def _transform_model_input(
        self,
        x: np.ndarray | "torch.Tensor",
        y: np.ndarray | None = None,
        compute_gradient: bool = False,
    ) -> tuple[dict, list]:
        """
        Transform the user input space into the model input space.

        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                  could have different lengths. A possible example of `x` could be:
                  `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
        :param y: Target values of shape (nb_samples). Each sample in `y` is a string, and it may possess different
                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
        :param compute_gradient: Indicate whether to compute gradients for the input `x`.
        :return: A tuple of a dictionary of batch and a list representing the original order of the batch
        """
        import torch
        from fairseq.data import data_utils

        def _collate_fn(batch: list) -> dict:
            """
            Collate function that transforms a list of numpy array or torch tensor representing a batch into a
            dictionary that Espresso takes as input.
            """
            # sort by seq length in descending order
            batch = sorted(batch, key=lambda t: t[0].size(0), reverse=True)
            batch_size = len(batch)
            max_seqlength = batch[0][0].size(0)
            src_frames = torch.zeros(batch_size, max_seqlength, 1)
            src_lengths = torch.zeros(batch_size, dtype=torch.long)

            for i, (sample, _) in enumerate(batch):
                seq_length = sample.size(0)
                src_frames[i, :seq_length, :] = sample.unsqueeze(1)
                src_lengths[i] = seq_length

            if compute_gradient:
                src_frames = torch.tensor(src_frames, requires_grad=True)
                src_frames.requires_grad = True

            # for input feeding in training
            if batch[0][1] is not None:
                pad_idx = self.dictionary.pad()
                eos_idx = self.dictionary.eos()
                target = data_utils.collate_tokens(
                    [s[1] for s in batch],
                    pad_idx,
                    eos_idx,
                    False,
                    False,
                    pad_to_length=None,
                    pad_to_multiple=1,
                )
                prev_output_tokens = data_utils.collate_tokens(
                    [s[1] for s in batch],
                    pad_idx,
                    eos_idx,
                    False,
                    True,
                    pad_to_length=None,
                    pad_to_multiple=1,
                )
                target = target.long().to(self._device)
                prev_output_tokens = prev_output_tokens.long().to(self._device)
                ntokens = sum(s[1].ne(pad_idx).int().sum().item() for s in batch)

            else:
                target = None
                prev_output_tokens = None
                ntokens = None

            batch_dict = {
                "ntokens": ntokens,
                "net_input": {
                    "src_tokens": src_frames.to(self._device),
                    "src_lengths": src_lengths.to(self._device),
                    "prev_output_tokens": prev_output_tokens,
                },
                "target": target,
            }

            return batch_dict

        # We must process each sequence separately due to the diversity of their length
        batch = []
        for i, _ in enumerate(x):
            # First process the target
            if y is None:
                target = None
            else:
                eap = self.spp.EncodeAsPieces(y[i])
                sp_string = " ".join(eap)
                target = self.dictionary.encode_line(sp_string, add_if_not_exist=False)  # target is a long tensor

            # Push the sequence to device
            if isinstance(x, np.ndarray):
                x[i] = x[i].astype(config.ART_NUMPY_DTYPE)
                x[i] = torch.tensor(x[i]).to(self._device)

            # Set gradient computation permission
            if compute_gradient:
                x[i].requires_grad = True

            # Re-scale the input audio to the magnitude used to train Espresso model
            x[i] = x[i] * 32767

            # Form the batch
            batch.append((x[i], target))

        # We must keep the order of the batch for later use as the following function will change its order
        batch_idx = sorted(range(len(batch)), key=lambda i: batch[i][0].size(0), reverse=True)

        # The collate function is important to convert input into model space
        batch_dict = _collate_fn(batch)

        # return inputs, targets, input_percentages, target_sizes, batch_idx
        return batch_dict, batch_idx

        Transform the user input space into the model input space.

        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                  could have different lengths. A possible example of `x` could be:
                  `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
        :param y: Target values of shape (nb_samples). Each sample in `y` is a string, and it may possess different
                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
        :param compute_gradient: Indicate whether to compute gradients for the input `x`.
        :return: A tuple of a dictionary of batch and a list representing the original order of the batch
        
art/estimators/speech_recognition/pytorch_espresso.py