def build_and_train_conv1_model(inputs, labels, num_of_labels, do_train=True, validation_data=None):
inputs_transpose, labels_many_to_one, input_len, num_of_features = Utils.convert_to_cnn_inputs_and_labels(inputs,
labels)
model = MyModel(input_len=input_len, num_features=num_of_features, num_labels=num_of_labels)
weights_path = None
if model_config.continue_train_existing_model:
weights_path = model_config.model_file_name
model.build_model("CONV1", [], [], weights_path=weights_path)
if do_train:
if validation_data is not None:
val_inputs_transpose, val_labels_many_to_one, input_len, num_of_features = Utils.convert_to_cnn_inputs_and_labels(
validation_data[0],
validation_data[1])
model.fit(inputs_transpose, labels_many_to_one, model_path=model_config.model_file_name,val_percentage=validation_perc,
early_stopping_patience=10, validation_data=(val_inputs_transpose,val_labels_many_to_one), batch_size=batch_size, num_epochs=num_epochs)
else:
model.fit(inputs_transpose, labels_many_to_one, model_path=model_config.model_file_name, early_stopping_patience=10,
val_percentage=validation_perc, batch_size=batch_size, num_epochs=num_epochs)
return model
def build_and_train_gru_model(inputs, labels, num_of_labels, do_train=True):
num_of_features = len(inputs[0][0])
model = MyModel(input_len=model_config.input_vec_size, num_features=num_of_features, num_labels=num_of_labels)
if model_config.continue_train_existing_model:
weights_path = model_config.model_file_name
if model_config.many_to_many:
model.build_model("GRU", [350, 300, 250], [0.05, 0.05, 0.05], weights_path)
if do_train:
model.fit(inputs, labels, model_path="model", early_stopping_patience=40, val_percentage=validation_perc,
batch_size=batch_size, num_epochs=num_epochs)
else:
# NOTE: train - many inputs to one label
labels_many_to_one = Utils.get_many_to_one_labels(labels, num_of_labels)
model.build_model("GRU_1", [350, 300, 250], [0.05, 0.05, 0.05], weights_path)
if do_train:
model.fit(inputs, labels_many_to_one, model_path="model", early_stopping_patience=40, val_percentage=validation_perc,
batch_size=batch_size, num_epochs=num_epochs)
return model
def main():
audio_path = 'wav_files\\long_dr1_7.wav'
# NOTE: no overlap in the input vectors frames
inputs = Utils.audio_to_model_inputs(audio_path,
model_config.input_vec_size)
inputs_transposed = Utils.convert_to_cnn_inputs(inputs)
input_len = len(inputs_transposed[0])
num_of_features = len(inputs_transposed[0][0])
num_of_labels = PhonesSet.get_num_labels()
model = MyModel(input_len=input_len,
num_features=num_of_features,
num_labels=num_of_labels)
model_file = model_config.model_file_name
model.build_model("CONV1", [], [], weights_path=model_file)
thread = threading.Thread(target=send_visemes_to_smartbody,
args=(model, inputs_transposed))
thread.daemon = True # Daemon thread
thread.start()
loader_audio_path = 'wav_files\\long_dr1_7_loader.wav'
play_wav(loader_audio_path)
