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 = performance_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 = performance_sequence_generator.PerformanceRnnSequenceGenerator(
model=performance_model.PerformanceRnnModel(config),
details=config.details,
steps_per_second=config.steps_per_second,
num_velocity_bins=config.num_velocity_bins,
control_signals=config.control_signals,
optional_conditioning=config.optional_conditioning,
checkpoint=get_checkpoint(),
bundle=bundle,
note_performance=config.note_performance)
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 create_sequence_generator(config, **kwargs):
return PerformanceRnnSequenceGenerator(
performance_model.PerformanceRnnModel(config), config.details,
steps_per_second=config.steps_per_second,
num_velocity_bins=config.num_velocity_bins,
control_signals=config.control_signals,
optional_conditioning=config.optional_conditioning,
fill_generate_section=False,
note_performance=config.note_performance,
**kwargs)
def create_sequence_generator(config, **kwargs):
return PerformanceRnnSequenceGenerator(
performance_model.PerformanceRnnModel(config),
config.details,
steps_per_second=config.steps_per_second,
num_velocity_bins=config.num_velocity_bins,
note_density_conditioning=config.density_bin_ranges is not None,
pitch_histogram_conditioning=(config.pitch_histogram_window_size
is not None),
fill_generate_section=False,
**kwargs)
def create_sequence_generator(config, **kwargs):
return PerformanceRnnSequenceGenerator(
performance_model.PerformanceRnnModel(config), config.details,
steps_per_second=config.steps_per_second,
num_velocity_bins=config.num_velocity_bins, fill_generate_section=False,
**kwargs)
