AcouSpike

A modern, lightweight library for Neuromorphic Audio Processing using Spiking Neural Networks

🌟 Overview

AcouSpike is a PyTorch-based framework designed for neuromorphic audio processing using Spiking Neural Networks (SNNs). It provides a flexible and efficient way to build, train, and deploy SNN models for various audio processing tasks.

🚀 Features

• Flexible Architecture

  • Build custom SNN models using PyTorch
  • Support for various neuron types and synaptic connections
  • Modular design for easy extension

• Audio Processing

  • Built-in support for common audio tasks
  • Efficient spike encoding for audio signals

• Developer Friendly

  • Minimal dependencies
  • Comprehensive documentation
  • Full test coverage
  • Easy-to-follow examples

🔧 Installation

[TODO] make the acouspike a pip package

pip install -i https://test.pypi.org/simple/ acouspike==0.0.0.1

📚 Documentation

Model Components

• Neuron Models
• Network Architectures

Tutorials

1. Getting Started
2. Building Your First SNN
3. Audio Processing Basics

💡 Quick Start

import acouspike as asp

# Create a simple SNN model
model = asp.models.SimpleSNN(
    input_size=64,
    hidden_size=128,
    output_size=10
)

# Train the model
trainer = asp.training.SNNTrainer(model)
trainer.train(dataset)

🎯 Examples

Ready-to-use examples are available in the recipes directory:

• Speaker Identification

cd recipes/speaker_identification
python run.sh

• Keyword Spotting

cd recipes/keyword_spotting
python run.sh

📊 Benchmarks

Performance benchmarks and comparisons are available in our benchmarks page.

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📄 License

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

📬 Contact

• Issue Tracker: GitHub Issues
• Email: maintainer@example.com

🙏 Acknowledgments

• List of contributors
• Supporting organizations
• Related projects and inspirations