Update wmms.py

wmms.py

@@ -17,7 +17,7 @@ SAMPLE_RATE = 16_000
 
 _COMPRESSED_FILENAME = 'watkins.zip'
 
-CLASSES = ['Atlantic_Spotted_Dolphin', 'Bearded_Seal', 'Beluga,_White_Whale', 'Bottlenose_Dolphin', 'Bowhead_Whale', 'Clymene_Dolphin', 'Common_Dolphin', 'False_Killer_Whale', 'Fin,_Finback_Whale', 'Frasers_Dolphin', 'Grampus,_Rissos_Dolphin', 'Harp_Seal', 'Humpback_Whale', 'Killer_Whale', 'Leopard_Seal', 'Long-Finned_Pilot_Whale', 'Melon_Headed_Whale', 'Minke_Whale', 'Narwhal', 'Northern_Right_Whale', 'Pantropical_Spotted_Dolphin', 'Ross_Seal', 'Rough-Toothed_Dolphin', 'Short-Finned_Pacific_Pilot_Whale', 'Southern_Right_Whale', 'Sperm_Whale', 'Spinner_Dolphin', 'Striped_Dolphin', 'Walrus', 'Weddell_Seal', 'White-beaked_Dolphin', 'White-sided_Dolphin']
+CLASSES = ['Atlantic_Spotted_Dolphin', 'Bearded_Seal', 'Beluga,_White_Whale', 'Bottlenose_Dolphin', 'Bowhead_Whale', 'Clymene_Dolphin', 'Common_Dolphin', 'False_Killer_Whale', 'Fin,_Finback_Whale', 'Frasers_Dolphin', 'Grampus,_Rissos_Dolphin', 'Harp_Seal', 'Humpback_Whale', 'Killer_Whale', 'Leopard_Seal', 'Long-Finned_Pilot_Whale', 'Melon_Headed_Whale', 'Minke_Whale', 'Narwhal', 'Northern_Right_Whale', 'Pantropical_Spotted_Dolphin', 'Ross_Seal', 'Rough-Toothed_Dolphin', 'Short-Finned_Pacific_Pilot_Whale', 'Southern_Right_Whale', 'Sperm_Whale', 'Spinner_Dolphin', 'Striped_Dolphin', 'Walrus', 'White-beaked_Dolphin', 'White-sided_Dolphin']
 
 
 class WmmsConfig(datasets.BuilderConfig):
@@ -59,33 +59,33 @@ class WMMS(datasets.GeneratorBasedBuilder):
         """Returns SplitGenerators."""
         archive_path = dl_manager.extract(_COMPRESSED_FILENAME)
         extensions = ['.wav']
-        _, filepaths = fast_scandir(archive_path, extensions, recursive=True)
-        labels = [default_find_classes(f) for f in filepaths]
 
-        # train_walker, test_walker = train_test_split(
-        #     _walker, test_size=0.2, random_state=914, stratify=[default_find_classes(f) for f in _walker]
-        # )
+        _remove_class = 'Weddell_Seal' # only 2 samples in the dataset
+        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+        filepaths = [f for f in _walker if default_find_classes(f) != _remove_class]
+        labels = [default_find_classes(f) for f in filepaths]
 
         # Step 1: Organize samples by class
         class_to_files = defaultdict(list)
         for filepath, label in zip(filepaths, labels):
             class_to_files[label].append(filepath)
         
-        # Step 2: Select exactly 2 samples per class for the test set
+        # Step 2: Select exactly n samples per class for the test set
+        n_shot = 5
         test_files, test_labels = [], []
         train_files, train_labels = [], []
         
         for label, files in class_to_files.items():
-            if len(files) < 2:
-                raise ValueError(f"Not enough samples for class {label}")  # Ensure each class has at least 2 samples
+            if len(files) < n_shot:
+                raise ValueError(f"Not enough samples for class {label}")  # Ensure each class has at least n_shot samples
         
             random.Random(914).shuffle(files)  # Shuffle to ensure randomness
         
-            test_files.extend(files[:2])  # Pick first 2 for test
-            test_labels.extend([label] * 2)
+            test_files.extend(files[:n_shot])  # Pick first n_shot for test
+            test_labels.extend([label] * n_shot)
         
-            train_files.extend(files[2:])  # Remaining go to train
-            train_labels.extend([label] * (len(files) - 2))
+            train_files.extend(files[n_shot:])  # Remaining go to train
+            train_labels.extend([label] * (len(files) - n_shot))
         
         return [
             datasets.SplitGenerator(