# Audio Processing Models

This directory contains implementations and examples for various audio processing models and tools.

## Whisper Integration

### Speech-to-Text
- Real-time transcription
- Batch processing
- Multi-language support
- Custom model fine-tuning

### Implementation Examples
```python
# Example: Whisper Speech-to-Text
import whisper

model = whisper.load_model("base")
result = model.transcribe("audio.mp3")
print(result["text"])
```

## Audio Generation

### Text-to-Speech
- Voice synthesis
- Voice cloning
- Multi-speaker support
- Emotion control

### Features
- Natural voice generation
- Custom voice training
- Audio post-processing
- Format conversion

## Best Practices

1. Audio preprocessing
2. Model selection
3. Resource management
4. Error handling
5. Output validation
6. Performance optimization
7. Quality control

## Performance Considerations

- Model size optimization
- Processing speed
- Memory usage
- GPU utilization
- Batch processing
- Real-time processing

## Contributing

Please follow these guidelines:
1. Include audio processing examples
2. Document model parameters
3. Add performance benchmarks
4. Include usage examples
5. Document dependencies

## Dependencies

- whisper
- torch
- numpy
- soundfile
- librosa
- transformers
- datasets