def testMelodyRNNPipeline(self):
note_sequence = magenta.common.testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
magenta.music.testing_lib.add_track_to_sequence(
note_sequence, 0, [(12, 100, 0.00, 2.0), (11, 55, 2.1, 5.0),
(40, 45, 5.1, 8.0), (55, 120, 8.1, 11.0),
(53, 99, 11.1, 14.1)])
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
melody_extractor = melody_pipelines.MelodyExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MelodyOneHotEncoding(self.config.min_note,
self.config.max_note))
quantized = quantizer.transform(note_sequence)[0]
melody = melody_extractor.transform(quantized)[0]
melody.squash(self.config.min_note, self.config.max_note,
self.config.transpose_to_key)
one_hot = pipelines_common.make_sequence_example(
*one_hot_encoding.encode(melody))
expected_result = {'training_melodies': [one_hot], 'eval_melodies': []}
pipeline_inst = melody_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testDrumsRNNPipeline(self):
note_sequence = magenta.common.testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
magenta.music.testing_lib.add_track_to_sequence(
note_sequence,
0, [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0),
(41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0),
(39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1),
(51, 40, 12.6, 13.0), (55, 100, 14.1, 15.0),
(54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)],
is_drum=True)
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7,
gap_bars=1.0)
one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MultiDrumOneHotEncoding())
quantized = quantizer.transform(note_sequence)[0]
drums = drums_extractor.transform(quantized)[0]
one_hot = pipelines_common.make_sequence_example(
*one_hot_encoding.encode(drums))
expected_result = {
'training_drum_tracks': [one_hot],
'eval_drum_tracks': []
}
pipeline_inst = drums_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def transform(self, input_object):
performance = input_object
if self._control_signals:
# Encode conditional on control signals.
control_sequences = []
for control in self._control_signals:
control_sequences.append(control.extract(performance))
control_sequence = list(zip(*control_sequences))
if self._optional_conditioning:
# Create two copies, one with and one without conditioning.
# pylint: disable=g-complex-comprehension
encoded = [
self._encoder_decoder.encode(
list(
zip([disable] * len(control_sequence),
control_sequence)), performance)
for disable in [False, True]
]
# pylint: enable=g-complex-comprehension
else:
encoded = [
self._encoder_decoder.encode(control_sequence, performance)
]
else:
# Encode unconditional.
encoded = [self._encoder_decoder.encode(performance)]
return [
pipelines_common.make_sequence_example(*enc) for enc in encoded
]
def transform(self, input_object):
melody = input_object
melody.squash(
self._min_note,
self._max_note,
self._transpose_to_key)
encoded = pipelines_common.make_sequence_example(
*self._melody_encoder_decoder.encode(melody))
return [encoded]
def testMelodyRNNPipeline(self):
note_sequence = magenta.common.testing_lib.parse_test_proto(
note_seq.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
note_seq.testing_lib.add_track_to_sequence(note_sequence, 0,
[(12, 100, 0.00, 2.0),
(11, 55, 2.1, 5.0),
(40, 45, 5.1, 8.0),
(55, 120, 8.1, 11.0),
(53, 99, 11.1, 14.1)])
note_seq.testing_lib.add_chords_to_sequence(note_sequence,
[('N.C.', 0.0),
('Am9', 5.0),
('D7', 10.0)])
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=False)
conditional_encoding = note_seq.ConditionalEventSequenceEncoderDecoder(
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MajorMinorChordOneHotEncoding()),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MelodyOneHotEncoding(self.config.min_note,
self.config.max_note)))
quantized = quantizer.transform(note_sequence)[0]
lead_sheet = lead_sheet_extractor.transform(quantized)[0]
lead_sheet.squash(self.config.min_note, self.config.max_note,
self.config.transpose_to_key)
encoded = pipelines_common.make_sequence_example(
*conditional_encoding.encode(lead_sheet.chords, lead_sheet.melody))
expected_result = {
'training_lead_sheets': [encoded],
'eval_lead_sheets': []
}
pipeline_inst = improv_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def transform(self, lead_sheet):
lead_sheet.squash(self._min_note, self._max_note,
self._transpose_to_key)
try:
encoded = [
self._conditional_encoder_decoder.encode(
lead_sheet.chords, lead_sheet.melody)
]
stats = []
except note_seq.ChordEncodingError as e:
tf.logging.warning('Skipped lead sheet: %s', e)
encoded = []
stats = [statistics.Counter('chord_encoding_exception', 1)]
except note_seq.ChordSymbolError as e:
tf.logging.warning('Skipped lead sheet: %s', e)
encoded = []
stats = [statistics.Counter('chord_symbol_exception', 1)]
self._set_stats(stats)
return [
pipelines_common.make_sequence_example(*enc) for enc in encoded
]
def transform(self, input_object):
seq = input_object
encoded = pipelines_common.make_sequence_example(
*self._encoder_decoder.encode(seq))
return [encoded]
def transform(self, seq):
encoded = pipelines_common.make_sequence_example(
*self._encoder_decoder.encode(seq))
return [encoded]
