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import json |
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import datasets |
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_DESCRIPTION = """ |
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Grapheme-to-Phoneme training, validation and test sets |
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""" |
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_BASE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress/resolve/main/dataset" |
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_HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress" |
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_NA = "N/A" |
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_SPLIT_TYPES = ["train", "valid", "test"] |
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_DATA_TYPES = ["lexicon", "sentence"] |
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_SPLITS = [ |
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f"{data_type}_{split_type}" |
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for data_type in _DATA_TYPES |
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for split_type in _SPLIT_TYPES] |
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class GraphemeToPhoneme(datasets.GeneratorBasedBuilder): |
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def __init__(self, base_url=None, splits=None, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.base_url = base_url or _BASE_URL |
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self.splits = splits or _SPLITS |
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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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"id": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"origin": datasets.Value("string"), |
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"char": datasets.Value("string"), |
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"phn": datasets.Sequence(datasets.Value("string")), |
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}, |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE_URL, |
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) |
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def _get_url(self, split): |
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return f'{self.base_url}/{split}.json' |
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def _split_generator(self, dl_manager, split): |
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url = self._get_url(split) |
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path = dl_manager.download_and_extract(url) |
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return datasets.SplitGenerator( |
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name=split, |
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gen_kwargs={"datapath": path, "datatype": split}, |
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) |
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def _split_generators(self, dl_manager): |
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return [ |
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self._split_generator(dl_manager, split) |
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for split in self.splits |
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] |
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def _generate_examples(self, datapath, datatype): |
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with open(datapath, encoding="utf-8") as f: |
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data = json.load(f) |
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for sentence_counter, (item_id, item) in enumerate(data.items()): |
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resp = { |
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"id": item_id, |
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"speaker_id": str(item.get("speaker_id") or _NA), |
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"origin": item["origin"], |
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"char": item["char"], |
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"phn": item["phn"], |
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} |
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yield sentence_counter, resp |
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