    def _get_source_audio(self, index: Union[int, List[int]]) -> torch.Tensor:
        """
        Gives source audio for given index with any relevant transforms
        applied. For ConcatAug, source audios for given indices are
        concatenated in given order.
        Args:
            index (int or List[int]): index—or in the case of ConcatAug,
            indices—to pull the source audio for
        Returns:
            source audios concatenated for given indices with
            relevant transforms appplied
        """
        if _is_int_or_np_int(index):
            source = get_features_or_waveform(
                self.audio_paths[index],
                need_waveform=self.cfg.use_audio_input,
                use_sample_rate=self.cfg.use_sample_rate,
                waveform_transforms=self.waveform_transforms,
            )
        else:
            source = np.concatenate(
                [
                    get_features_or_waveform(
                        self.audio_paths[i],
                        need_waveform=self.cfg.use_audio_input,
                        use_sample_rate=self.cfg.use_sample_rate,
                        waveform_transforms=self.waveform_transforms,
                    )
                    for i in index
                ]
            )
        if self.cfg.use_audio_input:
            source = torch.from_numpy(source).float()
            if self.cfg.standardize_audio:
                with torch.no_grad():
                    source = F.layer_norm(source, source.shape)
        else:
            if self.feature_transforms is not None:
                source = self.feature_transforms(source)
            source = torch.from_numpy(source).float()
        return source
Gives source audio for given index with any relevant transforms
applied. For ConcatAug, source audios for given indices are
concatenated in given order.
Args:
index (int or List[int]): index—or in the case of ConcatAug,
indices—to pull the source audio for
Returns:
source audios concatenated for given indices with
relevant transforms appplied
fairseq/data/audio/speech_to_text_dataset.py