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<img src="docs/source/_static/logo_banner.png" alt="autrainer — A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks">
</div>
# autrainer
[](https://pypi.org/project/autrainer/)
[](https://pypi.org/project/autrainer/)
[](https://huggingface.co/autrainer)
[](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/).
- `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).
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"description": "<div align=\"center\">\n <img src=\"docs/source/_static/logo_banner.png\" alt=\"autrainer \u2014 A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks\">\n</div>\n\n# autrainer\n\n[](https://pypi.org/project/autrainer/)\n[](https://pypi.org/project/autrainer/)\n[](https://huggingface.co/autrainer)\n[](https://github.com/autrainer/autrainer/blob/main/LICENSE)\n\nA Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks.\n\n_autrainer_ is built on top of [PyTorch](https://pytorch.org/) and [Hydra](https://hydra.cc/),\noffering a modular and extensible way to perform reproducible deep learning experiments\nfor computer audition tasks using YAML configuration files and the command line.\n\n## Installation\n\nTo install _autrainer_, first ensure that PyTorch (along with torchvision and torchaudio) version 2.0 or higher is installed.\nFor installation instructions, refer to the [PyTorch website](https://pytorch.org/get-started/locally/).\n\nIt is recommended to install _autrainer_ within a virtual environment.\nTo create a new virtual environment, refer to the [Python venv documentation](https://docs.python.org/3/library/venv.html).\n\nNext, install _autrainer_ using _pip_.\n\n```bash\npip install autrainer\n```\n\nThe following optional dependencies can be installed to enable additional features:\n\n- `latex` for LaTeX plotting (requires a LaTeX installation).\n- `mlflow` for [MLflow](https://mlflow.org/) logging.\n- `tensorboard` for [TensorBoard](https://www.tensorflow.org/tensorboard) logging.\n- `opensmile` for audio feature extraction with [openSMILE](https://audeering.com/opensmile/).\n- `albumentations` for image augmentations with [Albumentations](https://albumentations.ai/).\n- `torch-audiomentations` for audio augmentations with [torch-audiomentations](https://github.com/asteroid-team/torch-audiomentations).\n\nTo install _autrainer_ with all optional dependencies, use the following command:\n\n```bash\npip install autrainer[all]\n```\n\nTo install _autrainer_ from source, refer to the [contribution guide](https://autrainer.github.io/autrainer/development/contributing.html).\n\n## Next Steps\n\nTo get started using _autrainer_, the [quickstart guide](https://autrainer.github.io/autrainer/usage/quickstart.html) outlines the creation of a simple training configuration\nand [tutorials](https://autrainer.github.io/autrainer/usage/tutorials.html) provide examples for implementing custom modules including their configurations.\n\nFor 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).\n",
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