This repository contains the datasets and scripts required for ICASSP 2022 DNS Challenge, AKA DNS Challenge 4, or DNS4. For more details about the challenge, please see our website. For more details on the testing framework, please visit P.835.
- The datasets and datasets_fullband folders are placeholders for the datasets. That is, our data downloader script by default will place the downloaded audio data there. After the download, these directories will contain clean speech, noise, and room impulse responses required for creating the training data for wideband scenario. The script will also download here 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 for wideband.
- download-dns-challenge-4.sh - this is the script to download the data. By default, the data
will be placed into
./datasets/
and./datasets_fullband/
directories. Please take a look at the script and uncomment the perferred download method. Unmodified, the script performs a dry run and retrieves only the HTTP headers for each archive. - 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.
The default directory structure and the sizes of the datasets available for DNS Challenge are:
. 855G
+-- datasets 4.3G
| \-- impulse_responses 4.3G
\-- datasets_fullband 887G
+-- clean_fullband 827G
| +-- emotional_speech 2.3G
| +-- french_speech 63G
| +-- german_speech 263G
| +-- italian_speech 39G
| +-- read_speech 300G
| +-- russian_speech 12G
| +-- spanish_speech 66G
| +-- vctk_wav48_silence_trimmed 39G
| \-- VocalSet_48kHz_mono 1G
+-- dev_testset 3G
| +-- enrollment_data 644M
| \-- noisy_testclips 2.4G
\-- noise_fullband 58G
In all, you will need about 1TB to store the UNPACKED data. Archived, the same data takes 550GB total.
- Python 3.6 and above
- Python libraries: soundfile, librosa
NOTE: git LFS is no longer required for DNS Challenge. Please use the
download-dns-challenge-4.sh
script in this repo to download the data.
- Install Python libraries
pip3 install soundfile librosa
- Clone the repository.
git clone https://github.com/microsoft/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
python3 noisyspeech_synthesizer_singleprocess.py
If you use this dataset in a publication please cite the following paper:
@inproceedings{reddy2021interspeech,
title={INTERSPEECH 2021 Deep Noise Suppression Challenge},
author={Reddy, Chandan KA and Dubey, Harishchandra and Koishida, Kazuhito and Nair, Arun and Gopal, Vishak and Cutler, Ross and Braun, Sebastian and Gamper, Hannes and Aichner, Robert and Srinivasan, Sriram},
booktitle={INTERSPEECH},
year={2021}
}
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.835 test framework:
@inproceedings{naderi2021crowdsourcing,
title={Subjective Evaluation of Noise Suppression Algorithms in Crowdsourcing},
author={Naderi, Babak and Cutler, Ross},
booktitle={INTERSPEECH},
year={2021}
}
DNSMOS API:
@inproceedings{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},
booktitle={ICASSP},
year={2020}
}
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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
MIT License
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