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//

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.