def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An ImprovRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
all_transpositions = config.transpose_to_key is None
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence,
['eval_lead_sheets', 'training_lead_sheets'],
[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)
lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
name='LeadSheetExtractor_' + mode)
encoder_pipeline = EncoderPipeline(config, name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_lead_sheets']
dag[quantizer] = time_change_splitter
dag[lead_sheet_extractor] = quantizer
dag[encoder_pipeline] = lead_sheet_extractor
dag[dag_pipeline.DagOutput(mode + '_lead_sheets')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A DrumsRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence,
['eval_drum_tracks', 'training_drum_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)
drums_extractor = drum_pipelines.DrumsExtractor(
min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractor_' + mode)
encoder_pipeline = event_sequence_pipeline.EncoderPipeline(
magenta.music.DrumTrack, config.encoder_decoder,
name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_drum_tracks']
dag[quantizer] = time_change_splitter
dag[drums_extractor] = quantizer
dag[encoder_pipeline] = drums_extractor
dag[dag_pipeline.DagOutput(mode + '_drum_tracks')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
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(melody_encoder_decoder):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
melody_encoder_decoder: A melodies_lib.MelodyEncoderDecoder object.
Returns:
A pipeline.Pipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_beat=4)
melody_extractor = pipelines_common.MonophonicMelodyExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
encoder_pipeline = EncoderPipeline(melody_encoder_decoder)
partitioner = pipelines_common.RandomPartition(
tf.train.SequenceExample, ['eval_melodies', 'training_melodies'],
[FLAGS.eval_ratio])
dag = {
quantizer: dag_pipeline.Input(music_pb2.NoteSequence),
melody_extractor: quantizer,
encoder_pipeline: melody_extractor,
partitioner: encoder_pipeline,
dag_pipeline.Output(): partitioner
}
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A MelodyRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
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)
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[quantizer] = time_change_splitter
dag[melody_extractor] = quantizer
dag[encoder_pipeline] = melody_extractor
dag[dag_pipeline.DagOutput(mode + '_melodies')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A DrumsRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
drums_extractor_train = drum_pipelines.DrumsExtractor(
min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractorTrain')
drums_extractor_eval = drum_pipelines.DrumsExtractor(
min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractorEval')
encoder_pipeline_train = EncoderPipeline(config,
name='EncoderPipelineTrain')
encoder_pipeline_eval = EncoderPipeline(config, name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_drum_tracks', 'training_drum_tracks'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.Input(music_pb2.NoteSequence),
partitioner: quantizer,
drums_extractor_train: partitioner['training_drum_tracks'],
drums_extractor_eval: partitioner['eval_drum_tracks'],
encoder_pipeline_train: drums_extractor_train,
encoder_pipeline_eval: drums_extractor_eval,
dag_pipeline.Output('training_drum_tracks'): encoder_pipeline_train,
dag_pipeline.Output('eval_drum_tracks'): encoder_pipeline_eval
}
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: A MelodyRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
A id_pipeline.IDPipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
melody_extractor = melody_pipelines.MelodyExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
id_pipeline = pipelines_common.IDPipeline()
encoder_pipeline = EncoderPipeline(config)
partitioner = pipelines_common.RandomPartition(
tf.train.SequenceExample, ['eval_melodies', 'training_melodies'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
melody_extractor: quantizer,
id_pipeline: melody_extractor,
encoder_pipeline: id_pipeline,
partitioner: encoder_pipeline,
dag_pipeline.DagOutput(): partitioner
}
return dag_pipeline.DAGPipeline(dag), id_pipeline
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, steps_per_quarter, min_steps, max_steps, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An EventSequenceRnnConfig.
steps_per_quarter: How many steps per quarter to use when quantizing.
