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, 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 = pipelines_common.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
quantizer = pipelines_common.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 = encoder_decoder.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 = encoder_decoder.EncoderPipeline(
performance_lib.Performance, config.encoder_decoder,
name='EncoderPipeline_' + mode)
dag[sustain_pipeline] = partitioner[mode + '_performances']
dag[stretch_pipeline] = sustain_pipeline
dag[splitter] = stretch_pipeline
dag[quantizer] = splitter
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, 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)
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 = encoder_decoder.EncoderPipeline(
magenta.music.DrumTrack,
config.encoder_decoder,
name='EncoderPipelineTrain')
encoder_pipeline_eval = encoder_decoder.EncoderPipeline(
magenta.music.DrumTrack,
config.encoder_decoder,
name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_drum_tracks', 'training_drum_tracks'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.DagInput(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.DagOutput('training_drum_tracks'): encoder_pipeline_train,
dag_pipeline.DagOutput('eval_drum_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 = encoder_decoder.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)
