def decode_one_audio_mossformer2_sr_48k(model, device, inputs, args):
    """
    This function decodes a single audio input using a two-stage speech super-resolution model.
    Supports both offline decoding (for short audio) and online decoding (for long audio)
    with a sliding window approach.

    Parameters:
    -----------
    model : list
        A list of two-stage models:
        - model[0]: The transformer-based Mossformer model for feature enhancement.
        - model[1]: The vocoder for generating high-resolution waveforms.
    device : str or torch.device
        The computation device ('cpu' or 'cuda') where the models will run.
    inputs : torch.Tensor
        A tensor of shape (batch_size, num_samples) containing low-resolution audio signals.
        Only the first audio (inputs[0, :]) is processed.
    args : Namespace
        An object containing the following attributes:
        - sampling_rate: Sampling rate of the input audio (e.g., 48,000 Hz).
        - one_time_decode_length: Maximum duration (in seconds) for offline decoding.
        - decode_window: Window size (in seconds) for sliding window processing.
        - Other optional attributes used for Mel spectrogram extraction.

    Returns:
    --------
    numpy.ndarray
        The high-resolution audio waveform as a NumPy array, refined and upsampled.
    """
    inputs = inputs[0, :]  # Extract the first element from the input tensor
    input_len = inputs.shape[0]  # Get the length of the input audio
    #inputs = inputs * MAX_WAV_VALUE  # Normalize the input to the maximum WAV value

    # Check if input length exceeds the defined threshold for online decoding
    if input_len > args.sampling_rate * args.one_time_decode_length:  # 20 seconds
        online_decoding = True
        if online_decoding:
            window = int(args.sampling_rate * args.decode_window)  # Define window length (e.g., 4s for 48kHz)
            stride = int(window * 0.75)  # Define stride length (e.g., 3s for 48kHz)
            t = inputs.shape[0]  # Update length after potential padding

            # Pad input if necessary to match window size
            if t < window:
                inputs = np.concatenate([inputs, np.zeros(window - t)], 0)
            elif t < window + stride:
                padding = window + stride - t
                inputs = np.concatenate([inputs, np.zeros(padding)], 0)
            else:
                if (t - window) % stride != 0:
                    padding = t - (t - window) // stride * stride
                    inputs = np.concatenate([inputs, np.zeros(padding)], 0)

            audio = torch.from_numpy(inputs).type(torch.FloatTensor)  # Convert to Torch tensor
            t = audio.shape[0]  # Update length after conversion
            outputs = torch.from_numpy(np.zeros(t))  # Initialize output tensor
            give_up_length = (window - stride) // 2  # Determine length to ignore at the edges
            dfsmn_memory_length = 0  # Placeholder for potential memory length
            current_idx = 0  # Initialize current index for sliding window

            # Process audio in sliding window segments
            while current_idx + window <= t:
                # Select appropriate segment of audio for processing
                if current_idx < dfsmn_memory_length:
                    audio_segment = audio[0:current_idx + window]
                else:
                    audio_segment = audio[current_idx - dfsmn_memory_length:current_idx + window]

                # Pass filter banks through the model
                mel_segment = get_mel(audio_segment.unsqueeze(0), args)
                mossformer_output_segment = model[0](mel_segment.to(device))
                generator_output_segment = model[1](mossformer_output_segment)
                generator_output_segment = generator_output_segment.squeeze()
                offset = len(audio_segment) - len(generator_output_segment)
                # Store the output segment in the output tensor
                if current_idx == 0:
                    outputs[current_idx:current_idx + window - give_up_length] = generator_output_segment[:-give_up_length+offset]
                else:
                    generator_output_segment = generator_output_segment[-window:]  # Get the latest window of output
                    outputs[current_idx + give_up_length:current_idx + window - give_up_length] = generator_output_segment[give_up_length:-give_up_length+offset]
                
                current_idx += stride  # Move to the next segment

    else:
        # Process the entire audio at once if it is shorter than the threshold
        audio = torch.from_numpy(inputs).type(torch.FloatTensor)
        mel_input = get_mel(audio.unsqueeze(0), args)
        mossformer_output = model[0](mel_input.to(device))
        generator_output = model[1](mossformer_output)
        outputs = generator_output.squeeze()

    outputs = outputs.cpu().numpy()
    outputs = bandwidth_sub(inputs, outputs)
    return outputs

    This function decodes a single audio input using a two-stage speech super-resolution model.
    Supports both offline decoding (for short audio) and online decoding (for long audio)
    with a sliding window approach.

    Parameters:
    -----------
    model : list
        A list of two-stage models:
        - model[0]: The transformer-based Mossformer model for feature enhancement.
        - model[1]: The vocoder for generating high-resolution waveforms.
    device : str or torch.device
        The computation device ('cpu' or 'cuda') where the models will run.
    inputs : torch.Tensor
        A tensor of shape (batch_size, num_samples) containing low-resolution audio signals.
        Only the first audio (inputs[0, :]) is processed.
    args : Namespace
        An object containing the following attributes:
        - sampling_rate: Sampling rate of the input audio (e.g., 48,000 Hz).
        - one_time_decode_length: Maximum duration (in seconds) for offline decoding.
        - decode_window: Window size (in seconds) for sliding window processing.
        - Other optional attributes used for Mel spectrogram extraction.

    Returns:
    --------
    numpy.ndarray
        The high-resolution audio waveform as a NumPy array, refined and upsampled.
    
clearvoice/clearvoice/utils/decode.py