# Get dataset

data_loader = prepare_dataloader(args.data_path, "val.txt", batch_size=50, shuffle=False)

# Get loss function

Loss = model.get_criterion()

# Evaluation

mel_l_list = []

d_l_list = []

f_l_list = []

e_l_list = []

current_step = 0

for i, batch in enumerate(data_loader):

# Get Data

id_ = batch["id"]

sid, text, mel_target, D, log_D, f0, energy, \

src_len, mel_len, max_src_len, max_mel_len = model.parse_batch(batch)

with torch.no_grad():

# Forward

mel_output, _, _, log_duration_output, f0_output, energy_output, src_mask, mel_mask, out_mel_len = model(

text, src_len, mel_target, mel_len, D, f0, energy, max_src_len, max_mel_len)

# Cal Loss

mel_loss, d_loss, f_loss, e_loss = Loss(mel_output, mel_target,

log_duration_output, log_D, f0_output, f0, energy_output, energy, src_len, mel_len)

# Logger

m_l = mel_loss.item()

d_l = d_loss.item()

f_l = f_loss.item()

e_l = e_loss.item()

mel_l_list.append(m_l)

d_l_list.append(d_l)

f_l_list.append(f_l)

e_l_list.append(e_l)

current_step += 1

mel_l = sum(mel_l_list) / len(mel_l_list)

d_l = sum(d_l_list) / len(d_l_list)

f_l = sum(f_l_list) / len(f_l_list)

e_l = sum(e_l_list) / len(e_l_list)