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_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)
