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Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
| Date | Stars |
|---|---|
| 2026-07-24 | 1161 |
| 2026-07-25 | 1161 |
| 2026-07-28 | 1161 |
| 2026-07-30 | 1161 |
| 2026-07-31 | 1162 |
| 2026-08-06 | 1164 |
Today
+2 stars today
This week
+3 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.26%/day
---
[](https://codecov.io/gh/asteroid-team/torch-audiomentations)
[](https://github.com/ambv/black)
[](https://doi.org/10.5281/zenodo.14650813)
Audio data augmentation in PyTorch. Inspired by [audiomentations](https://github.com/iver56/audiomentations).
* Supports CPU and GPU (CUDA) - speed is a priority
* Supports batches of multichannel (or mono) audio
* Transforms extend `nn.Module`, so they can be integrated as a part of a pytorch neural network model
* Most transforms are differentiable
* Three modes: `per_batch`, `per_example` and `per_channel`
* Cross-platform compatibility
* Permissive MIT license
* Aiming for high test coverage
# Setup
[](https://pypi.org/project/torch-audiomentations/)
[](https://pypi.org/project/torch-audiomentations/)
`pip install torch-audiomentations`
# Usage example
```python
import torch
from torch_audiomentations import Compose, Gain, PolarityInversion
# Initialize augmentation callable
apply_augmentation = Compose(
transforms=[
Gain(
min_gain_in_db=-15.0,
max_gain_in_db=5.0,
p=0.5,
),
PolarityInversion(p=0.5)
]
)
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 s of 16 kHz audio.
audio_samples = torch.rand(size=(8, 2, 32000), dtype=torch.float32, device=torch_device) - 0.5
# Apply augmentation. This varies the gain and polarity of (some of)
# the audio snippets in the batch independently.
perturbed_audio_samples = apply_augmentation(audio_samples, sample_rate=16000)
```
# Known issues
* Target data processing is still in an experimental state ([#3](https://github.com/asteroid-team/torch-audiomentations/issues/3)). Workaround: Use `freeze_parameters` and `unfreeze_parameters` for now if the target data is audio with the same shape as the input.
* Using torch-audiomentations in a multiprocessing context can lead to memory leaks ([#132](https://github.com/asteroid-team/torch-audiomentations/issues/132)). Workaround: If using torch-audiomentations in a multiprocessing context, it'll probably work better to run the transforms on CPU.
* Multi-GPU / DDP is not officially supported ([#136](https://github.com/asteroid-team/torch-audiomentations/issues/136)). The author does not have a multi-GPU setup to test & fix this. Get in touch if you want to donate some hardware for this. Workaround: Run the transforms on single GPU instead.
* `PitchShift` does not support small pitch shifts, especially for low sample rates ([#151](https://github.com/asteroid-team/torch-audiomentations/issues/151)). Workaround: If you need small pitch shifts applied to low sample rates, use [PitchShift in audiomentations](https://iver56.github.io/audiomentations/waveform_transforms/pitch_shift/) or [torch-pitch-shift](https://github.com/KentoNishi/torch-pitch-shift/) directly without the function for calculating efficient pitch-shift targets.
# Contribute
Contributors welcome!
[Join the Asteroid's slack](https://join.slack.com/t/asteroid-dev/shared_invite/zt-cn9y85t3-QNHXKD1Et7qoyzu1Ji5bcA)
to start discussing about `torch-audiomentations` with us.
# Motivation: Speed
We don't want data augmentationExcerpt of 14,103 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:33556eac8100ad08, topic:deep-learning, topic:pytorch
matched fp:33556eac8100ad08, topic:data-augmentation, desc:data augmentation, readme:data augmentation