Building Spotify's Song Radio in 100 lines of Python

Somebody said that when it comes to deep learning, it is still very early days, Like 1995 (I was going to lookup the quote, but I can't find - it was probably Churchill or Mark Twain). I disagree. The early days are gone, it is more like 1958. Fortran has just been invented. The early days of having to implement the mechanics of neural networks is akin to writing machine code in the Fifties. Platforms like Tensorflow, Theano and Keras let us focus on what we can do, rather than how.

Marconi the inventor (from Wikipedia)

Marconi is a demonstration of this. It shows how to build a clone of Spotify's Song Radio in a less than a hundred of Python using open source libraries and data easily scraped from the Internet. If you include the code that scrapes and preprocesses the data, it is almost 400 lines, but still.

Pandora was the first company to successfully launch a product that produced playlists based on a song. It did this by employing humans who would listen to songs and characterize each on 450 musical dimensions. It worked well. Once you have all songs mapped into this multi-dimensional space, finding similar songs is a mere matter of finding songs that occupy a similar position in this space.

Employing humans is expensive though (even underpaid musicians). So how do we automatically map songs into a multi-dimensional space? These days the answer has to be Machine Learning. Now you could build some sort of model that really understands music. Probably. It sounds really hard though. Most likely you'd need more than a hundred if not more than a thousand lines of code.

Marconi doesn't know anything about the actual music. It is trained on playlists. The underlying notion here is that similar songs will occur near each other in playlists. When you are building a playlist, you do this based on the mood you are in, or maybe the mood you want to create.

A good analogy here is Word2Vec. Word2Vec learns word similarities by feeding it sentences. After a lot of sentences, it knows that "coffee" and "espresso" are similar, because it notices that in a sentence where you use the one, you might as well expect the other. It even learns deeper relationships between words, for example that the words "king" and "man" have the same relationship as the words "queen" and "woman".

Fascinating stuff. Usefully, the Python library GenSim contains a great implementation of Word2Vec. So if we feed this playlists containing song ids, rather than sentences containing words, it will after a while learn relationships between songs. Suggesting a playlist based on a song becomes than again a straightforward nearest neighbor search.

The trick is collecting the data. How do we get our hands on a large set of playlists? To their great credit, Spotify has a wonderful API that lets you get info on songs, artists and playlists. It does not, however, grant access to a dump of (public) playlists, which would be the ideal input for this project.

The workaround I use, is to search for words in the title of playlists. We start with the word 'a'. This will return a thousand playlists containing the word 'a'. We store those. Then we count for all playlists scraped so far, how often we see any of the words in the titles of those playlists. We pick the word that appears most often that we haven't searched for. Rinse and repeat. So after 'a', you'll see 'the', 'of' etc. After a while 'greatest' and 'hits' appear.

It works and quickly returns a largish set of public playlists. It's not ideal in that it is hardly a natural way to sample playlists. For example, by searching for the most popular words, chances are you'll get the most popular playlists. The playlists returned also seemed very long (hundreds of songs), but maybe that's normal.

Next up: get the tracks that are actually in those playlists. Thanks to Spotify's API, that's quite simple. Just keep calling:

res = session.user_playlist_tracks(owner_id,  playlist_id,
    fields='items(track(id, name, artists(name, id), duration_ms)),next')

There's a bunch of parsing, boiler plate and handling timeouts etc to make it work in practice, but it's all fairly straight-forward.

Once we have the training data, building the model is also quite easy. I throw out playlists that are too long or that have only songs from one artist and the model is trained with a lowish number of dimensions. To make this accessible online, I import the song vectors into postgres. Recommending music then becomes as simple as this sql statement:

SELECT song_id, song_name, artist_name, 

       cube_distance(cube(vec), cube(%(mid_vec)s)) as distance

FROM song2vec ORDER BY distance

LIMIT 10;

Where mid_vec is the vector representing the song that was used as an input, or the middle of a set of vectors if multiple songs were provided. 

How does it perform? Well, you can try for yourself, but I think it works pretty well!

There's a lot of room for more experiments here, I think. Building an artists only model would be a simple extension. Looking at the meta information of the songs and using it to build classifiers might also be interesting. Even more interesting would be to look at the actual music and see if there are features in the wave patterns that we can map onto the song vectors.