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csteinmetz1 committed Nov 12, 2023
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<div align="center">

<img src="docs/assets/dasp-no-bg.png" width="200px">
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/dasp-no-bg.png" width="200px">

# dasp

<i> Differentiable audio signal processors in PyTorch </i>

</div>

<img src="docs/assets/box.svg" width="30px"> &nbsp; Includes reverberation, distortion, dynamic range processing, equalization, stereo processing.
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/box.svg" width="30px"> &nbsp; Includes reverberation, distortion, dynamic range processing, equalization, stereo processing.

<img src="docs/assets/gear.svg" width="30px"> &nbsp; Enables virtual analog modeling, blind parameter estimation, automated DSP, and style transfer.
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/gear.svg" width="30px"> &nbsp; Enables virtual analog modeling, blind parameter estimation, automated DSP, and style transfer.

<img src="docs/assets/gpu-card.svg" width="30px"> &nbsp; Batching with operation on both CPU and GPU accelerators for fast training and reduced bottlenecks.
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/gpu-card.svg" width="30px"> &nbsp; Batching with operation on both CPU and GPU accelerators for fast training and reduced bottlenecks.

<img src="docs/assets/code-slash.svg" width="30px"> &nbsp; Open source and free to use for academic and commercial applications under Apache 2.0 license.
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/code-slash.svg" width="30px"> &nbsp; Open source and free to use for academic and commercial applications under Apache 2.0 license.

## Installation

```
pip install dasp-pytorch
```

Or, for a local installation.

```
git clone https://github.com/csteinmetz1/dasp-pytorch
cd dasp-pytorch
pip install -e .
```

Note: Coming to PyPi soon to enable `pip install dasp-pytorch`.

## Examples

`dasp-pytorch` is a Python library for constructing differentiable audio signal processors using PyTorch.
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Supported by the EPSRC UKRI Centre for Doctoral Training in Artificial Intelligence and Music (EP/S022694/1).

<p float="left">
<img src="docs/assets/logos/qm.png" height="50px"> &nbsp; &nbsp;
<img src="docs/assets/logos/aim.png" height="50px"> &nbsp; &nbsp;
<img src="docs/assets/logos/ukri.png" height="50px"> &nbsp; &nbsp;
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/logos/qm.png" height="50px"> &nbsp; &nbsp;
<img src="https://github.com/csteinmetz1/dasp-pytorch/raw/main/docs/assets/logos/aim.png" height="50px"> &nbsp; &nbsp;
<img src="https://github.com/csteinmetz1/dasp-pytorch/blob/main/docs/assets/logos/ukri.png" height="50px"> &nbsp; &nbsp;
</p>

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