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oil.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.
+
+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}")