from pathlib import Path from typing import List import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @misc{magichubLEXIndoIndonesian, author = {}, title = {LEX-INDO: AN INDONESIAN LEXICON}, year = {}, howpublished = {Online}, url = {https://magichub.com/datasets/indonesian-lexicon/}, note = {Accessed 19-03-2024}, } """ _DATASETNAME = "lex_indo" _DESCRIPTION = """This open-source lexicon consists of 2,000 common Indonesian words, with phoneme series attached. It is intended to be used as the lexicon for an automatic speech recognition system or a text-to-speech system. The dictionary presents words as well as their pronunciation transcribed with an ARPABET(phone set of CMU)-like phone set. Syllables are separated with dots. """ _HOMEPAGE = "https://magichub.com/datasets/indonesian-lexicon/" _LICENSE = Licenses.CC_BY_NC_ND_4_0.value _LOCAL = True _URLS = {} _SUPPORTED_TASKS = [Tasks.MULTILEXNORM] _LANGUAGES = ["ind"] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class LexIndo(datasets.GeneratorBasedBuilder): """This open-source lexicon consists of 2,000 common Indonesian words, with phoneme series attached""" 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} lexicon with source schema", schema="source", subset_id=_DATASETNAME, ) ] + [ SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} lexicon with SEACrowd schema", schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", subset_id=_DATASETNAME, ) ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: schema = self.config.schema if schema == "source": features = datasets.Features({"id": datasets.Value("string"), "word": datasets.Value("string"), "phoneme": datasets.Value("string")}) else: 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.""" if self.config.data_dir is None: raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.") else: data_dir = self.config.data_dir return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir, }, ) ] def _generate_examples(self, filepath: Path): """Yields examples as (key, example) tuples.""" try: with open(f"{filepath}/Indonesian_dic.txt", "r") as f: data = f.readlines() except FileNotFoundError: print("File not found. Please check the file path. Make sure Indonesian_dic.txt is in dest directory") except IOError: print("An error occurred while trying to read the file.") for idx, text in enumerate(data): word_i = text.split()[0] phoneme_i = " ".join(text.split()[1:]) if self.config.schema == "source": example = {"id": str(idx), "word": word_i, "phoneme": phoneme_i} elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": example = { "id": str(idx), "text_1": word_i, "text_2": phoneme_i, "text_1_name": _LANGUAGES[-1], "text_2_name": "phoneme", } else: raise ValueError(f"Invalid config: {self.config.name}") yield idx, example