def generate_data(self, data_dir, tmp_dir, task_id=-1):
del task_id
from magenta.models.score2perf import datagen_beam # pylint: disable=g-import-not-at-top,import-outside-toplevel
def augment_note_sequence(ns, stretch_factor, transpose_amount):
"""Augment a NoteSequence by time stretch and pitch transposition."""
augmented_ns = sequences_lib.stretch_note_sequence(ns,
stretch_factor,
in_place=False)
try:
_, num_deleted_notes = sequences_lib.transpose_note_sequence(
augmented_ns,
transpose_amount,
min_allowed_pitch=MIN_PITCH,
max_allowed_pitch=MAX_PITCH,
in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError(
'Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError(
'Transposition caused out-of-range pitch(es).')
return augmented_ns
augment_params = itertools.product(self.stretch_factors,
self.transpose_amounts)
augment_fns = [
functools.partial(augment_note_sequence,
stretch_factor=s,
transpose_amount=t) for s, t in augment_params
]
datagen_beam.generate_examples(
input_transform=self.performances_input_transform(tmp_dir),
output_dir=data_dir,
problem_name=self.dataset_filename(),
splits=self.splits,
min_hop_size_seconds=self.min_hop_size_seconds,
max_hop_size_seconds=self.max_hop_size_seconds,
min_pitch=MIN_PITCH,
max_pitch=MAX_PITCH,
num_replications=self.num_replications,
encode_performance_fn=self.performance_encoder(
).encode_note_sequence,
encode_score_fns=dict((name, encoder.encode_note_sequence)
for name, encoder in self.score_encoders()),
augment_fns=augment_fns,
absolute_timing=self.absolute_timing,
random_crop_length=self.random_crop_length_in_datagen)
def generate_data(self, data_dir, tmp_dir, task_id=-1):
del task_id
def augment_note_sequence(ns, stretch_factor, transpose_amount):
"""Augment a NoteSequence by time stretch and pitch transposition."""
augmented_ns = sequences_lib.stretch_note_sequence(
ns, stretch_factor, in_place=False)
try:
_, num_deleted_notes = sequences_lib.transpose_note_sequence(
augmented_ns, transpose_amount,
min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH,
in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError(
'Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError(
'Transposition caused out-of-range pitch(es).')
return augmented_ns
augment_fns = [
functools.partial(
augment_note_sequence,
stretch_factor=stretch_factor,
transpose_amount=transpose_amount)
for stretch_factor, transpose_amount in itertools.product(
self.stretch_factors, self.transpose_amounts)
]
datagen_beam.generate_examples(
input_transform=self.performances_input_transform(tmp_dir),
output_dir=data_dir,
problem_name=self.dataset_filename(),
splits=self.splits,
min_hop_size_seconds=self.min_hop_size_seconds,
max_hop_size_seconds=self.max_hop_size_seconds,
min_pitch=MIN_PITCH,
max_pitch=MAX_PITCH,
num_replications=self.num_replications,
encode_performance_fn=self.performance_encoder().encode_note_sequence,
encode_score_fns=dict((name, encoder.encode_note_sequence)
for name, encoder in self.score_encoders()),
augment_fns=augment_fns,
absolute_timing=self.absolute_timing)
def testGenerateExamples(self):
ns = music_pb2.NoteSequence()
testing_lib.add_track_to_sequence(
ns, 0, [(60, 100, 0.0, 1.0), (64, 100, 1.0, 2.0), (67, 127, 2.0, 3.0)])
input_transform = beam.transforms.Create([('0', ns.SerializeToString())])
output_dir = tempfile.mkdtemp()
encoder = music_encoders.MidiPerformanceEncoder(
steps_per_second=100,
num_velocity_bins=32,
min_pitch=21,
max_pitch=108)
datagen_beam.generate_examples(
input_transform=input_transform,
output_dir=output_dir,
problem_name='test_problem',
splits={'train': 1.0},
min_hop_size_seconds=3.0,
max_hop_size_seconds=3.0,
min_pitch=21,
max_pitch=108,
num_replications=1,
encode_performance_fn=encoder.encode_note_sequence)
def testGenerateExamples(self):
ns = music_pb2.NoteSequence()
testing_lib.add_track_to_sequence(
ns, 0, [(60, 100, 0.0, 1.0), (64, 100, 1.0, 2.0), (67, 127, 2.0, 3.0)])
input_transform = beam.transforms.Create([('0', ns.SerializeToString())])
output_dir = tempfile.mkdtemp()
encoder = music_encoders.MidiPerformanceEncoder(
steps_per_second=100,
num_velocity_bins=32,
min_pitch=21,
max_pitch=108)
datagen_beam.generate_examples(
input_transform=input_transform,
output_dir=output_dir,
problem_name='test_problem',
splits={'train': 1.0},
min_hop_size_seconds=3.0,
max_hop_size_seconds=3.0,
min_pitch=21,
max_pitch=108,
num_replications=1,
encode_performance_fn=encoder.encode_note_sequence)
