Upload thai_romanization.py with huggingface_hub

thai_romanization.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+The Thai Romanization dataset contains 648,241 Thai words \
+    that were transliterated into English, making Thai \
+    pronounciation easier for non-native Thai speakers. \
+    This is a valuable dataset for Thai language learners \
+    and researchers working on Thai language processing task. \
+    Each word in the Thai Romanization dataset is paired with \
+    its English phonetic representation, enabling accurate \
+    pronunciation guidance. This facilitates the learning and \
+    practice of Thai pronunciation for individuals who may not \
+    be familiar with the Thai script. The dataset aids in improving \
+    the accessibility and usability of Thai language resources, \
+    supporting applications such as speech recognition, text-to-speech \
+    synthesis, and machine translation. It enables the development of \
+    Thai language tools that can benefit Thai learners, tourists, \
+    and those interested in Thai culture and language.
+"""
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+# There are no citation available for this dataset.
+_CITATION = ""
+
+_DATASETNAME = "thai_romanization"
+
+_DESCRIPTION = """
+The Thai Romanization dataset contains 648,241 Thai words \
+    that were transliterated into English, making Thai \
+    pronounciation easier for non-native Thai speakers. \
+    This is a valuable dataset for Thai language learners \
+    and researchers working on Thai language processing task. \
+    Each word in the Thai Romanization dataset is paired with \
+    its English phonetic representation, enabling accurate \
+    pronunciation guidance. This facilitates the learning and \
+    practice of Thai pronunciation for individuals who may not \
+    be familiar with the Thai script. The dataset aids in improving \
+    the accessibility and usability of Thai language resources, \
+    supporting applications such as speech recognition, text-to-speech \
+    synthesis, and machine translation. It enables the development of \
+    Thai language tools that can benefit Thai learners, tourists, \
+    and those interested in Thai culture and language.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/datasets/wannaphong/thai-romanization/data"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+
+_LOCAL = False
+
+_URLS = {_DATASETNAME: "https://raw.githubusercontent.com/wannaphong/thai-romanization/master/dataset/data.csv"}
+
+_SUPPORTED_TASKS = [Tasks.TRANSLITERATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ThaiRomanizationDataset(datasets.GeneratorBasedBuilder):
+    """
+    Thai Romanization dataloader from Kaggle (Phong et al., 2018)
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features({"word": datasets.Value("string"), "romanization": datasets.Value("string")})
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir),
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath, delimiter="	")
+        df.columns = ["word", "romanization"]
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                example = {
+                    "id": str(index),
+                    "text_1": str(row["word"]),
+                    "text_2": str(row["romanization"]),
+                    "text_1_name": "word",
+                    "text_2_name": "romanization",
+                }
+
+            yield index, example