Convert dataset to Parquet (#9)

- Convert dataset to Parquet (b5b3d351c5370501a2b79fba8ccceed1f67886fe)
- Delete loading script (9954eea637812477cd5488498c088f2550bd267d)
- Delete data file (f0408e7e6180c2eeea89917247bdca3edd4a18ea)

GTZAN_genre_classification.py

@@ -1,116 +0,0 @@
-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"),
-                    "audio": datasets.Audio(sampling_rate=44100),
-                    "genre": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "artist": datasets.Value("string"),
-                    "tempo": datasets.Value("float"),
-                    "keys": datasets.Value("string"),
-                    "loudness": datasets.Value("float"),
-                    "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"),
-                }
-            ),
-            supervised_keys=("genre", "title"),
-            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):
-                # Correct the file paths by removing the 'data/gtzan/' prefix
-                file_path = os.path.join(data_dir, row["filepath"].replace("data/gtzan/", ""))
-                album_cover_path = os.path.join(data_dir, row["album_cover_path"].replace("data/gtzan/", ""))
-
-                # Debugging the file paths
-                logger.info(f"Processing file: {file_path}")
-                if not os.path.exists(file_path):
-                    logger.error(f"File not found: {file_path}")
-
-                yield id_, {
-                    "id": int(row["id"]),
-                    "audio": file_path,  # Include the audio path next to the ID
-                    "genre": row["genre"],
-                    "title": row["title"],
-                    "artist": row["artist"],
-                    "tempo": float(row["tempo"]),
-                    "keys": row["keys"],
-                    "loudness": float(row["loudness"]),
-                    "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": album_cover_path,
-                    # "key": row["key"],
-                    # "filepath": file_path,
-                }

README.md

@@ -35,18 +35,12 @@ dataset_info:
     dtype: float32
   - name: z_umap
     dtype: float32
-  - name: album_cover_path
-    dtype: string
-  - name: key
-    dtype: string
-  - name: filepath
-    dtype: string
   splits:
   - name: train
-    num_bytes: 1323465590.0
+    num_bytes: 1323109027.0
     num_examples: 999
-  download_size: 1305799028
-  dataset_size: 1323465590.0
+  download_size: 1305734429
+  dataset_size: 1323109027.0
 configs:
 - config_name: gtzan
   data_files:

data/gtzan.zip → gtzan/train-00000-of-00003.parquet

@@ -1,3 +1,3 @@
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-oid sha256:0e62a3b63c083298de9d7238edd74c4f2c4908d81c6514ef9d1f421f1c87f9ba
-size 1244166188
+oid sha256:2a19b0c8cd445a81aecbdd1b852bebd537cbcd50d6d691cdef84e3082e100833
+size 440616185

gtzan/train-00001-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb1f9b32a44d48e8080a1391cbb25cba37a8a50d6657556cdc267206ced15e4a
+size 429077505

gtzan/train-00002-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2765faff5c6d28916f5079d138c659c1242eeab82bf09c4a1e96a9daf84b633
+size 436040739