Book Image

Hands-On Music Generation with Magenta

By : Alexandre DuBreuil
Book Image

Hands-On Music Generation with Magenta

By: Alexandre DuBreuil

Overview of this book

The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book, you’ll follow a hands-on approach to using ML models for music generation, learning how to integrate them into an existing music production workflow. Complete with practical examples and explanations of the theoretical background required to understand the underlying technologies, this book is the perfect starting point to begin exploring music generation. The book will help you learn how to use the models in Magenta for generating percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. Through practical examples and in-depth explanations, you’ll understand ML models such as RNNs, VAEs, and GANs. Using this knowledge, you’ll create and train your own models for advanced music generation use cases, along with preparing new datasets. Finally, you’ll get to grips with integrating Magenta with other technologies, such as digital audio workstations (DAWs), and using Magenta.js to distribute music generation apps in the browser. By the end of this book, you'll be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Code in Action
Get in touch
1
Section 1: Introduction to Artwork Generation
Section 1: Introduction to Artwork Generation
Free Chapter
2
Introduction to Magenta and Generative Art
Introduction to Magenta and Generative Art
Technical requirements
Overview of generative art
New techniques with machine learning
Google's Magenta and TensorFlow in music generation
Installing Magenta and Magenta for GPU
Installing the music software and synthesizers
Installing the code editing software
Generating a basic MIDI file
Summary
Questions
Further reading
3
Section 2: Music Generation with Machine Learning
Section 2: Music Generation with Machine Learning
4
Generating Drum Sequences with the Drums RNN
Generating Drum Sequences with the Drums RNN
Technical requirements
The significance of RNNs in music generation
Using the Drums RNN on the command line
Using the Drums RNN in Python
Summary
Questions
Further reading
5
Generating Polyphonic Melodies
Generating Polyphonic Melodies
Technical requirements
LSTM for long-term dependencies
Generating melodies with the Melody RNN
Generating polyphony with the Polyphony RNN and Performance RNN
Summary
Questions
Further reading
6
Latent Space Interpolation with MusicVAE
Latent Space Interpolation with MusicVAE
Technical requirements
Continuous latent space in VAEs
Score transformation with MusicVAE and GrooVAE
Understanding TensorFlow code
Summary
Questions
Further reading
7
Audio Generation with NSynth and GANSynth
Audio Generation with NSynth and GANSynth
Technical requirements
Learning about WaveNet and temporal structures for music
Neural audio synthesis with NSynth
Using GANSynth as a generative instrument
Summary
Questions
Further reading
8
Section 3: Training, Learning, and Generating a Specific Style
Section 3: Training, Learning, and Generating a Specific Style
9
Data Preparation for Training
Data Preparation for Training
Technical requirements
Looking at existing datasets
Building a dance music dataset
Building a jazz dataset
Preparing the data using pipelines
Summary
Questions
Further reading
10
Training Magenta Models
Training Magenta Models
Technical requirements
Choosing the model and configuration
Training and tuning a model
Using Google Cloud Platform
Summary
Questions
Further reading
11
Section 4: Making Your Models Interact with Other Applications
Section 4: Making Your Models Interact with Other Applications
12
Magenta in the Browser with Magenta.js
Magenta in the Browser with Magenta.js
Technical requirements
Introducing Magenta.js and TensorFlow.js
Creating a Magenta.js web application
Making Magenta.js interact with other apps
Summary
Questions
Further reading
13
Making Magenta Interact with Music Applications
Making Magenta Interact with Music Applications
Technical requirements
Sending MIDI to a DAW or synthesizer
Looping the generated MIDI
Using Magenta as a standalone application with Magenta Studio
Summary
Questions
Further Reading
14
Assessments
Assessments
Chapter 1: Introduction to Magenta and Generative Art
Chapter 2: Generating Drum Sequences with the Drums RNN
Chapter 3: Generating Polyphonic Melodies
Chapter 4: Latent Space Interpolation with MusicVAE
Chapter 5: Audio Generation with NSynth and GANSynth
Chapter 6: Data Preparation for Training
Chapter 7: Training Magenta Models
Chapter 8: Magenta in the Browser with Magenta.js
Chapter 9: Making Magenta Interact with Music Applications
15
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Summary

In this chapter, we've looked at Tensorflow.js and Magenta.js, the JavaScript implementations of TensorFlow and Magenta. We've learned that TensorFlow.js is GPU accelerated using WebGL and that Magenta.js has a limited set of models available that can only be used for generation, not training. We've converted a Python-trained model from the previous chapter to a format that TensorFlow.js can load. We've also introduced Tone.js and the Web Audio API, which is used by Magenta.js to synthesize sound in the browser.

Then, we've created three music generation web applications. The first application used GANSynth to sample short audio notes. By doing so, we've learned how to import the required scripts, either using a big ES5 bundle or a smaller, split up, ES6 bundle. The second application used MusicVAE to sample a trio of instruments, with the...

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