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Generate Rap Lyrics – Fresh Machine Learning #4



Siraj Raval

This episode of Fresh Machine Learning is about generating rap lyrics! Lyrical generation is possible using either Hidden Markov Models or deep learning. In this episode, I go through a few past examples of what’s been done before, then dive into our own example that we can code in Python. Welcome to the machine MC revolution!

The demo code for this video can be found here:

https://github.com/llSourcell/Rap_Lyric_Generator

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Try it out live here:

http://deepbeat.org/

I introduce three papers in this video

Unsupervised Rhyme Scheme Identification in Hip Hop Lyrics Using Hidden Markov Models:

http://link.springer.com/chapter/10.1007%2F978-3-642-39593-2_3

Modeling Hip Hop Challenge-Response Lyrics as Machine Translation:

http://www.illc.uva.nl/LaCo/CLS/papers/wu_hiphop_itg.pdf

DopeLearning: A Computational Approach to Rap Lyrics Generation:

http://arxiv.org/abs/1505.04771

More info about Hidden Markov Models:

https://www.youtube.com/watch?v=TPRoLreU9lA

https://www.quora.com/What-is-a-simple-explanation-of-the-Hidden-Markov-Model-algorithm

http://www.developerstation.org/2011/11/hidden-markov-models-for-dummies.html

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39 thoughts on “Generate Rap Lyrics – Fresh Machine Learning #4
  1. Great videos. Love the short videos but I think you should consider making some longer in depth videos depending on what the viewers want to see. You're putting out great content so I'm sure you'll be rewarded richly over time

  2. I felt like you speed up the video of you talking, if you can not say it in time then make the video itself longer, I don't mind spending a few extra mins (I had to do .50 speed to understand) (Love your videos)

  3. So it basically just try to predict the next word, right? What could I do to implement rhymes? Oh, thanks for being so awesome! Your channel is definitely helping me A LOT

  4. I love your video so much!!
    How did you find these 2 papers form Hong Kong and Finland ? I looked up at Google Scholars it is only cited for less than 10 times.
    Also where you do normally go to look for machine learning papers?
    Thank you so much!

  5. I checked out the code from github, not really sure it works well with multiple training files, because the new counts would kind of overwrite the old transition probabilities. Also the code for the loops seemed very C-like and not very pythonic.

    I've made some adjustments and created a pull request. Would be great for you review and merge it.

    As a side note, inspired by the idea I started my own version of a Markov Model text generator. It uses ordered lists instead of dictionaries and numpy for faster computations, also makes some preprocessing on the text. Still a work in progress, but you can check it out on https://github.com/baldassarreFe/python-pankakes

    Great job with the Learn Python for Data Science series, keep it going!!

  6. THis is sooo cool and surprisingly its not even too complicated well to do at least understandings going to take a bit more messing around with

  7. the demo code line 9:
    for curr, succ in zip(words[1:], words[:-1]):

    i think it should be like this:
    for curr, succ in zip(words[:-1], words[1:]):

    right?

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