Local Audio Transcribe

Transcribe audio locally through Interpreter's app-side builtin-transcribe tool server. Do not install Python packages and do not ask for an OpenAI API key. The agent asks Interpreter to download/run local Whisper models; the app server owns the user-data install location and filesystem permissions.

Workflow

1. Collect the audio/video file path and ask whether the user wants a fast, small model or a larger, higher-quality model when that tradeoff matters.
2. Run interpreter-app tools builtin-transcribe list_transcription_models --json '{}' to check installed models and compare size/language/quality.
3. If the chosen model is not installed, ask the user before downloading it. Then run interpreter-app tools builtin-transcribe download_model --json '{"model":"tiny.en"}' or the selected model ID. Interpreter downloads into app user data and shows a download toast.
4. Run interpreter-app tools builtin-transcribe transcribe_audio --json '{"audioPath":"path/to/audio.mp3","model":"tiny.en","outputPath":"output/transcribe/transcript.txt"}'.
5. Validate the transcript and save outputs under output/transcribe/ when working in this repo unless the user requested another path.

Decision rules

• Default to tiny.en for quick English transcription.
• Use tiny for quick multilingual transcription.
• Use small.en or small when accuracy matters and a larger download is acceptable.
• Use medium.en, medium, or large-v3 only when the user accepts much larger downloads and slower local runtime.
• If transcribe_audio says the model is missing, do not work around it in shell. Ask the user whether to download a model, then call download_model.
• Speaker diarization and known-speaker labeling are not supported by the local builtin-transcribe tool.
• For video files, first extract audio with a normal local media workflow, then pass the audio file to transcribe_audio.

Output conventions

• Use output/transcribe/<job-id>/ for evaluation runs.
• Use outputPath for transcript files to avoid overwriting.

Tool quick start

interpreter-app tools builtin-transcribe list_transcription_models --json '{}'

interpreter-app tools builtin-transcribe download_model --json '{"model":"tiny.en"}'

interpreter-app tools builtin-transcribe transcribe_audio --json '{"audioPath":"interview.mp3","model":"tiny.en","outputPath":"output/transcribe/interview.txt"}'

Reference map

• references/api.md: local model IDs and supported audio format notes.