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README.md DELETED
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- # librig2p-nostress - Grapheme-To-Phoneme Dataset
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-
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- This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
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-
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- The dataset is derived from the following pre-existing datasets:
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-
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- * [LibriSpeech ASR Corpus](https://www.openslr.org/12)
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- * [LibriSpeech Alignments](https://github.com/CorentinJ/librispeech-alignments)
 
 
 
 
 
 
 
 
 
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librig2p-nostress.py DELETED
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- # coding=utf-8
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- # Copyright 2021 Artem Ploujnikov
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-
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-
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- # Lint as: python3
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- import json
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-
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- import datasets
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-
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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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-
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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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-
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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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-
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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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-
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- def _get_url(self, split):
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- return f'{self.base_url}/{split}.json'
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-
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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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-
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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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-
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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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-
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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