def decode_one_audio_frcrn_se_16k(model, device, inputs, args):
    """Decodes audio using the FRCRN 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 model's inference method is applied 
    to obtain the enhanced audio output.

    Args:
        model (nn.Module): The trained FRCRN model used for decoding.
        device (torch.device): The device (CPU or GPU) to perform computations on.
        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

    # Pad the inputs to meet the decoding window length requirements
    if t < window:
        # Pad with zeros if the input length is less than the window size
        inputs = np.concatenate([inputs, np.zeros((inputs.shape[0], window - t))], axis=1)
    elif t < window + stride:
        # Pad the input if its length is less than the window plus stride
        padding = window + stride - t
        inputs = np.concatenate([inputs, np.zeros((inputs.shape[0], padding))], axis=1)
    else:
        # Ensure the input length is a multiple of the stride
        if (t - window) % stride != 0:
            padding = t - (t - window) // stride * stride
            inputs = np.concatenate([inputs, np.zeros((inputs.shape[0], padding))], axis=1)

    # Convert inputs to a PyTorch tensor and move to the specified device
    inputs = torch.from_numpy(np.float32(inputs)).to(device)
    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 = model.inference(tmp_input).detach().cpu().numpy()  # 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
    else:
        # If no segmentation is required, process the entire input
        outputs = model.inference(inputs).detach().cpu().numpy()  # Inference on full input

    return outputs  # Return the decoded audio output
Decodes audio using the FRCRN 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 model's inference method is applied 
    to obtain the enhanced audio output.

    Args:
        model (nn.Module): The trained FRCRN model used for decoding.
        device (torch.device): The device (CPU or GPU) to perform computations on.
        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