update

.gitignore

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+data/.DS_Store

GTZAN_genre_classification.py

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+import os
+import csv
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@misc{gtzan2023,
+  title={GTZAN Music Genre Classification Dataset},
+  author={Your Name},
+  year={2023},
+  url={https://example.com},
+}
+"""
+
+_DESCRIPTION = """\
+The GTZAN dataset is a music genre classification dataset.
+It consists of 10 genres, each represented by 100 tracks.
+"""
+
+_HOMEPAGE_URL = "https://example.com"
+_DATA_URL = "data/gtzan.zip"
+
+class GTZANConfig(datasets.BuilderConfig):
+    """BuilderConfig for GTZAN"""
+
+    def __init__(self, **kwargs):
+        super(GTZANConfig, self).__init__(**kwargs)
+
+
+class GTZAN(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        GTZANConfig(name="gtzan", version=datasets.Version("1.0.0"), description="GTZAN Music Genre Classification Dataset")
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "genre": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "artist": datasets.Value("string"),
+                    "tempo": datasets.Value("float"),
+                    "keys": datasets.Value("string"),
+                    "loudness": datasets.Value("float"),
+                    "embeddings": datasets.Value("string"),
+                    "sorted_pred_genres": datasets.Value("string"),
+                    "x_tsne": datasets.Value("float"),
+                    "y_tsne": datasets.Value("float"),
+                    "z_tsne": datasets.Value("float"),
+                    "x_umap": datasets.Value("float"),
+                    "y_umap": datasets.Value("float"),
+                    "z_umap": datasets.Value("float"),
+                    "album_cover_path": datasets.Value("string"),
+                    "key": datasets.Value("string"),
+                    "filepath": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=44100),
+                }
+            ),
+            supervised_keys=("audio", "genre"),
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(_DATA_URL)
+        data_dir = os.path.join(archive_path, "gtzan")
+
+        # Debugging the paths
+        logger.info(f"Archive path: {archive_path}")
+        logger.info(f"Data directory: {data_dir}")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "metadata_file": os.path.join(data_dir, "metadata.csv"),
+                    "data_dir": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, metadata_file, data_dir):
+        with open(metadata_file, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for id_, row in enumerate(reader):
+                file_path = os.path.join(data_dir, row["filepath"])
+                
+                # Debugging the file paths
+                logger.info(f"Processing file: {file_path}")
+
+                yield id_, {
+                    "id": int(row["id"]),
+                    "genre": row["genre"],
+                    "title": row["title"],
+                    "artist": row["artist"],
+                    "tempo": float(row["tempo"]),
+                    "keys": row["keys"],
+                    "loudness": float(row["loudness"]),
+                    "embeddings": row["embeddings"],
+                    "sorted_pred_genres": row["sorted_pred_genres"],
+                    "x_tsne": float(row["x_tsne"]),
+                    "y_tsne": float(row["y_tsne"]),
+                    "z_tsne": float(row["z_tsne"]),
+                    "x_umap": float(row["x_umap"]),
+                    "y_umap": float(row["y_umap"]),
+                    "z_umap": float(row["z_umap"]),
+                    "album_cover_path": os.path.join(data_dir, row["album_cover_path"]),
+                    "key": row["key"],
+                    "filepath": file_path,
+                    "audio": file_path,
+                }