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.
Table of Contents (16 chapters)
1
Section 1: Introduction to Artwork Generation
3
Section 2: Music Generation with Machine Learning
8
Section 3: Training, Learning, and Generating a Specific Style
11
Section 4: Making Your Models Interact with Other Applications

Using the Drums RNN on the command line

Now that we understand how RNNs make for powerful tools of music generation, we'll use the Drums RNN model to do just that. The pre-trained models in Magenta are a good way of starting music generation straightaway. For the Drums RNN model, we'll be using the drum_kit pre-trained bundle, which was trained on thousands of percussion MIDI files.

This section will provide insight into the usage of Magenta on the command line. We'll be primarily using Python code to call Magenta, but using the command line has some advantages:

  • It is simple to use and useful for quick use cases.
  • It doesn't require writing any code or having any programming knowledge.
  • It encapsulates parameters in helpful commands and flags.

In this section, we'll use the Drums RNN model in the command line and learn to configure the generation though...