Create dataset.py

dataset.py

@@ -0,0 +1,55 @@
+import csv
+import datasets
+
+_CITATION = """\
+@dataset{qasim2025animalsounds,
+  title = {Animal Sound Classification Dataset},
+  author = {Muhammad Qasim},
+  year = {2025},
+  url = {https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification}
+}
+"""
+
+_DESCRIPTION = """\
+A meticulously curated dataset of labeled animal sounds (dogs, cats, cows) for audio classification tasks. The dataset contains trimmed and cleaned audio clips with labels.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification"
+
+_LICENSE = "mit"
+
+_URLS = {
+    "train": "ML1_features.csv"
+}
+
+class AnimalSoundClassification(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "audio": datasets.Value("string"),  # path to audio file or URL
+                "label": datasets.ClassLabel(names=["dog", "cat", "cow"]),
+            }),
+            supervised_keys=("audio", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_path = dl_manager.download_and_extract(_URLS["train"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_path},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as csv_file:
+            reader = csv.DictReader(csv_file)
+            for id_, row in enumerate(reader):
+                yield id_, {
+                    "audio": row["audio"],  # Make sure column name matches your CSV
+                    "label": row["label"].lower(),  # Lowercasing to match ClassLabel names
+                }