Transcribe local audio files to text using the Whisper CLI.
npx skills add https://github.com/majunbao/learn --skill openai-whisper-majunbao
Please help me install this Agent Skill. Skill: openai-whisper Source: https://github.com/majunbao/learn/tree/main/openclaw_tags/openclaw-2026.3.2/skills/openai-whisper Command: npx skills add https://github.com/majunbao/learn --skill openai-whisper-majunbao
Local, offline transcription of speech without needing an API key or online services, preserving privacy.
Transcribe a local audio file using whisper, for example whisper /path/audio.mp3 --model medium --output_format txt.
High-intent search queries and answers about installing and using this skill.
You can transcribe audio locally by running the Whisper CLI command, such as whisper /path/audio.mp3, to process speech into text directly on your device without internet access.
The Whisper CLI supports TXT, SRT, and VTT transcription output formats. You can specify your desired format using the --output_format flag to integrate transcribed speech into offline pipelines.
No, an API key is not required for on-device speech-to-text. Whisper performs local transcription offline, allowing you to process audio privately without needing API keys or external online services.
Yes, Whisper supports batch processing of media files for offline transcription in privacy-sensitive environments. All data remains on-device, making it ideal for transcribing interviews, lectures, and meetings securely.
To select a specific transcription model when converting speech to text, use the --model flag in your Whisper CLI command, such as specifying --model medium to balance accuracy and processing speed.
The best way to transcribe meetings while keeping data on-device is using local Whisper speech-to-text. It processes audio files offline, ensuring privacy without sending your recorded meeting data to external API servers.
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