def testExtractMelodiesMelodyTooLongWithPad(self):
music_testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 127, 2, 4),
(14, 50, 6, 15)])
music_testing_lib.add_track_to_sequence(self.note_sequence, 1,
[(12, 127, 2, 4),
(14, 50, 6, 18)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
expected = [[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT,
NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT
]]
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
max_steps_truncate=14,
max_steps_discard=18,
gap_bars=1,
min_unique_pitches=2,
ignore_polyphonic_notes=True,
pad_end=True)
melodies = [list(melody) for melody in melodies]
self.assertEqual(expected, melodies)
def testExtractMelodiesTooFewPitches(self):
# Test that extract_melodies discards melodies with too few pitches where
# pitches are equivalent by octave.
music_testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 100, 0, 1),
(13, 100, 1, 2),
(18, 100, 2, 3),
(24, 100, 3, 4),
(25, 100, 4, 5)])
music_testing_lib.add_track_to_sequence(self.note_sequence, 1,
[(12, 100, 0, 1),
(13, 100, 1, 2),
(18, 100, 2, 3),
(25, 100, 3, 4),
(26, 100, 4, 5)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
expected = [[12, 13, 18, 25, 26]]
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=1,
min_unique_pitches=4,
ignore_polyphonic_notes=True)
melodies = [list(melody) for melody in melodies]
self.assertEqual(expected, melodies)
def testExtractMultipleMelodiesFromSameTrack(self):
music_testing_lib.add_track_to_sequence(self.note_sequence,
0, [(12, 100, 2, 4),
(11, 1, 6, 11)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 8),
(50, 100, 33, 37),
(52, 100, 34, 37)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
expected = [[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 11, NO_EVENT,
NO_EVENT, NO_EVENT, NO_EVENT
], [
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT
], [NO_EVENT, 50, 52, NO_EVENT, NO_EVENT]]
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
melodies = sorted([list(melody) for melody in melodies])
self.assertEqual(expected, melodies)
def testExtractMelodiesMelodyTooShort(self):
music_testing_lib.add_track_to_sequence(self.note_sequence,
0, [(12, 127, 2, 4),
(14, 50, 6, 7)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 8)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
2, [(12, 127, 2, 4),
(14, 50, 6, 9)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
expected = [[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT
],
[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT,
14, NO_EVENT, NO_EVENT
]]
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=2,
gap_bars=1,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
melodies = [list(melody) for melody in melodies]
self.assertEqual(expected, melodies)
def testExtractChordsForMelodiesCoincidentChords(self):
music_testing_lib.add_track_to_sequence(self.note_sequence,
0, [(12, 100, 2, 4),
(11, 1, 6, 11)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 8),
(50, 100, 33, 37),
(52, 100, 34, 37)])
music_testing_lib.add_chords_to_sequence(self.note_sequence,
[('C', 2), ('G7', 6),
('E13', 8), ('Cmaj7', 8)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, self.steps_per_quarter)
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, stats = chord_pipelines.extract_chords_for_melodies(
quantized_sequence, melodies)
expected = [[NO_CHORD, NO_CHORD, 'C', 'C', 'C', 'C', 'G7', 'G7'],
['Cmaj7', 'Cmaj7', 'Cmaj7', 'Cmaj7', 'Cmaj7']]
stats_dict = dict((stat.name, stat) for stat in stats)
self.assertIsNone(chord_progressions[0])
self.assertEqual(expected,
[list(chords) for chords in chord_progressions[1:]])
self.assertEqual(stats_dict['coincident_chords'].count, 1)
def testExtractLeadSheetFragmentsCoincidentChords(self):
music_testing_lib.add_track_to_sequence(self.note_sequence,
0, [(12, 100, 2, 4),
(11, 1, 6, 11)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 8),
(50, 100, 33, 37),
(52, 100, 34, 37)])
music_testing_lib.add_chords_to_sequence(self.note_sequence,
[('C', 2), ('G7', 6),
('Cmaj7', 33), ('F', 33)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
lead_sheets, _ = lead_sheet_pipelines.extract_lead_sheet_fragments(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True,
require_chords=True)
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chord_pipelines.extract_chords_for_melodies(
quantized_sequence, melodies)
