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import os
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import datasets
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from huggingface_hub import HfFileSystem
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from typing import List, Tuple
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logger = datasets.logging.get_logger(__name__)
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fs = HfFileSystem()
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_CITATION = """
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"""
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_DESCRIPTION = """
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This dataset contain short clips of Vietnamese speakers.
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"""
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_HOMEPAGE = "https://github.com/tanthinhdt/vietnamese-av-asr"
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_REPO_PATH = "datasets/phdkhanh2507/vietnamese-speaker-clip"
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_REPO_URL = f"https://huggingface.co/{_REPO_PATH}/resolve/main"
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_URLS = {
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"meta": f"{_REPO_URL}/metadata/" + "{channel}.parquet",
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"visual": f"{_REPO_URL}/visual/" + "{channel}.zip",
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"audio": f"{_REPO_URL}/audio/" + "{channel}.zip",
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}
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_CONFIGS = ["all"]
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if fs.exists(_REPO_PATH + "/metadata"):
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_CONFIGS.extend([
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os.path.basename(file_name)[:-8]
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for file_name in fs.listdir(_REPO_PATH + "/metadata", detail=False)
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if file_name.endswith(".parquet")
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])
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class VietnameseSpeakerClipConfig(datasets.BuilderConfig):
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"""Vietnamese Speaker Clip configuration."""
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def __init__(self, name, **kwargs):
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"""
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:param name: Name of subset.
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:param kwargs: Arguments.
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"""
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super(VietnameseSpeakerClipConfig, self).__init__(
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name=name,
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version=datasets.Version("1.0.0"),
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description=_DESCRIPTION,
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**kwargs,
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)
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class VietnameseSpeakerClip(datasets.GeneratorBasedBuilder):
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"""Vietnamese Speaker Clip dataset."""
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BUILDER_CONFIGS = [VietnameseSpeakerClipConfig(name) for name in _CONFIGS]
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DEFAULT_CONFIG_NAME = "all"
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def _info(self) -> datasets.DatasetInfo:
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features = datasets.Features({
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"id": datasets.Value("string"),
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"channel": datasets.Value("string"),
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"visual": datasets.Value("string"),
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"duration": datasets.Value("float64"),
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"fps": datasets.Value("int8"),
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"audio": datasets.Value("string"),
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"sampling_rate": datasets.Value("int64"),
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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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citation=_CITATION,
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)
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def _split_generators(
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self, dl_manager: datasets.DownloadManager
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) -> List[datasets.SplitGenerator]:
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"""
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Get splits.
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:param dl_manager: Download manager.
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:return: Splits.
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"""
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config_names = _CONFIGS[1:] if self.config.name == "all" else [self.config.name]
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metadata_paths = dl_manager.download(
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[_URLS["meta"].format(channel=channel) for channel in config_names]
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)
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visual_dirs = dl_manager.download_and_extract(
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[_URLS["visual"].format(channel=channel) for channel in config_names]
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)
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audio_dirs = dl_manager.download_and_extract(
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[_URLS["audio"].format(channel=channel) for channel in config_names]
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)
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visual_dict = {
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channel: visual_dir for channel, visual_dir in zip(config_names, visual_dirs)
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}
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audio_dict = {
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channel: audio_dir for channel, audio_dir in zip(config_names, audio_dirs)
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}
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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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"metadata_paths": metadata_paths,
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"visual_dict": visual_dict,
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"audio_dict": audio_dict,
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},
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),
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]
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def _generate_examples(
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self, metadata_paths: List[str],
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visual_dict: dict,
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audio_dict: dict,
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) -> Tuple[int, dict]:
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"""
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Generate examples from metadata.
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:param metadata_paths: Paths to metadata.
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:param visual_dict: Paths to directory containing visual data.
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:param audio_dict: Paths to directory containing audio data.
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:yield: Example.
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"""
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dataset = datasets.load_dataset(
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"parquet",
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data_files=metadata_paths,
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split="train",
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)
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for i, sample in enumerate(dataset):
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channel = sample["channel"]
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visual_path = os.path.join(
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visual_dict[channel], channel, sample["id"] + ".mp4"
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)
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audio_path = os.path.join(
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audio_dict[channel], channel, sample["id"] + ".wav"
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)
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yield i, {
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"id": sample["id"],
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"channel": channel,
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"visual": visual_path,
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"duration": sample["duration"],
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"fps": sample["fps"],
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"audio": audio_path,
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"sampling_rate": sample["sampling_rate"],
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}
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