  This paper describes Asteroid, the PyTorch-based audio source separation
toolkit for researchers. Inspired by the most successful neural source
separation systems, it provides all neural building blocks required to build
such a system. To improve reproducibility, Kaldi-style recipes on common audio
source separation datasets are also provided. This paper describes the software
architecture of Asteroid and its most important features. By showing
experimental results obtained with Asteroid's recipes, we show that our
implementations are at least on par with most results reported in reference
papers. The toolkit is publicly available at
https://github.com/mpariente/asteroid .
