Add files using upload-large-folder tool

meter2800.py

@@ -1,3 +1,5 @@
+# meter2800.py
+
 from pathlib import Path
 import datasets
 import pandas as pd
@@ -18,6 +20,7 @@ Each audio file is annotated with a primary meter class label and an alternative
 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"
 
@@ -53,38 +56,31 @@ class Meter2800(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # download CSVs and tarball
-        csv_files = dl_manager.download({
+        csv_links = {
             split: f"https://huggingface.co/datasets/pianistprogrammer/Meter2800/resolve/main/data_{split}_{self.config.name}.csv"
             for split in ["train", "val", "test"]
-        })
-        archive = dl_manager.download_and_extract("https://huggingface.co/datasets/pianistprogrammer/Meter2800/resolve/main/data.tar.gz")
+        }
+        csv_files = dl_manager.download(csv_links)
+        archive = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/pianistprogrammer/Meter2800/resolve/main/data.tar.gz"
+        )
 
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"csv_path": csv_files["train"], "root": archive}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"csv_path": csv_files["val"], "root": archive}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"csv_path": csv_files["test"], "root": archive}
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"csv_path": csv_files["train"], "root": archive}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": csv_files["val"], "root": archive}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"csv_path": csv_files["test"], "root": archive}),
         ]
 
     def _generate_examples(self, csv_path, root):
         df = pd.read_csv(csv_path).dropna(subset=["filename", "label", "meter"]).reset_index(drop=True)
-
         for idx, row in df.iterrows():
-            rel = row["filename"].lstrip("/")  # strip leading slash if present
-            path = Path(root) / rel
-
+            rel = row["filename"].lstrip("/")  # ensure relative path, not absolute
+            audio_path = Path(root) / rel
+            if not audio_path.is_file():
+                raise FileNotFoundError(f"Missing audio file: {audio_path}")
             yield idx, {
                 "filename": rel,
-                "audio": str(path),
+                "audio": str(audio_path),
                 "label": row["label"],
                 "meter": str(row["meter"]),
                 "alt_meter": str(row.get("alt_meter", row["meter"])),