# Last lead sheet should be rejected for coincident chords.
self.assertEqual(list(melodies[:2]),
list(lead_sheet.melody for lead_sheet in lead_sheets))
self.assertEqual(list(chord_progressions[:2]),
list(lead_sheet.chords for lead_sheet in lead_sheets))
def testExtractChordsForMelodies(self):
music_testing_lib.add_track_to_sequence(self.note_sequence,
0, [(12, 100, 2, 4),
(11, 1, 6, 11)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 8),
(50, 100, 33, 37),
(52, 100, 34, 37)])
music_testing_lib.add_chords_to_sequence(self.note_sequence,
[('C', 2), ('G7', 6),
('Cmaj7', 33)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, self.steps_per_quarter)
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chord_pipelines.extract_chords_for_melodies(
quantized_sequence, melodies)
expected = [[
NO_CHORD, NO_CHORD, 'C', 'C', 'C', 'C', 'G7', 'G7', 'G7', 'G7',
'G7'
], [NO_CHORD, NO_CHORD, 'C', 'C', 'C', 'C', 'G7', 'G7'],
['G7', 'Cmaj7', 'Cmaj7', 'Cmaj7', 'Cmaj7']]
self.assertEqual(expected,
[list(chords) for chords in chord_progressions])
def testExtractLeadSheetFragments(self):
music_testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 100, .5, 1),
(11, 1, 1.5, 2.75)])
music_testing_lib.add_track_to_sequence(self.note_sequence, 1,
[(12, 127, .5, 1),
(14, 50, 1.5, 2),
(50, 100, 8.25, 9.25),
(52, 100, 8.5, 9.25)])
music_testing_lib.add_chords_to_sequence(self.note_sequence,
[('C', .5), ('G7', 1.5),
('Cmaj7', 8.25)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, self.steps_per_quarter)
lead_sheets, _ = lead_sheet_pipelines.extract_lead_sheet_fragments(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True,
require_chords=True)
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chord_pipelines.extract_chords_for_melodies(
quantized_sequence, melodies)
self.assertEqual(list(melodies),
list(lead_sheet.melody for lead_sheet in lead_sheets))
self.assertEqual(list(chord_progressions),
list(lead_sheet.chords for lead_sheet in lead_sheets))
def testExtractMelodiesStatistics(self):
music_testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 7), (10, 100, 8, 10), (9, 100, 11, 14),
(8, 100, 16, 40), (7, 100, 41, 42)])
music_testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 2, 8)])
music_testing_lib.add_track_to_sequence(
self.note_sequence, 2,
[(12, 127, 0, 1)])
music_testing_lib.add_track_to_sequence(
self.note_sequence, 3,
[(12, 127, 2, 4), (12, 50, 6, 8)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
_, stats = melody_pipelines.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=1, min_unique_pitches=2,
ignore_polyphonic_notes=False)
stats_dict = dict((stat.name, stat) for stat in stats)
self.assertEqual(stats_dict['polyphonic_tracks_discarded'].count, 1)
self.assertEqual(stats_dict['melodies_discarded_too_short'].count, 1)
self.assertEqual(stats_dict['melodies_discarded_too_few_pitches'].count, 1)
self.assertEqual(
stats_dict['melody_lengths_in_bars'].counters,
{float('-inf'): 0, 0: 0, 1: 0, 2: 0, 10: 1, 20: 0, 30: 0, 40: 0, 50: 0,
100: 0, 200: 0, 500: 0})
def testExtractMelodiesSimple(self):
music_testing_lib.add_track_to_sequence(self.note_sequence,
0, [(12, 100, 2, 4),
(11, 1, 6, 7)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 9)])
music_testing_lib.add_track_to_sequence(self.note_sequence,
9, [(13, 100, 2, 4),
(15, 25, 6, 8)],
is_drum=True)
