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()
