add dataset loading script

spectrograms.py

@@ -0,0 +1,62 @@
+from datasets import (
+    DatasetBuilder,
+    DownloadManager,
+    DatasetInfo,
+    Features,
+    Image,
+    ClassLabel,
+    Split,
+    SplitGenerator,
+)
+import pandas as pd
+
+from pathlib import Path
+
+
+_DESCRIPTION = """\
+This dataset includes spectrograms of ~950 afrobeats songs and 1k rock songs.
+The spectrograms were generated with `librosa`.
+"""
+
+_NAMES = [fdr.name for fdr in Path("./data").glob("*/")]
+
+_URLS = {
+    "afrobeats": "https://huggingface.co/datasets/Kabilan108/spectrograms/resolve/main/data/afrobeats.zip",
+    "rock": "https://huggingface.co/datasets/Kabilan108/spectrograms/resolve/main/data/rock.zip",
+}
+
+
+class SongSpectrograms(DatasetBuilder):
+    """ "Song-Spectrograms Images Dataset"""
+
+    def _info(self):
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            features=Features({"image": Image(), "label": ClassLabel(names=_NAMES)}),
+            supervised_keys=("image", "label"),
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "afrobeats_dir": Path(files["afrobeats"]) / "afrobeats",
+                    "rock_dir": Path(files["rock"]) / "rock",
+                },
+            )
+        ]
+
+    def _generate_examples(self, afrobeats_dir, rock_dir):
+        for genre_dir, label in [(afrobeats_dir, "afrobeats"), (rock_dir, "rock")]:
+            metadata = pd.read_csv(Path(genre_dir) / "metadata.tsv", sep="\t")
+
+            for _, row in metadata.iterrows():
+                path = Path(genre_dir) / row["file_name"]
+
+                yield (
+                    f"{label}_{row['file_name'].replace('.png', '')}",
+                    {"image": str(path), "label": label},
+                )