transcribe

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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.

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Sourceopenai/skills

Added on2026-02-07

NPX Install

npx skill4agent add openai/skills transcribe

SKILL.md Content

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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)

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.