def convert_waveform(
    waveform: Union[np.ndarray, torch.Tensor],
    sample_rate: int,
    normalize_volume: bool = False,
    to_mono: bool = False,
    to_sample_rate: Optional[int] = None,
) -> Tuple[Union[np.ndarray, torch.Tensor], int]:
    """convert a waveform:
    - to a target sample rate
    - from multi-channel to mono channel
    - volume normalization

    Args:
        waveform (numpy.ndarray or torch.Tensor): 2D original waveform
            (channels x length)
        sample_rate (int): original sample rate
        normalize_volume (bool): perform volume normalization
        to_mono (bool): convert to mono channel if having multiple channels
        to_sample_rate (Optional[int]): target sample rate
    Returns:
        waveform (numpy.ndarray): converted 2D waveform (channels x length)
        sample_rate (float): target sample rate
    """
    try:
        import torchaudio.sox_effects as ta_sox
    except ImportError:
        raise ImportError("Please install torchaudio: pip install torchaudio")

    effects = []
    if normalize_volume:
        effects.append(["gain", "-n"])
    if to_sample_rate is not None and to_sample_rate != sample_rate:
        effects.append(["rate", f"{to_sample_rate}"])
    if to_mono and waveform.shape[0] > 1:
        effects.append(["channels", "1"])
    if len(effects) > 0:
        is_np_input = isinstance(waveform, np.ndarray)
        _waveform = torch.from_numpy(waveform) if is_np_input else waveform
        converted, converted_sample_rate = ta_sox.apply_effects_tensor(
            _waveform, sample_rate, effects
        )
        if is_np_input:
            converted = converted.numpy()
        return converted, converted_sample_rate
    return waveform, sample_rate
convert a waveform:
    - to a target sample rate
    - from multi-channel to mono channel
    - volume normalization

    Args:
        waveform (numpy.ndarray or torch.Tensor): 2D original waveform
            (channels x length)
        sample_rate (int): original sample rate
        normalize_volume (bool): perform volume normalization
        to_mono (bool): convert to mono channel if having multiple channels
        to_sample_rate (Optional[int]): target sample rate
    Returns:
        waveform (numpy.ndarray): converted 2D waveform (channels x length)
        sample_rate (float): target sample rate
    
structured_prompting/fairseq-version/fairseq/fairseq/data/audio/audio_utils.py