This is the repo of the manuscript "Dual-branch Attention-In-Attention Transformer for speech enhancement"

Related tags

Deep LearningDB-AIAT
Overview

DB-AIAT: A Dual-branch attention-in-attention transformer for single-channel SE (https://arxiv.org/abs/2110.06467)

This is the repo of the manuscript "Dual-branch Attention-In-Attention Transformer for speech enhancement", which is accepted by ICASSP2022.

Abstract:Curriculum learning begins to thrive in the speech enhancement area, which decouples the original spectrum estimation task into multiple easier sub-tasks to achieve better performance. Motivated by that, we propose a dual-branch attention-in-attention transformer-based module dubbed DB-AIAT to handle both coarse- and fine-grained regions of spectrum in parallel. From a complementary perspective, a magnitude masking branch is proposed to estimate the overall spectral magnitude, while a complex refining branch is designed to compensate for the missing complex spectral details and implicitly derive phase information. Within each branch, we propose a novel attention-in-attention transformer-based module to replace the conventional RNNs and temporal convolutional network for temporal sequence modeling. Specifically, the proposed attention-in-attention transformer consists of adaptive temporal-frequency attention transformer blocks and an adaptive hierarchical attention module, which can capture long-term time-frequency dependencies and further aggregate global hierarchical contextual information. The experimental results on VoiceBank + Demand dataset show that DB-AIAT yields state-of-the-art performance (e.g., 3.31 PESQ, 95.6% STOI and 10.79dB SSNR) over previous advanced systems with a relatively light model size (2.81M).

Code:

You can use dual_aia_trans_merge_crm() in aia_trans.py for dual-branch SE, while aia_complex_trans_mag() and aia_complex_trans_ri() are single-branch aprroaches. The trained weights on VB dataset is also provided. You can directly perform inference or finetune the model by using vb_aia_merge_new.pth.tar.

requirements:

CUDA 10.1
torch == 1.8.0
pesq == 0.0.1
librosa == 0.7.2
SoundFile == 0.10.3

How to train

Step1

prepare your data. Run json_extract.py to generate json files, which records the utterance file names for both training and validation set

# Run json_extract.py
json_extract.py

Step2

change the parameter settings accroding to your directory (within config_merge.py)

Step3

Network Training (you can also use aia_complex_trans_mag() and aia_complex_trans_ri() network in aia_trans.py for single-branch SE)

# Run main.py to begin network training 
# solver_merge.py and train_merge.py contain detailed training process
main_merge.py

Inference:

The trained weights vb_aia_merge_new.pth.tar on VB dataset is also provided in BEST_MODEL.

# Run main.py to enhance the noisy speech samples.
enhance.py 

Comparison with SOTA:

image

Citation

If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.

@article{yu2021dual,
title={Dual-branch Attention-In-Attention Transformer for single-channel speech enhancement},
author={Yu, Guochen and Li, Andong and Wang, Yutian and Guo, Yinuo and Wang, Hui and Zheng, Chengshi},
journal={arXiv preprint arXiv:2110.06467},
year={2021}
}
Owner
Guochen Yu
Phd of Communication University of China and Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences
Guochen Yu
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