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@magenta/music-vae
Advanced tools
A machine learning model for exploring latent spaces of musical scores
This JavaScript implementation of MusicVAE uses Deeplearn.js for GPU-accelerated inference.
To use in your application, install the npm package @magenta/music-vae, or use the pre-built bundle.
You can then instantiate a MusicVAE
object with:
let mvae = new MusicVAE('/path/to/checkpoint')
See the demo for example usage.
For a complete guide on how to build an app with MusicVAE, read the Melody Mixer tutorial.
Several pre-trained MusicVAE checkpoints are hosted on GCS. While we do not plan to remove any of the current checkpoints, we will be adding more in the future, so your applications should reference the checkpoints.json file to see which checkpoints are available.
If your application has a high QPS, you must mirror these files on your own server.
See the demo for example usage.
yarn install
to get dependencies
yarn build
to produce a commonjs version with typescript definitions for MusicVAE in the es5/
folder that can then be consumed by others over NPM.
yarn bundle
to produce a bundled version in dist/
.
yarn build-demo
to build the demo.
http-server demo/
to see it working.
FAQs
A machine learning model for exploring latent spaces of musical scores
The npm package @magenta/music-vae receives a total of 5 weekly downloads. As such, @magenta/music-vae popularity was classified as not popular.
We found that @magenta/music-vae demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.
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