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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
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