https://github.com/jingyonghou/RPN_KWS

Region proposal network based small-footprint keyword spotting (Pytorch)
https://github.com/jingyonghou/RPN_KWS

Last synced: 12 days ago
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Region proposal network based small-footprint keyword spotting (Pytorch)

• Host: GitHub
• URL: https://github.com/jingyonghou/RPN_KWS
• Owner: jingyonghou
• License: mit
• Created: 2019-07-29T19:17:08.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-11-15T01:31:37.000Z (almost 1 year ago)
• Last Synced: 2024-08-02T09:28:48.446Z (3 months ago)
• Language: Python
• Homepage:
• Size: 138 KB
• Stars: 51
• Watchers: 3
• Forks: 17
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-keyword-spotting - Region Proposal Network Based Small-Footprint Keyword Spotting

README

        
# RPN_KWS

Pytorch code of paper "Region Proposal Network Based Small-Footprint Keyword Spotting"

https://ieeexplore.ieee.org/document/8807313

Please cite the work below if you want to use the code or want to do research related to our work

```

@ARTICLE{hou2019region, 

author={Hou, Jingyong and Shi, Yangyang and Ostendorf, Mari and  Hwang, Mei-Yuh

and Xie, Lei }, 

journal={IEEE Signal Processing Letters}, 

title={Region Proposal Network Based Small-Footprint Keyword Spotting}, 

year={2019}, 

volume={26}, 

number={10}, 

pages={1471-1475}

}

```

I will release a new version of RPN KWS with an Online Hard Example Mining (OHEM) algorithm, which will improve our system.

https://github.com/jingyonghou/RPN_KWS_OHEM

## Detection samples

![image](https://github.com/jingyonghou/RPN_KWS/raw/master/Picture1.png)

![image](https://github.com/jingyonghou/RPN_KWS/raw/master/Picture2.png)

Selected two utterances which contains predefined keyword. The red box is the ground-truth start-end area of keyword from forced-alignment, the blue box is the best anchor selected according to the classification score, the green box is the proposed region proposal corresponding to the best anchor.

## Running environment

### Python 2.7.15

### pytorch 0.4.1

### CUDA 8.0 or higher

### Kaldi

You should know basic knowledge of Kaldi before looking at the run script. I use Kaldi to extract Fbank features and do a global CMVN using the statictics from all training set. You should add cmd.sh, path.sh, steps and utils to your working dir before you run the script.

### Please follow the run_rpn_kws.sh script to learn how to run the code

## reference

https://github.com/jwyang/faster-rcnn.pytorch

https://github.com/vesis84/kaldi-io-for-python