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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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from huggingface_hub import HfFileSystem |
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from pyarrow import parquet as pq |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import SCHEMA_TO_FEATURES, TASK_TO_SCHEMA, Licenses, Tasks |
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_CITATION = """\ |
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@misc{Bud500, |
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author = {Anh Pham, Khanh Linh Tran, Linh Nguyen, Thanh Duy Cao, Phuc Phan, Duong A. Nguyen}, |
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title = {Bud500: A Comprehensive Vietnamese ASR Dataset}, |
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url = {https://github.com/quocanh34/Bud500}, |
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year = {2024} |
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} |
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""" |
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_DATASETNAME = "bud500" |
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_DESCRIPTION = """\ |
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Bud500 is a diverse Vietnamese speech corpus designed to support ASR research |
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community. With aprroximately 500 hours of audio, it covers a broad spectrum of |
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topics including podcast, travel, book, food, and so on, while spanning accents |
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from Vietnam's North, South, and Central regions. Derived from free public audio |
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resources, this publicly accessible dataset is designed to significantly enhance |
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the work of developers and researchers in the field of speech recognition. |
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Before using this dataloader, please accept the acknowledgement at |
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https://huggingface.co/datasets/linhtran92/viet_bud500 and use huggingface-cli |
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login for authentication. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/linhtran92/viet_bud500" |
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_LANGUAGES = ["vie"] |
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_LICENSE = Licenses.APACHE_2_0.value |
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_LOCAL = False |
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_BASE_URL = "https://huggingface.co/datasets/linhtran92/viet_bud500/resolve/main/data/{filename}" |
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_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION] |
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_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}" |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class Bud500Dataset(datasets.GeneratorBasedBuilder): |
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"""A diverse Vietnamese speech corpus with aprroximately 500 hours of audio.""" |
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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=_DATASETNAME, |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{_SEACROWD_SCHEMA}", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema=_SEACROWD_SCHEMA, |
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subset_id=_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( |
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{ |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"transcription": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == _SEACROWD_SCHEMA: |
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features = SCHEMA_TO_FEATURES[ |
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TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]] |
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] |
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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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"""Returns SplitGenerators.""" |
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file_list = HfFileSystem().ls("datasets/linhtran92/viet_bud500/data", detail=False) |
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train_urls, test_urls, val_urls = [], [], [] |
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for filename in file_list: |
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if filename.endswith(".parquet"): |
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filename = filename.split("/")[-1] |
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split = filename.split("-")[0] |
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url = _BASE_URL.format(filename=filename) |
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if split == "train": |
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train_urls.append(url) |
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elif split == "test": |
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test_urls.append(url) |
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elif split == "validation": |
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val_urls.append(url) |
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train_paths = list(map(Path, dl_manager.download(sorted(train_urls)))) |
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test_paths = list(map(Path, dl_manager.download(sorted(test_urls)))) |
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val_paths = list(map(Path, dl_manager.download(sorted(val_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={"data_paths": train_paths}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"data_paths": test_paths}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"data_paths": val_paths}, |
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), |
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] |
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def _generate_examples(self, data_paths: Path) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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key = 0 |
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for data_path in data_paths: |
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with open(data_path, "rb") as f: |
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pf = pq.ParquetFile(f) |
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for row_group in range(pf.num_row_groups): |
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df = pf.read_row_group(row_group).to_pandas() |
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for row in df.itertuples(): |
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if self.config.schema == "source": |
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yield key, { |
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"audio": row.audio, |
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"transcription": row.transcription, |
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} |
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elif self.config.schema == _SEACROWD_SCHEMA: |
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yield key, { |
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"id": str(key), |
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"path": None, |
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"audio": row.audio, |
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"text": row.transcription, |
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"speaker_id": None, |
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"metadata": None, |
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} |
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key += 1 |
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