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from datasets import (
DatasetBuilder,
DownloadManager,
DatasetInfo,
Features,
Image,
ClassLabel,
Split,
SplitGenerator,
)
import pandas as pd
import datasets
from pathlib import Path
_DESCRIPTION = """\
This dataset includes spectrograms of ~950 afrobeats songs and 1k rock songs.
The spectrograms were generated with `librosa`.
"""
_NAMES = ["afrobeats", "rock"]
_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 Spectrograms(DatasetBuilder):
class Spectrograms(datasets.GeneratorBasedBuilder):
"""Spectrograms Images Dataset"""
def _info(self):
return DatasetInfo(
description=_DESCRIPTION,
features=Features({"image": datasets.Value("string"), "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},
)
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