def main(unused_argv):
"""Saves bundle or runs generator based on flags."""
tf.logging.set_verbosity(FLAGS.log)
bundle = get_bundle()
config_id = bundle.generator_details.id if bundle else FLAGS.config
config = polyphony_model.default_configs[config_id]
config.hparams.parse(FLAGS.hparams)
# Having too large of a batch size will slow generation down unnecessarily.
config.hparams.batch_size = min(config.hparams.batch_size,
FLAGS.beam_size * FLAGS.branch_factor)
generator = polyphony_sequence_generator.PolyphonyRnnSequenceGenerator(
model=polyphony_model.PolyphonyRnnModel(config),
details=config.details,
steps_per_quarter=config.steps_per_quarter,
checkpoint=get_checkpoint(),
bundle=bundle)
if FLAGS.save_generator_bundle:
bundle_filename = os.path.expanduser(FLAGS.bundle_file)
if FLAGS.bundle_description is None:
tf.logging.warning('No bundle description provided.')
tf.logging.info('Saving generator bundle to %s', bundle_filename)
generator.create_bundle_file(bundle_filename, FLAGS.bundle_description)
else:
run_with_flags(generator)
def main(unused_argv):
"""Saves bundle or runs generator based on flags."""
tf.logging.set_verbosity(FLAGS.log)
config = polyphony_model.default_configs[FLAGS.config]
generator = polyphony_sequence_generator.PolyphonyRnnSequenceGenerator(
model=polyphony_model.PolyphonyRnnModel(config),
details=config.details,
steps_per_quarter=config.steps_per_quarter,
checkpoint=get_checkpoint(),
bundle=get_bundle())
if FLAGS.save_generator_bundle:
bundle_filename = os.path.expanduser(FLAGS.bundle_file)
if FLAGS.bundle_description is None:
tf.logging.warning('No bundle description provided.')
tf.logging.info('Saving generator bundle to %s', bundle_filename)
generator.create_bundle_file(bundle_filename, FLAGS.bundle_description)
else:
run_with_flags(generator)
def __init__(self, led):
self.button = Button()
self.player = Player(MusicGeneratorSettings.output_dir)
self.led = led
bundle_file = os.path.expanduser(MusicGeneratorSettings.bundle_file)
bundle = sequence_generator_bundle.read_bundle_file(bundle_file)
tf.logging.set_verbosity(MusicGeneratorSettings.log)
config_id = bundle.generator_details.id
config = polyphony_model.default_configs[config_id]
config.hparams.parse(MusicGeneratorSettings.hparams)
# Having too large of a batch size will slow generation down unnecessarily.
config.hparams.batch_size = min(
config.hparams.batch_size, MusicGeneratorSettings.beam_size *
MusicGeneratorSettings.branch_factor)
self.generator = polyphony_sequence_generator.PolyphonyRnnSequenceGenerator(
model=polyphony_model.PolyphonyRnnModel(config),
details=config.details,
steps_per_quarter=config.steps_per_quarter,
checkpoint=None,
bundle=bundle)
def create_sequence_generator(config, **kwargs):
return PolyphonyRnnSequenceGenerator(
polyphony_model.PolyphonyRnnModel(config),
config.details,
steps_per_quarter=config.steps_per_quarter,
**kwargs)
def create_sequence_generator(config, **kwargs):
return PolyphonyRnnSequenceGenerator(
polyphony_model.PolyphonyRnnModel(config), config.details,
**kwargs)
