torch-audiomentations 0.2.0

A Pytorch library for audio data augmentation. Inspired by audiomentations. Useful for deep learning.

torch-audiomentations

Audio data augmentation in PyTorch. Inspired by audiomentations.

Setup

pip install torch-audiomentations

Usage example

import torch
from torch_audiomentations import Gain


# Initialize augmentation callable
apply_gain_augmentation = Gain(
    min_gain_in_db=-15.0,
    max_gain_in_db=5.0,
    p=0.5,
)

# Note: torch-audiomentations can run on CPU or GPU
torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Make an example tensor with white noise.
# This tensor represents 8 audio snippets with 2 channels (stereo) and 2 seconds of 16 kHz audio.
audio_samples = torch.rand(size=(8, 2, 32000), dtype=torch.float32, device=torch_device) - 0.5

# Apply gain augmentation. This varies the gain of (some of) the audio snippets in the batch independently.
perturbed_audio_samples = apply_gain_augmentation(audio_samples, sample_rate=16000)

Contribute

Contributors welcome! Join the Asteroid's slack to start discussing about torch-audiomentations with us.

Motivation: Speed

We don't want data augmentation to be a bottle neck in model training speed. Here is a comparison of the time it takes to run 1D convolution:

Current state

torch-audiomentations is in a very early development stage, so it's not ready for prime time yet. Meanwhile, star the repo and stay tuned!

Version history

v0.2.0 (2020-10-19)

• Simplify API for using CUDA tensors. The device is now inferred from the input tensor.
• Implement PeakNormalization
• Expose convolve in the API

v0.1.0 (2020-10-12)

• Initial release with Gain and PolarityInversion

Development

Setup

A GPU-enabled development environment for torch-audiomentations can be created with conda:

• conda create --name torch-audiomentations python=3.7.3
• conda activate torch-audiomentations
• conda install pytorch cudatoolkit=10.1 -c pytorch
• conda env update

Run tests

pytest

Conventions

• Format python code with black
• Use Google-style docstrings
• Use explicit relative imports, not absolute imports

Acknowledgements

The development of torch-audiomentations is kindly backed by Nomono