This repository contains the datasets list and scripts required for the ConferencingSpeech challenge. For more details about the challenge, please see our website.

• baseline, this folder contains baseline system include inference model exported by onnx and inference scripts;

• eval, this folder contains evaluation scripts to calculate PESQ, STOI and SI-SNR;

• selected_lists, the selected wave about train speech and noise wave name from aishell-1, aishell-3, librispeech-360, VCTK, MUSAN, Audioset. Each participant is only allowed to use the selected speech and noise data below :

  • selected_lists/dev/circle.name circle RIR wave utt name of dev set
  • selected_lists/dev/linear.name linear RIR wave utt name of dev set
  • selected_lists/dev/non_uniform.name non uniform linear RIR wave utt name of dev set
  • selected_lists/dev/clean.name wave utt name of dev set used clean set
  • selected_lists/dev/noise.name wave utt name of dev set used noise set
  • selected_lists/train/aishell_1.name wave utt name from aishell-1 set used in train set
  • selected_lists/train/aishell_3.name wave utt name from aishell-3 set used in train set
  • selected_lists/train/librispeech_360.name wave utt name from librispeech-360 set used in train set
  • selected_lists/train/vctk.name wave utt name from VCTK set used in train set
  • selected_lists/train/audioset.name wave utt name from Audioset used in train set
  • selected_lists/train/musan.name wave utt name from MUSAN used in train set
  • selected_lists/train/circle.name circle wave RIR name of train set
  • selected_lists/train/linear.name linear wave RIR name of train set
  • selected_lists/train/non_uniform.name non unifrom linear RIR utt name of train set

• simulation, about simulation scripts, how to use to see ReadMe

  • simulation/mix_wav.py simulate dev set and train set
  • simulation/prepare.sh use selected_lists/*/*name to select used wave from downloaded raw data, or you can select them by yourself scripts.
  • simulation/quick_select.py quickly select the name by a name list instead of grep -r -f
  • simulation/challenge_rirgenerator.py the script to simulate RIRs in train and dev set
  • simulation/data/dev_circle_simu_mix.config dev circle set simulation setup, include clean wave, noise wave, rir wave, snr, volume scale, start point
  • simulation/data/dev_linear_simu_mix.config dev linear set simulation setup, include clean wave, noise wave, rir wave, snr, volume scale, start point
  • simulation/data/dev_non_uniform_linear_simu_mix.config dev non uniform linear set simulation setup, include clean wave, noise wave, rir wave, snr, volume scale, start point
  • simulation/data/train_simu_circle.config train circle set simulation setup, include clean wave, noise wave, rir wave, snr, volume scale, start point; please download it from dropbox.
  • simulation/data/train_simu_linear.config train linear set simulation setup, include clean wave, noise wave, rir wave, snr, volume scale, start point; please download it from dropbox.
  • simulation/data/train_simu_non_uniform.config train non uniform linear set simulation setup, include clean wave, noise wave, rir wave, snr, volume scale, start point; please download it from dropbox.

• requirements.txt, dependency

Notes:

1. \*.config file should be replaced with correct path of audio files.
2. Training config files have been released together with challenge data.

If you use this challenge dataset and baseline system in a publication, please cite the following paper:

@article{wei2021interspeech,
  title={{INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing}},
  author={Wei Rao and Yihui Fu and Yanxin Hu and Xin Xu and Yvkai Jv and Jiangyu Han and Zhongjie Jiang and Lei Xie and Yannan Wang and Shinji Watanabe and Zheng-Hua Tan and Hui Bu and Tao Yu and Shidong Shang},
  journal={arXiv preprint arXiv:2104.00960}
}

python3.6 or above

pip install -r requirements.txt

if you simulation RIRs by yourself with our scripts, you may better install this:

pyrirgen

Apache 2.0