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.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=steps_per_quarter)
# 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)
transposition_pipeline_train = sequences_lib.TranspositionPipeline(
transposition_range, name='TranspositionPipelineTrain')
transposition_pipeline_eval = sequences_lib.TranspositionPipeline(
transposition_range, name='TranspositionPipelineEval')
poly_extractor_train = PolyphonicSequenceExtractor(
min_steps=min_steps, max_steps=max_steps, name='PolyExtractorTrain')
poly_extractor_eval = PolyphonicSequenceExtractor(min_steps=min_steps,
max_steps=max_steps,
name='PolyExtractorEval')
encoder_pipeline_train = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence,
config.encoder_decoder,
name='EncoderPipelineTrain')
encoder_pipeline_eval = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence,
config.encoder_decoder,
name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
partitioner: quantizer,
transposition_pipeline_train: partitioner['training_poly_tracks'],
transposition_pipeline_eval: partitioner['eval_poly_tracks'],
poly_extractor_train: transposition_pipeline_train,
poly_extractor_eval: transposition_pipeline_eval,
encoder_pipeline_train: poly_extractor_train,
encoder_pipeline_eval: poly_extractor_eval,
dag_pipeline.DagOutput('training_poly_tracks'): encoder_pipeline_train,
dag_pipeline.DagOutput('eval_poly_tracks'): encoder_pipeline_eval
}
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 testRandomPartition(self):
random_partition = pipelines_common.RandomPartition(
str, ['a', 'b', 'c'], [0.1, 0.4])
random_nums = [0.55, 0.05, 0.34, 0.99]
choices = ['c', 'a', 'b', 'c']
random_partition.rand_func = functools.partial(six.next, iter(random_nums))
self.assertEqual(random_partition.input_type, str)
self.assertEqual(random_partition.output_type,
{'a': str, 'b': str, 'c': str})
for i, s in enumerate(['hello', 'qwerty', '1234567890', 'zxcvbnm']):
results = random_partition.transform(s)
self.assertTrue(isinstance(results, dict))
self.assertEqual(set(results.keys()), set(['a', 'b', 'c']))
self.assertEqual(len(results.values()), 3)
self.assertEqual(len([l for l in results.values() if l == []]), 2) # pylint: disable=g-explicit-bool-comparison
self.assertEqual(results[choices[i]], [s])
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, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An ImprovRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
all_transpositions = config.transpose_to_key is None
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
lead_sheet_extractor_train = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=3,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=all_transpositions,
name='LeadSheetExtractorTrain')
lead_sheet_extractor_eval = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=3,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=all_transpositions,
name='LeadSheetExtractorEval')
encoder_pipeline_train = EncoderPipeline(config,
name='EncoderPipelineTrain')
encoder_pipeline_eval = EncoderPipeline(config, name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_lead_sheets', 'training_lead_sheets'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
partitioner: quantizer,
lead_sheet_extractor_train: partitioner['training_lead_sheets'],
lead_sheet_extractor_eval: partitioner['eval_lead_sheets'],
encoder_pipeline_train: lead_sheet_extractor_train,
encoder_pipeline_eval: lead_sheet_extractor_eval,
dag_pipeline.DagOutput('training_lead_sheets'): encoder_pipeline_train,
dag_pipeline.DagOutput('eval_lead_sheets'): encoder_pipeline_eval
}
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, steps_per_quarter, min_steps, max_steps, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An EventSequenceRnnConfig.
steps_per_quarter: How many steps per quarter to use when quantizing.
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.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=steps_per_quarter)
poly_extractor_train = PolyphonicSequenceExtractor(
min_steps=min_steps, max_steps=max_steps, name='PolyExtractorTrain')
poly_extractor_eval = PolyphonicSequenceExtractor(
min_steps=min_steps, max_steps=max_steps, name='PolyExtractorEval')
encoder_pipeline_train = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence, config.encoder_decoder,
name='EncoderPipelineTrain')
encoder_pipeline_eval = encoder_decoder.EncoderPipeline(
polyphony_lib.PolyphonicSequence, config.encoder_decoder,
name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence,
['eval_poly_tracks', 'training_poly_tracks'],
[eval_ratio])
dag = {quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
partitioner: quantizer,
poly_extractor_train: partitioner['training_poly_tracks'],
poly_extractor_eval: partitioner['eval_poly_tracks'],
encoder_pipeline_train: poly_extractor_train,
encoder_pipeline_eval: poly_extractor_eval,
dag_pipeline.DagOutput('training_poly_tracks'): encoder_pipeline_train,
dag_pipeline.DagOutput('eval_poly_tracks'): encoder_pipeline_eval}
return dag_pipeline.DAGPipeline(dag)