This repository contains the datasets and scripts required for the DNS challenge. For more details about the challenge, please visit https://www.microsoft.com/en-us/research/dns-challenge/home. For more details on the testing framework, please visit P.808.
- The datasets directory contains the clean speech, noise and room impulse responses for creating training data. It also contains the test set that participants can use during the development stages.
- The NSNet2-baseline directory contains the inference scripts and the ONNX model for the baseline Speech Enhancement method.
- noisyspeech_synthesizer_singleprocess.py - is used to synthesize noisy-clean speech pairs for training purposes.
- noisyspeech_synthesizer.cfg - is the configuration file used to synthesize the data. Users are required to accurately specify different parameters and provide the right paths to the datasets required to synthesize noisy speech.
- audiolib.py - contains modules required to synthesize datasets.
- utils.py - contains some utility functions required to synthesize the data.
- unit_tests_synthesizer.py - contains the unit tests to ensure sanity of the data.
- requirements.txt - contains all the libraries required for synthesizing the data.
- Python 3.0 and above
- Soundfile (pip install pysoundfile), librosa
- Install librosa
pip install librosa
- Install Git Large File Storage for faster download of the datasets.
git lfs install
git lfs track "*.wav"
git add .gitattributes
- Clone the repository.
git clone https://github.com/microsoft/DNS-Challenge DNS-Challenge
- Edit noisyspeech_synthesizer.cfg to specify the required parameters described in the file and include the paths to clean speech, noise and impulse response related csv files. Also, specify the paths to the destination directories and store the logs.
- Create dataset
python noisyspeech_synthesizer_singleprocess.py
For the datasets and the DNS challenge:
@article{reddy2020icassp,
title={ICASSP 2021 Deep Noise Suppression Challenge},
author={Reddy, Chandan KA and Dubey, Harishchandra and Gopal, Vishak and Cutler, Ross and Braun, Sebastian and Gamper, Hannes and Aichner, Robert and Srinivasan, Sriram},
journal={arXiv preprint arXiv:2009.06122},
year={2020}
}The baseline NSNet noise suppression:
@INPROCEEDINGS{9054254,
author={Y. {Xia} and S. {Braun} and C. K. A. {Reddy} and H. {Dubey} and R. {Cutler} and I. {Tashev}},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP)},
title={Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement},
year={2020}, volume={}, number={}, pages={871-875},}@misc{braun2020data,
title={Data augmentation and loss normalization for deep noise suppression},
author={Sebastian Braun and Ivan Tashev},
year={2020},
eprint={2008.06412},
archivePrefix={arXiv},
primaryClass={eess.AS}
}The P.808 test framework:
@article{naderi2020open,
title={An Open source Implementation of ITU-T Recommendation P. 808 with Validation},
author={Naderi, Babak and Cutler, Ross},
journal={arXiv preprint arXiv:2005.08138},
year={2020}
}DNSMOS API:
@article{reddy2020dnsmos,
title={DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors},
author={Reddy, Chandan KA and Gopal, Vishak and Cutler, Ross},
journal={arXiv e-prints},
pages={arXiv--2010},
year={2020}
}This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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MICROSOFT PROVIDES THE DATASETS ON AN "AS IS" BASIS. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, GUARANTEES OR CONDITIONS WITH RESPECT TO YOUR USE OF THE DATASETS. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAW, MICROSOFT DISCLAIMS ALL LIABILITY FOR ANY DAMAGES OR LOSSES, INLCUDING DIRECT, CONSEQUENTIAL, SPECIAL, INDIRECT, INCIDENTAL OR PUNITIVE, RESULTING FROM YOUR USE OF THE DATASETS.
The datasets are provided under the original terms that Microsoft received such datasets. See below for more information about each dataset.
The datasets used in this project are licensed as follows:
- Clean speech:
- https://librivox.org/; License: https://librivox.org/pages/public-domain/
- PTDB-TUG: Pitch Tracking Database from Graz University of Technology https://www.spsc.tugraz.at/databases-and-tools/ptdb-tug-pitch-tracking-database-from-graz-university-of-technology.html; License: http://opendatacommons.org/licenses/odbl/1.0/
- Edinburgh 56 speaker dataset: https://datashare.is.ed.ac.uk/handle/10283/2791; License: https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/license_text?sequence=11&isAllowed=y
- VocalSet: A Singing Voice Dataset https://zenodo.org/record/1193957#.X1hkxYtlCHs; License: Creative Commons Attribution 4.0 International
- Emotion data corpus: CREMA-D (Crowd-sourced Emotional Multimodal Actors Dataset) https://github.com/CheyneyComputerScience/CREMA-D; License: http://opendatacommons.org/licenses/dbcl/1.0/
- The VoxCeleb2 Dataset http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html; License: http://www.robots.ox.ac.uk/~vgg/data/voxceleb/ The VoxCeleb dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found here.
- VCTK Dataset: https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html; License: This corpus is licensed under Open Data Commons Attribution License (ODC-By) v1.0. http://opendatacommons.org/licenses/by/1.0/
- Noise:
- Audioset: https://research.google.com/audioset/index.html; License: https://creativecommons.org/licenses/by/4.0/
- Freesound: https://freesound.org/ Only files with CC0 licenses were selected; License: https://creativecommons.org/publicdomain/zero/1.0/
- Demand: https://zenodo.org/record/1227121#.XRKKxYhKiUk; License: https://creativecommons.org/licenses/by-sa/3.0/deed.en_CA
- RIR datasets: OpenSLR26 and OpenSLR28:
- http://www.openslr.org/26/
- http://www.openslr.org/28/
- License: Apache 2.0
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