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BytesAgain β€Ί Skills β€Ί Faster Whisper Gpu
πŸ¦€ ClawHub

Faster Whisper Gpu

by @felipeoff

High-performance local speech-to-text transcription using Faster Whisper with NVIDIA GPU acceleration. Transcribe audio files locally without sending data to...

Versionv0.1.0
Downloads917
#writing#video#audio#legal#communication
TERMINAL
clawhub install faster-whisper-gpu

πŸ“– About This Skill

name: faster-whisper-gpu description: High-performance local speech-to-text transcription using Faster Whisper with NVIDIA GPU acceleration. Transcribe audio files locally without sending data to external services. homepage: https://github.com/FelipeOFF/faster-whisper-gpu metadata: clawdbot: emoji: πŸŽ™οΈ category: audio tags: - transcription - stt - speech-to-text - whisper - gpu - cuda - local - privacy requires: bins: - python3 python_packages: - faster-whisper - torch install: - id: pip kind: pip packages: - faster-whisper - torch label: Install faster-whisper and PyTorch

πŸŽ™οΈ Faster Whisper GPU

High-performance local speech-to-text transcription using Faster Whisper with NVIDIA GPU acceleration.

✨ Features

  • πŸš€ GPU Accelerated: Uses NVIDIA CUDA for blazing-fast transcription
  • πŸ”’ 100% Local: No data leaves your machine. Complete privacy.
  • πŸ’° Free Forever: No API costs. Run unlimited transcriptions.
  • 🌍 Multilingual: Supports 99 languages with automatic detection
  • πŸ“ Multiple Formats: Input: MP3, WAV, FLAC, OGG, M4A. Output: TXT, SRT, JSON
  • 🎯 Multiple Models: From tiny (fast) to large-v3 (most accurate)
  • 🎬 Subtitle Generation: Create SRT files with word-level timestamps

    • πŸ“‹ Requirements

    Hardware

  • NVIDIA GPU with CUDA support (recommended: 4GB+ VRAM)
  • Or CPU-only mode (slower but works on any machine)

    • Software

  • Python 3.8+
  • NVIDIA drivers (for GPU support)
  • CUDA Toolkit 11.8+ or 12.x

    • πŸš€ Quick Start

    Installation

    # Install dependencies
pip install faster-whisper torch
    Verify GPU is available
    
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

    Basic Usage

    # Transcribe an audio file (auto-detects GPU)
python transcribe.py audio.mp3
    Specify language explicitly
    
python transcribe.py audio.mp3 --language pt
    Output as SRT subtitles
    
python transcribe.py audio.mp3 --format srt --output subtitles.srt
    Use larger model for better accuracy
    
python transcribe.py audio.mp3 --model large-v3

    πŸ”§ Advanced Usage

    Command Line Options

    python transcribe.py  [options]
    Options:
  --model {tiny,base,small,medium,large-v1,large-v2,large-v3}
                        Model size to use (default: base)
  --language LANG       Language code (e.g., 'pt', 'en', 'es'). Auto-detect if not specified.
  --format {txt,srt,json,vtt}
                        Output format (default: txt)
  --output FILE         Output file path (default: stdout)
  --device {cuda,cpu}   Device to use (default: cuda if available)
  --compute_type {int8,int8_float16,int16,float16,float32}
                        Computation precision (default: float16)
  --task {transcribe,translate}
                        Task: transcribe or translate to English (default: transcribe)
  --vad_filter          Enable voice activity detection filter
  --vad_parameters MIN_DURATION_ON,MIN_DURATION_OFF
                        VAD parameters as comma-separated values
  --condition_on_previous_text
                        Condition on previous text (default: True)
  --initial_prompt PROMPT
                        Initial prompt to guide transcription
  --word_timestamps     Include word-level timestamps (for SRT/JSON)
  --hotwords WORDS      Comma-separated hotwords to boost recognition

    Examples

    #### Portuguese Transcription with SRT Output 

    python transcribe.py meeting.mp3 --language pt --format srt --output meeting.srt

    #### English Translation from Any Language 

    python transcribe.py japanese_audio.mp3 --task translate --format txt

    #### High-Accuracy Mode with Large Model 

    python transcribe.py podcast.mp3 --model large-v3 --vad_filter --word_timestamps

    #### CPU-Only Mode (no GPU) 

    python transcribe.py audio.mp3 --device cpu --compute_type int8

    🐍 Python API

    from faster_whisper import WhisperModel
    Load model
    
model = WhisperModel("base", device="cuda", compute_type="float16")
    Transcribe
    
segments, info = model.transcribe("audio.mp3", language="pt")
    print(f"Detected language: {info.language} (probability: {info.language_probability:.2f})")
    for segment in segments:
    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")

    πŸ“Š Model Sizes & VRAM Requirements

    | Model | Parameters | VRAM Required | Relative Speed | Accuracy | |----------|------------|---------------|----------------|----------| | tiny | 39 M | ~1 GB | ~32x | Basic | | base | 74 M | ~1 GB | ~16x | Good | | small | 244 M | ~2 GB | ~6x | Better | | medium | 769 M | ~5 GB | ~2x | Great | | large-v3 | 1550 M | ~10 GB | 1x | Best |

    *Benchmarks measured on NVIDIA RTX 4090*

    πŸ” Supported Languages

    Faster Whisper supports 99 languages including:

  • Portuguese (pt)
  • English (en)
  • Spanish (es)
  • French (fr)
  • German (de)
  • Italian (it)
  • Japanese (ja)
  • Chinese (zh)
  • Russian (ru)
  • And 90+ more...

    • πŸ› οΈ Troubleshooting

    CUDA Out of Memory

    # Use smaller model
python transcribe.py audio.mp3 --model tiny
    Or use CPU
    
python transcribe.py audio.mp3 --device cpu
    Or reduce precision
    
python transcribe.py audio.mp3 --compute_type int8

    Model Download Issues

    Models are automatically downloaded on first use to ~/.cache/huggingface/hub/. If behind a proxy, set: 
    export HF_HOME=/path/to/custom/cache

    Slow Transcription

  • Ensure GPU is being used: check nvidia-smi during transcription
  • Use smaller model for faster results
  • Enable VAD filter to skip silent parts

    • 🀝 Contributing

    Contributions are welcome! Please: 1. Fork the repository 2. Create a feature branch 3. Submit a pull request

    πŸ“œ License

    MIT License - See LICENSE for details.

    Faster Whisper is developed by SYSTRAN and based on OpenAI's Whisper.

    πŸ™ Acknowledgments

  • OpenAI Whisper - Original model
  • Faster Whisper - Optimized implementation
  • CTranslate2 - Fast inference engine

    • Made with ❀️ for the OpenClaw community

    πŸ’‘ Examples

    #### Portuguese Transcription with SRT Output 

    python transcribe.py meeting.mp3 --language pt --format srt --output meeting.srt

    #### English Translation from Any Language 

    python transcribe.py japanese_audio.mp3 --task translate --format txt

    #### High-Accuracy Mode with Large Model 

    python transcribe.py podcast.mp3 --model large-v3 --vad_filter --word_timestamps

    #### CPU-Only Mode (no GPU) 

    python transcribe.py audio.mp3 --device cpu --compute_type int8