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
expected = [[NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 11],
[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT,
14, NO_EVENT, NO_EVENT
]]
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=1,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
self.assertEqual(2, len(melodies))
self.assertIsInstance(melodies[0], melodies_lib.Melody)
self.assertIsInstance(melodies[1], melodies_lib.Melody)
melodies = sorted([list(melody) for melody in melodies])
self.assertEqual(expected, melodies)
def emissionModelTrainer(self):
ALL_CHORD_LIST = ['N.C', 'C', 'Cm', 'C#', 'C#m', 'D', 'Dm', 'Eb', 'Ebm', 'E', 'Em', 'F', 'Fm', 'F#', 'F#m', 'G',
'Gm', 'G#', 'G#m', 'A', 'Am', 'A#', 'A#m', 'B', 'Bm']
Same_Chord = {'Db': 'C#', 'Dbm': 'C#m', 'D#': 'Eb', 'D#m': 'Ebm', 'Gb': 'F#', 'Gbm': 'F#m', 'Ab': 'G#',
'Abm': 'G#m', 'Bb': 'A#', 'Bbm': 'A#m'}
ALL_NOTE_LIST = ['C', 'C#', 'D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
path = "D:\FAI\Wikifonia"
for file in glob.glob(path):
mxlObject = musicxml_parser.MusicXMLDocument(file)
mxlSequence = musicxml_reader.musicxml_to_sequence_proto(mxlObject)
quantizedNoteSequence = sequences_lib.quantize_note_sequence(mxlSequence, 1)
melodies, stats = melody_pipelines.extract_melodies(quantizedNoteSequence)
chord_prog, stats = chord_pipelines.extract_chords_for_melodies(quantizedNoteSequence, melodies)
if not chord_prog:
continue
for i in range(len(list(chord_prog[0]))):
curChord = list(chord_prog[0])[i]
curMel = list(melodies[0])[i]
while (curMel > 71):
curMel = curMel - 12
while (curMel < 60):
curMel = curMel + 12
curChord = re.sub(r'\d+', '', curChord)
curChord = curChord[:3]
if curChord not in 'N.C.':
if len(curChord) == 3 and curChord[2] not in 'm':
curChord = curChord[:2]
if curChord[1] not in ['#', 'b']:
curChord = curChord[:1]
if curChord in Same_Chord:
curChord = Same_Chord[curChord]
if curChord in 'Cb':
curChord = 'B'
if curChord in 'Fb':
curChord = 'E'
if curChord in 'Cbm':
curChord = 'D'
if curChord in 'Fbm':
curChord = 'Em'
a = ALL_CHORD_LIST.index(re.sub(r'\d+', '', curChord))
b = curMel
self.mo_matrix[a][b - 60] = self.mo_matrix[a][b - 60] + 1
normed_mo_matrix = normalize(self.mo_matrix, axis=1, norm='l1')
self.mo_matrix = normed_mo_matrix
def extractPitch(self, song):
seq = midi_io.midi_file_to_note_sequence(song)
qseq = sequences_lib.quantize_note_sequence(seq, 1)
melodies, stats = melody_pipelines.extract_melodies(qseq)
rounded_pitches = []
for mel in list(melodies[0]):
while (mel > 71):
mel = mel - 12
while (mel < 60):
mel = mel + 12
rounded_pitches.append(mel)
return rounded_pitches
def cleanDataset(self):
path = "D:\FAI\Wikifonia"
count = 0
for file in glob.glob(path):
try:
mxlObject = musicxml_parser.MusicXMLDocument(file)
mxlSequence = musicxml_reader.musicxml_to_sequence_proto(mxlObject)
quantizedNoteSequence = sequences_lib.quantize_note_sequence(mxlSequence, 1)
chord_prog, stats = chord_pipelines.extract_chords(quantizedNoteSequence)
melodies, stats = melody_pipelines.extract_melodies(quantizedNoteSequence)
ac, stats = chord_pipelines.extract_chords_for_melodies(quantizedNoteSequence, melodies)
except:
os.remove(file)
print(file)
count = count + 1
def load_primer(self):
"""Loads default MIDI primer file.
Also assigns the steps per bar of this file to be the model's defaults.
