Update ravdess-script.py

ravdess-script.py

@@ -92,16 +92,7 @@ class RAVDESS(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         archive_path = dl_manager.extract(_COMPRESSED_FILENAME)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"}
-            ), 
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
-            ), 
-        ]
 
-    def _generate_examples(self, archive_path, split=None):
         extensions = ['.wav']
         _, _walker = fast_scandir(archive_path, extensions, recursive=True)
         _walker_with_fold = [(fileid, default_find_fold(fileid)) for fileid in _walker]
@@ -122,27 +113,27 @@ class RAVDESS(datasets.GeneratorBasedBuilder):
             train_fold = ['1', '2', '3', '4']
             test_fold = ['5']
 
-        if split == 'train':
-            train_walker = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
-            for guid, audio_path in enumerate(train_walker):
-                yield guid, {
-                    "id": str(guid), 
-                    "file": audio_path, 
-                    "audio": audio_path, 
-                    "emotion": default_find_classes(audio_path), 
-                    "label": default_find_classes(audio_path), 
-                }
-        elif split == 'test':
-            test_walker = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
-            for guid, audio_path in enumerate(test_walker):
-                yield guid, {
-                    "id": str(guid), 
-                    "file": audio_path, 
-                    "audio": audio_path, 
-                    "emotion": default_find_classes(audio_path), 
-                    "label": default_find_classes(audio_path), 
-                }
+        train_walker = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
+        test_walker = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_walker, "split": "train"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"audio_paths": test_walker, "split": "test"}
+            ), 
+        ]
 
+    def _generate_examples(self, audio_paths, split=None):
+        for guid, audio_path in enumerate(audio_paths):
+            yield guid, {
+                "id": str(guid), 
+                "file": audio_path, 
+                "audio": audio_path, 
+                "emotion": default_find_classes(audio_path), 
+                "label": default_find_classes(audio_path), 
+            }
 
 def default_find_classes(audio_path):
     return RAVDESS_EMOTIONS_MAPPING.get(Path(audio_path).name.split('-')[2])