Initial import

.gitattributes

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 *.mp3 filter=lfs diff=lfs merge=lfs -text
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+dataset/sentence_valid.json filter=lfs diff=lfs merge=lfs -text
+dataset/lexicon_test.json filter=lfs diff=lfs merge=lfs -text
+dataset/lexicon_train.json filter=lfs diff=lfs merge=lfs -text
+dataset/lexicon_valid.json filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-license: other
----
+# 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/lexicon_test.json

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dataset/lexicon_train.json

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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://raw.githubusercontent.com/flexthink/librig2p-nostress/develop/dataset"
+
+_HOMEPAGE_URL = "https://github.com/flexthink/librig2p-nostress/tree/develop"
+
+_PHONEMES = [
+    "AA",
+    "AE",
+    "AH",
+    "AO",
+    "AW",
+    "AY",
+    "B",
+    "CH",
+    "D",
+    "DH",
+    "EH",
+    "ER",
+    "EY",
+    "F",
+    "G",
+    "HH",
+    "IH",
+    "IY",
+    "JH",
+    "K",
+    "L",
+    "M",
+    "N",
+    "NG",
+    "OW",
+    "OY",
+    "P",
+    "R",
+    "S",
+    "SH",
+    "T",
+    "TH",
+    "UH",
+    "UW",
+    "V",
+    "W",
+    "Y",
+    "Z",
+    "ZH",
+]
+_ORIGINS = ["librispeech", "librispeech-lex"]
+_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.ClassLabel(names=_ORIGINS),
+                    "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