VoiceAccess

VoiceAccess is an open-source project dedicated to bringing automatic speech recognition (ASR) to low-resource and endangered languages. By leveraging transfer learning, data augmentation, and community collaboration, we aim to preserve linguistic diversity and enable technology access for underserved communities.

🎯 Mission

Our mission is to democratize speech recognition technology by:

• Providing state-of-the-art ASR models for languages with limited training data
• Enabling rapid adaptation of existing models to new languages
• Building tools that respect and preserve linguistic diversity
• Creating an inclusive platform for community-driven language preservation

✨ Key Features

• Transfer Learning: Adapt pre-trained models (Wav2Vec2, Whisper, Conformer) to new languages with minimal data
• Data Augmentation: Advanced techniques to enhance limited training datasets
• Multi-Language Support: Framework designed for easy addition of new languages
• Low-Resource Optimization: Efficient models that work with as little as 1 hour of transcribed audio
• Community Tools: Easy-to-use interfaces for non-technical language communities
• Modular Architecture: Plug-and-play components for custom ASR pipelines

📊 Performance

| Language Type | Training Data | WER | CER | |--------------|---------------|-----|-----| | High-resource | >100 hours | 8-12% | 2-4% | | Medium-resource | 10-100 hours | 15-25% | 5-10% | | Low-resource | 1-10 hours | 25-40% | 10-20% | | Zero-shot | 0 hours | 40-60% | 20-35% |

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/openimpactai/VoiceAccess.git
cd VoiceAccess

# Install dependencies
pip install -r requirements.txt

# Install VoiceAccess
pip install -e .

Basic Usage

from voiceaccess import ASREngine, Config

# Load configuration
config = Config.from_file("configs/default.yaml")

# Initialize ASR engine
engine = ASREngine(config)

# Load a pre-trained model
engine.load_model("models/wav2vec2-base.pt", model_type="wav2vec2")

# Transcribe audio
transcription = engine.transcribe("path/to/audio.wav")
print(transcription)

Adapt to a New Language

# Adapt model to a new language
engine.adapt_to_language(
    language_code="xyz",  # Your language code
    adaptation_data_path="data/xyz_language/"
)

# Save adapted model
engine.model.save_checkpoint("models/wav2vec2-xyz-adapted.pt")

🏗️ Architecture

VoiceAccess/
├── src/
│   ├── core/              # Core ASR engine and configuration
│   ├── models/            # Model architectures (Wav2Vec2, Whisper, etc.)
│   ├── languages/         # Language-specific adaptations
│   ├── preprocessing/     # Audio processing utilities
│   ├── augmentation/      # Data augmentation techniques
│   ├── evaluation/        # Metrics and evaluation tools
│   └── api/              # REST API for model serving
├── data/                  # Dataset storage
├── models/               # Model checkpoints
├── configs/              # Configuration files
├── notebooks/            # Jupyter notebooks for experiments
├── scripts/              # Training and evaluation scripts
├── tests/                # Unit and integration tests
└── examples/             # Usage examples

🤝 Contributing

We welcome contributions from researchers, developers, and language communities! Please see our Contributing Guide for details on:

• Adding support for new languages
• Improving model architectures
• Contributing datasets
• Documentation and tutorials

📚 Documentation

• User Guide - Detailed usage instructions
• API Reference - Complete API documentation
• Model Zoo - Pre-trained models for various languages
• Training Guide - How to train models for new languages

🌍 Supported Languages

Currently supported languages include:

• Well-resourced: English, Spanish, French, German, Chinese
• Low-resource: Quechua, Maori, Welsh, Basque
• Endangered: Various indigenous languages (contact us for details)

See languages/README.md for the full list and how to add your language.

🔧 Requirements

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.8+ (optional, for GPU acceleration)
• 8GB+ RAM (16GB recommended)
• 10GB+ free disk space

📈 Roadmap

• [ ] Support for 100+ low-resource languages
• [ ] Real-time streaming ASR
• [ ] Mobile deployment (iOS/Android)
• [ ] Federated learning for privacy-preserving training
• [ ] Integration with language documentation tools
• [ ] Multi-speaker diarization
• [ ] Code-switching support

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Mozilla Common Voice for multilingual speech datasets
• Hugging Face for transformer models
• All language communities contributing to this project

📧 Contact

• Email: voiceaccess@openimpactai.org
• GitHub Issues: Report bugs or request features
• Discord: Join our community

📖 Citation

If you use VoiceAccess in your research, please cite:

@software{voiceaccess2024,
  title = {VoiceAccess: Automatic Speech Recognition for Low-Resource Languages},
  author = {OpenImpactAI},
  year = {2024},
  url = {https://github.com/openimpactai/VoiceAccess}
}

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<p align="center"> Made with ❤️ by <a href="https://github.com/openimpactai">OpenImpactAI</a> </p>