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Whisper

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whisper

Whisper is an Agent Skill for documents published in the trpc-group/trpc-agent-go repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Transcribe audio files to text using OpenAI Whisper

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/trpc-group/trpc-agent-go/blob/HEAD/examples/skill/skills/whisper/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/whisper, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Whisper Audio Transcription Skill

Transcribe audio files to text using OpenAI Whisper.

Capabilities

  • Transcribe audio files (MP3, WAV, M4A, FLAC, OGG, etc.) to text
  • Support for 90+ languages with auto-detection
  • Optional timestamp generation
  • Multiple model sizes (tiny/base/small/medium/large)
  • Output in plain text or JSON format

Usage

Basic Transcription

python3 scripts/transcribe.py <audio_file> <output_file>

With Options

# Specify model size (default: base)
python3 scripts/transcribe.py audio.mp3 transcript.txt --model medium

# Specify language (improves accuracy)
python3 scripts/transcribe.py audio.mp3 transcript.txt --language zh

# Include timestamps
python3 scripts/transcribe.py audio.mp3 transcript.txt --timestamps

# JSON output with metadata
python3 scripts/transcribe.py audio.mp3 output.json --format json

Parameters

  • audio_file (required): Path to input audio file
  • output_file (required): Path to output text/JSON file
  • --model: Whisper model size (tiny/base/small/medium/large, default: base)
  • --language: Language code (e.g., en, zh, es, fr, auto for detection)
  • --timestamps: Include word-level timestamps in output
  • --format: Output format (text/json, default: text)

Model Sizes

ModelParametersSpeedAccuracyMemory
tiny39M~32xGood~1GB
base74M~16xBetter~1GB
small244M~6xGreat~2GB
medium769M~2xExcellent~5GB
large1.5B1xBest~10GB

Supported Audio Formats

MP3, WAV, M4A, FLAC, OGG, AAC, WMA, and more (via FFmpeg)

Dependencies

  • Python 3.8+
  • openai-whisper
  • ffmpeg

Installation

pip install openai-whisper
sudo apt-get install ffmpeg  # Ubuntu/Debian