chest_falsetto.py

import os
import random
import datasets
from datasets.tasks import ImageClassification

_NAMES = {
    "all": ["m_chest", "f_chest", "m_falsetto", "f_falsetto"],
    "gender": ["female", "male"],
    "singing_method": ["falsetto", "chest"],
}

_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"

_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"

_URLS = {
    "audio": f"{_DOMAIN}/audio.zip",
    "mel": f"{_DOMAIN}/mel.zip",
    "eval": f"{_DOMAIN}/eval.zip",
}


class chest_falsetto(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=(
                datasets.Features(
                    {
                        "audio": datasets.Audio(sampling_rate=22050),
                        "mel": datasets.Image(),
                        "label": datasets.features.ClassLabel(names=_NAMES["all"]),
                        "gender": datasets.features.ClassLabel(names=_NAMES["gender"]),
                        "singing_method": datasets.features.ClassLabel(
                            names=_NAMES["singing_method"]
                        ),
                    }
                )
                if self.config.name == "default"
                else datasets.Features(
                    {
                        "mel": datasets.Image(),
                        "cqt": datasets.Image(),
                        "chroma": datasets.Image(),
                        "label": datasets.features.ClassLabel(names=_NAMES["all"]),
                        "gender": datasets.features.ClassLabel(names=_NAMES["gender"]),
                        "singing_method": datasets.features.ClassLabel(
                            names=_NAMES["singing_method"]
                        ),
                    }
                )
            ),
            supervised_keys=("mel", "label"),
            homepage=_HOMEPAGE,
            license="CC-BY-NC-ND",
            version="1.2.0",
            task_templates=[
                ImageClassification(
                    task="image-classification",
                    image_column="mel",
                    label_column="label",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        dataset = []
        if self.config.name == "default":
            files = {}
            audio_files = dl_manager.download_and_extract(_URLS["audio"])
            mel_files = dl_manager.download_and_extract(_URLS["mel"])
            for fpath in dl_manager.iter_files([audio_files]):
                fname: str = os.path.basename(fpath)
                if fname.endswith(".wav"):
                    item_id = fname.split(".")[0]
                    files[item_id] = {"audio": fpath}

            for fpath in dl_manager.iter_files([mel_files]):
                fname = os.path.basename(fpath)
                if fname.endswith(".jpg"):
                    item_id = fname.split(".")[0]
                    files[item_id]["mel"] = fpath

            dataset = list(files.values())

        else:
            data_files = dl_manager.download_and_extract(_URLS["eval"])
            for fpath in dl_manager.iter_files([data_files]):
                if "mel" in fpath and os.path.basename(fpath).endswith(".jpg"):
                    dataset.append(fpath)

        categories = {}
        for name in _NAMES["all"]:
            categories[name] = []

        for data in dataset:
            fpath = data["audio"] if self.config.name == "default" else data
            filename: str = os.path.basename(fpath)[:-4]
            label = "_".join(filename.split("_")[1:3])
            categories[label].append(data)

        testset, validset, trainset = [], [], []
        for cls in categories:
            random.shuffle(categories[cls])
            count = len(categories[cls])
            p60 = int(count * 0.6)
            p80 = int(count * 0.8)
            trainset += categories[cls][:p60]
            validset += categories[cls][p60:p80]
            testset += categories[cls][p80:]

        random.shuffle(trainset)
        random.shuffle(validset)
        random.shuffle(testset)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"files": trainset}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"files": validset}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"files": testset}
            ),
        ]

    def _generate_examples(self, files):
        if self.config.name == "default":
            for i, fpath in enumerate(files):
                file_name = os.path.basename(fpath["audio"])
                sex = file_name.split("_")[1]
                method = file_name.split("_")[2].split(".")[0]
                yield i, {
                    "audio": fpath["audio"],
                    "mel": fpath["mel"],
                    "label": f"{sex}_{method}",
                    "gender": "male" if sex == "m" else "female",
                    "singing_method": method,
                }

        else:
            for i, fpath in enumerate(files):
                file_name: str = os.path.basename(fpath)
                sex = file_name.split("_")[1]
                method = file_name.split("_")[2]
                yield i, {
                    "mel": fpath,
                    "cqt": fpath.replace("mel", "cqt"),
                    "chroma": fpath.replace("mel", "chroma"),
                    "label": f"{sex}_{method}",
                    "gender": "male" if sex == "m" else "female",
                    "singing_method": method,
                }