def METHOD_NAME(dataset_dir: str, sample_rate: int, speaker_id: int, youtube_id: int, utterance_id: int, seed: int):
    """Create and save audio samples to corresponding files
    Args:
        dataset_dir (str): The directory of the dataset.
        sample_rate (int): Sample rate of waveform.
        speaker_id (int): The index of speaker sub directory.
        youtube_id (int): The index of youtube sub directory.
        utterance_id (int): The utterance index.
        seed (int): The seed to generate the waveform.
    Returns:
        Tuple[torch.Tensor, int, int, str, str]
        The waveform Tensor, sample rate, speaker label, file_name, and the file path.
    """
    # add random string before youtube_id
    youtube_id = "Zxhsj" + str(youtube_id)
    path = os.path.join(dataset_dir, "id10" + str(speaker_id), youtube_id)
    os.makedirs(path, exist_ok=True)
    filename = str(utterance_id) + ".wav"
    file_path = os.path.join(path, filename)
    waveform = get_whitenoise(
        sample_rate=sample_rate,
        duration=0.01,
        n_channels=1,
        seed=seed,
    )
    save_wav(file_path, waveform, sample_rate)
    file_name = "-".join(["id10" + str(speaker_id), youtube_id, str(utterance_id)])
    file_path = "/".join(["id10" + str(speaker_id), youtube_id, str(utterance_id) + ".wav"])
    return waveform, sample_rate, speaker_id, file_name, file_path