ChatGPT Codex CLI OpenAI Skill

transcribe

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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Part of openai/skills — 44 skills

Install

skills CLI npx skills add https://github.com/openai/skills/tree/main/skills/.curated/transcribe
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install openai-skills@llmmart
Git git clone https://github.com/openai/skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole openai/skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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 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.
Files (skills)
  • agents
    • openai.yaml 414 B 
      interface:
  display_name: "Audio Transcribe"
  short_description: "Transcribe audio using OpenAI, with optional speaker diarization when requested. Prefer the bundled CLI for deterministic, repeatable runs."
  icon_small: "./assets/transcribe-small.svg"
  icon_large: "./assets/transcribe.png"
  default_prompt: "Transcribe this audio or video, include speaker labels when possible, and provide a clean summary."
  • assets
    • transcribe-small.svg 750 B · in bundle
    • transcribe.png 1.3 KB · in bundle
  • references
    • api.md 457 B 
      # gpt-4o-transcribe-diarize quick reference

- Input formats: mp3, mp4, mpeg, mpga, m4a, wav, webm.
- Max file size: 25 MB per request.
- response_format options: text, json, diarized_json.
- For audio longer than ~30 seconds, pass chunking_strategy (use "auto" to split into chunks).
- Known speakers: up to 4 references via extra_body known_speaker_names + known_speaker_references (data URLs).
- Prompting is not supported for gpt-4o-transcribe-diarize.
  • scripts
    • transcribe_diarize.py 8.5 KB 
      #!/usr/bin/env python3
"""Transcribe audio (optionally with speaker diarization) using OpenAI."""

from __future__ import annotations

import argparse
import base64
import json
import mimetypes
import os
from pathlib import Path
import sys
from typing import Any, Dict, List, Optional, Tuple

DEFAULT_MODEL = "gpt-4o-mini-transcribe"
DEFAULT_RESPONSE_FORMAT = "text"
DEFAULT_CHUNKING_STRATEGY = "auto"
MAX_AUDIO_BYTES = 25 * 1024 * 1024
MAX_KNOWN_SPEAKERS = 4

ALLOWED_RESPONSE_FORMATS = {"text", "json", "diarized_json"}


def _die(message: str, code: int = 1) -> None:
    print(f"Error: {message}", file=sys.stderr)
    raise SystemExit(code)


def _warn(message: str) -> None:
    print(f"Warning: {message}", file=sys.stderr)


def _ensure_api_key(dry_run: bool) -> None:
    if os.getenv("OPENAI_API_KEY"):
        print("OPENAI_API_KEY is set.", file=sys.stderr)
        return
    if dry_run:
        _warn("OPENAI_API_KEY is not set; dry-run only.")
        return
    _die("OPENAI_API_KEY is not set. Export it before running.")


def _normalize_response_format(value: Optional[str]) -> str:
    if not value:
        return DEFAULT_RESPONSE_FORMAT
    fmt = value.strip().lower()
    if fmt not in ALLOWED_RESPONSE_FORMATS:
        _die(
            "response-format must be one of: "
            + ", ".join(sorted(ALLOWED_RESPONSE_FORMATS))
        )
    return fmt


def _normalize_chunking_strategy(value: Optional[str]) -> Any:
    if not value:
        return DEFAULT_CHUNKING_STRATEGY
    raw = str(value).strip()
    if raw.startswith("{"):
        try:
            return json.loads(raw)
        except json.JSONDecodeError:
            _die("chunking-strategy JSON is invalid")
    return raw


def _guess_mime_type(path: Path) -> str:
    mime, _ = mimetypes.guess_type(str(path))
    if mime:
        return mime
    return "audio/wav"


def _encode_data_url(path: Path) -> str:
    data = path.read_bytes()
    mime = _guess_mime_type(path)
    encoded = base64.b64encode(data).decode("ascii")
    return f"data:{mime};base64,{encoded}"


def _parse_known_speakers(raw_items: List[str]) -> Tuple[List[str], List[str]]:
    names: List[str] = []
    refs: List[str] = []
    for raw in raw_items:
        if "=" not in raw:
            _die("known-speaker must be NAME=PATH")
        name, path_str = raw.split("=", 1)
        name = name.strip()
        path = Path(path_str.strip())
        if not name or not path_str.strip():
            _die("known-speaker must be NAME=PATH")
        if not path.exists():
            _die(f"Known speaker file not found: {path}")
        names.append(name)
        refs.append(_encode_data_url(path))
    if len(names) > MAX_KNOWN_SPEAKERS:
        _die(f"known speakers must be <= {MAX_KNOWN_SPEAKERS}")
    return names, refs


def _output_extension(response_format: str) -> str:
    return "txt" if response_format == "text" else "json"


def _build_output_path(
    audio_path: Path,
    response_format: str,
    out: Optional[str],
    out_dir: Optional[str],
) -> Path:
    ext = "." + _output_extension(response_format)
    if out:
        path = Path(out)
        if path.exists() and path.is_dir():
            return path / f"{audio_path.stem}.transcript{ext}"
        if path.suffix == "":
            return path.with_suffix(ext)
        return path
    if out_dir:
        base = Path(out_dir)
        base.mkdir(parents=True, exist_ok=True)
        return base / f"{audio_path.stem}.transcript{ext}"
    return Path(f"{audio_path.stem}.transcript{ext}")


