import tgt
np.random.seed(42)
tf.random.set_seed(42)
dynamic_memory_allocation()
parser = basic_train_parser()
args = parser.parse_args()
config = Config(config_path=args.config, asr=True)
config_dict = config.config
config.create_remove_dirs(clear_dir=args.clear_dir,
clear_logs=args.clear_logs,
clear_weights=args.clear_weights)
config.dump_config()
config.print_config()
model = config.get_model()
config.compile_model(model)
data_handler = ASRDataset.from_config(config,
tokenizer=model.text_pipeline.tokenizer,
kind='valid')
dataset = data_handler.get_dataset(
bucket_batch_sizes=config_dict['bucket_batch_sizes'],
bucket_boundaries=config_dict['bucket_boundaries'],
shuffle=False)
# create logger and checkpointer and restore latest model
summary_manager = SummaryManager(model=model,
log_dir=config.log_dir,
tag=f'Validation/predicted_mel_{fname[0].numpy().decode("utf-8")}')
# residual = abs(model_out['mel_linear'] - model_out['final_output'])
# summary_manager.display_mel(mel=residual[0], tag=f'Validation/conv-linear_residual')
summary_manager.display_mel(
mel=val_mel[0],
tag=f'Validation/target_mel_{fname[0].numpy().decode("utf-8")}')
return val_loss['loss']
config_manager = Config(config_path=args.config, model_kind='autoregressive')
config = config_manager.config
config_manager.create_remove_dirs(clear_dir=args.clear_dir,
clear_logs=args.clear_logs,
clear_weights=args.clear_weights)
config_manager.dump_config()
config_manager.print_config()
#
# get model, prepare data for model, create datasets
model = config_manager.get_model()
config_manager.compile_model(model)
data_prep = AutoregressivePreprocessor.from_config(
config_manager, tokenizer=model.text_pipeline.tokenizer)
train_data_handler = TextMelDataset.from_config(config_manager,
preprocessor=data_prep,
kind='train')
valid_data_handler = TextMelDataset.from_config(config_manager,
preprocessor=data_prep,
kind='valid')
train_dataset = train_data_handler.get_dataset(