We are excited to release our first tutorial model, a recurrent neural network that generates music. It serves as an end-to-end primer on how to build a recurrent network in TensorFlow. It also demonstrates a sampling of what’s to come in Magenta. In addition, we are releasing code that converts MIDI files to a format that TensorFlow can understand, making it easy to create training datasets from any collection of MIDI files.

This tutorial will allow you to to generate music with a recurrent neural network. It’s purposefully a simple model, so don’t expect stellar music results. We’ll post more complex models soon.

Background on Recurrent Neural Networks

A recurrent neural network (RNN) has looped, or recurrent, connections which allow the network to hold information across inputs. These connections can be thought of as similar to memory. RNNs are particularly useful for learning sequential data like music.

In TensorFlow, the recurrent connections in a graph are unrolled into an equivalent feed-forward network. That network is then trained using a gradient descent technique called backpropagation through time (BPTT).

An RNN's recurrent connection unrolled through time. Image courtesy of Chris Olah.
Unrolled RNN

There are endless ways that an RNN can connect back to itself with recurrent connections. People typically stick to a few common patterns, the most common being Long Short-Term Memory (LSTM) cells and Gated Recurrent Units (GRU). These both have multiplicative gates that protect their internal memory from being overwritten too easily, allowing them to handle longer sequences. We use LSTMs in this model. To learn more about RNNs and specifically LSTMs, check out Chris Olah’s fantastic post. Experts in the field might also like to look at Goodfellow, Bengio and Courville’s RNN chapter from their book “Deep Learning.”

This Release

This RNN is the first in a series of models we will be releasing which predict the next note given a sequence of previous notes. They do this by learning a probability distribution over the next notes given all the previous notes. By sampling from that distribution and feeding the chosen note back into the model at the next step, the RNN can dream up an entire melody. Generative models are typically unsupervised, meaning that there are samples but no labels. However we turn the problem of melody generation into a supervised one by trying to predict the next note in a sequence, that way labels can be derived from any dataset of just music and nothing else. This allows us to use RNNs which are supervised models.

It takes a bit of work to put together a training set of melodies, so we are providing code that reads an archive of MIDI files and outputs monophonic melody lines extracted from them in a format TensorFlow can understand. After you have that ready, instructions to build and run the model are here.


As always, we are excited to hear from you. Let us know what you liked, didn’t like, and want to see in the future from Magenta. You can add some code to our GitHub or join our discussion group.