def get_config(batch_size, data_path):
return configs.Config(
model=MusicVAE(lstm_models.BidirectionalLstmEncoder(),
lstm_models.CategoricalLstmDecoder()),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=512,
max_seq_len=32, # 2 bars w/ 16 steps per bar
z_size=512,
enc_rnn_size=[2048],
dec_rnn_size=[2048, 2048, 2048],
free_bits=0,
max_beta=0.5,
beta_rate=0.99999,
sampling_schedule='inverse_sigmoid',
sampling_rate=1000,
)),
note_sequence_augmenter=data.NoteSequenceAugmenter(
transpose_range=(-5, 5)),
data_converter=data.OneHotMelodyConverter(
valid_programs=data.MEL_PROGRAMS,
skip_polyphony=False,
max_bars=100, # Truncate long melodies before slicing.
slice_bars=2,
steps_per_quarter=4),
train_examples_path=data_path,
eval_examples_path=data_path,
)
def update_config(config, update_dict):
config_dict = config.values()
config_dict.update(update_dict)
return Config(**config_dict)
CONFIG_MAP = {}
# Melody
CONFIG_MAP['cat-mel_2bar_small'] = Config(
model=MusicVAE(lstm_models.BidirectionalLstmEncoder(),
lstm_models.CategoricalLstmDecoder()),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=512,
max_seq_len=32, # 2 bars w/ 16 steps per bar
z_size=256,
enc_rnn_size=[512],
dec_rnn_size=[256, 256],
free_bits=0,
max_beta=0.2,
beta_rate=0.99999,
sampling_schedule='inverse_sigmoid',
sampling_rate=1000,
)),
note_sequence_augmenter=data.NoteSequenceAugmenter(transpose_range=(-5,
5)),
data_converter=data.OneHotMelodyConverter(
def update_config(config, update_dict):
config_dict = config.values()
config_dict.update(update_dict)
return Config(**config_dict)
CONFIG_MAP = {}
# Melody
CONFIG_MAP['cat-mel_2bar_small'] = Config(
model=MusicVAE(lstm_models.BidirectionalLstmEncoder(),
lstm_models.CategoricalLstmDecoder()),
hparams=merge_hparams(
lstm_models.get_default_hparams(),
HParams(
batch_size=512,
max_seq_len=32, # 2 bars w/ 16 steps per bar
z_size=256,
enc_rnn_size=[512],
dec_rnn_size=[256, 256],
)),
note_sequence_augmenter=None,
note_sequence_converter=data.OneHotMelodyConverter(
valid_programs=data.MEL_PROGRAMS,
skip_polyphony=False,
max_bars=100, # Truncate long melodies before slicing.
slice_bars=2,
steps_per_quarter=4),
train_examples_path=None,
