Upload mumospee_small.py with huggingface_hub

mumospee_small.py

@@ -73,26 +73,26 @@ class MumospeeDataset(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"csv_path": csv_path, "split": "train", "language": _LANGUAGES, "tag": _TAGS}
+                gen_kwargs={"filepath": csv_path, "split": "train", "language": _LANGUAGES, "tag": _TAGS}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"csv_path": csv_path, "split": "validation", "language": _LANGUAGES, "tag": _TAGS}
+                gen_kwargs={"filepath": csv_path, "split": "validation", "language": _LANGUAGES, "tag": _TAGS}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"csv_path": csv_path, "split": "test", "language": _LANGUAGES, "tag": _TAGS}
+                gen_kwargs={"filepath": csv_path, "split": "test", "language": _LANGUAGES, "tag": _TAGS}
             ),
         ]
 
 
-    def _generate_examples(self, csv_file, split, language, tag):
+    def _generate_examples(self, filepath, split, language, tag):
         """Generate examples from the CSV data and audio URLs."""
         # Use dl_manager to get the CSV file from the Hugging Face repo
         # csv_file = dl_manager.download_and_extract("metadata.csv")
         # Read the CSV metadata
 
-        data = pd.read_csv(csv_file)
+        data = pd.read_csv(filepath)
         # Filter by primary split
         data_split = data[data["split"] == split]