A Machine Learning project that trains a Hidden Markov Model over a dataset of rap song lyrics. You can generate a rap using a single word as the starting point.
The two text files have the rap lyrics that the model learns from.
To run:
python RapLyrics.py
When prompted,enter the first word with which you want to generate the rap lyrics
Hidden Markov Model
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states.Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition,part-of-speech tagging, musical score etc.
In simpler Markov models (like a Markov chain), the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but the output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore, the sequence of tokens generated by an HMM gives some information about the sequence of states. The adjective 'hidden' refers to the state sequence through which the model passes, not to the parameters of the model; the model is still referred to as a 'hidden' Markov model even if these parameters are known exactly.