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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: 🎙️ 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

🎙️ Faster Whisper GPU

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

✨ Features

• 🚀 GPU Accelerated: Uses NVIDIA CUDA for blazing-fast transcription
• 🔒 100% Local: No data leaves your machine. Complete privacy.
• 💰 Free Forever: No API costs. Run unlimited transcriptions.
• 🌍 Multilingual: Supports 99 languages with automatic detection
• 📁 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

📋 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

🚀 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

🔧 Advanced Usage

Command Line Options

python transcribe.py <audio_file> [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}")

📊 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

🔍 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...

🛠️ 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

📜 License

MIT License - See LICENSE for details.

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

🙏 Acknowledgments

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

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Made with ❤️ for the OpenClaw community