https://github.com/solarliner/latentwt

Generate wavetables, with AI.
https://github.com/solarliner/latentwt

autoencoder deep-learning wavetables

Last synced: 3 months ago
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Generate wavetables, with AI.

• Host: GitHub
• URL: https://github.com/solarliner/latentwt
• Owner: SolarLiner
• License: mit
• Created: 2022-09-16T21:38:18.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-09-20T15:50:58.000Z (over 3 years ago)
• Last Synced: 2025-02-12T13:55:23.950Z (over 1 year ago)
• Topics: autoencoder, deep-learning, wavetables
• Language: Vue
• Homepage: https://latentwt.netlify.app
• Size: 6.41 MB
• Stars: 8
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# [Latent Wavetables](https://latentwt.netlify.app)

An autoencoder for wavetables

This project aims to create a latent space of wavetables, such that exploring them is as easy as hovering over a 2D graph. You can generate wavetable sequences from dragging around the graph.

The model is provided at `public/latentwt.onnx` in ONNX format, and runs in the browser.

## Wavetable licences

The wavetables from the web frontend are from [WaveEdit Online](https://waveeditonline.com/), which are CC0 - any wavetable generated from this tool is also CC0.

## Training

To train the model, you'll need a corpus of wavetable sequences to feed to the model. This project feeds from the [WaveEdit Online](https://waveeditonline.com/), but should be flexible enough to be trained on any other wavetable sequences that you may have.

The `model.py` is a runnable script with a CLI interface that allows you to train and save the model with your own wavetables.

```

usage: model.py [-h] -w WAVETABLES [-W WIDTH] [-d {cuda,cpu}] [-e EPOCHS] output

positional arguments:

  output                Output ONNX model

options:

  -h, --help            show this help message and exit

  -w WAVETABLES, --wavetables WAVETABLES

                        Path to the folder containing wavetable sequence wav files 

  -W WIDTH, --width WIDTH

                        Wavetable width (default: 256)

  -d {cuda,cpu}, --device {cuda,cpu}

                        Use CPU/GPU to compute the model (default is detected) 

  -e EPOCHS, --epochs EPOCHS

                        Number of training epochs (default: 50)

```