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 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 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 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 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 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 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
