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ONSSEN: An Open-source Speech Separation and Enhancement Library

Onssen, pronounced as おんせん(温泉, Japanese hot spring), is a PyTorch-based library for speech separation, speech enhancement, or speech style transformation.

Development plan:

  • Provide template classes for data, model, and evaluation
  • Move models to separate folders (i.e. Kaldi style)
  • Reproduce scores and upload pretrained models
  • Finish inference method for online separation

2020-04-20 Updates:

  • Add evaluation method for deep clustering
  • Use W_{MR} weight in deep clustering
  • Minor changes

Supported Models

  • Deep Clustering
  • Chimera Net
  • Chimera++
  • Phase Estimation Network
  • Speech Enhancement with Restoration Layers

Supported Dataset

Requirements

  • PyTorch
  • LibRosa
  • NumPy

Usage

You can simply use the existing config JSON file or customize your config file to train the enhancement or separation model. under the egs/wsj0-2mix/deep_clustering/ directory:

python run.py -c config.json

Citing

If you use onssen for your research project, please cite one of the following bibtex citations:

@article{ni2019onssen,
title={Onssen: an open-source speech separation and enhancement library},
author={Ni, Zhaoheng and Mandel, Michael I},
journal={arXiv preprint arXiv:1911.00982},
year={2019}
}

@Misc{onssen,
    author = {Zhaoheng Ni and Michael Mandel},
    title = "ONSSEN: An Open-source Speech Separation and Enhancement Library",
    howpublished = {\url{https://github.com/speechLabBcCuny/onssen}},
    year =        {2019}
}

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An open-source speech separation and enhancement library

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