def generate(data_fn, out_fn, N_epochs):
# model settings
max_len = 20
max_tries = 1000
diversity = 0.5
# musical settings
bpm = 130
# get data
chords, abstract_grammars = get_musical_data(data_fn)
corpus, values, val_indices, indices_val = get_corpus_data(
abstract_grammars)
print("corpus length:", len(corpus))
print("total # of values:", len(values))
# build model
model = lstm.build_model(corpus=corpus,
val_indices=val_indices,
max_len=max_len,
N_epochs=N_epochs)
# set up audio stream
out_stream = stream.Stream()
# generation loop
curr_offset = 0.0
loopEnd = len(chords)
for loopIndex in range(1, loopEnd):
# get chords from file
curr_chords = stream.Voice()
for j in chords[loopIndex]:
curr_chords.insert((j.offset % 4), j)
# generate grammar
curr_grammar = __generate_grammar(
model=model,
corpus=corpus,
abstract_grammars=abstract_grammars,
values=values,
val_indices=val_indices,
indices_val=indices_val,
max_len=max_len,
max_tries=max_tries,
diversity=diversity,
)
curr_grammar = curr_grammar.replace(" A", " C").replace(" X", " C")
# Pruning #1: smoothing measure
curr_grammar = prune_grammar(curr_grammar)
# Get notes from grammar and chords
curr_notes = unparse_grammar(curr_grammar, curr_chords)
# Pruning #2: removing repeated and too close together notes
curr_notes = prune_notes(curr_notes)
# quality assurance: clean up notes
curr_notes = clean_up_notes(curr_notes)
# print # of notes in curr_notes
print("After pruning: %s notes" %
(len([i for i in curr_notes if isinstance(i, note.Note)])))
# insert into the output stream
for m in curr_notes:
out_stream.insert(curr_offset + m.offset, m)
for mc in curr_chords:
out_stream.insert(curr_offset + mc.offset, mc)
curr_offset += 4.0
out_stream.insert(0.0, tempo.MetronomeMark(number=bpm))
# Play the final stream through output (see 'play' lambda function above)
# play = lambda x: midi.realtime.StreamPlayer(x).play()
# play(out_stream)
# save stream
mf = midi.translate.streamToMidiFile(out_stream)
mf.open(out_fn, "wb")
mf.write()
mf.close()
"""Modified from the original data_utils.py file from the Deep Learning Specialization assignment"""
from grammar import unparse_grammar
from music import data_processing
from music21 import *
from preprocess import get_corpus_data, get_musical_data
from qa import clean_up_notes, prune_grammar, prune_notes
chords, abstract_grammars = get_musical_data('data/original_metheny.mid')
corpus, tones, tones_indices, indices_tones = get_corpus_data(
abstract_grammars)
def load_data():
N_tones = len(set(corpus))
X, Y, N_tones = data_processing(corpus, tones_indices, 60, 30)
return (X, Y, N_tones, indices_tones)
def generate_music(sampling_fn):
"""
Generates music using the given sampling function that returns the indices of musical values.
Creates an audio stream to save the music.
"""
# Set up audio stream
out_stream = stream.Stream()
# Initialize chord variables
curr_offset = 0.0 # variable used to write sounds to the Stream.
num_chords = int(len(chords) / 3) # number of different set of chords