The Rap Machine is a product of machine learning that utilizes spaCy, an open-source NLP library, as well as a recurrent neural network architecture called LSTM in order to generate lyrics from a Kaggle dataset containing more than 55,000 lyrics across several genres. The second part of this project involves predicting which artist would most likely be associated in terms of style with the machine-generated lyrics.
-You will need access to Jupyter Notebook in order to run through the code yourself. Feel free to mess around and see what you can create! Using this dataset, as well as downloading Jupyter Notebook from here, you can do a lot with this code.
-You will also need to install Tensorflow and Keras, as well as have NumPy and Pandas (two popular Python libraries) installed on your machine. Of course, you can use simple pip commands to install these libraries as well.
Cody Liu @ https://github.com/liu-cody
Rohit Gangupantulu @ https://github.com/rgangu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so. If any doubts arise, feel free to contact the authors.
- Clarification in usage of the Keras API: https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py
- Thanks to Henry Ehrenberg. Follow him at https://github.com/henryre.
Warning - Some of the output may contain vulgar language - this is due to the neural network learning from the lyrical contents of the artists.