Build an AI Composer - Machine Learning for Hackers #2

Key Takeaways

[Music] I actually didn't play anything you just heard AI generated music hello world welcome to rology in this episode we're going to train a neural network to compose music all by itself machine generated music the technical term for this is music language modeling and it has a long history of research behind it marov models and restricted boltzman machines which kind of sounds like something out of halflife or Bioshock hold on babe I've got to go save the world using the restricted Bolton Machine music is how we communicate our emotions and Passions and it's completely based on mathematical relationships octaves chords scales Keys all of it is math at the lowest level music is a series of sound waves that create pockets of air pressure and the pitch we hear depends on the frequency of changes in this air pressure sheet music is The annotation we've created to help us map these sounds into a repeatable set of instructions so if machine learning is all about feeding data into models Define patterns and make predictions could we use it to feed in music data to predict new songs absolutely we're going to build an app that learns how to compose British folk music by training on a British folk data set I was going to go with American but I didn't think it'd be age appropriate there's too much sax and violins get it we'll be using tensorflow the sickest machine learning library ever to do this in just 10 lines of python we're going to follow our tried and true four-step machine learning methodology to do this collect the data set build a model train the model and test the model to start off we want to collect our data set so let's import the URL live module which is going to let us download the file from the web once we import it we can call the URL retrieve method to do just that we'll set the parameters to a link in the data dat set and the name will call the downloaded file we're using the Nottingham data set for this demo which is a collection of a thousand British folk songs in MIDI format midi format is perfect for us since it encodes all the note and time information exactly how it would be written in music annotation it comes in a zip file we want to unzip it as well we can do this programmatically using the zip file module we'll extract the data from the zip and place it in the data directory Okay so we've got our data it's time to create the model but before we do that we need to think about how we want to represent our input data there are two things happening there's the main tune or Melody and then there the supporting notes are harmony let's represent each as a vector and to make things easier we'll make two assumptions the first is that the melody is monophonic that means only one note is played at each time step the second is that the harmony at each time step can be classified into a chord class so that's two different vectors one for Melody and one for harmony we'll then combine them into one big vector and use that as our input we can just import our ml helper class and then call the create model method to do this music plays out over a period of time it's a sequence of notes so we need to use a sequence learning model it has to accept a sequence of notes as an input and output a new sequence of notes plain old neuron Nets can't do this they accept fixed size inputs like an image or a number we need a special kind of neural network a recurrent neural network those can deal with sequences since data doesn't just flow one way it Loops this allows the network to have a kind of short-term memory unlike Donald Trump but wait we want our Network to not just remember the most recent music it's heard but all the music it's heard like a piece of music can have multiple themes in different parts of it hopeful sad cheery and if Network remembers only the most recent part which was cheery it's just going to compose cheery stuff we need a special type of recurrent neural network called a long shortterm memory Network this type of network can remember things from way back in the sequence of data and it uses everything it remembers to generate new sequences our model will generate the sequences and chord mapping file to a file in the data folder this is a serialized by stream representation of our music that we can input directly into our model to train by the way every machine learning model has a set of water called hyper parameters these are the parameters that we human set for how our model operates like knobs on a control panel how many layers do we want how many iterations for training how many neurons you can just use an existing model with pre-tuned hyperparameters to build something awesome so now we're ready to train our model we can just call the train model method of our neuronet class to do this this will get the network to start collecting the input data piece by piece it took me about 2 hours to train this on my 2013 MacBook Pro but you don't have to wait until it's completely done to training to test it out just wait until you see the best law so far encountered saving model message once you see that you can type in RNN sample into terminal with the config file flag and point it to the newly generated config file in your model's folder that will generate a new song using the newly trained model you've just created to generate music we just sample the melody and Harmony at each time step and plug it into our trained model the model will then predict what the next notes will be the collection of all the predicted notes is our newly generated song there's some improvements that could be made for sure the time signature is is kind of sporadic and in terms of long-term structure there seems to be a lack of repeated themes and phrases the solution may well be more data and more computing power it usually is when it comes to machine learning with deep neuron Nets machine learning can help us learn the fundamental nature of how Music Works in ways that we haven't even thought about links with more info below check them out and please subscribe for more machine learning videos for now I've got to go fix a runtime error so thanks for watching