def _decode_one_audio_mossformergan_se_16k(model, device, inputs, norm_factor, args):
    """Processes audio inputs through the MossFormerGAN model for speech enhancement.

    This function performs the following steps:
    1. Pads the input audio tensor to fit the model requirements.
    2. Computes a normalization factor for the input tensor.
    3. Applies Short-Time Fourier Transform (STFT) to convert the audio into the frequency domain.
    4. Processes the STFT representation through the model to predict the real and imaginary components.
    5. Uncompresses the predicted spectrogram and applies Inverse STFT (iSTFT) to convert back to time domain audio.
    6. Normalizes the output audio.

    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.
        norm_factor (torch.Tensor): A norm tensor to regularize input amplitude
        args (Namespace): Contains arguments for STFT parameters and normalization.

    Returns:
        numpy.ndarray: The decoded audio output, which has been enhanced by the model.
    """
    input_len = inputs.size(-1)  # Get the length of the input audio
    nframe = int(np.ceil(input_len / args.win_inc))  # Calculate the number of frames based on window increment
    padded_len = int(nframe * args.win_inc)  # Calculate the padded length to fit the model
    padding_len = padded_len - input_len  # Determine how much padding is needed

    # Pad the input audio with the beginning of the input
    inputs = torch.cat([inputs, inputs[:, :padding_len]], dim=-1)

    # Prepare inputs for STFT by transposing and normalizing
    inputs = torch.transpose(inputs, 0, 1)  # Change shape for STFT
    inputs = torch.transpose(inputs * norm_factor, 0, 1)  # Apply normalization factor and transpose back

    # Perform Short-Time Fourier Transform (STFT) on the normalized inputs
    inputs_spec = stft(inputs, args, center=True, periodic=True, onesided=True)
    inputs_spec = inputs_spec.to(torch.float32)  # Ensure the spectrogram is in float32 format

    # Compress the power of the spectrogram to improve model performance
    inputs_spec = power_compress(inputs_spec).permute(0, 1, 3, 2)

    # Pass the compressed spectrogram through the model to get predicted real and imaginary parts
    out_list = model(inputs_spec)
    pred_real, pred_imag = out_list[0].permute(0, 1, 3, 2), out_list[1].permute(0, 1, 3, 2)

    # Uncompress the predicted spectrogram to get the magnitude and phase
    pred_spec_uncompress = power_uncompress(pred_real, pred_imag).squeeze(1)

    # Perform Inverse STFT (iSTFT) to convert back to time domain audio
    outputs = istft(pred_spec_uncompress, args, center=True, periodic=True, onesided=True)

    # Normalize the output audio by dividing by the normalization factor
    outputs = outputs.squeeze(0) / norm_factor

    return outputs[:input_len].detach().cpu().numpy()  # Return the output as a numpy array
Processes audio inputs through the MossFormerGAN model for speech enhancement.

    This function performs the following steps:
    1. Pads the input audio tensor to fit the model requirements.
    2. Computes a normalization factor for the input tensor.
    3. Applies Short-Time Fourier Transform (STFT) to convert the audio into the frequency domain.
    4. Processes the STFT representation through the model to predict the real and imaginary components.
    5. Uncompresses the predicted spectrogram and applies Inverse STFT (iSTFT) to convert back to time domain audio.
    6. Normalizes the output audio.

    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.
        norm_factor (torch.Tensor): A norm tensor to regularize input amplitude
        args (Namespace): Contains arguments for STFT parameters and normalization.

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