Metadata-Version: 2.1
Name: torchaudio-augmentations
Version: 0.2.0
Summary: Audio augmentations library for PyTorch, for audio in the time-domain.
Home-page: https://github.com/spijkervet/torchaudio-augmentations
Author: Janne Spijkervet
Author-email: janne.spijkervet@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: fancy feature
License-File: LICENSE


# Audio Augmentations
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4748582.svg)](https://zenodo.org/record/4748582#)

Audio augmentations library for PyTorch for audio in the time-domain, with support for stochastic data augmentations as used often in self-supervised / contrastive learning.


## Usage
We can define several audio augmentations, which will be applied sequentially to a raw audio waveform:
```
from audio_augmentations import *

audio, sr = torchaudio.load("tests/classical.00002.wav")

num_samples = sr * 5
transforms = [
    RandomResizedCrop(n_samples=num_samples),
    RandomApply([PolarityInversion()], p=0.8),
    RandomApply([Noise(min_snr=0.3, max_snr=0.5)], p=0.3),
    RandomApply([Gain()], p=0.2),
    RandomApply([HighLowPass(sample_rate=sr)], p=0.8),
    RandomApply([Delay(sample_rate=sr)], p=0.5),
    RandomApply([PitchShift(
        n_samples=num_samples,
        sample_rate=sr
    )], p=0.4),
    RandomApply([Reverb(sample_rate=sr)], p=0.3)
]
```

We can return either one or many versions of the same audio example:
```
transform = Compose(transforms=transforms)
transformed_audio =  transform(audio)
>> transformed_audio.shape[0] = 1
```

```
audio = torchaudio.load("testing/classical.00002.wav")
transform = ComposeMany(transforms=transforms, num_augmented_samples=4)
transformed_audio = transform(audio)
>> transformed_audio.shape[0] = 4
```

Similar to the `torchvision.datasets` interface, an instance of the `Compose` or `ComposeMany` class can be supplied to a torchaudio dataloaders that accept `transform=`.


## Optional
Install WavAugment for reverberation / pitch shifting:
```
pip install git+https://github.com/facebookresearch/WavAugment
```

# Cite
You can cite this work with the following BibTeX:
```
@misc{spijkervet_torchaudio_augmentations,
  doi = {10.5281/ZENODO.4748582},
  url = {https://zenodo.org/record/4748582},
  author = {Spijkervet,  Janne},
  title = {Spijkervet/torchaudio-augmentations},
  publisher = {Zenodo},
  year = {2021},
  copyright = {MIT License}
}
```


