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 testStretchPipeline(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
time: 1.0
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
note_sequence, 0,
[(11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50), (55, 120, 4.0, 4.01)])
expected_sequences = [
sequences_lib.stretch_note_sequence(note_sequence, 0.5),
sequences_lib.stretch_note_sequence(note_sequence, 1.0),
sequences_lib.stretch_note_sequence(note_sequence, 1.5)]
unit = note_sequence_pipelines.StretchPipeline(
stretch_factors=[0.5, 1.0, 1.5])
self._unit_transform_test(unit, note_sequence, expected_sequences)
