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support not_ew too

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James Betker 2022-05-25 08:58:23 -06:00
parent 5188866bd5
commit 00e133afa9
2 changed files with 16 additions and 5 deletions
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7
codes/data/audio/unsupervised_audio_dataset.py
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@ -98,8 +98,11 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
for exc in opt['exclusions']: for exc in opt['exclusions']:
with open(exc, 'r') as f: with open(exc, 'r') as f:
exclusions.extend(f.read().splitlines()) exclusions.extend(f.read().splitlines())
ew = opt_get(opt, ['endswith']) ew = opt_get(opt, ['endswith'], [])
self.audiopaths = load_paths_from_cache(path, cache_path, exclusions, ew) assert isinstance(ew, list)
not_ew = opt_get(opt, ['not_endswith'], [])
assert isinstance(not_ew, list)
self.audiopaths = load_paths_from_cache(path, cache_path, exclusions, endswith=ew, not_endswith=not_ew)
# Parse options # Parse options
self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050) self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)

14
codes/data/util.py
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@ -578,7 +578,7 @@ def imresize_np(img, scale, antialiasing=True):
return out_2.numpy() return out_2.numpy()
def load_paths_from_cache(paths, cache_path, exclusion_list=[], endswith=None): def load_paths_from_cache(paths, cache_path, exclusion_list=[], endswith=[], not_endswith=[]):
if not isinstance(paths, list): if not isinstance(paths, list):
paths = [paths] paths = [paths]
if os.path.exists(cache_path): if os.path.exists(cache_path):
@ -597,8 +597,16 @@ def load_paths_from_cache(paths, cache_path, exclusion_list=[], endswith=None):
print(f"Excluded {before-len(output)} files.") print(f"Excluded {before-len(output)} files.")
if endswith is not None: if endswith is not None:
before = len(output) before = len(output)
output = list(filter(lambda p: p.endswith(endswith), output)) def filter_fn(p):
print(f"Excluded {before-len(output)} files with endswith mask. For total of {len(output)} files") for e in endswith:
if not p.endswith(endswith):
return False
for e in not_endswith:
if p.endswith(e):
return False
return True
output = list(filter(filter_fn, output))
print(f"!!Excluded {before-len(output)} files with endswith mask. For total of {len(output)} files")
print("Done.") print("Done.")
torch.save(output, cache_path) torch.save(output, cache_path)
return output return output