> **transcribe** — chapter 36 of 44 in [openai/skills](https://skillsdocs.com/openai/skills).
>
> Book (all chapters, one file): https://skillsdocs.com/openai/skills.md
> Machine manifest: https://skillsdocs.com/openai/skills/.well-known/agent-skills/index.json
> Install the book: `npx skills add openai/skills`
> Upstream: https://github.com/openai/skills/blob/main/skills/.curated/transcribe/SKILL.md @ `main`
> Raw bytes, no header: https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/SKILL.md
> Base for relative paths: https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/
> Licence: Declared in LICENSE.txt — https://github.com/openai/skills/blob/main/skills/.curated/transcribe/LICENSE.txt
>
> Bundled files (6), referenced from this skill's directory:
>   - `agents/openai.yaml` — https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/agents/openai.yaml
>   - `assets/transcribe-small.svg` — https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/assets/transcribe-small.svg
>   - `assets/transcribe.png` — https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/assets/transcribe.png
>   - `LICENSE.txt` — https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/LICENSE.txt
>   - `references/api.md` — https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/references/api.md
>   - `scripts/transcribe_diarize.py` — https://raw.githubusercontent.com/openai/skills/main/skills/.curated/transcribe/scripts/transcribe_diarize.py
>
> Content © its authors, served unmodified. Takedown: https://github.com/kyleledbetter/skillsdocs/issues/new?labels=takedown&title=Takedown+request

<!-- Verbatim upstream SKILL.md follows, YAML frontmatter included. -->

---
name: "transcribe"
description: "Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings."
---


# Audio Transcribe

Transcribe audio using OpenAI, with optional speaker diarization when requested. Prefer the bundled CLI for deterministic, repeatable runs.

## Workflow
1. Collect inputs: audio file path(s), desired response format (text/json/diarized_json), optional language hint, and any known speaker references.
2. Verify `OPENAI_API_KEY` is set. If missing, ask the user to set it locally (do not ask them to paste the key).
3. Run the bundled `transcribe_diarize.py` CLI with sensible defaults (fast text transcription).
4. Validate the output: transcription quality, speaker labels, and segment boundaries; iterate with a single targeted change if needed.
5. Save outputs under `output/transcribe/` when working in this repo.

## Decision rules
- Default to `gpt-4o-mini-transcribe` with `--response-format text` for fast transcription.
- If the user wants speaker labels or diarization, use `--model gpt-4o-transcribe-diarize --response-format diarized_json`.
- If audio is longer than ~30 seconds, keep `--chunking-strategy auto`.
- Prompting is not supported for `gpt-4o-transcribe-diarize`.

## Output conventions
- Use `output/transcribe/<job-id>/` for evaluation runs.
- Use `--out-dir` for multiple files to avoid overwriting.

## Dependencies (install if missing)
Prefer `uv` for dependency management.

```
uv pip install openai
```
If `uv` is unavailable:
```
python3 -m pip install openai
```

## Environment
- `OPENAI_API_KEY` must be set for live API calls.
- If the key is missing, instruct the user to create one in the OpenAI platform UI and export it in their shell.
- Never ask the user to paste the full key in chat.

## Skill path (set once)

```bash
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export TRANSCRIBE_CLI="$CODEX_HOME/skills/transcribe/scripts/transcribe_diarize.py"
```

User-scoped skills install under `$CODEX_HOME/skills` (default: `~/.codex/skills`).

## CLI quick start
Single file (fast text default):
```
python3 "$TRANSCRIBE_CLI" \
  path/to/audio.wav \
  --out transcript.txt
```

Diarization with known speakers (up to 4):
```
python3 "$TRANSCRIBE_CLI" \
  meeting.m4a \
  --model gpt-4o-transcribe-diarize \
  --known-speaker "Alice=refs/alice.wav" \
  --known-speaker "Bob=refs/bob.wav" \
  --response-format diarized_json \
  --out-dir output/transcribe/meeting
```

Plain text output (explicit):
```
python3 "$TRANSCRIBE_CLI" \
  interview.mp3 \
  --response-format text \
  --out interview.txt
```

## Reference map
- `references/api.md`: supported formats, limits, response formats, and known-speaker notes.