def decode_one_audio_mossformergan_se_16k(model, device, inputs, args):
    """Decodes audio using the MossFormerGAN model for speech enhancement at 16kHz.

    This function processes the input audio tensor either in segments or as a whole, 
    depending on the length of the input. The `_decode_one_audio_mossformergan_se_16k` 
    function is called to perform the model inference and return the enhanced audio output.

    Args:
        model (nn.Module): The trained MossFormerGAN model used for decoding.
        device (torch.device): The device (CPU or GPU) for computation.
        inputs (torch.Tensor): Input audio tensor of shape (B, T), where B is the batch size 
                              and T is the number of time steps.
        args (Namespace): Contains arguments for decoding configuration.

    Returns:
        numpy.ndarray: The decoded audio output, which has been enhanced by the model.
    """
    decode_do_segment = False  # Flag to determine if segmentation is needed
    window = int(args.sampling_rate * args.decode_window)  # Decoding window length
    stride = int(window * 0.75)  # Decoding stride for segmenting the input
    b, t = inputs.shape  # Get batch size (b) and input length (t)

    # Check if input length exceeds one-time decode length to decide on segmentation
    if t > args.sampling_rate * args.one_time_decode_length:
        decode_do_segment = True  # Enable segment decoding for long sequences

    # Convert inputs to a PyTorch tensor and move to the specified device
    inputs = torch.from_numpy(np.float32(inputs)).to(device)

    # Compute normalization factor based on the input
    norm_factor = torch.sqrt(inputs.size(-1) / torch.sum((inputs ** 2.0), dim=-1))

    b, t = inputs.shape  # Update batch size and input length after conversion

    # Process the inputs in segments if necessary
    if decode_do_segment:
        outputs = np.zeros(t)  # Initialize the output array
        give_up_length = (window - stride) // 2  # Calculate length to give up at each segment
        current_idx = 0  # Initialize current index for segmentation

        while current_idx + window <= t:
            tmp_input = inputs[:, current_idx:current_idx + window]  # Get segment input
            tmp_output = _decode_one_audio_mossformergan_se_16k(model, device, tmp_input, norm_factor, args)  # Inference on segment

            # For the first segment, use the whole segment minus the give-up length
            if current_idx == 0:
                outputs[current_idx:current_idx + window - give_up_length] = tmp_output[:-give_up_length]
            else:
                # For subsequent segments, account for the give-up length
                outputs[current_idx + give_up_length:current_idx + window - give_up_length] = tmp_output[give_up_length:-give_up_length]

            current_idx += stride  # Move to the next segment

        return outputs  # Return the accumulated outputs from segments
    else:
        # If no segmentation is required, process the entire input
        return _decode_one_audio_mossformergan_se_16k(model, device, inputs, norm_factor, args)  # Inference on full input
Decodes audio using the MossFormerGAN model for speech enhancement at 16kHz.

    This function processes the input audio tensor either in segments or as a whole, 
    depending on the length of the input. The `_decode_one_audio_mossformergan_se_16k` 
    function is called to perform the model inference and return the enhanced audio output.

    Args:
        model (nn.Module): The trained MossFormerGAN model used for decoding.
        device (torch.device): The device (CPU or GPU) for computation.
        inputs (torch.Tensor): Input audio tensor of shape (B, T), where B is the batch size 
                              and T is the number of time steps.
        args (Namespace): Contains arguments for decoding configuration.

    Returns:
        numpy.ndarray: The decoded audio output, which has been enhanced by the model.
    
clearvoice/clearvoice/utils/decode.py