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Audio Transcribe

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audio-transcribe

Audio Transcribe is an Agent Skill for documents published in the AtomicBot-ai/atomic-agent repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/AtomicBot-ai/atomic-agent/blob/HEAD/starter-skills/audio-transcribe/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/audio-transcribe, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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"] } }]
  1. Read the produced transcript (whisper names it <basename>.txt):
[{ "tool": "os.fs.read", "args": { "path": "audio.txt" } }]
  1. 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.