the medium model and output the text to the current directory.
High-intent search queries and answers about installing and using this skill.
Local speech-to-text transcription converts spoken audio into written text directly on your machine. This Skill uses the Whisper CLI to process audio files locally, eliminating the need for cloud-based services or external API keys.
Yes, you need the Whisper binary installed and configured for local model execution. This prerequisite allows the Skill to perform local speech-to-text transcription of voice notes and meeting recordings without relying on external servers.
To transcribe a meeting recording, use the Whisper CLI to process the audio file at a specified path. You can select a model like the medium model to balance speed and accuracy, outputting the transcribed text directly to your chosen directory.
Yes, local speech-to-text transcription allows choosing different Whisper models. This feature enables you to select a specific model to achieve the desired balance between transcription speed and text accuracy for your audio files.
Yes, local speech-to-text transcription supports various output formats for the transcribed text. This allows you to convert audio content into written documentation or analysis formats that best suit your workflow requirements.
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