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.gitattributes

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README.md

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-# librig2p-nostress - Grapheme-To-Phoneme Dataset
-
-This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
-
-The dataset is derived from the following pre-existing datasets:
-
-* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
-* [LibriSpeech Alignments](https://github.com/CorentinJ/librispeech-alignments)

dataset/sentence_train.json

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dataset/lexicon_valid.json → default/librig2p-nostress-lexicon_test.parquet

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dataset/lexicon_train.json → default/librig2p-nostress-sentence_train.parquet

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default/librig2p-nostress-sentence_valid.parquet

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librig2p-nostress.py

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-# coding=utf-8
-# Copyright 2021 Artem Ploujnikov
-
-
-# Lint as: python3
-import json
-
-import datasets
-
-_DESCRIPTION = """
-Grapheme-to-Phoneme training, validation and test sets
-"""
-
-_BASE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress/resolve/main/dataset"
-_HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress"
-_NA = "N/A"
-_SPLIT_TYPES = ["train", "valid", "test"]
-_DATA_TYPES = ["lexicon", "sentence"]
-_SPLITS = [
-    f"{data_type}_{split_type}"
-    for data_type in _DATA_TYPES
-    for split_type in _SPLIT_TYPES]
-
-class GraphemeToPhoneme(datasets.GeneratorBasedBuilder):
-    def __init__(self, base_url=None, splits=None, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.base_url = base_url or _BASE_URL
-        self.splits = splits or _SPLITS
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "speaker_id": datasets.Value("string"),
-                    "origin": datasets.Value("string"),
-                    "char": datasets.Value("string"),
-                    "phn": datasets.Sequence(datasets.Value("string")),
-                },
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE_URL,
-        )
-
-    def _get_url(self, split):
-        return f'{self.base_url}/{split}.json'
-
-    def _split_generator(self, dl_manager, split):
-        url = self._get_url(split)
-        path = dl_manager.download_and_extract(url)
-        return datasets.SplitGenerator(
-            name=split,
-            gen_kwargs={"datapath": path, "datatype": split},
-        )
-
-    def _split_generators(self, dl_manager):
-        return [
-            self._split_generator(dl_manager, split)
-            for split in self.splits
-        ]
-
-    def _generate_examples(self, datapath, datatype):
-        with open(datapath, encoding="utf-8") as f:
-            data = json.load(f)
-
-        for sentence_counter, (item_id, item) in enumerate(data.items()):
-            resp = {
-                "id": item_id,
-                "speaker_id": str(item.get("speaker_id") or _NA),
-                "origin": item["origin"],
-                "char": item["char"],
-                "phn": item["phn"],
-            }
-            yield sentence_counter, resp