Audio augmentations library for PyTorch for audio in the time-domain

Related tags

Audiotorchaudio-augmentations
Overview

Audio Augmentations

DOI

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
> transformed_audio.shape[0] = 4 "> 
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}
}
Comments
  • Delay augmentation on cuda

    Delay augmentation on cuda

    Hi. Currently the delay augmentation doesn't work on gpu since part of the signal is on cpu. I think making thebeginning tensor same as the audio tensor device should fix it. Thanks. https://github.com/Spijkervet/torchaudio-augmentations/blob/d044f9d020e12032ab9280acf5f34a337e72d212/torchaudio_augmentations/augmentations/delay.py#L31

    opened by sidml 2
  • Correctness unit test would be great

    Correctness unit test would be great

    For some transforms, we can test if the values are actually correct by manually computing the expected value. For example, PolarityInversion could be test with some tiny tensors like [[0.1, 0.5, -1.0]]. Reverse as well. Probably only those two? Still, it'd be better than not having any.

    opened by keunwoochoi 2
  • Default value of `max_snr` in `Noise`

    Default value of `max_snr` in `Noise`

    1.0 of SNR with signal and white noise would be a really heavily corrupted signal. Could we set it to be a little more reasonable value?

    Related; it'd be great if one can hear some examples of the augmented result.

    opened by keunwoochoi 2
  • End-to-end PitchShift transform tests

    End-to-end PitchShift transform tests

    This merge requests adds end-to-end pitch transformation detection with librosa's pYIN pitch detection, to test if the applied transformation yields the expected pitch transposition.

    opened by Spijkervet 0
  • Unittests

    Unittests

    This adds various unittests and fixes to multi-channel input for Reverb, Pitch, Reverse and HighLowPass filter augmentations. It also removes Essentia as a dependency, and instead uses julius for IRR filtering.

    opened by Spijkervet 0
  • import error

    import error

    when i import torchaudio_augmentation

    I got the error

    RuntimeError : torchaudio.sox_effects.sox_effects.effect_names requires module: torchaudio._torchaudio

    how can I deal with it?

    opened by EavnJeong 0
  • Snr db

    Snr db

    Hi, Thanks for the interesting work. Allow me to suggest this change for two reasons:

    • Expressing SNR in dB cancels the doubt there might be between power SNR and RMS SNR.
    • When sampling an SNR, it feels to me like it makes more sense to uniformly sample from the log scale of the dB than on the linear range. This way you ensure that your low SNR have as much chances as your high SNR.

    I hope it makes sens. I'd be glad to discuss further about that.

    opened by wesbz 0
  • sanity check for duration

    sanity check for duration

    In transforms where the duration may change, if the input audio is shorter than n_samples, the error message is not intuitive. I forgot but in some case, the multiprocessing-based dataloader silently died. Maybe it's worth checking it somewhere?

    opened by keunwoochoi 0
  • Shapes are still a bit confusing

    Shapes are still a bit confusing

    From ComposeMany.__call__(), is x also a ch, time shape 2-dim tensor? And I'm sure what would be the expected behavior by this function, especially the shape of the output.

    opened by keunwoochoi 3
Releases(v0.2.3)
Owner
Janne
Music producer, machine learning in MIR & occasional ethical hacker
Janne
GitHub Repository
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