def _create_client():
    try:
        from openai import OpenAI
    except ImportError:
        _die("openai SDK not installed. Install with `uv pip install openai`.")
    return OpenAI()


def _format_output(result: Any, response_format: str) -> str:
    if response_format == "text":
        text = getattr(result, "text", None)
        return text if isinstance(text, str) else str(result)
    if hasattr(result, "model_dump"):
        return json.dumps(result.model_dump(), indent=2)
    if isinstance(result, (dict, list)):
        return json.dumps(result, indent=2)
    return json.dumps({"text": getattr(result, "text", str(result))}, indent=2)


def _validate_audio(path: Path) -> None:
    if not path.exists():
        _die(f"Audio file not found: {path}")
    size = path.stat().st_size
    if size > MAX_AUDIO_BYTES:
        _warn(
            f"Audio file exceeds 25MB limit ({size} bytes): {path}"
        )


def _build_payload(
    args: argparse.Namespace,
    known_speaker_names: List[str],
    known_speaker_refs: List[str],
) -> Dict[str, Any]:
    payload: Dict[str, Any] = {
        "model": args.model,
        "response_format": args.response_format,
        "chunking_strategy": args.chunking_strategy,
    }
    if args.language:
        payload["language"] = args.language
    if args.prompt:
        payload["prompt"] = args.prompt
    if known_speaker_names:
        payload["extra_body"] = {
            "known_speaker_names": known_speaker_names,
            "known_speaker_references": known_speaker_refs,
        }
    return payload


def _run_one(
    client: Any,
    audio_path: Path,
    payload: Dict[str, Any],
) -> Any:
    with audio_path.open("rb") as audio_file:
        return client.audio.transcriptions.create(
            file=audio_file,
            **payload,
        )


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Transcribe audio (optionally with speaker diarization) using OpenAI."
    )
    parser.add_argument("audio", nargs="+", help="Audio file(s) to transcribe")
    parser.add_argument(
        "--model",
        default=DEFAULT_MODEL,
        help=f"Model to use (default: {DEFAULT_MODEL})",
    )
    parser.add_argument(
        "--response-format",
        default=DEFAULT_RESPONSE_FORMAT,
        help="Response format: text, json, or diarized_json",
    )
    parser.add_argument(
        "--chunking-strategy",
        default=DEFAULT_CHUNKING_STRATEGY,
        help="Chunking strategy (use 'auto' for long audio)",
    )
    parser.add_argument("--language", help="Optional language hint (e.g. 'en')")
    parser.add_argument("--prompt", help="Optional prompt to guide transcription")
    parser.add_argument(
        "--known-speaker",
        action="append",
        default=[],
        help="Known speaker reference as NAME=PATH (repeatable, max 4)",
    )
    parser.add_argument("--out", help="Output file path (single audio only)")
    parser.add_argument("--out-dir", help="Output directory for transcripts")
    parser.add_argument(
        "--stdout",
        action="store_true",
        help="Write transcript to stdout instead of a file",
    )
    parser.add_argument(
        "--dry-run",
        action="store_true",
        help="Validate inputs and print payload without calling the API",
    )

    args = parser.parse_args()
    args.response_format = _normalize_response_format(args.response_format)
    args.chunking_strategy = _normalize_chunking_strategy(args.chunking_strategy)

    if args.out and len(args.audio) > 1:
        _die("--out only supports a single audio file")
    if args.stdout and (args.out or args.out_dir):
        _die("--stdout cannot be combined with --out or --out-dir")
    if args.stdout and len(args.audio) > 1:
        _die("--stdout only supports a single audio file")

    if args.prompt and "transcribe-diarize" in args.model:
        _die("prompt is not supported with gpt-4o-transcribe-diarize")
    if args.response_format == "diarized_json" and "transcribe-diarize" not in args.model:
        _die("diarized_json requires gpt-4o-transcribe-diarize")

    _ensure_api_key(args.dry_run)

    audio_paths = [Path(p) for p in args.audio]
    for path in audio_paths:
        _validate_audio(path)

    known_names, known_refs = _parse_known_speakers(args.known_speaker)
    if known_names and "transcribe-diarize" not in args.model:
        _warn("known-speaker references are only supported for gpt-4o-transcribe-diarize")
    payload = _build_payload(args, known_names, known_refs)

    if args.dry_run:
        print(json.dumps(payload, indent=2))
        return

    client = _create_client()

    for path in audio_paths:
        result = _run_one(client, path, payload)
        output = _format_output(result, args.response_format)
        if args.stdout:
            print(output)
            continue
        out_path = _build_output_path(path, args.response_format, args.out, args.out_dir)
        out_path.parent.mkdir(parents=True, exist_ok=True)
        out_path.write_text(output, encoding="utf-8")
        print(f"Wrote {out_path}")


if __name__ == "__main__":
    main()
  • LICENSE.txt 10.5 KB 
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  • SKILL.md 2.8 KB 
    ---
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.

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