def testExtractLeadSheetFragments(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track(self.quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
testing_lib.add_quantized_track(self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8),
(50, 100, 33, 37), (52, 100, 34, 37)])
testing_lib.add_quantized_chords(self.quantized_sequence,
[('C', 2), ('G7', 6), ('Cmaj7', 33)])
lead_sheets, _ = lead_sheets_lib.extract_lead_sheet_fragments(
self.quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True,
require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
self.quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.extract_chords_for_melodies(
self.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):
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)])
testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 2, 8)])
testing_lib.add_track_to_sequence(
self.note_sequence, 2,
[(12, 127, 0, 1)])
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 = melodies_lib.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 testExtractMelodiesTooFewPitches(self):
# Test that extract_melodies discards melodies with too few pitches where
# pitches are equivalent by octave.
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(self.quantized_sequence, 0,
[(12, 100, 0, 1),
(13, 100, 1, 2),
(18, 100, 2, 3),
(24, 100, 3, 4),
(25, 100, 4, 5)])
testing_lib.add_quantized_track_to_sequence(self.quantized_sequence, 1,
[(12, 100, 0, 1),
(13, 100, 1, 2),
(18, 100, 2, 3),
(25, 100, 3, 4),
(26, 100, 4, 5)])
expected = [[12, 13, 18, 25, 26]]
melodies, _ = melodies_lib.extract_melodies(
self.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 testExtractMelodiesSimple(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 7)])
testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 9)])
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, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=1, min_unique_pitches=2,
ignore_polyphonic_notes=True)
self.assertEqual(2, len(melodies))
self.assertTrue(isinstance(melodies[0], melodies_lib.Melody))
self.assertTrue(isinstance(melodies[1], melodies_lib.Melody))
melodies = sorted([list(melody) for melody in melodies])
self.assertEqual(expected, melodies)
def testExtractMultipleMelodiesFromSameTrack(self):
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
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, _ = melodies_lib.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 testExtractChordsForMelodiesCoincidentChords(self):
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
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)])
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, _ = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, stats = chords_lib.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 testExtractChordsForMelodies(self):
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
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)])
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, _ = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=1,
gap_bars=2,
min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.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 testExtractLeadSheetFragmentsNoChords(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8),
(50, 100, 33, 37), (52, 100, 34, 37)])
testing_lib.add_quantized_chords_to_sequence(
self.quantized_sequence,
[('C', 2), ('G7', 6), (NO_CHORD, 10)])
lead_sheets, stats = lead_sheets_lib.extract_lead_sheet_fragments(
self.quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True, require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
self.quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.extract_chords_for_melodies(
self.quantized_sequence, melodies)
stats_dict = dict([(stat.name, stat) for stat in stats])
# Last lead sheet should be rejected for having no 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))
self.assertEqual(stats_dict['empty_chord_progressions'].count, 1)
def testExtractChordsForMelodiesCoincidentChords(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
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)])
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, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, stats = chords_lib.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):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8),
(50, 100, 33, 37), (52, 100, 34, 37)])
testing_lib.add_quantized_chords_to_sequence(
self.quantized_sequence,
[('C', 2), ('G7', 6), ('Cmaj7', 33), ('F', 33)])
lead_sheets, _ = lead_sheets_lib.extract_lead_sheet_fragments(
self.quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True, require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
self.quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.extract_chords_for_melodies(
self.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 testExtractLeadSheetFragmentsCoincidentChords(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
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)])
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_sheets_lib.extract_lead_sheet_fragments(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True, require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.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 testExtractLeadSheetFragmentsNoChords(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
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)])
testing_lib.add_chords_to_sequence(
self.note_sequence,
[('C', 2), ('G7', 6), (NO_CHORD, 10)])
quantized_sequence = sequences_lib.quantize_note_sequence(
