def eval(args):
# load model
model = get_model(args,
0,
args.r,
from_ckpt=True,
train=False,
grocery=args.grocery)
model.load(args.logname) # from default checkpoint
if args.wav_file_list:
with open(args.wav_file_list) as f:
for line in f:
try:
print((line.strip()))
if (args.speaker == 'single'):
upsample_wav(
'../data/vctk/VCTK-Corpus/wav48/p225/' +
line.strip(), args, model)
else:
upsample_wav(
'../data/vctk/VCTK-Corpus/' + line.strip(), args,
model)
except EOFError:
print('WARNING: Error reading file:', line.strip())
def eval(args):
# load model
# model = get_model(args, 0, args.r, from_ckpt=True, train=False)
# model.load(args.logname) # from default checkpoint
num = 49
model = torch.load(folder + "model_epoch_"+str(num)+".pth")
avg_psnr = 0
avg_snr = 0
sum_x = 0
sum_y = 0
val_dir = '../data/vctk/vctk-speaker1-val.4.16000.8192.4096.h5'
file_list = '../data/vctk/speaker1/speaker1-val-files.txt'
# X_val, Y_val = load_h5(args.val)
# dataset = loading(root_dir, transform=None)
valset1 = loading(val_dir, transform=None)
valset = DataLoader(valset1, batch_size=1, shuffle=False, num_workers=4)
nb_batch = valset.__len__()
with torch.no_grad():
for i_batch, val in enumerate(valset):
# for batch in range(nb_batch):
# input, target = batch[0].to(device), batch[1].to(device)
X_val, Y_val = val['lr'], val['hr']
# print(X_val.numpy()[0].shape)
# x_temp = X_val.numpy()[0]
# y_temp = Y_val.numpy()[0]
# x_S = computeSNR(x_temp,2048)
# y_S = computeSNR(y_temp,2048)
# sum_x += x_S
# sum_y += y_S
X_val = X_val.float()
Y_val = Y_val.float()
X_val = Variable(X_val.cuda(), requires_grad=False).permute(0, 2, 1) # compute N, C L
Y_val = Variable(Y_val.cuda(), requires_grad=False).permute(0, 2, 1)
# print(X_val.size())
# print(Y_val.size())
prediction = model(X_val)
mse = loss_function(prediction, Y_val)
psnr = 10 * log10(1 / mse.item())
snr = 10 * log10(1 / mse.cpu().data.numpy())
avg_psnr += psnr
avg_snr += snr
print("===> Avg. SNR: {:.4f} dB".format(avg_snr / len(valset)))
# print("===> X. SNR: {:.4f} dB".format(sum_x / len(valset)))
# print("===> Y. SNR: {:.4f} dB".format(sum_y / len(valset)))
with open(file_list) as f:
for line in f:
try:
print(line.strip())
upsample_wav(line.strip(), model)
except EOFError:
print('WARNING: Error reading file:', line.strip())
def eval(args):
# load model
model = get_model(args, 0, args.r, from_ckpt=True, train=False)
model.load(args.logname) # from default checkpoint
if args.wav_file_list:
with open(args.wav_file_list) as f:
for line in f:
try:
print line.strip()
upsample_wav(line.strip(), args, model)
except EOFError:
print 'WARNING: Error reading file:', line.strip()
def eval(args):
# load model
model = get_model(args, 0, args.r, from_ckpt=True, train=False)
model.load(args.logname) # from default checkpoint
upsample_wav(args.wav_file_list, args, model)