"""
if not os.path.exists(self.midi_primer):
tf.logging.warn('ERROR! No such primer file exists! %s', self.midi_primer)
return
self.primer_sequence = midi_io.midi_file_to_sequence_proto(self.midi_primer)
quantized_seq = sequences_lib.quantize_note_sequence(
self.primer_sequence, steps_per_quarter=4)
extracted_melodies, _ = melody_pipelines.extract_melodies(
quantized_seq, min_bars=0, min_unique_pitches=1)
self.primer = extracted_melodies[0]
self.steps_per_bar = self.primer.steps_per_bar
def testExtractMelodiesLateStart(self):
music_testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 102, 103), (13, 100, 104, 106)])
music_testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 100, 100, 101), (13, 100, 102, 105)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
expected = [[NO_EVENT, NO_EVENT, 12, NOTE_OFF, 13, NO_EVENT],
[12, NOTE_OFF, 13, NO_EVENT, NO_EVENT]]
melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=1, min_unique_pitches=2,
ignore_polyphonic_notes=True)
melodies = sorted([list(melody) for melody in melodies])
self.assertEqual(expected, melodies)
def extract_lead_sheet_fragments(quantized_sequence,
search_start_step=0,
min_bars=7,
max_steps_truncate=None,
max_steps_discard=None,
gap_bars=1.0,
min_unique_pitches=5,
ignore_polyphonic_notes=True,
pad_end=False,
filter_drums=True,
require_chords=False,
all_transpositions=False):
"""Extracts a list of lead sheet fragments from a quantized NoteSequence.
This function first extracts melodies using melodies_lib.extract_melodies,
then extracts the chords underlying each melody using
chords_lib.extract_chords_for_melodies.
Args:
quantized_sequence: A quantized NoteSequence object.
search_start_step: Start searching for a melody at this time step. Assumed
to be the first step of a bar.
min_bars: Minimum length of melodies in number of bars. Shorter melodies are
discarded.
max_steps_truncate: Maximum number of steps in extracted melodies. If
defined, longer melodies are truncated to this threshold. If pad_end is
also True, melodies will be truncated to the end of the last bar below
this threshold.
max_steps_discard: Maximum number of steps in extracted melodies. If
defined, longer melodies are discarded.
gap_bars: A melody comes to an end when this number of bars (measures) of
silence is encountered.
min_unique_pitches: Minimum number of unique notes with octave equivalence.
Melodies with too few unique notes are discarded.
ignore_polyphonic_notes: If True, melodies will be extracted from
`quantized_sequence` tracks that contain polyphony (notes start at the
same time). If False, tracks with polyphony will be ignored.
pad_end: If True, the end of the melody will be padded with NO_EVENTs so
that it will end at a bar boundary.
filter_drums: If True, notes for which `is_drum` is True will be ignored.
require_chords: If True, only return lead sheets that have at least one
chord other than NO_CHORD. If False, lead sheets with only melody will
also be returned.
all_transpositions: If True, also transpose each lead sheet fragment into
all 12 keys.
Returns:
A python list of LeadSheet instances.
Raises:
NonIntegerStepsPerBarError: If `quantized_sequence`'s bar length
(derived from its time signature) is not an integer number of time
steps.
"""
sequences_lib.assert_is_relative_quantized_sequence(quantized_sequence)
stats = dict([('empty_chord_progressions',
statistics.Counter('empty_chord_progressions'))])
melodies, melody_stats = melody_pipelines.extract_melodies(
quantized_sequence,
search_start_step=search_start_step,
min_bars=min_bars,
max_steps_truncate=max_steps_truncate,
max_steps_discard=max_steps_discard,
gap_bars=gap_bars,
min_unique_pitches=min_unique_pitches,
ignore_polyphonic_notes=ignore_polyphonic_notes,
pad_end=pad_end,
filter_drums=filter_drums)
chord_progressions, chord_stats = chord_pipelines.extract_chords_for_melodies(
quantized_sequence, melodies)
lead_sheets = []
for melody, chords in zip(melodies, chord_progressions):
