Asteroid is a Pytorch-based source separation and speech enhancement API that enables fast experimentation on common datasets. It comes with a source code written to support a large range of architectures and a set of recipes to reproduce some papers.
Asteroid is intended to be a community-based project so hop on and help us !

• User friendliness. Asteroid's API offers simple solutions for most common use cases.
• Modularity. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a flexible way to create new systems.
• Extensibility. Extending Asteroid with new features is simple. Add a new filterbank, separator, architecture, dataset or even recipe very easily.
• Reproducibility. Recipes provide an easy way to reproduce results with data preparation, training and evaluation in a same script.

In order to install Asteroid, clone the repo and install it using pip or python :

git clone https://github.com/mpariente/AsSteroid
cd AsSteroid
# Install with pip (in editable mode)
pip install -e .
# Install with python
python setup.py install

cd egs/wham/ConvTasNet
./run.sh

More information in egs/README.md.

• ConvTasnet (Luo et al.)
• Tasnet (Luo et al.)
• Deep clustering (Hershey et al. and Isik et al.)
• Chimera ++ (for ) (Luo et al. and Wang et al.)
• FurcaNeXt (Shi et al.)
• DualPathRNN (Luo et al.)
• Two step learning (Tzinis et al.)

See our contributing guidelines.

├── asteroid                 # Python package / Source code
│   ├── data                 # Data classes, DalatLoaders maker.
│   ├── engine               # Training classes : losses, optimizers and trainer.
│   ├── filterbanks          # Common filterbanks and related classes.
│   ├── masknn               # Separation building blocks and architectures.
│   └── utils.py
├── examples                 # Simple asteroid examples 
└── egs                      # Recipes for all datasets and systems.
│   ├── wham                 # Recipes for one dataset (WHAM) 
│   │   ├── ConvTasNet       # ConvTasnet systme on the WHAM dataset.
│   │   │   └── ...          # Recipe's structure. See egs/README.md for more info
│   │   ├── Your recipe      # More recipes on the same dataset (Including yours)
│   │   ├── ...
│   │   └── DualPathRNN
│   └── Your dataset         # More datasets (Including yours)

Audio source separation and speech enhancement are fast evolving fields with a growing number of papers submitted to conferences each year. While datasets such as wsj0-{2, 3}mix, WHAM or MS-SNSD are being shared, there has been little effort to create common codebases for development and evaluation of source separation and speech enhancement algorithms. Here is one !