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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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import pandas as pd |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = "" |
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_DATASETNAME = "kheng_info" |
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_DESCRIPTION = """\ |
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The Kheng.info Speech dataset was derived from recordings of Khmer words on the Khmer dictionary website kheng.info. |
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The recordings were recorded by a native Khmer speaker. |
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The recordings are short, generally ranging between 1 to 2 seconds only. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/seanghay/khmer_kheng_info_speech" |
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_LANGUAGES = ["khm"] |
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_LICENSE = Licenses.UNKNOWN.value |
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_LOCAL = False |
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_URLS = { |
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_DATASETNAME: "https://huggingface.co/datasets/seanghay/khmer_kheng_info_speech/resolve/main/data/train-00000-of-00001-4e7ad082a34164d1.parquet", |
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} |
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_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class KhengInfoDataset(datasets.GeneratorBasedBuilder): |
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"""This is the Kheng.info Speech dataset, which wasderived from recordings on the Khmer dictionary website kheng.info""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_sptext", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_sptext", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features({"word": datasets.Value("string"), "duration_ms": datasets.Value("int64"), "audio": datasets.Audio(sampling_rate=16_000)}) |
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elif self.config.schema == "seacrowd_sptext": |
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features = schemas.speech_text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_dir, |
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}, |
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) |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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df = pd.read_parquet(filepath, engine="pyarrow") |
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if self.config.schema == "source": |
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for _id, row in df.iterrows(): |
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yield _id, {"word": row["word"], "duration_ms": row["duration_ms"], "audio": row["audio"]} |
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elif self.config.schema == "seacrowd_sptext": |
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for _id, row in df.iterrows(): |
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yield _id, { |
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"id": _id, |
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"path": row["audio"], |
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"audio": row["audio"], |
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"text": row["word"], |
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"speaker_id": None, |
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"metadata": { |
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"speaker_age": None, |
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"speaker_gender": None, |
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}, |
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} |
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