self.note_sequence, steps_per_quarter=1)
lead_sheets, stats = lead_sheets_lib.extract_lead_sheet_fragments(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True, require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.extract_chords_for_melodies(
quantized_sequence, melodies)
stats_dict = dict((stat.name, stat) for stat in stats)
# Last lead sheet should be rejected for having no 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))
self.assertEqual(stats_dict['empty_chord_progressions'].count, 1)
def testExtractMelodiesStatistics(self):
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)])
testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 2, 8)])
testing_lib.add_track_to_sequence(
self.note_sequence, 2,
[(12, 127, 0, 1)])
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 = melodies_lib.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 testExtractMelodiesPadEnd(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 127, 2, 4), (14, 50, 6, 7)])
testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8)])
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,
NOTE_OFF],
[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, NOTE_OFF, NO_EVENT, NO_EVENT]]
melodies, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, 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 testExtractMelodiesMelodyTooLong(self):
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(12, 127, 2, 4), (14, 50, 6, 15)])
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] * 7,
[NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14] +
[NO_EVENT] * 7
]
melodies, _ = melodies_lib.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)
melodies = [list(melody) for melody in melodies]
self.assertEqual(expected, melodies)
def testExtractLeadSheetFragments(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, .5, 1), (11, 1, 1.5, 2.75)])
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)])
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_sheets_lib.extract_lead_sheet_fragments(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True, require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.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 testExtractMelodiesSimple(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 7)])
testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 9)])
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, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=1, min_unique_pitches=2,
ignore_polyphonic_notes=True)
self.assertEqual(2, len(melodies))
self.assertTrue(isinstance(melodies[0], melodies_lib.Melody))
self.assertTrue(isinstance(melodies[1], melodies_lib.Melody))
melodies = sorted([list(melody) for melody in melodies])
self.assertEqual(expected, melodies)
def testExtractMelodiesPadEnd(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 127, 2, 4), (14, 50, 6, 7)])
testing_lib.add_track_to_sequence(
self.note_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8)])
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,
NOTE_OFF],
[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, NOTE_OFF, NO_EVENT, NO_EVENT]]
melodies, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, 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 testExtractLeadSheetFragments(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(12, 100, .5, 1), (11, 1, 1.5, 2.75)])
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)])
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_sheets_lib.extract_lead_sheet_fragments(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True, require_chords=True)
melodies, _ = melodies_lib.extract_melodies(
quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.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 extract_lead_sheet_fragments(quantized_sequence,
min_bars=7,
gap_bars=1.0,
min_unique_pitches=5,
ignore_polyphonic_notes=True,
require_chords=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.
min_bars: Minimum length of melodies in number of bars. Shorter 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.
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.
Returns:
A python list of LeadSheet instances.
Raises:
NonIntegerStepsPerBarException: If `quantized_sequence`'s bar length
(derived from its time signature) is not an integer number of time
steps.
"""
sequences_lib.assert_is_quantized_sequence(quantized_sequence)
stats = dict([('empty_chord_progressions',
statistics.Counter('empty_chord_progressions'))])
melodies, melody_stats = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=min_bars,
gap_bars=gap_bars,
min_unique_pitches=min_unique_pitches,
ignore_polyphonic_notes=ignore_polyphonic_notes)
chord_progressions, chord_stats = chords_lib.extract_chords_for_melodies(
quantized_sequence, melodies)
lead_sheets = []
for melody, chords in zip(melodies, chord_progressions):
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)
lead_sheets.append(lead_sheet)
return lead_sheets, stats.values() + melody_stats + chord_stats
def extract_lead_sheet_fragments(quantized_sequence,
min_bars=7,
gap_bars=1.0,
min_unique_pitches=5,
ignore_polyphonic_notes=True,
require_chords=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.
min_bars: Minimum length of melodies in number of bars. Shorter 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.
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.
Returns:
A python list of LeadSheet instances.
Raises:
NonIntegerStepsPerBarException: If `quantized_sequence`'s bar length
(derived from its time signature) is not an integer number of time
steps.
