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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.
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses
_CITATION = """\
@inproceedings{maxwelll-smith-foley-2023-automated,
title = "Automated speech recognition of {I}ndonesian-{E}nglish language lessons on {Y}ou{T}ube using transfer learning",
author = "Maxwell-Smith, Zara and Foley, Ben",
editor = "Serikov, Oleg
and Voloshina, Ekaterina
and Postnikova, Anna
and Klyachko, Elena
and Vylomova, Ekaterina
and Shavrina, Tatiana
and Le Ferrand, Eric
and Malykh, Valentin
and Tyers, Francis
and Arkhangelskiy, Timofey
and Mikhailov, Vladislav",
booktitle = "Proceedings of the Second Workshop on NLP Applications to Field Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.fieldmatters-1.1",
doi = "10.18653/v1/2023.fieldmatters-1.1",
pages = "1--16",
abstract = "Experiments to fine-tune large multilingual models with limited data from a specific domain or setting has potential
to improve automatic speech recognition (ASR) outcomes. This paper reports on the use of the Elpis ASR pipeline to fine-tune two
pre-trained base models, Wav2Vec2-XLSR-53 and Wav2Vec2-Large-XLSR-Indonesian, with various mixes of data from 3 YouTube channels
teaching Indonesian with English as the language of instruction. We discuss our results inferring new lesson audio (22-46%
word error rate) in the context of speeding data collection in diverse and specialised settings. This study is an example of how
ASR can be used to accelerate natural language research, expanding ethically sourced data in low-resource settings.",
}
"""
_DATASETNAME = "oil"
_DESCRIPTION = """\
The Online Indonesian Learning (OIL) dataset or corpus currently contains lessons from three Indonesian teachers who have posted content on YouTube.
"""
_HOMEPAGE = "https://huggingface.co/datasets/ZMaxwell-Smith/OIL"
_LANGUAGES = ["eng", "ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
_LOCAL = False
_URLS = {
_DATASETNAME: {"train": "https://huggingface.co/api/datasets/ZMaxwell-Smith/OIL/parquet/default/train/0.parquet"},
}
_SUPPORTED_TASKS = []
_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class OIL(datasets.GeneratorBasedBuilder):
"""The Online Indonesian Learning (OIL) dataset or corpus currently contains lessons from three Indonesian teachers who have posted content on YouTube."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"audio": datasets.Audio(decode=False),
"label": datasets.ClassLabel(num_classes=98),
}
)
else:
raise ValueError(f"Invalid config: {self.config.name}")
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]
train_path = dl_manager.download_and_extract(urls["train"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
df = pd.read_parquet(filepath)
for index, row in df.iterrows():
yield index, row.to_dict()
else:
raise ValueError(f"Invalid config: {self.config.name}")
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