transcribe

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)

export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export

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

CLI quick start

Single file (fast text default):

Diarization with known speakers (up to 4):

Plain text output (explicit):

Reference map

• references/api.md: supported formats, limits, response formats, and known-speaker notes.