"""
sequences_lib.assert_is_quantized_sequence(quantized_sequence)
stats = dict([('empty_chord_progressions',
statistics.Counter('empty_chord_progressions'))])
melodies, melody_stats = melodies_lib.extract_melodies(
quantized_sequence, min_bars=min_bars, gap_bars=gap_bars,
min_unique_pitches=min_unique_pitches,
ignore_polyphonic_notes=ignore_polyphonic_notes)
chord_progressions, chord_stats = chords_lib.extract_chords_for_melodies(
quantized_sequence, melodies)
lead_sheets = []
for melody, chords in zip(melodies, chord_progressions):
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)
lead_sheets.append(lead_sheet)
return lead_sheets, stats.values() + melody_stats + chord_stats
def transform(self, quantized_sequence):
try:
melodies, stats = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=self.min_bars,
min_unique_pitches=self.min_unique_pitches,
gap_bars=self.gap_bars,
ignore_polyphonic_notes=self.ignore_polyphonic_notes)
except events_lib.NonIntegerStepsPerBarException as detail:
tf.logging.warning('Skipped sequence: %s', detail)
melodies = []
stats = [statistics.Counter('non_integer_steps_per_bar', 1)]
self._set_stats(stats)
return melodies
def transform(self, quantized_sequence):
try:
melodies, stats = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=self.min_bars,
min_unique_pitches=self.min_unique_pitches,
gap_bars=self.gap_bars,
ignore_polyphonic_notes=self.ignore_polyphonic_notes)
except melodies_lib.NonIntegerStepsPerBarException as detail:
tf.logging.warning('Skipped sequence: %s', detail)
melodies = []
stats = [statistics.Counter('non_integer_steps_per_bar', 1)]
self._set_stats(stats)
return melodies
def testExtractMelodiesMelodyTooShort(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 127, 2, 4), (14, 50, 6, 7)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 9)])
expected = [[NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14,
NO_EVENT, NO_EVENT]]
melodies, _ = melodies_lib.extract_melodies(
self.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 testExtractMelodiesLateStart(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 100, 102, 103), (13, 100, 104, 106)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 100, 100, 101), (13, 100, 102, 105)])
expected = [[NO_EVENT, NO_EVENT, 12, NOTE_OFF, 13, NO_EVENT],
[12, NOTE_OFF, 13, NO_EVENT, NO_EVENT]]
melodies, _ = melodies_lib.extract_melodies(
self.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 testExtractMelodiesMelodyTooLongWithPad(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 127, 2, 4), (14, 50, 6, 15)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 18)])
expected = [[NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14,
NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT]]
melodies, _ = melodies_lib.extract_melodies(
self.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.
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 100, 0, 1), (13, 100, 1, 2), (18, 100, 2, 3),
(24, 100, 3, 4), (25, 100, 4, 5)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 100, 0, 1), (13, 100, 1, 2), (18, 100, 2, 3),
(25, 100, 3, 4), (26, 100, 4, 5)])
expected = [[12, 13, 18, 25, 26]]
melodies, _ = melodies_lib.extract_melodies(
self.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 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, _ = melodies_lib.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 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, _ = melodies_lib.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 testExtractMultipleMelodiesFromSameTrack(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8),
(50, 100, 33, 37), (52, 100, 34, 37)])
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, _ = melodies_lib.extract_melodies(
self.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 transform(self, quantized_sequence):
try:
melodies, stats = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=self._min_bars,
max_steps_truncate=self._max_steps,
min_unique_pitches=self._min_unique_pitches,
gap_bars=self._gap_bars,
ignore_polyphonic_notes=self._ignore_polyphonic_notes,
filter_drums=self._filter_drums)
except events_lib.NonIntegerStepsPerBarException as detail:
tf.logging.warning('Skipped sequence: %s', detail)
melodies = []
stats = [statistics.Counter('non_integer_steps_per_bar', 1)]
except events_lib.ZeroDivisionError as detail:
tf.logging.warning('Skipped sequence: %s', detail)
melodies = []
stats = [statistics.Counter('zero_division_error', 1)]
self._set_stats(stats)
return melodies
def testExtractMelodiesMelodyTooLongWithPad(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track(self.quantized_sequence, 0,
[(12, 127, 2, 4), (14, 50, 6, 15)])
testing_lib.add_quantized_track(self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 18)])
expected = [[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT,
NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT
]]
melodies, _ = melodies_lib.extract_melodies(
self.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 testExtractChordsForMelodies(self):
self.quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 11)])
testing_lib.add_quantized_track_to_sequence(
self.quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8),
(50, 100, 33, 37), (52, 100, 34, 37)])
testing_lib.add_quantized_chords_to_sequence(
self.quantized_sequence,
[('C', 2), ('G7', 6), ('Cmaj7', 33)])
melodies, _ = melodies_lib.extract_melodies(
self.quantized_sequence, min_bars=1, gap_bars=2, min_unique_pitches=2,
ignore_polyphonic_notes=True)
chord_progressions, _ = chords_lib.extract_chords_for_melodies(
self.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 _generate(self, generate_sequence_request):
if len(generate_sequence_request.generator_options.generate_sections
) != 1:
raise sequence_generator.SequenceGeneratorException(
'This model supports only 1 generate_sections message, but got %s'
% (len(generate_sequence_request.generator_options.
