def decode_one_audio_mossformer2_se_48k(model, device, inputs, args):
    """Processes audio inputs through the MossFormer2 model for speech enhancement at 48kHz.

    This function decodes audio input using the following steps:
    1. Normalizes the audio input to a maximum WAV value.
    2. Checks the length of the input to decide between online decoding and batch processing.
    3. For longer inputs, processes the audio in segments using a sliding window.
    4. Computes filter banks and their deltas for the audio segment.
    5. Passes the filter banks through the model to get a predicted mask.
    6. Applies the mask to the spectrogram of the audio segment and reconstructs the audio.
    7. For shorter inputs, processes them in one go without segmentation.
    
    Args:
        model (nn.Module): The trained MossFormer2 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 sampling rate, window size, and other parameters.

    Returns:
        numpy.ndarray: The decoded audio output, normalized to the range [-1, 1].
    """
    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]

                # Compute filter banks for the audio segment
                fbanks = compute_fbank(audio_segment.unsqueeze(0), args)
                
                # Compute deltas for filter banks
                fbank_tr = torch.transpose(fbanks, 0, 1)  # Transpose for delta computation
                fbank_delta = torchaudio.functional.compute_deltas(fbank_tr)  # First-order delta
                fbank_delta_delta = torchaudio.functional.compute_deltas(fbank_delta)  # Second-order delta
                
                # Transpose back to original shape
                fbank_delta = torch.transpose(fbank_delta, 0, 1)
                fbank_delta_delta = torch.transpose(fbank_delta_delta, 0, 1)

                # Concatenate the original filter banks with their deltas
                fbanks = torch.cat([fbanks, fbank_delta, fbank_delta_delta], dim=1)
                fbanks = fbanks.unsqueeze(0).to(device)  # Add batch dimension and move to device

                # Pass filter banks through the model
                Out_List = model(fbanks)
                pred_mask = Out_List[-1]  # Get the predicted mask from the output

                # Apply STFT to the audio segment
                spectrum = stft(audio_segment, args)
                pred_mask = pred_mask.permute(2, 1, 0)  # Permute dimensions for masking
                masked_spec = spectrum.cpu() * pred_mask.detach().cpu()  # Apply mask to the spectrum
                masked_spec_complex = masked_spec[:, :, 0] + 1j * masked_spec[:, :, 1]  # Convert to complex form

                # Reconstruct audio from the masked spectrogram
                output_segment = istft(masked_spec_complex, args, len(audio_segment))

                # Store the output segment in the output tensor
                if current_idx == 0:
                    outputs[current_idx:current_idx + window - give_up_length] = output_segment[:-give_up_length]
                else:
                    output_segment = output_segment[-window:]  # Get the latest window of output
                    outputs[current_idx + give_up_length:current_idx + window - give_up_length] = output_segment[give_up_length:-give_up_length]
                
                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)
        fbanks = compute_fbank(audio.unsqueeze(0), args)

        # Compute deltas for filter banks
        fbank_tr = torch.transpose(fbanks, 0, 1)
        fbank_delta = torchaudio.functional.compute_deltas(fbank_tr)
        fbank_delta_delta = torchaudio.functional.compute_deltas(fbank_delta)
        fbank_delta = torch.transpose(fbank_delta, 0, 1)
        fbank_delta_delta = torch.transpose(fbank_delta_delta, 0, 1)

        # Concatenate the original filter banks with their deltas
        fbanks = torch.cat([fbanks, fbank_delta, fbank_delta_delta], dim=1)
        fbanks = fbanks.unsqueeze(0).to(device)  # Add batch dimension and move to device

        # Pass filter banks through the model
        Out_List = model(fbanks)
        pred_mask = Out_List[-1]  # Get the predicted mask
        spectrum = stft(audio, args)  # Apply STFT to the audio
        pred_mask = pred_mask.permute(2, 1, 0)  # Permute dimensions for masking
        masked_spec = spectrum * pred_mask.detach().cpu()  # Apply mask to the spectrum
        masked_spec_complex = masked_spec[:, :, 0] + 1j * masked_spec[:, :, 1]  # Convert to complex form
        
        # Reconstruct audio from the masked spectrogram
        outputs = istft(masked_spec_complex, args, len(audio))

    return outputs.numpy() / MAX_WAV_VALUE  # Return the output normalized to [-1, 1]
Processes audio inputs through the MossFormer2 model for speech enhancement at 48kHz.

    This function decodes audio input using the following steps:
    1. Normalizes the audio input to a maximum WAV value.
    2. Checks the length of the input to decide between online decoding and batch processing.
    3. For longer inputs, processes the audio in segments using a sliding window.
    4. Computes filter banks and their deltas for the audio segment.
    5. Passes the filter banks through the model to get a predicted mask.
    6. Applies the mask to the spectrogram of the audio segment and reconstructs the audio.
    7. For shorter inputs, processes them in one go without segmentation.
    
    Args:
        model (nn.Module): The trained MossFormer2 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 sampling rate, window size, and other parameters.

    Returns:
        numpy.ndarray: The decoded audio output, normalized to the range [-1, 1].
    
clearvoice/clearvoice/utils/decode.py