def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> tuple[np.ndarray, np.ndarray] | np.ndarray:
        """
        Perform batch-wise prediction for given inputs.

        :param x: Samples of shape `(nb_samples)` with values in range `[-32768, 32767]`. 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 batch_size: Size of batches.
        :return: Array of predicted transcriptions of shape `(nb_samples)`. A possible example of a transcription
                 return is `np.array(['SIXTY ONE', 'HELLO'])`.
        """
        if x[0].ndim != 1:  # pragma: no cover
            raise ValueError(
                "The LingvoASR estimator can only be used temporal data of type mono. Please remove any channel"
                "dimension."
            )
        # if inputs have 32-bit floating point wav format, the preprocessing argument is required
        is_normalized = max(map(max, np.abs(x))) <= 1.0  # type: ignore
        if is_normalized and self.preprocessing is None:  # pragma: no cover
            raise ValueError(
                "The LingvoASR estimator requires input values in the range [-32768, 32767] or normalized input values"
                " with correct preprocessing argument (mean=0, stddev=1/normalization_factor)."
            )

        nb_samples = x.shape[0]
        assert nb_samples % batch_size == 0, "Number of samples must be divisible by batch_size"

        # Apply preprocessing
        x, _ = self._apply_preprocessing(x, y=None, fit=False)

        y = []
        nb_batches = int(np.ceil(nb_samples / float(batch_size)))
        for m in range(nb_batches):
            # batch indices
            begin, end = m * batch_size, min((m + 1) * batch_size, nb_samples)

            x_batch_padded, _, mask_frequency = self._pad_audio_input(x[begin:end])

            feed_dict = {
                self._x_padded: x_batch_padded,
                self._y_target: np.array(["DUMMY"] * batch_size),
                self._mask_frequency: mask_frequency,
            }
            # run prediction
            y_batch = self.sess.run(self._predict_batch_op, feed_dict)

            # extract and append transcription result
            y += y_batch["topk_decoded"][:, 0].tolist()

        y_decoded = [item.decode("utf-8").upper() for item in y]
        return np.array(y_decoded, dtype=str)

        Perform batch-wise prediction for given inputs.

        :param x: Samples of shape `(nb_samples)` with values in range `[-32768, 32767]`. 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 batch_size: Size of batches.
        :return: Array of predicted transcriptions of shape `(nb_samples)`. A possible example of a transcription
                 return is `np.array(['SIXTY ONE', 'HELLO'])`.
        
art/estimators/speech_recognition/tensorflow_lingvo.py