This repository contains the datasets and scripts required for the DNS challenge. For more details about the challenge, please visit https://dns-challenge.azurewebsites.net/ and refer to our paper.
- The datasets directory contains the clean speech and noise clips.
- The NSNet-baseline directory contains the inference scripts and the ONNX model for the baseline Speech Enhancer called Noise Suppression Net (NSNet)
- 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.
- 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
- Python 3.0 and above
- Soundfile (pip install pysoundfile), librosa
- Clone the repository
- Edit noisyspeech_synthesizer.cfg to include the paths to clean speech and noise directories. Also, specify the paths to the destination directories and store logs.
- Run python noisyspeech_synthesizer_singleprocess.py to synthesize the data.
For the datasets and the DNS challenge:
@misc{ch2020interspeech,
title={The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework},
author={Chandan K. A. Reddy and Ebrahim Beyrami and Harishchandra Dubey and Vishak Gopal and Roger Cheng and Ross Cutler and Sergiy Matusevych and Robert Aichner and Ashkan Aazami and Sebastian Braun and Puneet Rana and Sriram Srinivasan and Johannes Gehrke},
year={2020},
eprint={2001.08662},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
The baseline NSNet noise suppression:
@misc{xia2020weighted,
title={Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement},
author={Yangyang Xia and Sebastian Braun and Chandan K. A. Reddy and Harishchandra Dubey and Ross Cutler and Ivan Tashev},
year={2020},
eprint={2001.10601},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
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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
- 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
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