def decode_one_audio_mossformer2_ss_16k(model, device, inputs, args):
    """Decodes audio using the MossFormer2 model for speech separation at 16kHz.

    This function handles the audio decoding process by processing the input tensor
    in segments, if necessary, and applies the model to obtain separated audio outputs.

    Args:
        model (nn.Module): The trained MossFormer2 model 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:
        list: A list of decoded audio outputs for each speaker.
    """
    out = []  # Initialize the list to store outputs
    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 if segmentation is used
    b, t = inputs.shape  # Get batch size and input length

    rms_input = (inputs ** 2).mean() ** 0.5

    # 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 ensure they meet the decoding window length requirements
    if t < window:
        inputs = np.concatenate([inputs, np.zeros((inputs.shape[0], window - t))], axis=1)
    elif t < window + stride:
        padding = window + stride - t
        inputs = np.concatenate([inputs, np.zeros((inputs.shape[0], padding))], axis=1)
    else:
        if (t - window) % stride != 0:
            padding = t - (t - window) // stride * stride
            inputs = np.concatenate([inputs, np.zeros((inputs.shape[0], padding))], axis=1)

    inputs = torch.from_numpy(np.float32(inputs)).to(device)  # Convert inputs to torch tensor and move 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((args.num_spks, t))  # Initialize output array for each speaker
        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_out_list = model(tmp_input)  # Forward pass through the model
            for spk in range(args.num_spks):
                # Convert output for the current speaker to numpy
                tmp_out_list[spk] = tmp_out_list[spk][0, :].detach().cpu().numpy()
                if current_idx == 0:
                    # For the first segment, use the whole segment minus the give-up length
                    outputs[spk, current_idx:current_idx + window - give_up_length] = tmp_out_list[spk][:-give_up_length]
                else:
                    # For subsequent segments, account for the give-up length at both ends
                    outputs[spk, current_idx + give_up_length:current_idx + window - give_up_length] = tmp_out_list[spk][give_up_length:-give_up_length]
            current_idx += stride  # Move to the next segment
        for spk in range(args.num_spks):
            out.append(outputs[spk, :])  # Append outputs for each speaker
    else:
        # If no segmentation is required, process the entire input
        out_list = model(inputs)
        for spk in range(args.num_spks):
            out.append(out_list[spk][0, :].detach().cpu().numpy())  # Append output for each speaker

    # Normalize the outputs back to the input magnitude for each speaker
    for spk in range(args.num_spks):
        rms_out = (out[spk] ** 2).mean() ** 0.5
        out[spk] = out[spk] / rms_out * rms_input
    return out  # Return the list of normalized outputs
Decodes audio using the MossFormer2 model for speech separation at 16kHz.

    This function handles the audio decoding process by processing the input tensor
    in segments, if necessary, and applies the model to obtain separated audio outputs.

    Args:
        model (nn.Module): The trained MossFormer2 model 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:
        list: A list of decoded audio outputs for each speaker.
    
clearvoice/clearvoice/utils/decode.py