Refactor Meter2800 dataset configuration and example generation logic

meter2800.py

@@ -23,20 +23,44 @@ It is split into training, validation, and test sets, each available in two clas
 _HOMEPAGE = "https://huggingface.co/datasets/pianistprogrammer/Meter2800"
 _LICENSE = "mit"
 
-# Define the labels used in the "label" and "meter"/"alt_meter" columns
-LABELS_4 = ["three", "four", "five" , "seven"]
-LABELS_2 = ["three", "four"]  # Example if using a binary grouping
+# Define the labels - adjust these based on your actual data
+LABELS_4 = ["three", "four", "five", "seven"]
+LABELS_2 = ["simple", "complex"]  # or whatever your 2-class grouping actually is
+
+class Meter2800Config(datasets.BuilderConfig):
+    """BuilderConfig for Meter2800."""
+    def __init__(self, name, **kwargs):
+        super(Meter2800Config, self).__init__(
+            name=name,
+            version=datasets.Version("1.0.0"),
+            **kwargs
+        )
 
 class Meter2800(datasets.GeneratorBasedBuilder):
+    """Meter2800 dataset."""
+    
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="4_classes", version=datasets.Version("1.0.0"),
-                               description="4-class meter classification"),
-        datasets.BuilderConfig(name="2_classes", version=datasets.Version("1.0.0"),
-                               description="2-class meter classification"),
+        Meter2800Config(
+            name="4_classes",
+            description="4-class meter classification"
+        ),
+        Meter2800Config(
+            name="2_classes", 
+            description="2-class meter classification"
+        ),
     ]
+    
+    DEFAULT_CONFIG_NAME = "4_classes"
 
     def _info(self):
-        label_names = LABELS_4 if self.config.name == "4_classes" else LABELS_2
+        if self.config.name == "4_classes":
+            label_names = LABELS_4
+        elif self.config.name == "2_classes":
+            label_names = LABELS_2
+        else:
+            # Fallback - shouldn't happen with proper configs
+            label_names = LABELS_4
+            
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features({
@@ -53,34 +77,45 @@ class Meter2800(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        root = Path(__file__).parent
-        suffix = "4_classes" if self.config.name == "4_classes" else "2_classes"
-
+        # Get the data directory
+        data_dir = dl_manager.download_and_extract("")
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"csv_file": root / f"data_train_{suffix}.csv"},
+                gen_kwargs={
+                    "csv_file": f"{data_dir}/data_train_{self.config.name}.csv",
+                    "data_dir": data_dir
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"csv_file": root / f"data_val_{suffix}.csv"},
+                gen_kwargs={
+                    "csv_file": f"{data_dir}/data_val_{self.config.name}.csv",
+                    "data_dir": data_dir
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"csv_file": root / f"data_test_{suffix}.csv"},
+                gen_kwargs={
+                    "csv_file": f"{data_dir}/data_test_{self.config.name}.csv",
+                    "data_dir": data_dir
+                },
             ),
         ]
 
-    def _generate_examples(self, csv_file):
+    def _generate_examples(self, csv_file, data_dir):
         df = pd.read_csv(csv_file)
         df = df.dropna(subset=["filename", "label", "meter"]).reset_index(drop=True)
 
         for idx, row in df.iterrows():
-            audio_path = Path(__file__).parent / row["filename"].lstrip("/")
+            # Construct the full audio path
+            audio_path = f"{data_dir}/{row['filename']}"
+            
             yield idx, {
                 "filename": row["filename"],
-                "audio": str(audio_path),
+                "audio": audio_path,
                 "label": row["label"],
                 "meter": str(row["meter"]),
                 "alt_meter": str(row.get("alt_meter", row["meter"])),
-            }
+            }