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

Model	Parameters	Speed	Accuracy	Memory
tiny	39M	~32x	Good	~1GB
base	74M	~16x	Better	~1GB
small	244M	~6x	Great	~2GB
medium	769M	~2x	Excellent	~5GB
large	1.5B	1x	Best	~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