# If `chords` is None, it's because a chord progression could not be
# extracted for this particular melody.
if chords is not None:
if require_chords and all(chord == chords_lib.NO_CHORD
for chord in chords):
stats['empty_chord_progressions'].increment()
else:
lead_sheet = LeadSheet(melody, chords)
if all_transpositions:
for amount in range(-6, 6):
transposed_lead_sheet = copy.deepcopy(lead_sheet)
transposed_lead_sheet.transpose(amount)
lead_sheets.append(transposed_lead_sheet)
else:
lead_sheets.append(lead_sheet)
return lead_sheets, list(stats.values()) + melody_stats + chord_stats
def _generate(self, input_sequence, generator_options):
if len(generator_options.input_sections) > 1:
raise sequence_generator.SequenceGeneratorError(
'This model supports at most one input_sections message, but got %s' %
len(generator_options.input_sections))
if len(generator_options.generate_sections) != 1:
raise sequence_generator.SequenceGeneratorError(
'This model supports only 1 generate_sections message, but got %s' %
len(generator_options.generate_sections))
if input_sequence and input_sequence.tempos:
qpm = input_sequence.tempos[0].qpm
else:
qpm = note_seq.DEFAULT_QUARTERS_PER_MINUTE
steps_per_second = note_seq.steps_per_quarter_to_steps_per_second(
self.steps_per_quarter, qpm)
generate_section = generator_options.generate_sections[0]
if generator_options.input_sections:
input_section = generator_options.input_sections[0]
primer_sequence = note_seq.trim_note_sequence(input_sequence,
input_section.start_time,
input_section.end_time)
input_start_step = note_seq.quantize_to_step(
input_section.start_time, steps_per_second, quantize_cutoff=0)
else:
primer_sequence = input_sequence
input_start_step = 0
if primer_sequence.notes:
last_end_time = max(n.end_time for n in primer_sequence.notes)
else:
last_end_time = 0
if last_end_time > generate_section.start_time:
raise sequence_generator.SequenceGeneratorError(
'Got GenerateSection request for section that is before the end of '
'the NoteSequence. This model can only extend sequences. Requested '
'start time: %s, Final note end time: %s' %
(generate_section.start_time, last_end_time))
# Quantize the priming sequence.
quantized_sequence = note_seq.quantize_note_sequence(
primer_sequence, self.steps_per_quarter)
# Setting gap_bars to infinite ensures that the entire input will be used.
extracted_melodies, _ = melody_pipelines.extract_melodies(
quantized_sequence, search_start_step=input_start_step, min_bars=0,
min_unique_pitches=1, gap_bars=float('inf'),
ignore_polyphonic_notes=True)
assert len(extracted_melodies) <= 1
start_step = note_seq.quantize_to_step(
generate_section.start_time, steps_per_second, quantize_cutoff=0)
# Note that when quantizing end_step, we set quantize_cutoff to 1.0 so it
# always rounds down. This avoids generating a sequence that ends at 5.0
# seconds when the requested end time is 4.99.
end_step = note_seq.quantize_to_step(
generate_section.end_time, steps_per_second, quantize_cutoff=1.0)
if extracted_melodies and extracted_melodies[0]:
melody = extracted_melodies[0]
else:
# If no melody could be extracted, create an empty melody that starts 1
# step before the request start_step. This will result in 1 step of
# silence when the melody is extended below.
steps_per_bar = int(
note_seq.steps_per_bar_in_quantized_sequence(quantized_sequence))
melody = note_seq.Melody([],
start_step=max(0, start_step - 1),
steps_per_bar=steps_per_bar,
steps_per_quarter=self.steps_per_quarter)
# Ensure that the melody extends up to the step we want to start generating.
melody.set_length(start_step - melody.start_step)
# Extract generation arguments from generator options.
arg_types = {
'temperature': lambda arg: arg.float_value,
'beam_size': lambda arg: arg.int_value,
'branch_factor': lambda arg: arg.int_value,
'steps_per_iteration': lambda arg: arg.int_value
}
args = dict((name, value_fn(generator_options.args[name]))
for name, value_fn in arg_types.items()
if name in generator_options.args)
generated_melody = self._model.generate_melody(
end_step - melody.start_step, melody, **args)
generated_sequence = generated_melody.to_sequence(qpm=qpm)
assert (generated_sequence.total_time - generate_section.end_time) <= 1e-5
return generated_sequence