Add exclusion_lists to unsupervised_audio_dataset

codes/data/audio/unsupervised_audio_dataset.py

@@ -56,7 +56,12 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
     def __init__(self, opt):
         path = opt['path']
         cache_path = opt['cache_path']  # Will fail when multiple paths specified, must be specified in this case.
-        self.audiopaths = load_paths_from_cache(path, cache_path)
+        exclusions = []
+        if 'exclusions' in opt.keys():
+            for exc in opt['exclusions']:
+                with open(exc, 'r') as f:
+                    exclusions.extend(f.read().splitlines())
+        self.audiopaths = load_paths_from_cache(path, cache_path, exclusions)
 
         # Parse options
         self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)

codes/data/util.py

@@ -578,7 +578,7 @@ def imresize_np(img, scale, antialiasing=True):
     return out_2.numpy()
 
 
-def load_paths_from_cache(paths, cache_path):
+def load_paths_from_cache(paths, cache_path, exclusion_list=[]):
     if not isinstance(paths, list):
         paths = [paths]
     if os.path.exists(cache_path):
@@ -588,6 +588,10 @@ def load_paths_from_cache(paths, cache_path):
         output = []
         for p in paths:
             output.extend(find_files_of_type('img', p, qualifier=is_audio_file)[0])
+        if exclusion_list is not None and len(exclusion_list) > 0:
+            print(f"Removing exclusion lists..")
+            output = filter(lambda p: p not in exclusion_list, output)
+        print("Done.")
         torch.save(output, cache_path)
     return output