Local Whisper Voice Transcription

Problem

When working with local audio files, developers often need a simpler path than sending recordings to a cloud service. The goal is to perform transcription, translation, and subtitle generation on the machine, using existing audio assets such as meeting recordings, interviews, podcast clips, or video media.

Core Workflow

This skill centers on Whisper CLI for local audio transcription. It is oriented around file-based processing: the input is an existing audio file, and the output is text or a subtitle-ready format.

• Local transcription: reads files such as audio.mp3 or audio.m4a
• Model selection: smaller models favor speed, while larger models favor accuracy
• Output formats: supports common targets such as txt and srt
• Translation: can use the translate task to produce English text from audio
• Model caching: models are downloaded to ~/.cache/whisper on first run and reused locally afterwards

The default model is turbo, which is convenient for everyday use. If latency or resource usage matters, the model size can be adjusted explicitly.

Boundaries

This is best suited to offline, file-based transcription, such as turning a recording into a text draft or generating a subtitle file for media assets. It is not a real-time streaming speech recognition service and should not be treated as a professional audio workstation. For heavy background noise, overlapping speakers, strong accents, or subtitle work that requires high editorial accuracy, human review is still necessary.