def main(config):
# For fast training.
cudnn.benchmark = True
# Data loader.
vcc_loader = get_loader(config.data_dir, config.batch_size, config.len_crop)
solver = Solver(vcc_loader, config)
solver.train()
def main(config):
# For fast training.
cudnn.benchmark = True
print('starting up')
# Data loader.
vcc_loader = get_loader(config.data_dir, config.batch_size, config.len_crop)
print('data loaded')
solver = Solver(vcc_loader, config)
print('solver loaded, training...')
solver.train()
def main(config):
# For fast training.
cudnn.benchmark = True
# Data loader.
vcc_loader = get_loader(config.data_dir,
config.data_train_meta_path,
config.batch_size,
config.max_len,
shuffle=True)
print(vcc_loader)
print('len:', len(vcc_loader))
# 对于验证集, 也存在每个音频起点的随机性, 不过先不管
val_loader = get_loader(config.data_dir,
config.data_val_meta_path,
config.batch_size,
config.max_len,
shuffle=False)
print(val_loader)
print('len:', len(val_loader))
solver = Solver(vcc_loader, val_loader, config)
solver.train()
name="test")
test_scorer.extract_embeddings(Encoder)
logging.info("EER on test set : " + str(test_scorer.compute_EER()) + " %")
train_scorer = Scoring(train_loader,
loader.get_dataset("train"),
device,
name="train")
train_scorer.extract_embeddings(Encoder)
logging.info("EER on train set : " + str(train_scorer.compute_EER()) +
" %")
val_scorer = Scoring(val_loader,
loader.get_dataset("val"),
device,
name="val")
val_scorer.extract_embeddings(Encoder)
logging.info("EER on val set : " + str(val_scorer.compute_EER()) + " %")
# Initiating Solver
solver = Solver((train_loader, val_loader),
config,
Encoder,
scorers=(train_scorer, val_scorer, test_scorer))
logging.info("solver initialized")
# Training
solver.train()
logging.info("### Training Finished ###")