| from datasets import ( | |
| DatasetBuilder, | |
| DownloadManager, | |
| DatasetInfo, | |
| Features, | |
| Image, | |
| ClassLabel, | |
| Split, | |
| SplitGenerator, | |
| ) | |
| import pandas as pd | |
| from pathlib import Path | |
| _DESCRIPTION = """\ | |
| This dataset includes spectrograms of ~950 afrobeats songs and 1k rock songs. | |
| The spectrograms were generated with `librosa`. | |
| """ | |
| _NAMES = [fdr.name for fdr in Path("./data").glob("*/")] | |
| _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 SongSpectrograms(DatasetBuilder): | |
| """ "Song-Spectrograms Images Dataset""" | |
| def _info(self): | |
| return DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=Features({"image": Image(), "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}, | |
| ) | |