generate_sections)))
generate_section = (
generate_sequence_request.generator_options.generate_sections[0])
primer_sequence = generate_sequence_request.input_sequence
notes_by_end_time = sorted(primer_sequence.notes,
key=lambda n: n.end_time)
last_end_time = notes_by_end_time[
-1].end_time if notes_by_end_time else 0
if last_end_time > generate_section.start_time_seconds:
raise sequence_generator.SequenceGeneratorException(
'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_seconds,
notes_by_end_time[-1].end_time))
# Quantize the priming sequence.
quantized_sequence = sequences_lib.QuantizedSequence()
quantized_sequence.from_note_sequence(primer_sequence,
self._steps_per_quarter)
# Setting gap_bars to infinite ensures that the entire input will be used.
extracted_melodies, _ = melodies_lib.extract_melodies(
quantized_sequence,
min_bars=0,
min_unique_pitches=1,
gap_bars=float('inf'),
ignore_polyphonic_notes=True)
assert len(extracted_melodies) <= 1
qpm = (primer_sequence.tempos[0].qpm
if primer_sequence and primer_sequence.tempos else
constants.DEFAULT_QUARTERS_PER_MINUTE)
start_step = self._seconds_to_steps(
generate_section.start_time_seconds, qpm)
end_step = self._seconds_to_steps(generate_section.end_time_seconds,
qpm)
if extracted_melodies and extracted_melodies[0]:
melody = extracted_melodies[0]
else:
tf.logging.warn(
'No melodies were extracted from the priming sequence. '
'Melodies will be generated from scratch.')
melody = melodies_lib.MonophonicMelody()
melody.from_event_list([
random.randint(self._melody_encoder_decoder.min_note,
self._melody_encoder_decoder.max_note)
])
start_step += 1
transpose_amount = melody.squash(
self._melody_encoder_decoder.min_note,
self._melody_encoder_decoder.max_note,
self._melody_encoder_decoder.transpose_to_key)
# Ensure that the melody extends up to the step we want to start generating.
melody.set_length(start_step)
inputs = self._session.graph.get_collection('inputs')[0]
initial_state = self._session.graph.get_collection('initial_state')[0]
final_state = self._session.graph.get_collection('final_state')[0]
softmax = self._session.graph.get_collection('softmax')[0]
final_state_ = None
for i in range(end_step - len(melody)):
if i == 0:
inputs_ = self._melody_encoder_decoder.get_inputs_batch(
[melody], full_length=True)
initial_state_ = self._session.run(initial_state)
else:
inputs_ = self._melody_encoder_decoder.get_inputs_batch(
[melody])
initial_state_ = final_state_
feed_dict = {inputs: inputs_, initial_state: initial_state_}
final_state_, softmax_ = self._session.run([final_state, softmax],
feed_dict)
self._melody_encoder_decoder.extend_event_sequences([melody],
softmax_)
melody.transpose(-transpose_amount)
generate_response = generator_pb2.GenerateSequenceResponse()
generate_response.generated_sequence.CopyFrom(
melody.to_sequence(qpm=qpm))
return generate_response
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:
NonIntegerStepsPerBarException: 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 = melodies_lib.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 = chords_lib.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 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:
NonIntegerStepsPerBarException: If `quantized_sequence`'s bar length
(derived from its time signature) is not an integer number of time
steps.
"""
sequences_lib.assert_is_quantized_sequence(quantized_sequence)
stats = dict([('empty_chord_progressions',
statistics.Counter('empty_chord_progressions'))])
melodies, melody_stats = melodies_lib.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 = chords_lib.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, stats.values() + melody_stats + chord_stats
