def testToNoteSequence(self):
converter = data.TrioConverter(steps_per_quarter=1,
slice_bars=2,
max_tensors_per_notesequence=1)
mel_oh = data.np_onehot(self.expected_sliced_labels[3][0], 90)
bass_oh = data.np_onehot(self.expected_sliced_labels[3][1], 90)
drums_oh = data.np_onehot(self.expected_sliced_labels[3][2], 512)
output_tensors = np.concatenate([mel_oh, bass_oh, drums_oh], axis=-1)
sequences = converter.to_notesequences([output_tensors])
self.assertEqual(1, len(sequences))
self.assertProtoEquals(
"""
ticks_per_quarter: 220
tempos < qpm: 120 >
notes <
instrument: 0 pitch: 52 start_time: 2.0 end_time: 4.0 program: 0
velocity: 80
>
notes <
instrument: 1 pitch: 50 start_time: 1.0 end_time: 2.5 program: 33
velocity: 80
>
notes <
instrument: 9 pitch: 36 start_time: 0.0 end_time: 0.5 velocity: 80
is_drum: True
>
notes <
instrument: 9 pitch: 38 start_time: 2.0 end_time: 2.5 velocity: 80
is_drum: True
>
total_time: 4.0
""", sequences[0])
def testSliced(self):
converter = data.TrioConverter(
steps_per_quarter=1, gap_bars=1, slice_bars=2,
max_tensors_per_notesequence=None)
in_tensors, out_tensors = converter.to_tensors(self.sequence)
self.assertArraySetsEqual(in_tensors, out_tensors)
actual_sliced_labels = [
np.stack(np.argmax(s, axis=-1) for s in np.split(t, [90, 180], axis=-1))
for t in out_tensors]
self.assertArraySetsEqual(self.expected_sliced_labels, actual_sliced_labels)
def testSlicedChordConditioned(self):
converter = data.TrioConverter(
steps_per_quarter=1, gap_bars=1, slice_bars=2,
max_tensors_per_notesequence=None,
chord_encoding=mm.MajorMinorChordOneHotEncoding())
tensors = converter.to_tensors(self.sequence)
self.assertArraySetsEqual(tensors.inputs, tensors.outputs)
actual_sliced_labels = [
np.stack(np.argmax(s, axis=-1) for s in np.split(t, [90, 180], axis=-1))
for t in tensors.outputs]
actual_sliced_chord_labels = [
np.argmax(t, axis=-1) for t in tensors.controls]
self.assertArraySetsEqual(self.expected_sliced_labels, actual_sliced_labels)
self.assertArraySetsEqual(
self.expected_sliced_chord_labels, actual_sliced_chord_labels)
def testToNoteSequenceChordConditioned(self):
converter = data.TrioConverter(
steps_per_quarter=1,
slice_bars=2,
max_tensors_per_notesequence=1,
chord_encoding=mm.MajorMinorChordOneHotEncoding())
mel_oh = data.np_onehot(self.expected_sliced_labels[3][0], 90)
bass_oh = data.np_onehot(self.expected_sliced_labels[3][1], 90)
drums_oh = data.np_onehot(self.expected_sliced_labels[3][2], 512)
chords_oh = data.np_onehot(self.expected_sliced_chord_labels[3], 25)
output_tensors = np.concatenate([mel_oh, bass_oh, drums_oh], axis=-1)
sequences = converter.to_notesequences([output_tensors], [chords_oh])
self.assertEqual(1, len(sequences))
self.assertProtoEquals(
"""
ticks_per_quarter: 220
tempos < qpm: 120 >
notes <
instrument: 0 pitch: 52 start_time: 2.0 end_time: 4.0 program: 0
velocity: 80
>
notes <
instrument: 1 pitch: 50 start_time: 1.0 end_time: 2.5 program: 33
velocity: 80
>
notes <
instrument: 9 pitch: 36 start_time: 0.0 end_time: 0.5 velocity: 80
is_drum: True
>
notes <
instrument: 9 pitch: 38 start_time: 2.0 end_time: 2.5 velocity: 80
is_drum: True
>
text_annotations <
text: 'N.C.' annotation_type: CHORD_SYMBOL
>
text_annotations <
time: 2.0 text: 'C' annotation_type: CHORD_SYMBOL
>
total_time: 4.0
""", sequences[0])
)),
note_sequence_augmenter=None,
data_converter=data.DrumsConverter(
max_bars=100, # Truncate long drum sequences before slicing.
pitch_classes=data.FULL_DRUM_PITCH_CLASSES,
slice_bars=2,
steps_per_quarter=4,
roll_input=True,
roll_output=True),
train_examples_path=None,
eval_examples_path=None,
)
# Trio Models
trio_16bar_converter = data.TrioConverter(steps_per_quarter=4,
slice_bars=16,
gap_bars=2)
CONFIG_MAP['flat-trio_16bar'] = Config(
model=MusicVAE(
lstm_models.BidirectionalLstmEncoder(),
lstm_models.MultiOutCategoricalLstmDecoder(output_depths=[
90, # melody
90, # bass
512, # drums
])),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=256,
max_seq_len=256,
90, # melody
90, # bass
512, # drums
])),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=256,
max_seq_len=256,
z_size=512,
enc_rnn_size=[2048, 2048],
dec_rnn_size=[2048, 2048, 2048],
)),
note_sequence_augmenter=None,
note_sequence_converter=data.TrioConverter(steps_per_quarter=4,
slice_bars=16,
gap_bars=2),
train_examples_path=None,
eval_examples_path=None,
)
config_map['hiercat-trio_16bar_big'] = Config(
model=MusicVAE(
lstm_models.BidirectionalLstmEncoder(),
lstm_models.HierarchicalMultiOutLstmDecoder(
core_decoders=[
lstm_models.CategoricalLstmDecoder(),
lstm_models.CategoricalLstmDecoder(),
lstm_models.CategoricalLstmDecoder()
],
output_depths=[