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import os
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import datasets
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import pandas as pd
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from datasets.tasks import AudioClassification
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_NAMES = [
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"gao_hu",
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"er_hu",
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"zhong_hu",
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"ge_hu",
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"di_yin_ge_hu",
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"jing_hu",
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"ban_hu",
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"bang_di",
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"qu_di",
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"xin_di",
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"da_di",
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"gao_yin_sheng",
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"zhong_yin_sheng",
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"di_yin_sheng",
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"gao_yin_suo_na",
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"zhong_yin_suo_na",
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"ci_zhong_yin_suo_na",
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"di_yin_suo_na",
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"gao_yin_guan",
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"zhong_yin_guan",
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"di_yin_guan",
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"bei_di_yin_guan",
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"ba_wu",
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"xun",
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"xiao",
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"liu_qin",
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"xiao_ruan",
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"pi_pa",
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"yang_qin",
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"zhong_ruan",
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"da_ruan",
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"gu_zheng",
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"gu_qin",
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"kong_hou",
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"san_xian",
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"yun_luo",
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"bian_zhong",
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"violin",
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"viola",
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"cello",
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"double_bass",
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"piccolo",
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"flute",
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"oboe",
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"clarinet",
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"bassoon",
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"saxophone",
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"trumpet",
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"trombone",
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"horn",
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"tuba",
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"harp",
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"tubular_bells",
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"bells",
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"xylophone",
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"vibraphone",
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"marimba",
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"piano",
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"clavichord",
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"accordion",
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"organ",
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]
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_DBNAME = os.path.basename(__file__).split(".")[0]
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_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic-database/{_DBNAME}/repo?Revision=master&FilePath=data"
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_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{_DBNAME}"
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_CITATION = """\
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@dataset{zhaorui_liu_2021_5676893,
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author = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
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title = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
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month = {mar},
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year = {2024},
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publisher = {HuggingFace},
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version = {1.2},
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url = {https://huggingface.co/ccmusic-database}
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}
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"""
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_DESCRIPTION = """\
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The raw dataset encompasses subjective timbre evaluation scores comprising 16 terms, such as bright, dark, raspy, etc, evaluated across 37 Chinese instruments and 24 Western instruments by 14 participants with musical backgrounds in a subjective evaluation experiment. Additionally, it includes 10 reports on spectrogram analysis of 10 instruments.
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During the integration, we have crafted the Chinese part and the Non-Chinese part into two splits. Each split is composed of multiple data entries, with each entry structured across 18 columns. The Chinese split encompasses 37 entries, while the Non-Chinese split includes 24 entries. The premier column of each data entry presents the instrument recordings in the .wav format, sampled at a rate of 22,050 Hz. The second column provides the Chinese pinyin or English name of the instrument. The subsequent 16 columns correspond to the 10-point score of the 16 terms. This dataset is suitable for conducting timber analysis of musical instruments and can also be utilized for various single or multiple regression tasks related to term scoring.
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"""
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_URLS = {
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"audio": f"{_DOMAIN}/audio.zip",
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"mel": f"{_DOMAIN}/mel.zip",
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"Chinese": f"{_DOMAIN}/Chinese.csv",
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"Western": f"{_DOMAIN}/Western.csv",
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}
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class instrument_timbre(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"audio": datasets.Audio(sampling_rate=22050),
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"mel": datasets.Image(),
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"instrument": datasets.features.ClassLabel(names=_NAMES),
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"slim": datasets.Value("float32"),
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"bright": datasets.Value("float32"),
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"dim": datasets.Value("float32"),
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"sharp": datasets.Value("float32"),
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"thick": datasets.Value("float32"),
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"thin": datasets.Value("float32"),
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"solid": datasets.Value("float32"),
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"clear": datasets.Value("float32"),
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"dry": datasets.Value("float32"),
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"plump": datasets.Value("float32"),
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"rough": datasets.Value("float32"),
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"pure": datasets.Value("float32"),
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"hoarse": datasets.Value("float32"),
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"harmonious": datasets.Value("float32"),
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"soft": datasets.Value("float32"),
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"turbid": datasets.Value("float32"),
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}
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),
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supervised_keys=("audio", "instrument"),
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homepage=_HOMEPAGE,
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license="CC-BY-NC-ND",
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version="1.2.0",
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citation=_CITATION,
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task_templates=[
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AudioClassification(
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task="audio-classification",
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audio_column="audio",
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label_column="instrument",
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)
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],
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)
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def _split_generators(self, dl_manager):
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audio_files = dl_manager.download_and_extract(_URLS["audio"])
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mel_files = dl_manager.download_and_extract(_URLS["mel"])
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cn_ins_eval = dl_manager.download(_URLS["Chinese"])
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en_ins_eval = dl_manager.download(_URLS["Western"])
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cn_labels = pd.read_csv(cn_ins_eval, index_col="instrument_id")
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en_labels = pd.read_csv(en_ins_eval, index_col="instrument_id")
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cn_dataset, en_dataset = {}, {}
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for path in dl_manager.iter_files([audio_files]):
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fname: str = os.path.basename(path)
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i = int(fname.split(".wa")[0]) - 1
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if fname.endswith(".wav"):
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region = os.path.basename(os.path.dirname(path))
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labels = cn_labels if region == "Chinese" else en_labels
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data = {
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"audio": path,
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"mel": "",
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"instrument": labels.iloc[i]["instrument_name"],
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"slim": labels.iloc[i]["slim"],
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"bright": labels.iloc[i]["bright"],
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"dim": labels.iloc[i]["dim"],
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"sharp": labels.iloc[i]["sharp"],
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"thick": labels.iloc[i]["thick"],
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"thin": labels.iloc[i]["thin"],
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"solid": labels.iloc[i]["solid"],
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"clear": labels.iloc[i]["clear"],
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"dry": labels.iloc[i]["dry"],
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"plump": labels.iloc[i]["plump"],
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"rough": labels.iloc[i]["rough"],
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"pure": labels.iloc[i]["pure"],
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"hoarse": labels.iloc[i]["hoarse"],
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"harmonious": labels.iloc[i]["harmonious"],
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"soft": labels.iloc[i]["soft"],
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"turbid": labels.iloc[i]["turbid"],
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}
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if region == "Chinese":
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cn_dataset[i] = data
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else:
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en_dataset[i] = data
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for path in dl_manager.iter_files([mel_files]):
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fname = os.path.basename(path)
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i = int(fname.split(".jp")[0]) - 1
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if fname.endswith(".jpg"):
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if os.path.basename(os.path.dirname(path)) == "Chinese":
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cn_dataset[i]["mel"] = path
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else:
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en_dataset[i]["mel"] = path
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return [
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datasets.SplitGenerator(
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name="Chinese",
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gen_kwargs={
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"files": [cn_dataset[k] for k in sorted(cn_dataset)],
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},
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),
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datasets.SplitGenerator(
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name="Western",
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gen_kwargs={
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"files": [en_dataset[k] for k in sorted(en_dataset)],
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},
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),
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]
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def _generate_examples(self, files):
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for i, path in enumerate(files):
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yield i, path
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