https://github.com/torchsynth/torchsynth

A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.
https://github.com/torchsynth/torchsynth

audio gpu machine-learning pytorch synthesizers torch

Last synced: 3 months ago
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A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

• Host: GitHub
• URL: https://github.com/torchsynth/torchsynth
• Owner: torchsynth
• License: apache-2.0
• Created: 2020-12-18T22:02:29.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2026-02-02T11:29:30.000Z (5 months ago)
• Last Synced: 2026-02-03T00:57:17.694Z (5 months ago)
• Topics: audio, gpu, machine-learning, pytorch, synthesizers, torch
• Language: Python
• Homepage: https://torchsynth.rtfd.io/
• Size: 2.67 MB
• Stars: 366
• Watchers: 13
• Forks: 16
• Open Issues: 153
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

          


# torchsynth

The fastest synth in the universe.





## Introduction

torchsynth is based upon traditional modular synthesis written in

pytorch. It is GPU-optional and differentiable.

Most synthesizers are fast in terms of latency. torchsynth is fast

in terms of throughput. It synthesizes audio 16200x faster than

realtime (714MHz) on a single GPU. This is of particular interest

to audio ML researchers seeking large training corpora.

Additionally, all synthesized audio is returned with the underlying

latent parameters used for generating the corresponding audio. This

is useful for multi-modal training regimes.



[Documentation](https://torchsynth.rtfd.io/en/latest/)

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/torchsynth/torchsynth/blob/main/examples/examples.ipynb)

[![PyPI](https://img.shields.io/pypi/v/torchsynth)](https://pypi.org/project/torchsynth/)

![PyPI - Wheel](https://img.shields.io/pypi/wheel/torchsynth)

![PyPI - License](https://img.shields.io/pypi/l/torchsynth)

[![codecov.io](https://codecov.io/gh/torchsynth/torchsynth/branch/main/graphs/badge.svg?logoWidth=18)](https://codecov.io/github/torchsynth/torchsynth?branch=master)

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[![Travis CI build status](https://app.travis-ci.com/torchsynth/torchsynth.svg?branch=main)](https://app.travis-ci.com/github/torchsynth/torchsynth)

[![Documentation Status](https://readthedocs.org/projects/torchsynth/badge/?version=latest)](https://torchsynth.readthedocs.io/en/latest/?badge=latest)



## Installation

```

pip3 install torchsynth

```

Note that torchsynth requires PyTorch version 1.8 or greater.

## Listen

If you'd like to hear torchsynth, check out

[synth1K1](https://github.com/torchsynth/synth1K1), a dataset of

1024 4-second sounds rendered from the

[Voice](https://torchsynth.readthedocs.io/en/latest/api/synth.html#torchsynth.synth.Voice)

synthesizer, or listen [on SoundCloud](https://soundcloud.com/user-357924775/synth1k1).

## Citation

If you use this work in your research, please cite:

```

@inproceedings{turian2021torchsynth,

	title        = {One Billion Audio Sounds from {GPU}-enabled Modular Synthesis},

	author       = {Joseph Turian and Jordie Shier and George Tzanetakis and Kirk McNally and Max Henry},

	year         = 2021,

	month        = Sep,

	booktitle    = {Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx2020)},

	location     = {Vienna, Austria}

}

```