AUX is an audio processing library in JAX, for JAX.

JAX is a library resulting from the union of Autograd and XLA for high-performance machine learning research. It provides NumPy, SciPy, automatic differentiation and first-class GPU/TPU support.

AUX, built on top of JAX, provides audio processing functions and tools to JAX. It is a sister library of PIX designed for image processing in JAX. Likewise, all operations in AUX can be optimized through jax.jit.

AUX is a pure-Python package using JAX for accelerated and optimized linear algebra.

First, follow JAX installation instructions to install JAX with the relevant accelerator support.

Then, assume you are in the dm_aux directory, install AUX using pip:

$ pip install -e . -r requirements/requirements.txt

AUX is a module containing tools that work on the raw waveform (PCM) and spectrogram. For example, assume that we want to add some additive Gaussian noise to a raw audio waveform.

import dm_aux as aux
import jax

# Load an waveform into a NumPy array with your preferred library.
x = load_waveform()

key = jax.random.PRNGKey(0)

x_with_noise = aux.additive_gaussian(key, x, noise_level_in_db=-30)

All the functions in AUX can be jax.jited. You can leverage it to speed up the audio processing.

# `jax.jit`ed function.
x_with_noise = jax.jit(aux.additive_gaussian)(key, x, noise_level_in_db=-30)

You may use our unit tests to test your development environment and to know more about the usage of the tools and functions. All the tests are in the files with the _test suffix, and can be executed using pytest:

$ pip install -e . -r requirements/requirements-test.txt
$ python -m pytest [-n <NUMCPUS>] dm_aux

This repository is part of the DeepMind JAX Ecosystem, to cite AUX please use the DeepMind JAX Ecosystem citation.

We are very happy to accept contributions!

Please read our contributing guidelines and send us PRs!