--- name: whisper-stt description: | Free local speech-to-text transcription using OpenAI Whisper. Transcribe audio files (mp3, wav, m4a, ogg, etc.) to text without API costs. Use when: (1) User needs audio/video transcription, (2) Converting voice memos to text, (3) Generating subtitles (SRT/VTT), (4) Free local STT without cloud API costs. ---

Whisper STT Skill

Free, local speech-to-text using OpenAI Whisper.

Prerequisites

Install dependencies (one-time setup):

pip install openai-whisper torch

Optional: Install ffmpeg for broader format support:

• macOS: brew install ffmpeg
• Ubuntu: sudo apt install ffmpeg

Usage

Transcribe an audio file

python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py <audio_file>

Options

| Option | Description | |--------|-------------| | --model | Model size: tiny, base, small, medium, large, large-v3-turbo (default: base) | | --language, -l | Language code: zh, en, ja, etc. (auto-detect if not specified) | | --output, -o | Output format: json, txt, srt, vtt (default: json) |

Examples

Chinese audio to text:

python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py recording.m4a --language zh --output txt

Generate subtitles (SRT):

python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py video.mp4 --output srt > subtitles.srt

Use faster model:

python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py audio.mp3 --model tiny --output txt

High accuracy (slower):

python ~/.openclaw/skills/whisper-stt/scripts/transcribe.py audio.mp3 --model large-v3 --output txt

Model Selection Guide

| Model | Speed | Accuracy | VRAM/RAM | Best For | |-------|-------|----------|----------|----------| | tiny | ~32x | Basic | ~1GB | Quick tests, low resource | | base | ~16x | Good | ~1GB | Balanced speed/accuracy | | small | ~6x | Better | ~2GB | Better accuracy | | medium | ~2x | Very Good | ~5GB | High accuracy | | large | 1x | Excellent | ~10GB | Best quality | | large-v3-turbo | ~8x | Excellent | ~6GB | Fast + accurate (recommended) |

Troubleshooting

"ModuleNotFoundError: No module named 'whisper'" → Run: pip install openai-whisper torch

"ffmpeg not found" → Install ffmpeg or convert audio to WAV format first

Slow transcription → Use smaller model (tiny/base) or ensure GPU is available (Apple Silicon MPS, NVIDIA CUDA)

Poor accuracy on Chinese → Use --language zh explicitly and consider larger model (medium/large)

Output Formats

• json: Full result with segments, timestamps, and metadata
• txt: Plain text transcription only
• srt: SubRip subtitle format with timing
• vtt: WebVTT subtitle format for web players

Credits

Powered by OpenAI Whisper - open source speech recognition.