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64f6984
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Parent(s):
434f180
Delete loading script
Browse files- GTZAN_genre_classification.py +0 -116
GTZAN_genre_classification.py
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import os
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import csv
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import datasets
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logger = datasets.logging.get_logger(__name__)
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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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_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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_HOMEPAGE_URL = "https://example.com"
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_DATA_URL = "data/gtzan.zip"
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class GTZANConfig(datasets.BuilderConfig):
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"""BuilderConfig for GTZAN"""
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def __init__(self, **kwargs):
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super(GTZANConfig, self).__init__(**kwargs)
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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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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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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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# 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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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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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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# 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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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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}
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