def METHOD_NAME(
    *,
    sample_rate: int = 16000,
    duration: float = 1,  # seconds
    n_channels: int = 1,
    seed: int = 0,
    dtype: Union[str, torch.dtype] = "float32",
    device: Union[str, torch.device] = "cpu",
    channels_first=True,
    scale_factor: float = 1,
):
    """Generate pseudo audio data with whitenoise
    Args:
        sample_rate: Sampling rate
        duration: Length of the resulting Tensor in seconds.
        n_channels: Number of channels
        seed: Seed value used for random number generation.
            Note that this function does not modify global random generator state.
        dtype: Torch dtype
        device: device
        channels_first: whether first dimension is n_channels
        scale_factor: scale the Tensor before clamping and quantization
    Returns:
        Tensor: shape of (n_channels, sample_rate * duration)
    """
    if isinstance(dtype, str):
        dtype = getattr(torch, dtype)
    if dtype not in [torch.float64, torch.float32, torch.int32, torch.int16, torch.uint8]:
        raise NotImplementedError(f"dtype {dtype} is not supported.")
    # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices,
    # so we only fork on CPU, generate values and move the data to the given device
    with torch.random.fork_rng([]):
        torch.random.manual_seed(seed)
        tensor = torch.randn([n_channels, int(sample_rate * duration)], dtype=torch.float32, device="cpu")
    tensor /= 2.0
    tensor *= scale_factor
    tensor.clamp_(-1.0, 1.0)
    if not channels_first:
        tensor = tensor.t()
    tensor = tensor.to(device)
    return convert_tensor_encoding(tensor, dtype)