def test(model, device, noisy_path, clean_path, enhance_path, score_path, args):
model = model.to(device)
# load model
model.eval()
torch.no_grad()
#checkpoint = torch.load(model_path)
#model.SEmodel.load_state_dict(checkpoint['model'])
# load data
test_files = np.array(getfilename(noisy_path))
c_dict = get_cleanwav_dic(clean_path)
#open score file
if os.path.exists(score_path):
os.remove(score_path)
check_folder(score_path)
print('Save PESQ&STOI results to:', score_path)
with open(score_path, 'a') as f:
f.write('Filename,PESQ,STOI\n')
print('Testing...')
for test_file in tqdm(test_files):
write_score(model, device, test_file, c_dict, enhance_path, score_path)
data = pd.read_csv(score_path)
pesq_mean = data['PESQ'].to_numpy().astype('float').mean()
stoi_mean = data['STOI'].to_numpy().astype('float').mean()
with open(score_path, 'a') as f:
f.write(','.join(('Average',str(pesq_mean),str(stoi_mean)))+'\n')
def test(model, device, noisy_path, clean_path, asr_dict, enhance_path, score_path, args):
model = model.to(device)
# load model
model.eval()
torch.no_grad()
# load data
if args.test_num is None:
test_files = np.array(getfilename(noisy_path,"test"))
else:
test_files = np.array(getfilename(noisy_path,"test")[:args.test_num])
c_dict = get_cleanwav_dic(clean_path, args.corpus)
#open score file
google_asr = True
if google_asr:
score_path = score_path.replace(".csv","_wer.csv")
if os.path.exists(score_path):
os.remove(score_path)
check_folder(score_path)
if google_asr:
print('Save WER results to:', score_path)
with open(score_path, 'a') as f:
f.write('Filename,PESQ,STOI,WER,CleanWER\n')
else:
print('Save PESQ&STOI results to:', score_path)
with open(score_path, 'a') as f:
f.write('Filename,PESQ,STOI\n')
print('Testing...')
for test_file in tqdm(test_files):
name=test_file.split('/')[-1].replace('.wav','')
ilen, y=asr_dict[name][0],asr_dict[name][1]
write_score(model, device, test_file, c_dict, enhance_path, ilen, y, score_path, args.asr_result, args.corpus)
data = pd.read_csv(score_path)
pesq_mean = data['PESQ'].to_numpy().astype('float').mean()
stoi_mean = data['STOI'].to_numpy().astype('float').mean()
with open(score_path, 'a') as f:
f.write(','.join(('Average',str(pesq_mean),str(stoi_mean)))+'\n')
def Load_data(args):
train_paths = []
val_paths = []
train_spec_noisy_list = getfilename(
os.path.join(args.out_path, 'spec', 'train/noisy'), "train")
n_files = np.array(train_spec_noisy_list)
if args.train_num is None:
train_paths, val_paths = train_test_split(n_files,
test_size=args.val_ratio,
random_state=999)
else:
train_paths, val_paths = train_test_split(n_files[:args.train_num],
test_size=args.val_ratio,
random_state=999)
print('Reading json files...')
asr_y_path = [item for item in args.asr_y_path.split(',')]
asr_dict = {}
for json_path in asr_y_path:
asr_dict = load_asr_data(json_path, asr_dict, args.TMHINT)
train_dataset, val_dataset = CustomDataset(train_paths, asr_dict,
args.TMHINT), CustomDataset(
val_paths, asr_dict,
args.TMHINT)
# [Yo] Add padding collate_fn
loader = {
'train':
DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True,
collate_fn=pad_collate),
'val':
DataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=8,
pin_memory=True,
collate_fn=pad_collate)
}
return loader
def Load_data(args):
train_paths = []
val_paths = []
train_spec_noisy_list = getfilename(
os.path.join(args.out_path, 'spec', 'train/noisy'), "train")
n_files = np.array(train_spec_noisy_list)
if args.train_num is None:
train_paths, val_paths = train_test_split(n_files,
test_size=args.val_ratio,
random_state=999)
else:
train_paths, val_paths = train_test_split(n_files[:args.train_num],
test_size=args.val_ratio,
random_state=999)
asr_dict = load_y_dict(args)
train_dataset, val_dataset = CustomDataset(train_paths, asr_dict,
args.corpus), CustomDataset(
val_paths, asr_dict,
args.corpus)
# [Yo] Add padding collate_fn
loader = {
'train':
DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True,
collate_fn=pad_collate),
'val':
DataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=8,
pin_memory=True,
collate_fn=pad_collate)
}
return loader