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  1. GTZAN_genre_classification.py +116 -0
GTZAN_genre_classification.py ADDED
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+ import os
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+ import csv
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+ import datasets
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+
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+ logger = datasets.logging.get_logger(__name__)
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+
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+ _CITATION = """\
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+ @misc{gtzan2023,
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+ title={GTZAN Music Genre Classification Dataset},
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+ author={Your Name},
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+ year={2023},
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+ url={https://example.com},
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The GTZAN dataset is a music genre classification dataset.
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+ It consists of 10 genres, each represented by 100 tracks.
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+ """
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+
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+ _HOMEPAGE_URL = "https://example.com"
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+ _DATA_URL = "data/gtzan.zip"
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+
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+ class GTZANConfig(datasets.BuilderConfig):
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+ """BuilderConfig for GTZAN"""
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+
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+ def __init__(self, **kwargs):
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+ super(GTZANConfig, self).__init__(**kwargs)
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+
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+
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+ class GTZAN(datasets.GeneratorBasedBuilder):
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+ BUILDER_CONFIGS = [
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+ GTZANConfig(name="gtzan", version=datasets.Version("1.0.0"), description="GTZAN Music Genre Classification Dataset")
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "id": datasets.Value("int32"),
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+ "audio": datasets.Audio(sampling_rate=44100),
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+ "genre": datasets.Value("string"),
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+ "title": datasets.Value("string"),
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+ "artist": datasets.Value("string"),
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+ "tempo": datasets.Value("float"),
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+ "keys": datasets.Value("string"),
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+ "loudness": datasets.Value("float"),
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+ "sorted_pred_genres": datasets.Value("string"),
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+ "x_tsne": datasets.Value("float"),
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+ "y_tsne": datasets.Value("float"),
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+ "z_tsne": datasets.Value("float"),
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+ "x_umap": datasets.Value("float"),
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+ "y_umap": datasets.Value("float"),
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+ "z_umap": datasets.Value("float"),
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+ "album_cover_path": datasets.Value("string"),
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+ "key": datasets.Value("string"),
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+ "filepath": datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=("genre", "title"),
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+ homepage=_HOMEPAGE_URL,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ archive_path = dl_manager.download_and_extract(_DATA_URL)
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+ data_dir = os.path.join(archive_path, "gtzan")
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+
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+ # Debugging the paths
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+ logger.info(f"Archive path: {archive_path}")
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+ logger.info(f"Data directory: {data_dir}")
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+
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "metadata_file": os.path.join(data_dir, "metadata.csv"),
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+ "data_dir": data_dir,
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, metadata_file, data_dir):
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+ with open(metadata_file, "r", encoding="utf-8") as f:
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+ reader = csv.DictReader(f)
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+ for id_, row in enumerate(reader):
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+ # Correct the file paths by removing the 'data/gtzan/' prefix
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+ file_path = os.path.join(data_dir, row["filepath"].replace("data/gtzan/", ""))
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+ album_cover_path = os.path.join(data_dir, row["album_cover_path"].replace("data/gtzan/", ""))
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+
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+ # Debugging the file paths
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+ logger.info(f"Processing file: {file_path}")
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+ if not os.path.exists(file_path):
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+ logger.error(f"File not found: {file_path}")
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+
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+ yield id_, {
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+ "id": int(row["id"]),
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+ "audio": file_path, # Include the audio path next to the ID
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+ "genre": row["genre"],
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+ "title": row["title"],
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+ "artist": row["artist"],
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+ "tempo": float(row["tempo"]),
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+ "keys": row["keys"],
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+ "loudness": float(row["loudness"]),
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+ "sorted_pred_genres": row["sorted_pred_genres"],
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+ "x_tsne": float(row["x_tsne"]),
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+ "y_tsne": float(row["y_tsne"]),
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+ "z_tsne": float(row["z_tsne"]),
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+ "x_umap": float(row["x_umap"]),
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+ "y_umap": float(row["y_umap"]),
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+ "z_umap": float(row["z_umap"]),
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+ "album_cover_path": album_cover_path,
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+ "key": row["key"],
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+ "filepath": file_path,
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+ }