def __init__(self, checkpoint=None, bundle=None):
details = generator_pb2.GeneratorDetails(id='test_generator',
description='Test Generator')
super(SeuenceGenerator, self).__init__(Model(),
details,
checkpoint=checkpoint,
bundle=bundle)
def testUseMatchingGeneratorId(self):
bundle = generator_pb2.GeneratorBundle(
generator_details=generator_pb2.GeneratorDetails(
id='test_generator'),
checkpoint_file=[b'foo.ckpt'],
metagraph_file=b'foo.ckpt.meta')
SeuenceGenerator(bundle=bundle)
bundle.generator_details.id = 'blarg'
with self.assertRaises(sequence_generator.SequenceGeneratorError):
SeuenceGenerator(bundle=bundle)
def testSpecifyEitherCheckPointOrBundle(self):
bundle = generator_pb2.GeneratorBundle(
generator_details=generator_pb2.GeneratorDetails(
id='test_generator'),
checkpoint_file=[b'foo.ckpt'],
metagraph_file=b'foo.ckpt.meta')
with self.assertRaises(sequence_generator.SequenceGeneratorError):
SeuenceGenerator(checkpoint='foo.ckpt', bundle=bundle)
with self.assertRaises(sequence_generator.SequenceGeneratorError):
SeuenceGenerator(checkpoint=None, bundle=None)
SeuenceGenerator(checkpoint='foo.ckpt')
SeuenceGenerator(bundle=bundle)
def testGetBundleDetails(self):
# Test with non-bundle generator.
seq_gen = SeuenceGenerator(checkpoint='foo.ckpt')
self.assertEqual(None, seq_gen.bundle_details)
# Test with bundle-based generator.
bundle_details = generator_pb2.GeneratorBundle.BundleDetails(
description='bundle of joy')
bundle = generator_pb2.GeneratorBundle(
generator_details=generator_pb2.GeneratorDetails(
id='test_generator'),
bundle_details=bundle_details,
checkpoint_file=[b'foo.ckpt'],
metagraph_file=b'foo.ckpt.meta')
seq_gen = SeuenceGenerator(bundle=bundle)
self.assertEqual(bundle_details, seq_gen.bundle_details)
50, 30, 62, 76, 83], # high tom
# closed hi-hat
[42, 44, 54, 68, 69, 70, 71, 73, 78, 80, 22],
# open hi-hat
[46, 67, 72, 74, 79, 81, 26,
49, 52, 55, 57, 58, # crash
51, 53, 59, 82], # ride
]
# Default configurations.
default_configs = {
'one_drum':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(
id='one_drum', description='Drums RNN with 2-state encoding.'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MultiDrumOneHotEncoding(
[[39] + # use hand clap as default when decoding
list(range(note_seq.MIN_MIDI_PITCH, 39)) +
list(range(39, note_seq.MAX_MIDI_PITCH + 1))])),
contrib_training.HParams(
batch_size=128,
rnn_layer_sizes=[128, 128],
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001),
steps_per_quarter=2),
'drum_kit':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(
control_encoder = note_seq.OptionalEventSequenceEncoder(control_encoder)
encoder_decoder = note_seq.ConditionalEventSequenceEncoderDecoder(
control_encoder, encoder_decoder)
super(PerformanceRnnConfig, self).__init__(
details, encoder_decoder, hparams)
self.num_velocity_bins = num_velocity_bins
self.control_signals = control_signals
self.optional_conditioning = optional_conditioning
self.note_performance = note_performance
default_configs = {
'performance':
PerformanceRnnConfig(
generator_pb2.GeneratorDetails(
id='performance', description='Performance RNN'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.PerformanceOneHotEncoding()),
contrib_training.HParams(
batch_size=64,
rnn_layer_sizes=[512, 512, 512],
dropout_keep_prob=1.0,
clip_norm=3,
learning_rate=0.001)),
'performance_with_dynamics':
PerformanceRnnConfig(
generator_pb2.GeneratorDetails(
id='performance_with_dynamics',
description='Performance RNN with dynamics'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.PerformanceOneHotEncoding(num_velocity_bins=32)),
branch_factor,
steps_per_iteration,
modify_events_callback=modify_events_callback)
def polyphonic_sequence_log_likelihood(self, sequence):
"""Evaluate the log likelihood of a polyphonic sequence.
