def METHOD_NAME(self, split: str, task_cfg: FairseqDataclass = None, **kwargs):
    data_path = self.cfg.data
    task_cfg = task_cfg or self.cfg
    # upgrade old task
    if isinstance(task_cfg, Namespace):
        if not hasattr(task_cfg, "autoregressive"):
            task_cfg.autoregressive = not task_cfg.criterion == "ctc"
    text_compression_level = getattr(
        TextCompressionLevel, str(self.cfg.text_compression_level)
    )
    if getattr(task_cfg, "binarized_dataset", False):
        self.datasets[split] = BinarizedAudioDataset(
            data_path,
            split=split,
            sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate),
            max_sample_size=self.cfg.max_sample_size,
            min_sample_size=self.cfg.min_sample_size,
            pad=task_cfg.labels is not None or task_cfg.enable_padding,
            normalize=task_cfg.normalize,
            num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu),
            compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu),
            **self._get_mask_precompute_kwargs(task_cfg),
        )
    else:
        manifest_path = os.path.join(data_path, "{}.tsv".format(split))
        self.datasets[split] = FileAudioDataset(
            manifest_path=manifest_path,
            sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate),
            max_sample_size=self.cfg.max_sample_size,
            min_sample_size=self.cfg.min_sample_size,
            pad=task_cfg.labels is not None or task_cfg.enable_padding,
            normalize=task_cfg.normalize,
            num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu),
            compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu),
            text_compression_level=text_compression_level,
            **self._get_mask_precompute_kwargs(task_cfg),
        )
    if self.cfg.tpu and task_cfg.inferred_w2v_config.mask_channel_prob == 0.0:
        logger.info(
            "Pretraining on TPUs may suffer convergence "
            "issues when training with `mask_channel_prob` value of "
            "0. You may want to set this to a low value close to 0."
        )