Audio Transcribe

Audio Transcribe

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

Workflow

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