  Speech separation is an essential task for multi-talker speech recognition.
Recently many deep learning approaches are proposed and have been constantly
refreshing the state-of-the-art performances. The lack of algorithm
implementations limits researchers to use the same dataset for comparison.
Building a generic platform can benefit researchers by easily implementing
novel separation algorithms and comparing them with the existing ones on
customized datasets. We introduce "onssen": an open-source speech separation
and enhancement library. onssen is a library mainly for deep learning
separation and enhancement algorithms. It uses LibRosa and NumPy libraries for
the feature extraction and PyTorch as the back-end for model training. onssen
supports most of the Time-Frequency mask-based separation algorithms (e.g. deep
clustering, chimera net, chimera++, and so on) and also supports customized
datasets. In this paper, we describe the functionality of modules in onssen and
show the algorithms implemented by onssen achieve the same performances as
reported in the original papers.
