coding-sharks/21CST_Guoqiang-Hu

A Keyword spotting system implemented using wekws (this project does not include a dataset, please download and modify the code yourself)

WeKws

Code is here and run by the script

Run：./run.sh

You should download dataset by yourself

Roadmap | Paper

Installation

• Clone the repo

git clone https://github.com/wenet-e2e/wekws.git

• Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
• Create Conda env:

conda create -n wekws python=3.8
conda activate wekws
pip install -r requirements.txt
conda install pytorch=1.10.0 torchaudio=0.10.0 cudatoolkit=11.1 -c pytorch -c conda-forge

## Reference

* Mining Effective Negative Training Samples for Keyword Spotting
  ([github]( https://github.com/jingyonghou/KWS_Max-pooling_RHE),
   [paper](https://www.microsoft.com/en-us/research/uploads/prod/2020/04/ICASSP2020_Max_pooling_KWS.pdf))
* Max-pooling Loss Training of Long Short-term Memory Networks for Small-footprint Keyword Spotting
  ([paper](https://arxiv.org/pdf/1705.02411.pdf))
* A depthwise separable convolutional neural network for keyword spotting on an embedded system
  ([github](https://github.com/PeterMS123/KWS-DS-CNN-for-embedded),
   [paper](https://asmp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13636-020-00176-2.pdf))
* Hello Edge: Keyword Spotting on Microcontrollers
  ([github](https://github.com/ARM-software/ML-KWS-for-MCU),
   [paper](https://arxiv.org/pdf/1711.07128.pdf))
* An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
  ([github](http://github.com/locuslab/TCN),
   [paper](https://arxiv.org/pdf/1803.01271.pdf))