| name | transcribe |
| description | Transcribe audio files locally with Interpreter's builtin-transcribe tool server. Use when a user asks to transcribe speech from recordings without cloud transcription or API keys. |
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
- 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.
- Run
interpreter-app tools builtin-transcribe list_transcription_models --json '{}'
to check installed models and compare size/language/quality.
- 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.
- Run
interpreter-app tools builtin-transcribe transcribe_audio --json '{"audioPath":"path/to/audio.mp3","model":"tiny.en","outputPath":"output/transcribe/transcript.txt"}'.
- 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.
