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import os, sys
import random
import datetime
import glob
import markov
import pickle
corpus = []
song = []
"Generate sheet music for a new song from a corpus of existing songs in abc format"
# get the list of filenames (abc files downloaded from http://www.norbeck.nu/abc/)
# getdirs = []
# dirs = ["hn201612/i/*.abc", "hn201612/s/*.abc"]
# dirs = ["data/*.abc"]
# dirs = ["data"]
# for dir1 in dirs:
# for filename in glob.iglob(dir1):
# getdirs += [filename]
#Finds all absolute paths in directory
#https://stackoverflow.com/questions/9816816/get-absolute-paths-of-all-files-in-a-directory
def abs_paths(dir):
for dir_path,_,filenames in os.walk(dir):
for f in filenames:
yield os.path.abspath(os.path.join(dir_path, f))
# ex_filename = "hn201612/i/hnsong1.abc"
# parsing on file to extract songs and add them to corpus
for filename in abs_paths("n-grams/data/hard"):
with open(filename) as f:
lines = f.readlines()
last = len(lines)
for index, line in enumerate(lines):
if (line.find("|") < 0 and index - 1 == last):
# if the next line does not have pipes add song to corpus and then set song variable empty again
corpus.append(song)
song = []
else:
if line.find("|") > -1:
# a line should be split on "|" and copied to the corpus if it has pipes
sline = line.split("|")
# add the list of measures to the song
song += [x.strip("\r\n") for x in sline if len(x.strip("\r\n")) > 0]
last = index
print("Training on {} songs...".format(len(corpus)))
# MARKOV PART
# n-gram length for markov model
n = 1
model = markov.generate_model_from_token_lists(corpus, n)
# save pickle
with open('markov_chain.pickle', 'wb') as handle:
pickle.dump(model, handle)
def nextword(word):
return markov.generate(model, 3, seed=word, max_iterations=1)
def writesong(songlength, first):
song = [first]
for i in range(songlength):
song += nextword(str(song[-1]))
return song
# choose a random song length from list of song lengths in corpus
lengthofsong = random.choice([len(x) for x in corpus if len(x) > 10])
print("Song length will be {}".format(lengthofsong))
firstnote = markov.generate(model, n, max_iterations=3)[0]
# print "first note: {}".format(firstnote)
print("Here is the song in abc format:")
song = writesong(lengthofsong, firstnote)
dob = datetime.datetime.now().strftime('%H%M')
print(dob)
print(song)
# make song file
songname = "n-grams/gen_songs_abc/gen_song_{}.abc".format(dob)
print("\n\nYou can find the song in {}".format(songname))
lastpart = lengthofsong - lengthofsong%4
# hack to include dictionary at the beginning of every abc file
# will add a more sophisticated way to generate the values in the future
title = "Markov Song {}".format(dob)
songbeginning = ['X:1','T:' + title, 'R:song', 'C:Visakh Ajith', 'Z:id:hn-song-111', 'M:3/4', 'L:1/8', 'Q:1/4=120', 'K:G'
]
songbeginning = [x+"\n" for x in songbeginning]
# convert song to abc format and write to file
newsong = open(songname, 'w')
newsong.writelines(songbeginning)
for i in range(lastpart):
newsong.write(" | ".join(song[i:i+3]) + "\n")
newsong.write(" | ".join(song[lastpart:lengthofsong]))
#abc2ly markov.abc
# lilypond -fpng markov.ly |