def clean_Datafolder(input_dir,
output_dir,
model,
batch_size,
cuda=False,
samples=None):
# Load dataset
make_identical_dir_structure(input_dir, output_dir)
test_set = Datafolder_soundfiles(y_paths=walk_dir(input_dir),
transform=model.transform)
output_paths = format_paths(test_set.y_paths,
input_dir,
output_dir,
extention='.wav')
sampler = None
if samples is not None:
if type(slice(0)) == type(samples):
sampler = SubsetSampler(indices=range(test_set.length),
slice_=samples)
else:
sampler = SubsetSampler(indices=samples)
test_data_loader = DataLoader(test_set,
batch_size=batch_size,
num_workers=2,
pin_memory=cuda,
sampler=sampler)
if cuda:
model.cuda()
model.eval()
pool = Pool(processes=2)
jobs = []
for (indexs, (data, Y_m, Y_a, length)) in test_data_loader:
try:
mask = compute_mask_batch(model, data, cuda=cuda)
except:
for index in indexs:
print(index, output_paths[index], 'failed')
else:
mask, Y_m, Y_a, length = (k.numpy()
for k in (mask, Y_m, Y_a, length))
for job in jobs:
job.wait()
jobs = []
for I, index in enumerate(indexs):
# clean_sample_(model, mask[I], Y_m[I], Y_a[I], length[I], output_paths[index])
jobs.append(
pool.apply_async(
clean_sample_(model, mask[I], Y_m[I], Y_a[I],
length[I], output_paths[index])))
pool.close()
pool.join()
print('Finished clearning', input_dir)
def enhance_Datafolder(model, input_dir, output_dir, batch_size, cuda=False):
# Load dataset
make_identical_dir_structure(input_dir, output_dir)
output_paths = format_paths(test_set.y_paths,
input_dir,
output_dir,
extention='.wav')
y_paths = walk_dir(input_dir)
enhance_soundfiles(model, y_paths, output_paths, batch_size, cuda)
def PESQ_evalfolder(reference_dir, degraded_dir, output_path, fs):
"""Compute the PESQ scores for all wavefiles in a folder.
Walks though a directory of degraded wavefiles and computes all the scores
with similar named refrence wavefiles in the refrence directory.
Saves a csv file with the filenames, mos, and mos_lqo scores.
Parameters
----------
reference_dir : str
The path to the refrence wavfile.
degraded_dir : str
The path to the degraded wavfile.
fs : int
The sample frequency should be 8000 or 16000.
"""
reference_paths = walk_dir(reference_dir)
degraded_paths = format_paths(reference_paths, reference_dir, degraded_dir)
assert set(degraded_paths) == set(walk_dir(degraded_dir))
print('Warning: PESQ will save a file in the current working directory')
mos_list, mos_lqo_list = PESQ_evalpaths(reference_paths,
degraded_paths,
output_path,
fs=fs)
print('PESQ failed for {} samples'.format(np.isnan(mos_list).sum()))
makedir(os.path.dirname(output_path))
io.savemat(
output_path, {
'data_mos':
mos_list,
'data_mos_lqo':
mos_lqo_list,
'names':
list(os.path.relpath(s, reference_dir) for s in reference_paths),
'reference_dir':
reference_dir,
'degraded_dir':
degraded_dir
})