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 !

• Installation
• Tutorials
• Running a recipe
• Available recipes
• Supported datasets
• Our guiding principles

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

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

Few notebooks showing example usage of asteroid's features.

• Permutation invariant training wrapper PITLossWrapper .
• Filterbank API

Running the recipes requires additional packages in most cases, we recommend running :

# from asteroid/
pip install -r requirements.txt

Then choose the recipe you want to run and run it !

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 ++ (Luo et al. and Wang et al.)
• FurcaNeXt (Shi et al.)
• DualPathRNN (Luo et al.)
• Two step learning (Tzinis et al.)

• WSJ0-2mix / WSJ03mix (Hershey et al.)
• WHAM (Wichern et al.)
• WHAMR (Maciejewski et al.)
• Microsoft DNS Challenge (Chandan et al.)
• SMS_WSJ (Drude et al.)

See our contributing guidelines.

To build the docs, you'll need Sphinx, a theme and some other package

# Start by installing the required packages
cd docs/
pip install -r requirements.txt
# Build the docs
make html
# View it ! (Change firefox by your favorite browser)
firefox build/html/index.html

If you rebuild the docs, don't forget to run make clean before it.

You can add this to your .bashrc, source it and run run_docs from the docs/ folder

alias run_docs='make clean; make html; firefox build/html/index.html'

├── 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)

# Launch tensorboard remotely (default port is 6006)
tensorboard --logdir exp/tmp/lightning_logs/ --port tf_port

# Open port-forwarding connection. Add -Nf option not to open remote. 
ssh -L local_port:localhost:tf_port user@ip

Then open http://localhost:local_port/.

• 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, system design, training and evaluation in a same script. This is an essential tool for the community !