torchaudio-augmentations 0.2.4
Audio augmentations library for PyTorch, for audio in the time-domain.
License: MIT
Requires Python: >=3.6.0


# PyTorch Audio Augmentations
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Audio data augmentations library for PyTorch for audio in the time-domain. The focus of this repository is to:
- Provide many audio transformations in an easy Python interface.
- Have a high test coverage.
- Easily control stochastic (sequential) audio transformations.
- Make every audio transformation differentiable with PyTorch's `nn.Module`.
- Optimise audio transformations for CPU and GPU.

It supports stochastic transformations as used often in self-supervised, semi-supervised learning methods. One can apply a single stochastic augmentation or create as many stochastically transformed audio examples from a single interface.

This package follows the conventions set out by `torchvision` and `torchaudio`, with audio defined as a tensor of `[channel, time]`, or a batched representation `[batch, channel, time]`. Each individual augmentation can be initialized on its own, or be wrapped around a `RandomApply` interface which will apply the augmentation with probability `p`.


## Usage
We can define a single or several audio augmentations, which are applied sequentially to an audio waveform.
```python
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=0.001, max_snr=0.005)], p=0.3),
    RandomApply([Gain()], p=0.2),
    HighLowPass(sample_rate=sr), # this augmentation will always be applied in this aumgentation chain!
    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 also define a stochastic augmentation on multiple transformations. The following will apply both polarity inversion and white noise with a probability of 80%, a gain of 20%, and delay and reverb with a probability of 50%:
```python
transforms = [
    RandomResizedCrop(n_samples=num_samples),
    RandomApply([PolarityInversion(), Noise(min_snr=0.001, max_snr=0.005)], p=0.8),
    RandomApply([Gain()], p=0.2),
    RandomApply([Delay(sample_rate=sr), Reverb(sample_rate=sr)], p=0.5)
]
```

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

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

Similar to the `torchvision.datasets` interface, an instance of the `Compose` or `ComposeMany` class can be supplied to `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}
}
```