Args:
sequence: The PolyphonicSequence object for which to evaluate the log
likelihood.
Returns:
The log likelihood of `sequence` under this model.
"""
return self._evaluate_log_likelihood([sequence])[0]
default_configs = {
'polyphony':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(id='polyphony',
description='Polyphonic RNN'),
note_seq.OneHotEventSequenceEncoderDecoder(
polyphony_encoder_decoder.PolyphonyOneHotEncoding()),
contrib_training.HParams(batch_size=64,
rnn_layer_sizes=[256, 256, 256],
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001)),
}
"""Evaluate the log likelihood of a drum track under the model.
Args:
drums: The DrumTrack object for which to evaluate the log likelihood.
Returns:
The log likelihood of `drums` under this model.
"""
return self._evaluate_log_likelihood([drums])[0]
# Default configurations.
default_configs = {
'one_drum':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(
id='one_drum', description='Drums RNN with 2-state encoding.'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MultiDrumOneHotEncoding(
[[39] + # use hand clap as default when decoding
list(range(note_seq.MIN_MIDI_PITCH, 39)) +
list(range(39, note_seq.MAX_MIDI_PITCH + 1))])),
contrib_training.HParams(batch_size=128,
rnn_layer_sizes=[128, 128],
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001),
steps_per_quarter=2),
'drum_kit':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(
id='drum_kit',
Returns:
The generated PianorollSequence object (which begins with the provided
primer track).
"""
return self._generate_events(num_steps=num_steps,
primer_events=primer_sequence,
temperature=None,
beam_size=beam_size,
branch_factor=branch_factor,
steps_per_iteration=steps_per_iteration)
default_configs = {
'rnn-nade':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(id='rnn-nade', description='RNN-NADE'),
note_seq.PianorollEncoderDecoder(),
contrib_training.HParams(batch_size=64,
rnn_layer_sizes=[128, 128, 128],
nade_hidden_units=128,
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001)),
'rnn-nade_attn':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(id='rnn-nade_attn',
description='RNN-NADE with attention.'),
note_seq.PianorollEncoderDecoder(),
contrib_training.HParams(batch_size=48,
rnn_layer_sizes=[128, 128],
attn_length=32,
and (transpose_to_key < 0
or transpose_to_key > note_seq.NOTES_PER_OCTAVE - 1)):
raise ValueError('transpose_to_key must be >= 0 and <= 11. '
'transpose_to_key is %d.' % transpose_to_key)
self.min_note = min_note
self.max_note = max_note
self.transpose_to_key = transpose_to_key
# Default configurations.
default_configs = {
'basic_improv':
ImprovRnnConfig(
generator_pb2.GeneratorDetails(
id='basic_improv',
description='Basic melody-given-chords RNN with one-hot triad '
'encoding for chords.'),
note_seq.ConditionalEventSequenceEncoderDecoder(
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.TriadChordOneHotEncoding()),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MelodyOneHotEncoding(min_note=DEFAULT_MIN_NOTE,
max_note=DEFAULT_MAX_NOTE))),
contrib_training.HParams(batch_size=128,
rnn_layer_sizes=[64, 64],
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001)),
'attention_improv':
ImprovRnnConfig(
generator_pb2.GeneratorDetails(
if (transpose_to_key is not None
and (transpose_to_key < 0
or transpose_to_key > note_seq.NOTES_PER_OCTAVE - 1)):
raise ValueError('transpose_to_key must be >= 0 and <= 11. '
'transpose_to_key is %d.' % transpose_to_key)
self.min_note = min_note
self.max_note = max_note
self.transpose_to_key = transpose_to_key
# Default configurations.
default_configs = {
'basic_rnn':
MelodyRnnConfig(
generator_pb2.GeneratorDetails(
id='basic_rnn', description='Melody RNN with one-hot encoding.'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MelodyOneHotEncoding(min_note=DEFAULT_MIN_NOTE,
max_note=DEFAULT_MAX_NOTE)),
contrib_training.HParams(batch_size=128,
rnn_layer_sizes=[128, 128],
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001)),
'mono_rnn':
MelodyRnnConfig(generator_pb2.GeneratorDetails(
id='mono_rnn', description='Monophonic RNN with one-hot encoding.'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MelodyOneHotEncoding(min_note=0,
max_note=128)),
contrib_training.HParams(batch_size=128,
