This Git repository for the official PyTorch implementation of "FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement", accepted by ICASSP 2022.
📜[Full Paper] ▶[Demo] 💿[Checkpoint]
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Linux or macOS
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python>=3.6
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Anaconda or Miniconda
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NVIDIA GPU + CUDA CuDNN (CPU can also be supported)
Install Anaconda or Miniconda, and then install conda and pip packages:
# Create conda environment
conda create --name speech_enhance python=3.6
conda activate speech_enhance
# Install conda packages
# Check python=3.8, cudatoolkit=10.2, pytorch=1.7.1, torchaudio=0.7
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
conda install tensorboard joblib matplotlib
# Install pip packages
# Check librosa=0.8
pip install Cython
pip install librosa pesq pypesq pystoi tqdm toml colorful mir_eval torch_complex
# (Optional) If you want to load "mp3" format audio in your dataset
conda install -c conda-forge ffmpegClone the repository:
git clone https://github.com/hit-thusz-RookieCJ/FullSubNet-plus.git
cd FullSubNet-plusDownload the pre-trained checkpoint, and input commands:
source activate speech_enhance
python -m speech_enhance.tools.inference \
-C config/inference.toml \
-M $MODEL_DIR \
-I $INPUT_DIR \
-O $OUTPUT_DIRgit clone https://github.com/hit-thusz-RookieCJ/FullSubNet-plus.git
cd FullSubNet-plusPlease prepare your data in the data dir as like:
- data/DNS-Challenge/DNS-Challenge-interspeech2020-master/
- data/DNS-Challenge/DNS-Challenge-master/
and set the train dir in the script run.sh.
Then:
source activate speech_enhance
bash run.sh 0 # peprare training list or meta filePlease prepare your test cases dir like: data/test_cases_<name>, and set the test dir in the script run.sh.
First, you need to modify the various configurations in config/train.toml for training.
Then you can run training:
source activate speech_enhance
bash run.sh 1 After training, you can enhance noisy speech. Before inference, you first need to modify the configuration in config/inference.toml.
You can also run inference:
source activate speech_enhance
bash run.sh 2Or you can just use inference.sh:
source activate speech_enhance
bash inference.shCalculating bjective metrics (SI_SDR, STOI, WB_PESQ, NB_PESQ, etc.) :
bash metrics.shObtain subjective scores (DNS_MOS):
python ./speech_enhance/tools/dns_mos.py --testset_dir $YOUR_TESTSET_DIR --score_file $YOUR_SAVE_DIRIf you find our work useful in your research, please consider citing:
@inproceedings{chen2022fullsubnet+,
title={FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement},
author={Chen, Jun and Wang, Zilin and Tuo, Deyi and Wu, Zhiyong and Kang, Shiyin and Meng, Helen},
booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7857--7861},
year={2022},
organization={IEEE}
}
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