full_id = 'soft-base-9900' + idd
folder = 'Dense_train'
model = Adapt(config_model=config_model, pretraining=False)
model.create_saver()
path = os.path.join(config.workdir, 'floydhub_model', "pretraining")
# path = os.path.join(config.log_dir, "pretraining")
model.restore_model(path, full_id)
## Connect DAS model to the front end
from models.dense import Dense_net as Dense
with model.graph.as_default():
model.connect_front(Dense)
model.sepNet.output = model.sepNet.prediction
model.back
model.cost
model.optimize
# model.freeze_front()
# model.optimize
model.tensorboard_init()
from itertools import compress
with model.graph.as_default():
global_vars = tf.global_variables()
is_not_initialized = model.sess.run([~(tf.is_variable_initialized(var)) \
for var in global_vars])
not_initialized_vars = list(compress(global_vars, is_not_initialized))
if len(not_initialized_vars):
####
#### NEW MODEL CONFIGURATION
####
config_model["type"] = "L41_finetuning"
learning_rate = 0.001
batch_size = 64
config_model["chunk_size"] = chunk_size
config_model["alpha"] = learning_rate
config_model["batch_size"] = batch_size
model = Adapt(config_model=config_model, pretraining=False)
with model.graph.as_default():
model.connect_front(L41Model)
var_list = [v for v in tf.global_variables() if ('front' in v.name)]
model.create_saver(var_list)
model.restore_model(path_adapt, full_id_adapt)
model.sepNet.prediction
model.sepNet.separate
model.sepNet.output = model.sepNet.enhance
var_list = [
v for v in tf.global_variables()
if ('prediction' in v.name or 'speaker_centroids' in v.name
or 'enhance' in v.name)
]
model.create_saver(var_list)
model.restore_model(path, full_id)
model.separator
model.back
