@magenta/music
Advanced tools
This JavaScript implementation of Magenta's musical note-based models uses TensorFlow.js for GPU-accelerated inference.
Complete documentation is available at https://tensorflow.github.io/magenta-js/music.
For the Python TensorFlow implementations, see the main Magenta repo.
Here are a few applications built with @magenta/music:
You can also try our hosted demos for each model and have a look at the demo code.
We have made an effort to port our most useful models, but please file an issue if you think something is missing, or feel free to submit a Pull Request!
OnsetsAndFrames implements Magenta's piano transcription model for converting raw audio to MIDI in the browser. While it is somewhat flexible, it works best on solo piano recordings. The algorithm takes half the duration of audio to run on most browsers, but due to a Webkit bug, audio resampling will make this it significantly slower on Safari.
Demo Application: Piano Scribe
MusicRNN implements Magenta's LSTM-based language models. These include MelodyRNN, DrumsRNN, ImprovRNN, and PerformanceRNN.
Demo Application: Neural Drum Machine
MusicVAE implements several configurations of Magenta's variational autoencoder model called MusicVAE including melody and drum "loop" models, 4- and 16-bar "trio" models, chord-conditioned multi-track models, and drum performance "humanizations" with GrooVAE.
Demo Application: Endless Trios
Piano Genie is a VQ-VAE model that that maps 8-button input to a full 88-key piano in real time.
Demo Application: Piano Genie
There are two main ways to get MagentaMusic.js in your JavaScript project: via script tags or by installing it from NPM and using a build tool like yarn.
Add the following code to an HTML file:
<html>
<head>
<!-- Load @magenta/music -->
<script src="https://cdn.jsdelivr.net/npm/@magenta/music@^1.0.0"></script>
<script>
// Instantiate model by loading desired config.
const model = new mm.MusicVAE(
'https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/trio_4bar');
const player = new mm.Player();
function play() {
player.resumeContext(); // enable audio
model.sample(1)
.then((samples) => player.start(samples[0], 80));
}
</script>
</head>
<body><button onclick="play()"><h1>Play Trio</h1></button></body>
</html>
Open up that html file in your browser (or click here for a hosted version) and the code will run. Click the "Play Trio" button to hear 4-bar trios that are randomly generated by MusicVAE.
It's also easy to add the ability to download MIDI for generated outputs, which is demonstrated in this example.
Add MagentaMusic.js to your project using yarn or npm.
For example, with yarn you can simply call yarn add @magenta/music.
Then, you can use the library in your own code as in the following example:
import * as mm from '@magenta/music';
const model = new mm.MusicVAE('/path/to/checkpoint');
const player = new mm.Player();
model
.initialize()
.then(() => model.sample(1))
.then(samples => {
player.resumeContext();
player.start(samples[0])
});
See our demos for example usage.
yarn install to install dependencies.
yarn test to run tests.
yarn bundle to produce a bundled version in dist/.
yarn run-demos to build and run the demo.
Since MagentaMusic.js does not support training models, you must use weights from a model trained with the Python-based Magenta models. We are also making available our own hosted pre-trained checkpoints.
Several pre-trained MusicRNN and MusicVAE checkpoints are hosted on GCS. The full list can is available in this table and can be accessed programmatically via a JSON index at https://goo.gl/magenta/js-checkpoints-json.
More information is available at https://goo.gl/magenta/js-checkpoints.
To use your own checkpoints with one of our models, you must first convert the weights to the appropriate format using the provided checkpoint_converter script.
This tool is dependent on tfjs-converter, which you must first install using pip install tensorflowjs. Once installed, you can execute the script as follows:
../scripts/checkpoint_converter.py /path/to/model.ckpt /path/to/output_dir
There are additonal flags available to reduce the size of the output by removing unused (training) variables or using weight quantization. Call ../scripts/checkpoint_converter.py -h to list the avilable options.
The model configuration should be placed in a JSON file named config.json in the same directory as your checkpoint. This configuration file contains all the information needed (besides the weights) to instantiate and run your model: the model type and data converter specification plus optional chord encoding, auxiliary inputs, and attention length. An example config.json file might look like:
{
"type": "MusicRNN",
"dataConverter": {
"type": "MelodyConverter",
"args": {
"minPitch": 48,
"maxPitch": 83
}
},
"chordEncoder": "PitchChordEncoder"
}
This configuration corresponds to a chord-conditioned melody MusicRNN model.
FAQs
Make music with machine learning, in the browser.
The npm package @magenta/music receives a total of 1,074 weekly downloads. As such, @magenta/music popularity was classified as popular.
We found that @magenta/music demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 6 open source maintainers collaborating on the project.
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