Transcribe local audio and video files into timestamped JSON transcripts using whisper.cpp.
npx skills add https://github.com/adbertram/cli-tools --skill whisper-cli
Please help me install this Agent Skill. Skill: whisper-cli Source: https://github.com/adbertram/cli-tools/tree/main/_repo/skills/whisper-cli Command: npx skills add https://github.com/adbertram/cli-tools --skill whisper-cli
This skill removes the need for cloud-based transcription services by providing a secure, private, and offline interface for converting audio and video files into text.
Use the whisper-cli skill to transcribe the audio file located at path/to/audio.wav and save the output.
High-intent search queries and answers about installing and using this skill.
To transcribe audio files offline, you process media entirely on your local machine using a local ggml model and the whisper-cli binary. This removes the need for cloud-based transcription services and generates text without internet connectivity.
Yes, offline speech-to-text transcription supports both audio and video file processing. You provide the media file path, and the whisper.cpp engine converts the spoken content into text without sending your data to external servers.
Timestamped audio transcripts are generated as JSON files. You can produce transcripts with timestamped segments or extract word-level precision data, which makes the output compatible with standard documentation and analysis workflows.
Yes, local speech-to-text transcription requires a local ggml model to execute. You must configure this model alongside the whisper-cli binary to perform the transcription tasks without any network dependencies.
The best way to ensure private transcription of meeting recordings is using a local, offline interface. By converting your audio files into text entirely on your local machine, you guarantee that no data is sent to external servers.
Offline transcription provides a secure and private environment for converting media into text. By using the local whisper.cpp engine with a ggml model, you avoid uploading sensitive audio or video files to cloud-based transcription services.
mangiapanejohn-devTranscribe audio files to text locally using the Whisper CLI.
shige1014-devTranscribe audio files into timestamped text with word-level timing.
imchangchangTranscribe audio files locally using Whisper models via Whisper.cpp.
framerslabTranscribe audio files to text offline using the Vosk library.
