Pytorch implementation of SELF-ATTENTIVE VAD, ICASSP 2021

Last update: Dec 23, 2022

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Overview

SELF-ATTENTIVE VAD: CONTEXT-AWARE DETECTION OF VOICE FROM NOISE (ICASSP 2021)

Pytorch implementation of SELF-ATTENTIVE VAD | Paper | Dataset

Yong Rae Jo, Youngki Moon, Won Ik Cho , and Geun Sik Jo

Voithru Inc., Inha University, Seoul National University.

2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Abstract

Recent voice activity detection (VAD) schemes have aimed at leveraging the decent neural architectures, but few were successful with applying the attention network due to its high reliance on the encoder-decoder framework. This has often let the built systems have a high dependency on the re- current neural networks, which are costly and sometimes less context-sensitive considering the scale and property of acoustic frames. To cope with this issue with the self- attention mechanism and achieve a simple, powerful, and environment-robust VAD, we first adopt the self-attention architecture in building up the modules for voice detection and boosted prediction. Our model surpasses the previous neural architectures in view of low signal-to-ratio and noisy real-world scenarios, at the same time displaying the robust- ness regarding the noise types. We make the test labels on movie data publicly available for the fair competition and future progress.

Getting started

Installation

$ git clone https://github.com/voithru/voice-activity-detection.git
$ cd voice-activity-detection

Linux

$ pip install -r requirements.txt

Main

$ python main.py --help

Training

$ python main.py train --help
Usage: main.py train [OPTIONS] CONFIG_PATH

Evaluation

$ python main.py evaluate --help
Usage: main.py evaluate [OPTIONS] EVAL_PATH CHECKPOINT_PATH

Inference

$ python main.py predict --help
Usage: main.py predict [OPTIONS] AUDIO_PATH CHECKPOINT_PATH

Overview

Figure. Overall architecture

Results

Figure. Test result - Noisex92

Figure. Test result - Real-world audio dataset

Citation

@INPROCEEDINGS{9413961,
  author={Jo, Yong Rae and Ki Moon, Young and Cho, Won Ik and Sik Jo, Geun},
  booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Self-Attentive VAD: Context-Aware Detection of Voice from Noise}, 
  year={2021},
  volume={},
  number={},
  pages={6808-6812},
  doi={10.1109/ICASSP39728.2021.9413961}}

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Comments

result cannot reproduct after changing feature into kaldi fbank

hi, following ur recipe ,result is good when training my data. but when i change feature into other tool's implement, like kaldi fbank and a torch implement librosa fbank(feature value is not same as librosa python implement), result is getting worse. is there any suggestion for a compareable result like librorsa python fbank implement. aslo i find the kaldi fbank looks not clear like librosa's, kaldi fbank is also successfully proved well in many audio task. but it's result getting worse when following ur recipe in vad task.

opened by holdurhorses 0

Pytorch implementation of SELF-ATTENTIVE VAD, ICASSP 2021

Related tags

Overview

SELF-ATTENTIVE VAD: CONTEXT-AWARE DETECTION OF VOICE FROM NOISE (ICASSP 2021)

Abstract

Getting started

Installation

Linux

Main

Training

Evaluation

Inference

Overview

Results

Citation

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