https://github.com/demiseom/specaugment

A Implementation of SpecAugment with Tensorflow & Pytorch, introduced by Google Brain
https://github.com/demiseom/specaugment

data-augmentation python pytorch specaugment speech speech-recognition tensorflow

Last synced: about 1 month ago
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A Implementation of SpecAugment with Tensorflow & Pytorch, introduced by Google Brain

• Host: GitHub
• URL: https://github.com/demiseom/specaugment
• Owner: DemisEom
• License: apache-2.0
• Created: 2019-04-24T03:29:51.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-04-05T00:46:37.000Z (over 2 years ago)
• Last Synced: 2024-10-11T04:41:43.923Z (about 1 month ago)
• Topics: data-augmentation, python, pytorch, specaugment, speech, speech-recognition, tensorflow
• Language: Python
• Homepage:
• Size: 428 KB
• Stars: 638
• Watchers: 11
• Forks: 135
• Open Issues: 24
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SpecAugment [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

This is a implementation of SpecAugment that speech data augmentation method which directly process the spectrogram with Tensorflow & Pytorch, introduced by Google Brain[1]. This is currently under the Apache 2.0, Please feel free to use for your project. Enjoy!

## How to use

First, you need to have python 3 installed along with [Tensorflow](https://www.tensorflow.org/install/).

Next, you need to install some audio libraries work properly. To install the requirement packages. Run the following command:

```bash

pip3 install SpecAugment

```

And then, run the specAugment.py program. It modifies the spectrogram by warping it in the time direction, masking blocks of consecutive frequency channels, and masking blocks of utterances in time.

#### *Try your audio file SpecAugment*

```shell

$ python3

```

```python

>>> import librosa

>>> from specAugment import spec_augment_tensorflow

# If you are Pytorch, then import spec_augment_pytorch instead of spec_augment_tensorflow

>>> audio, sampling_rate = librosa.load(audio_path)

>>> mel_spectrogram = librosa.feature.melspectrogram(y=audio,

                                                     sr=sampling_rate,

                                                     n_mels=256,

                                                     hop_length=128,

                                                     fmax=8000)

>>> warped_masked_spectrogram = spec_augment_tensorflow.spec_augment(mel_spectrogram=mel_spectrogram)

>>> print(warped_masked_spectrogram)

'

[[1.54055389e-01 7.51822486e-01 7.29588015e-01 ... 1.03616300e-01

  1.04682689e-01 1.05411769e-01]

 [2.21608739e-01 1.38559084e-01 1.01564167e-01 ... 4.19907116e-02

  4.86430404e-02 5.27331798e-02]

 [3.62784019e-01 2.09934399e-01 1.79158230e-01 ... 2.42307431e-01

  3.18662338e-01 3.67405599e-01]

 ...

 [6.36117335e-07 8.06897948e-07 8.55346431e-07 ... 2.84445018e-07

  4.02975952e-07 5.57131738e-07]

 [6.27753429e-07 7.53681318e-07 8.13035033e-07 ... 1.35111146e-07

  2.74058225e-07 4.56901031e-07]

 [0.00000000e+00 7.48416680e-07 5.51771037e-07 ... 1.13901361e-07

  2.56365068e-07 4.43868592e-07]]

'

```

Learn more examples about how to do specific tasks in SpecAugment at the test code.

```bash

python spec_augment_test.py

```

In test code, we using one of the [LibriSpeech dataset](http://www.openslr.org/12/).



  

  



# Reference

1. https://arxiv.org/pdf/1904.08779.pdf