https://github.com/autrainer/autrainer

A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks.
https://github.com/autrainer/autrainer

autrainer computer-audition deep-learning deep-neural-networks pytorch

Last synced: about 2 months ago
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A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks.

• Host: GitHub
• URL: https://github.com/autrainer/autrainer
• Owner: autrainer
• License: mit
• Created: 2024-09-03T06:17:58.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-10-29T21:11:42.000Z (about 2 months ago)
• Last Synced: 2024-10-31T09:07:19.356Z (about 2 months ago)
• Topics: autrainer, computer-audition, deep-learning, deep-neural-networks, pytorch
• Language: Python
• Homepage: https://autrainer.github.io/autrainer/
• Size: 3.57 MB
• Stars: 11
• Watchers: 5
• Forks: 0
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# autrainer

[![autrainer PyPI Version](https://img.shields.io/pypi/v/autrainer?logo=pypi&logoColor=b4befe&color=b4befe)](https://pypi.org/project/autrainer/)

[![autrainer Python Versions](https://img.shields.io/pypi/pyversions/autrainer?logo=python&logoColor=b4befe&color=b4befe)](https://pypi.org/project/autrainer/)

[![autrainer Hugging Face](https://img.shields.io/badge/Hugging_Face-autrainer-b4befe?logo=huggingface&logoColor=b4befe)](https://huggingface.co/autrainer)

[![autrainer GitHub License](https://img.shields.io/badge/license-MIT-b4befe?logo=c)](https://github.com/autrainer/autrainer/blob/main/LICENSE)

A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks.

_autrainer_ is built on top of [PyTorch](https://pytorch.org/) and [Hydra](https://hydra.cc/),

offering a modular and extensible way to perform reproducible deep learning experiments

for computer audition tasks using YAML configuration files and the command line.

## Installation

To install _autrainer_, first ensure that PyTorch (along with torchvision and torchaudio) version 2.0 or higher is installed.

For installation instructions, refer to the [PyTorch website](https://pytorch.org/get-started/locally/).

It is recommended to install _autrainer_ within a virtual environment.

To create a new virtual environment, refer to the [Python venv documentation](https://docs.python.org/3/library/venv.html).

Next, install _autrainer_ using _pip_.

```bash

pip install autrainer

```

The following optional dependencies can be installed to enable additional features:

- `latex` for LaTeX plotting (requires a LaTeX installation).

- `mlflow` for [MLflow](https://mlflow.org/) logging.

- `tensorboard` for [TensorBoard](https://www.tensorflow.org/tensorboard) logging.

- `opensmile` for audio feature extraction with [openSMILE](https://audeering.com/opensmile/).

- `albumentations` for image augmentations with [Albumentations](https://albumentations.ai/).

- `audiomentations` for audio augmentations with [audiomentations](https://github.com/iver56/audiomentations).

- `torch-audiomentations` for audio augmentations with [torch-audiomentations](https://github.com/asteroid-team/torch-audiomentations).

To install _autrainer_ with all optional dependencies, use the following command:

```bash

pip install autrainer[all]

```

To install _autrainer_ from source, refer to the [contribution guide](https://autrainer.github.io/autrainer/development/contributing.html).

## Next Steps

To get started using _autrainer_, the [quickstart guide](https://autrainer.github.io/autrainer/usage/quickstart.html) outlines the creation of a simple training configuration

and [tutorials](https://autrainer.github.io/autrainer/usage/tutorials.html) provide examples for implementing custom modules including their configurations.

For a complete list of available CLI commands, refer to the [CLI reference](https://autrainer.github.io/autrainer/usage/cli_reference.html) or the [CLI wrapper](https://autrainer.github.io/autrainer/usage/cli_wrapper.html).