SpecAugment

KO | EN

gitlite — search

#ai #typescript #go #android #machine-learning #rust #deep-learning #linux #docker #java #awesome #vue #security #open-source

(★ 96)

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

파일 탐색기

최종 버전 다운로드 (.zip)

- __init__.py
- base.png
- demo.ipynb
- freq_mask.png
- time_mask.png
- time_warp.png
augment.py
main.py
README.md

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

> SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
> Park, Daniel S. and Chan, William and Zhang, Yu and Chiu, Chung-Cheng and Zoph, Barret and Cubuk, Ekin D. and Le, Quoc V.
> Interspeech 2019
> [[Paper]](http://dx.doi.org/10.21437/Interspeech.2019-2680)
# About
This repository contains a implementation of the augmentation methodology proposed in the above paper.

##### Base Input
<figure>
  <img src="demo/base.png"/>
</figure>

##### SpecAugmented Output (Policy = 'LB')
<figure>
  <img src="demo/time_mask.png"/>
</figure>

### Requirements:
1. python3
2. librosa
3. libsndfile
4. audioread
5. ffmpeg
5. numpy
6. tensorflow
7. tensorflow_addons

### Usage:
```
main.py [--dir][--policy]
```

--dir    | path/to/dataset | default='./LibriSpeech/'\
--policy | augmentation policy to use from {'LB','LD', 'SS', 'SM'} | deafault='LD'

OR

```
refer to demo/demo.ipynb for jupyter notebook demo
```

### References:
1. @article{Park_2019,
   title={SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition},
   url={http://dx.doi.org/10.21437/Interspeech.2019-2680},
   DOI={10.21437/interspeech.2019-2680},
   journal={Interspeech 2019},
   publisher={ISCA},
   author={Park, Daniel S. and Chan, William and Zhang, Yu and Chiu, Chung-Cheng and Zoph, Barret and Cubuk, Ekin D. and Le, Quoc V.},
   year={2019},
   month={Sep}
}

SpecAugment

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

SpecAugment

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README