song_structure.py

import os
import datasets
from datasets.tasks import AudioClassification


# Once upload a new piano brand, please register its name here
_NAMES = ["intro", "chorus", "verse", "pre-chorus", "post-chorus", "bridge"]

_DBNAME = os.path.basename(__file__).split('.')[0]

_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME

_CITATION = """\
@dataset{zhaorui_liu_2021_5676893,
  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
  title        = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
  month        = nov,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.5676893},
  url          = {https://doi.org/10.5281/zenodo.5676893}
}
"""

_DESCRIPTION = """\
This database contains 300 pop songs (.mp3 format, downloaded from NetEase Cloud Music), 
as well as a structure annotation file (.txt format) for each song. 
The song structure is labeled as follows: 
intro, chorus, verse, pre-chorus, post-chorus, bridge, ending.
"""

_URL = _HOMEPAGE + "/resolve/main/data/labels.zip"


class piano_sound_quality(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "time": datasets.Value('time32'),
                    "audio": datasets.Value('binary'),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("time", "label"),
            homepage=_HOMEPAGE,
            license="mit",
            citation=_CITATION,
            task_templates=[
                AudioClassification(
                    task="audio-classification",
                    audio_column="time",
                    label_column="label",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files]),
                },
            )
        ]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith(".wav"):
                yield i, {
                    "time": 0,
                    "audio": path,
                    "label": 0,
                }