Add Meter2800 dataset implementation with metadata and example generation

.ipynb_checkpoints/data_test_2_classes-checkpoint.csv

@@ -0,0 +1,601 @@
+filename,label,meter,alt_meter
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meter2800.py

@@ -0,0 +1,86 @@
+from pathlib import Path
+import datasets
+import pandas as pd
+
+_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 (e.g., 'two', 'three', 'four') 
+and an alternative meter (numerical, e.g., 2, 3, 4, 6). 
+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 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
+
+class Meter2800(datasets.GeneratorBasedBuilder):
+    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"),
+    ]
+
+    def _info(self):
+        label_names = 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=None),
+                "label": datasets.ClassLabel(names=label_names),
+                "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):
+        root = Path(__file__).parent
+        suffix = "4_classes" if self.config.name == "4_classes" else "2_classes"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"csv_file": root / f"data_train_{suffix}.csv"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"csv_file": root / f"data_val_{suffix}.csv"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"csv_file": root / f"data_test_{suffix}.csv"},
+            ),
+        ]
+
+    def _generate_examples(self, csv_file):
+        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("/")
+            yield idx, {
+                "filename": row["filename"],
+                "audio": str(audio_path),
+                "label": row["label"],
+                "meter": str(row["meter"]),
+                "alt_meter": str(row.get("alt_meter", row["meter"])),
+            }