Add files using upload-large-folder tool

.gitattributes

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+data.tar.gz filter=lfs diff=lfs merge=lfs -text

data.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c4509dc14e7bff0cb02a9ee09676371c0cdaa4030445e358b846e38e9cee30d
+size 9299442890

meter2800.py

@@ -1,18 +1,4 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The Meter2800 dataset."""
-
+# meter2800.py
 from pathlib import Path
 import datasets
 import pandas as pd
@@ -29,59 +15,35 @@ _CITATION = """\
 
 _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 (e.g., 'two', 'three', 'four') 
-and an alternative meter (numerical, e.g., 2, 3, 4, 6). 
+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"
 
-# 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
+LABELS_2 = ["three", "four"]
 
 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
-        )
+        super().__init__(name=name, version=datasets.Version("1.0.0"), **kwargs)
 
 class Meter2800(datasets.GeneratorBasedBuilder):
-    """Meter2800 dataset."""
-    
     BUILDER_CONFIGS = [
-        Meter2800Config(
-            name="4_classes",
-            description="4-class meter classification"
-        ),
-        Meter2800Config(
-            name="2_classes", 
-            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):
-        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
-            
+        labels = LABELS_4 if self.config.name == "4_classes" else LABELS_2
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features({
                 "filename": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=22_050),
-                "label": datasets.ClassLabel(names=label_names),
+                "audio": datasets.Audio(sampling_rate=22050),
+                "label": datasets.ClassLabel(names=labels),
                 "meter": datasets.Value("string"),
                 "alt_meter": datasets.Value("string"),
             }),
@@ -92,44 +54,35 @@ class Meter2800(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Get the data directory
-        data_dir = dl_manager.download_and_extract("")
-        
+        # download CSVs and tarball
+        csv_files = dl_manager.download({
+            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")
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "csv_file": f"{data_dir}/data_train_{self.config.name}.csv",
-                    "data_dir": data_dir
-                },
+                gen_kwargs={"csv_path": csv_files["train"], "root": archive}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "csv_file": f"{data_dir}/data_val_{self.config.name}.csv",
-                    "data_dir": data_dir
-                },
+                gen_kwargs={"csv_path": csv_files["val"], "root": archive}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={
-                    "csv_file": f"{data_dir}/data_test_{self.config.name}.csv",
-                    "data_dir": data_dir
-                },
+                gen_kwargs={"csv_path": csv_files["test"], "root": archive}
             ),
         ]
 
-    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)
-
+    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():
-            # Construct the full audio path
-            audio_path = f"{data_dir}/{row['filename']}"
-            
+            path = Path(root) / row["filename"]
             yield idx, {
                 "filename": row["filename"],
-                "audio": audio_path,
+                "audio": str(path),
                 "label": row["label"],
                 "meter": str(row["meter"]),
                 "alt_meter": str(row.get("alt_meter", row["meter"])),