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)