def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Expanding the graph with enhance layer self.model.connect_front(self.separator) self.model.sepNet.output = self.model.sepNet.separate self.model.back self.model.create_saver() self.model.restore_model(self.args['model_folder']) self.model.finish_construction() self.model.initialize_non_init()
def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Restoring the front layer: # Expanding the graph with enhance layer self.model.connect_front(self.separator) self.model.sepNet.output = self.model.sepNet.enhance self.model.back self.model.create_saver() self.model.restore_model(self.args['model_folder']) # Initialize only non restored values self.model.initialize_non_init()
def build(self): if self.args['model_previous'] is not None: self.model = Adapt.load(self.args['model_previous'], self.args) self.model.connect_front(self.separator) self.model.sepNet.output = self.model.sepNet.prediction self.model.cost_model = self.model.sepNet.cost self.model.back # To save the back values ! self.model.create_saver() self.model.restore_model(self.args['model_previous']) self.model.finish_construction() self.model.freeze_all_with('front/') self.model.freeze_all_with('back/') self.model.optimize self.model.tensorboard_init() self.model.initialize_non_init() else: self.model = Adapt.load(self.args['model_folder'], self.args) self.model.connect_only_front_to_separator(self.separator) # Initialize only non restored values self.model.initialize_non_init()
def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Expanding the graph with enhance layer self.model.connect_front(self.separator) self.model.sepNet.output = self.model.sepNet.separate self.model.back self.model.create_saver() self.model.restore_model(self.args['model_folder']) self.model.cost_model = self.model.cost self.model.finish_construction() self.model.freeze_all_except('prediction', 'speaker_centroids') self.model.optimize self.model.tensorboard_init() # Initialize only non restored values self.model.initialize_non_init()
def build_model(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Restoring previous Model: self.model.restore_front_separator(self.args['model_folder'], self.separator) # Expanding the graph with enhance layer with self.model.graph.as_default(): self.model.sepNet.output = self.model.sepNet.enhance self.model.cost_model = self.model.sepNet.enhance_cost self.model.finish_construction() self.model.freeze_all_except('enhance') self.model.optimize self.model.tensorboard_init() # Initialize only non restored values self.model.initialize_non_init()
def build_model(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Expanding the graph with enhance layer with self.model.graph.as_default(): self.model.connect_front(self.separator) self.model.sepNet.output = self.model.sepNet.separate self.model.back self.model.restore_model(self.args['model_folder']) self.model.cost_model = self.model.cost self.model.finish_construction() self.model.optimize self.model.tensorboard_init() # Initialize only non restored values self.model.initialize_non_init()
def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) self.model.front self.model.pretraining = True self.model.separator self.model.back self.model.create_saver() self.model.restore_model(self.args['model_folder']) self.model.enhance self.model.cost_model = self.model.enhance_cost self.model.finish_construction() self.model.freeze_all_with('front/') self.model.freeze_all_with('back/') self.model.optimize self.model.tensorboard_init() self.model.initialize_non_init()
def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Restoring the front layer: # Expanding the graph with enhance layer self.model.connect_front(self.separator) self.model.sepNet.output = self.model.sepNet.enhance self.model.back self.model.create_saver() self.model.restore_model(self.args['model_folder']) self.model.cost_model = self.model.cost_finetuning self.model.finish_construction() to_train = [] for var in self.model.trainable_variables: for p in self.args['train']: if p in var.name: to_train.append(var) self.model.trainable_variables = to_train # self.model.freeze_all_except('prediction', 'speaker_centroids', 'enhance') self.model.optimize self.model.tensorboard_init() # Initialize only non restored values self.model.initialize_non_init()
males = H5PY_RW() males.open_h5_dataset('test_raw.h5py', subset=males_keys(H5_dico)) males.set_chunk(5 * 4 * 512) males.shuffle() print 'Male voices loaded: ', males.length(), ' items' fem = H5PY_RW() fem.open_h5_dataset('test_raw.h5py', subset=females_keys(H5_dico)) fem.set_chunk(5 * 4 * 512) fem.shuffle() print 'Female voices loaded: ', fem.length(), ' items' Mixer = Mixer([males, fem], with_mask=False, with_inputs=True) adapt_model = Adapt.load('jolly-firefly-9628', pretraining=False, separator=DPCL) # adapt_model.init() print 'Model DAS created' testVar = raw_input("Model loaded : Press Enter") cost_valid_min = 1e10 Mixer.select_split(0) learning_rate = 0.01 for i in range(config.max_iterations): X_in, X_mix, Ind = Mixer.get_batch(1) if (i + 1) % 100 == 0: learning_rate /= 10 c = adapt_model.train(X_mix, X_in, learning_rate, i)
def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) # Restoring previous Model: self.model.connect_enhance_to_separator(self.separator) self.model.initialize_non_init()
def build(self): self.model = Adapt.load(self.args['model_folder'], self.args) self.model.connect_only_front_to_separator(self.separator) # Initialize only non restored values self.model.initialize_non_init()