insecta_sounds.py

import os
import time
import random
import requests
import datasets
from tqdm import tqdm
from io import BytesIO
from PIL import Image

_URL = "https://www.missevan.com"

_HEADER = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36 Edg/144.0.0.0"
}


class insecta_sounds(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "audio": datasets.Value("string"),
                    "image": datasets.Image(),
                    "label": datasets.Value("string"),
                    "latin": datasets.Value("string"),
                }
            ),
            supervised_keys=("audio", "latin"),
            homepage=f"https://www.modelscope.cn/datasets/Genius-Society/{os.path.basename(__file__)[:-3]}",
            license="CC-BY-NC-ND",
            version="0.0.1",
        )

    def _get_files(self, drama_id=73247, page_size=100):
        try:
            response = requests.get(
                f"{_URL}/dramaapi/getdramaepisodedetails",
                params={"drama_id": drama_id, "p": 1, "page_size": page_size},
                headers=_HEADER,
            )
            response.raise_for_status()
            return response.json()["info"]["Datas"]

        except Exception as e:
            print(f"{e}, retrying...")
            time.sleep(random.randint(3, 5))
            return self._get_files()

    def _dld_img(self, url: str):
        try:
            response = requests.get(url, headers=_HEADER)
            response.raise_for_status()
            return Image.open(BytesIO(response.content))

        except Exception as e:
            print(f"{e}, retrying...")
            return self._dld_img(url)

    def _split_generators(self, _):
        dataset = []
        files = self._get_files()
        for file in tqdm(files, desc="Parsing classes"):
            label, latin = str(file["soundstr"]).split(" ", 1)
            dataset.append(
                {
                    "audio": f"{_URL}/soundiframe/{file['id']}?type=small",
                    "image": self._dld_img(file["front_cover"]),
                    "label": label.strip(),
                    "latin": latin.strip(),
                }
            )

        random.shuffle(dataset)

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

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            yield i, path