def testTranspositionPipelineIgnoreOutOfRangeNotes(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
tp = note_sequence_pipelines.TranspositionPipeline(
range(-1, 2), ignore_out_of_range_notes=True, min_pitch=0, max_pitch=12)
testing_lib.add_track_to_sequence(
note_sequence, 0,
[(10, 100, 1.0, 2.0), (12, 100, 2.0, 4.0), (13, 100, 4.0, 5.0)])
transposed = tp.transform(note_sequence)
self.assertEqual(3, len(transposed))
self.assertEqual(3, len(transposed[0].notes))
self.assertEqual(2, len(transposed[1].notes))
self.assertEqual(1, len(transposed[2].notes))
self.assertEqual(9, transposed[0].notes[0].pitch)
self.assertEqual(10, transposed[1].notes[0].pitch)
self.assertEqual(11, transposed[2].notes[0].pitch)
self.assertEqual(11, transposed[0].notes[1].pitch)
self.assertEqual(12, transposed[1].notes[1].pitch)
self.assertEqual(12, transposed[0].notes[2].pitch)
def testTranspositionPipeline(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
tp = note_sequence_pipelines.TranspositionPipeline(range(0, 2))
testing_lib.add_track_to_sequence(
note_sequence, 0,
[(12, 100, 1.0, 4.0)])
testing_lib.add_track_to_sequence(
note_sequence, 1,
[(36, 100, 2.0, 2.01)],
is_drum=True)
transposed = tp.transform(note_sequence)
self.assertEqual(2, len(transposed))
self.assertEqual(2, len(transposed[0].notes))
self.assertEqual(2, len(transposed[1].notes))
self.assertEqual(12, transposed[0].notes[0].pitch)
self.assertEqual(13, transposed[1].notes[0].pitch)
self.assertEqual(36, transposed[0].notes[1].pitch)
self.assertEqual(36, transposed[1].notes[1].pitch)
def get_pipeline(config, min_events, max_events, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A PerformanceRnnConfig.
min_events: Minimum number of events for an extracted sequence.
max_events: Maximum number of events for an extracted sequence.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
# Stretch by -5%, -2.5%, 0%, 2.5%, and 5%.
stretch_factors = [0.95, 0.975, 1.0, 1.025, 1.05]
# Transpose no more than a major third.
transposition_range = range(-3, 4)
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_performances', 'training_performances'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
sustain_pipeline = note_sequence_pipelines.SustainPipeline(
name='SustainPipeline_' + mode)
stretch_pipeline = note_sequence_pipelines.StretchPipeline(
stretch_factors, name='StretchPipeline_' + mode)
splitter = note_sequence_pipelines.Splitter(hop_size_seconds=30.0,
name='Splitter_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_second=config.steps_per_second, name='Quantizer_' + mode)
transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
transposition_range, name='TranspositionPipeline_' + mode)
perf_extractor = PerformanceExtractor(
min_events=min_events,
max_events=max_events,
num_velocity_bins=config.num_velocity_bins,
name='PerformanceExtractor_' + mode)
encoder_pipeline = EncoderPipeline(config,
name='EncoderPipeline_' + mode)
dag[sustain_pipeline] = partitioner[mode + '_performances']
if mode == 'eval':
# No stretching in eval.
dag[splitter] = sustain_pipeline
else:
dag[stretch_pipeline] = sustain_pipeline
dag[splitter] = stretch_pipeline
dag[quantizer] = splitter
if mode == 'eval':
# No transposition in eval.
dag[perf_extractor] = quantizer
else:
dag[transposition_pipeline] = quantizer
dag[perf_extractor] = transposition_pipeline
dag[encoder_pipeline] = perf_extractor
dag[dag_pipeline.DagOutput(mode + '_performances')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio=0.0):
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_melodies', 'training_melodies'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
repeat_sequence = RepeatSequence(min_duration=16,
name='RepeatSequence_' + mode)
transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
(0, ), name='TranspositionPipeline_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_quarter=config.steps_per_quarter,
name='Quantizer_' + mode)
melody_extractor = melody_pipelines.MelodyExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=True,
name='MelodyExtractor_' + mode)
encoder_pipeline = EncoderPipeline(config,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_melodies']
dag[repeat_sequence] = time_change_splitter
dag[quantizer] = repeat_sequence
dag[transposition_pipeline] = quantizer
dag[melody_extractor] = transposition_pipeline
dag[encoder_pipeline] = melody_extractor
dag[dag_pipeline.DagOutput(mode + '_melodies')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, min_steps, max_steps, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An EventSequenceRnnConfig.
min_steps: Minimum number of steps for an extracted sequence.
max_steps: Maximum number of steps for an extracted sequence.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
# Transpose up to a major third in either direction.
# Because our current dataset is Bach chorales, transposing more than a major
# third in either direction probably doesn't makes sense (e.g., because it is
# likely to exceed normal singing range).
transposition_range = range(-4, 5)
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_quarter=config.steps_per_quarter,
name='Quantizer_' + mode)
transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
transposition_range, name='TranspositionPipeline_' + mode)
poly_extractor = PolyphonicSequenceExtractor(min_steps=min_steps,
max_steps=max_steps,
name='PolyExtractor_' +
mode)
encoder_pipeline = event_sequence_pipeline.EncoderPipeline(
polyphony_lib.PolyphonicSequence,
config.encoder_decoder,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_poly_tracks']
dag[quantizer] = time_change_splitter
dag[transposition_pipeline] = quantizer
dag[poly_extractor] = transposition_pipeline
dag[encoder_pipeline] = poly_extractor
dag[dag_pipeline.DagOutput(mode + '_poly_tracks')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, min_steps, max_steps, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An EventSequenceRnnConfig.
min_steps: Minimum number of steps for an extracted sequence.
max_steps: Maximum number of steps for an extracted sequence.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
# Transpose up to a major third in either direction.
transposition_range = list(range(-4, 5))
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence,
['eval_pianoroll_tracks', 'training_pianoroll_tracks'], [eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_quarter=config.steps_per_quarter,
name='Quantizer_' + mode)
transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
transposition_range, name='TranspositionPipeline_' + mode)
pianoroll_extractor = PianorollSequenceExtractor(
min_steps=min_steps,
max_steps=max_steps,
name='PianorollExtractor_' + mode)
encoder_pipeline = event_sequence_pipeline.EncoderPipeline(
mm.PianorollSequence,
config.encoder_decoder,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_pianoroll_tracks']
dag[quantizer] = time_change_splitter
dag[transposition_pipeline] = quantizer
dag[pianoroll_extractor] = transposition_pipeline
dag[encoder_pipeline] = pianoroll_extractor
dag[dag_pipeline.DagOutput(mode +
'_pianoroll_tracks')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, transposition_range=(0, ), eval_ratio=0.0):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A MelodyRnnConfig object.
transposition_range: Collection of integer pitch steps to transpose.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
partitioner = pipelines_common.RandomPartition(
note_seq.NoteSequence, ['eval_melodies', 'training_melodies'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(note_seq.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
transposition_range, name='TranspositionPipeline_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_quarter=config.steps_per_quarter,
name='Quantizer_' + mode)
melody_extractor = melody_pipelines.MelodyExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False,
name='MelodyExtractor_' + mode)
encoder_pipeline = EncoderPipeline(config,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_melodies']
dag[quantizer] = time_change_splitter
dag[transposition_pipeline] = quantizer
dag[melody_extractor] = transposition_pipeline
dag[encoder_pipeline] = melody_extractor
dag[dag_pipeline.DagOutput(mode + '_melodies')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)