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name: audio-transcribe description: Transcribe speech from audio files (mp3, m4a, wav, ogg, flac, webm) to text using the local whisper CLI — no API key. Use whenever a task hinges on the spoken content of an audio attachment. version: 1.0.0 requires_tools:

• os.shell.run
• os.fs.read dangerous: false

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audio-transcribe

Turn spoken audio into text locally with whisper. Whisper writes a plain-text transcript next to the audio; you then read that file and answer from its content. The chat model never "hears" the audio — whisper does the listening as a separate process, so this works regardless of model size.

Setup health check (run first, every session)

Verify with one solo step:

[{ "tool": "os.shell.run", "args": { "cmd": "whisper", "args": ["--help"] } }]

Outcome map:

• exit 0 + usage text → ready, proceed.
• command not found: whisper → enter Setup playbook → "whisper missing".

Setup playbook (when prerequisites are missing)

whisper missing

Reply (solo reply step):

"whisper is not installed. I can install it: brew install openai-whisper (also needs ffmpeg). Install it?"

On yes:

[{ "tool": "os.shell.run", "args": { "cmd": "brew", "args": ["install", "openai-whisper", "ffmpeg"] } }]

On Linux: pipx install openai-whisper (or pip install openai-whisper) plus apt-get install ffmpeg. The first transcription downloads the model weights to ~/.cache/whisper.

When to use

• The task references an audio attachment (.mp3, .m4a, .wav, .ogg, .flac, .webm) and the answer depends on what is said in it.
• "What does the speaker say…", "list the ingredients mentioned…", "which page numbers are read aloud…".

When NOT to use

• The audio only needs format conversion / trimming — that's the ffmpeg skill.
• The attachment is an image / document — use vision or fs.read_document.

How to transcribe

1. Transcribe to a .txt next to the audio (one solo step). Pick the model by need: small is a good speed/accuracy default; use medium when accuracy matters and the clip is short.

[{ "tool": "os.shell.run", "args": { "cmd": "whisper", "args": ["audio.mp3", "--model", "small", "--output_format", "txt", "--output_dir", ".", "--language", "en"] } }]

2. Read the produced transcript (whisper names it <basename>.txt):

[{ "tool": "os.fs.read", "args": { "path": "audio.txt" } }]

3. Answer strictly from the transcript text. If a list/order is requested, preserve the spoken order exactly.

Rules

1. Always write the transcript to a NEW .txt; never overwrite the source audio.
2. Drop --language only when the spoken language is unknown; setting it correctly improves accuracy and speed.
3. Long clips re-encode slowly — set realistic expectations and prefer a smaller --model for multi-minute audio.
4. Base the final answer on the transcript content, not on the file name or metadata.