Upload g2p_si_1.py

g2p_si_1.py

@@ -0,0 +1,84 @@
+# 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-space/resolve/main/dataset"
+_HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress-space"
+_NA = "N/A"
+_SPLIT_TYPES = ["train", "valid", "test"]
+_DATA_TYPES = ["lexicon", "sentence", "homograph"]
+_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(
+                {
+                    "sample_id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "origin": datasets.Value("string"),
+                    "char": datasets.Value("string"),
+                    "phn": datasets.Sequence(datasets.Value("string")),
+                    "homograph": datasets.Value("string"),
+                    "homograph_wordid": datasets.Value("string"),
+                    "homograph_char_start": datasets.Value("int32"),
+                    "homograph_char_end": datasets.Value("int32"),
+                    "homograph_phn_start": datasets.Value("int32"),
+                    "homograph_phn_end": datasets.Value("int32"),
+                },
+            ),
+            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, (sample_id, item) in enumerate(data.items()):
+            resp = {
+                "sample_id": sample_id,
+                "speaker_id": str(item.get("speaker_id") or _NA),
+                "origin": item["origin"],
+                "char": item["char"],
+                "phn": item["phn"],
+                "homograph": item.get("homograph", _NA),
+                "homograph_wordid": item.get("homograph_wordid", _NA),
+                "homograph_char_start": item.get("homograph_char_start", 0),
+                "homograph_char_end": item.get("homograph_char_end", 0),
+                "homograph_phn_start": item.get("homograph_phn_start", 0),
+                "homograph_phn_end": item.get("homograph_phn_end", 0),
+            }
+            yield sentence_counter, resp