args = parser.parse_args()
#####################################################
# train parameters
#####################################################
n_epochs = args.n_epochs
batch_size = args.batch_size
model_size = args.model_size
ckpt_dir = args.ckpt_dir
#####################################################
# datasets
#####################################################
dataset = Voxceleb1("/tmp/sv_set/voxc1/fbank64")
train_x, train_y = dataset.get_norm("dev/train", scale=24)
val_x, val_y = dataset.get_norm("dev/val", scale=24)
n_train_samples = len(train_x)
steps_per_epoch = n_train_samples // batch_size
input_shape = (train_x.shape[1], train_x.shape[2], train_x.shape[3])
def train_generator():
for x, y in zip(train_x, train_y):
yield x, y
def val_generator():
for x, y in zip(val_x, val_y):
####################################################
# train parameters
####################################################
n_epochs = args.n_epochs
batch_size = args.batch_size
model_size = args.model_size
ckpt_dir = args.ckpt_dir
model_file = args.model_file
####################################################
# datasets
####################################################
dataset = Voxceleb1(args.root_dir)
train_x, train_y = dataset.get_norm("dev/train", scale=24)
val_x, val_y = dataset.get_norm("dev/val", scale=24)
input_shape = (train_x.shape[1], train_x.shape[2], train_x.shape[3])
def train_generator():
for x, y in zip(train_x, train_y):
yield x, y
def val_generator():
for x, y in zip(val_x, val_y):
yield x, y