markov_music_generator.py

#

You can see the actual code file here.

#

music generations with Markov chains 

import random
from music21 import corpus, stream, note
#

Find ngrams from a given list 

def get_ngrams(seq, n):
#

Given a list seq returns a list of ngrams where each element is an n-tuple.

Example:
>>> seq = [1, 2, 1, 3, 1, 2, 7]
>>> find_ngrams(seq, 2)
[(1, 2), (2, 1), (1, 3), (3, 1), (1, 2), (2, 7)]
>>> find_ngrams(seq, 3)
[(1, 2, 1), (2, 1, 3), (1, 3, 1), (3, 1, 2), (1, 2, 7)] 

    ngrams = []
#

Iterate over seq in n-sized slices each converted to an n-tuple and appended to ngrams.

    for i in range(len(seq) - n + 1):
        new_tuple = tuple(seq[i:i+n])
        ngrams.append(new_tuple)
    return ngrams
#

Create Markov model from a given list 


    

  

  

  

    

      

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Given a list seq returns a Markov model of the given order
as a dictionary. The keys are order-sized tuples. The value for each
key is a list of elements in seq that immediately follow
occurances of the key in the seq.


Example:

>>> seq = [1, 2, 1, 3, 1, 2, 7]

>>> markov_model(seq, 1)

{(2,): [1, 7], (3,): [1], (1,): [2, 3, 2]}

>>> markov_model(seq, 2)

{(1, 2): [1, 7], (1, 3): [1], (3, 1): [2], (2, 1): [3]}  

    

    

      
def markov_model(seq, order):

    

  

  

  

    

      

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    markov = {}

    

  

  

  

    

      

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First use find_ngrams to slice seq appropriately.

    

    

      
    ngrams = get_ngrams(seq, order+1)

    

  

  

  

    

      

        #
      

      
For each ngram in ngrams (where n = order + 1) the first
(n - 1) elements are used as the key and the last
element is appended to the list of possible
continuations.

    

    

      
    for ngram in ngrams:
        try:
            markov[ngram[:-1]].append(ngram[-1])
        except KeyError:
            markov[ngram[:-1]] = [ngram[-1]]
    return markov

    

  

  

  

    

      

        #
      

      
 Generate Markov chain 

    

    

      

    

  

  

  

    

      

        #
      

      
Given a list seq returns a Markov chain of
the given order with a maximum length of the integer max_length.

    

    

      
def markov_chain(markov, order, max_length=20):

    

  

  

  

    

      

        #
      

      
    

    

      

    

  

  

  

    

      

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The start_state for the Markov chain is chosen randomly from a list
of keys() in the markov dictionary.

    

    

      
    start_state = random.choice(list(markov.keys()))

    

  

  

  

    

      

        #
      

      
Markov chain is a list beginning with the start_state.

    

    

      
    chain = list(start_state)

    

  

  

  

    

      

        #
      

      
while loop to generate markov chain.

    

    

      
    while True:

    

  

  

  

    

      

        #
      

      
Choose a random continuation, next_state from the elements of
the value for the current start_state.

    

    

      
        next_state = random.choice(markov[start_state])
        chain.append(next_state)
        if len(chain) >= max_length:
            break

    

  

  

  

    

      

        #
      

      
Concatenate next_state to start_state and use the appropriate
slice of the resulting tuple as the new start_state.

    

    

      
        start_state = (start_state + (next_state,))[1:]
    return chain

    

  

  

  

    

      

        #
      

      
create empty dictionary for Markov model

    

    

      
markov = {}

    

  

  

  

    

      

        #
      

      
set order of the Markov model; experiment with changing the order

    

    

      
order = 2

sourceMaterial = corpus.parse('bach/bwv7.7')
for part in sourceMaterial.parts:
        notes = part.flat.notes

    

  

  

  

    

      

        #
      

      
Use tuples of note pitches and duration to create Markov model

    

    

      
        note_tuples = [(n.nameWithOctave, n.quarterLength) for n in notes]
        markov.update(markov_model(note_tuples, order))
chain = markov_chain(markov, order)

output = stream.Stream()

    

  

  

  

    

      

        #
      

      
Convert chain of (pitch, duration) tuples to music21 note objects
and append to output stream.

    

    

      
for ptch, dur in chain:
    output.append(note.Note(ptch, quarterLength=dur))
output.show()