Refactor Meter2800 dataset class by removing unused variables and simplifying example generation logic

meter2800.py

@@ -1,115 +1,42 @@
 import datasets
 import pandas as pd
-from pathlib import Path
-
-_CITATION = """\
-@misc{meter2800_dataset,
-  author       = {PianistProgrammer},
-  title        = {{Meter2800}: A Dataset for Music Time signature detection / Meter Classification},
-  year         = {2025},
-  publisher    = {Hugging Face},
-  url          = {https://huggingface.co/datasets/pianistprogrammer/Meter2800}
-}
-"""
-
-_DESCRIPTION = """\
-Meter2800 is a dataset of 2,800 music audio samples for automatic meter classification.
-Each audio file is annotated with a primary meter class label and an alternative meter.
-It is split into training, validation, and test sets, each available in two class configurations:
-2-class and 4-class. All audio is 16-bit WAV format.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/pianistprogrammer/Meter2800"
-_LICENSE = "mit"
 
 class Meter2800(datasets.GeneratorBasedBuilder):
-    """Meter2800 dataset for music meter classification."""
+    """Minimal test version."""
 
-    VERSION = datasets.Version("1.0.0")
-    
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="4_classes",
-            version=VERSION,
-            description="4-class meter classification",
-        ),
-        datasets.BuilderConfig(
-            name="2_classes",
-            version=VERSION,
-            description="2-class meter classification",
-        ),
+        datasets.BuilderConfig(name="4_classes", description="4 class version"),
+        datasets.BuilderConfig(name="2_classes", description="2 class version"),
     ]
-
+    
     DEFAULT_CONFIG_NAME = "4_classes"
 
     def _info(self):
-        # We'll determine the labels dynamically from the CSV files
-        # For now, use a generic ClassLabel that will be updated later
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
             features=datasets.Features({
                 "filename": datasets.Value("string"),
                 "audio": datasets.Audio(sampling_rate=None),
-                "label": datasets.Value("string"),  # We'll convert to ClassLabel later
+                "label": datasets.Value("string"),
                 "meter": datasets.Value("string"),
                 "alt_meter": datasets.Value("string"),
             }),
-            supervised_keys=("audio", "label"),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        
-        # The files should be in the root of the repo
-        config_suffix = self.config.name
-        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "csv_file": f"data_train_{config_suffix}.csv",
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "csv_file": f"data_val_{config_suffix}.csv",
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "csv_file": f"data_test_{config_suffix}.csv",
-                    "split": "test",
-                },
+                gen_kwargs={"csv_file": f"data_train_{self.config.name}.csv"},
             ),
         ]
 
-    def _generate_examples(self, csv_file, split):
-        """Yields examples."""
-        
-        # Read CSV directly - the file should be available in the repo
-        df = pd.read_csv(csv_file)
-        
-        # Read CSV
+    def _generate_examples(self, csv_file):
         df = pd.read_csv(csv_file)
-        df = df.dropna(subset=["filename", "label"]).reset_index(drop=True)
-
         for idx, row in df.iterrows():
-            # The audio files should be in subdirectories
-            audio_file = row["filename"]
-            if not audio_file.startswith("/"):
-                audio_file = "/" + audio_file
-            
             yield idx, {
                 "filename": row["filename"],
-                "audio": audio_file.lstrip("/"),  # Remove leading slash for HF
-                "label": str(row["label"]),
-                "meter": str(row.get("meter", "")),
-                "alt_meter": str(row.get("alt_meter", row.get("meter", ""))),
+                "audio": row["filename"].lstrip("/"),
+                "label": row["label"],
+                "meter": str(row["meter"]),
+                "alt_meter": str(row["alt_meter"]),
             }