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, 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 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=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 testTimeChangeSplitter(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
time: 2.0
numerator: 3
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
note_sequence, 0,
[(12, 100, 0.01, 10.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)])
expected_sequences = sequences_lib.split_note_sequence_on_time_changes(
note_sequence)
unit = note_sequence_pipelines.TimeChangeSplitter()
self._unit_transform_test(unit, note_sequence, expected_sequences)