def create_RNN_model(args, load_weight_from=None): ''' A wrapper for creating a 'class RNN' instance ''' # Update some dependent args if hasattr(args, "classes"): classes = args.classes elif hasattr(args, "classes_txt"): classes = lib_io.read_list(args.classes_txt) else: raise RuntimeError("The classes are no loaded into the RNN model.") args.num_classes = len(classes) args.save_log_to = args.save_model_to + "log.txt" args.save_fig_to = args.save_model_to + "fig.jpg" # Create model device = args.device model = RNN(args.input_size, args.hidden_size, args.num_layers, args.num_classes, device).to(device) model.set_classes(classes) # Load weights if load_weight_from: print(f"Load weights from: {load_weight_from}") weights = torch.load(load_weight_from) load_weights(model, weights) return model
def test_synthesize_audio(): texts = ["hello"] texts = lib_io.read_list("config/classes_kaggle.names") for text in texts: audio = synthesize_audio(text, PRINT=True) audio.play_audio() # audio.write_to_file(f"synthesized_audio_{text}.wav") audio.write_to_file(f"{text}.wav")
def test_synthesize_audio(): texts = ["hello"] texts = lib_io.read_list("config/classes_kaggle.names") import os, sys if not os.path.exists("output/"): os.makedirs("output/") for text in texts: print("=" * 80) print("Synthesizing " + text + " ...") audio = synthesize_audio(text, is_print=True) audio.play_audio() # audio.write_to_file(f"synthesized_audio_{text}.wav") audio.write_to_file(f"output/{text}.wav")
def create_RNN_model(args, load_weights_from=None): ''' A wrapper for creating a 'class RNN' instance ''' # Update some dependent args args.num_classes = len(lib_io.read_list( args.classes_txt)) # read from "config/classes.names" args.save_log_to = args.save_model_to + "log.txt" args.save_fig_to = args.save_model_to + "fig.jpg" # Create model device = args.device model = RNN(args.input_size, args.hidden_size, args.num_layers, args.num_classes, device).to(device) # Load weights if load_weights_from: print(f"Load weights from: {load_weights_from}") weights = torch.load(load_weights_from) load_weights(model, weights) return model
def setup_classes_labels(load_classes_from, model): classes = lib_io.read_list(load_classes_from) print(f"{len(classes)} classes: {classes}") model.set_classes(classes)