class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer in
  Espresso.
  
  | Paper link: https://arxiv.org/abs/1909.08723
class_docstr_tok: ["This","class","implements","a","model-specific","automatic","speech","recognizer","using","the","end-to-end","speech","recognizer","in","Espresso",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1909.08723"]
docstr: Perform prediction for a batch of inputs.
  
  :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.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
  :param batch_size: Batch size.
  :return: Transcription as a numpy array of characters. A possible example of a transcription return
           is `np.array(['SIXTY ONE', 'HELLO'])`.
docstr_tok: ["Perform","prediction","for","a","batch","of","inputs",".",":","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.array","(","[","np.array","(","[","0.1",",","0.2",",","0.1",",","0.4","]",")",",","np.array","(","[","0.3",",","0.1","]",")","]",")","`",".",":","param","batch_size",":","Batch","size",".",":","return",":","Transcription","as","a","numpy","array","of","characters",".","A","possible","example","of","a","transcription","return","is","`","np.array","(","[","'SIXTY","ONE","'",",","'HELLO","'","]",")","`","."]
code: def predict(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> np.ndarray:
      """
      Perform prediction for a batch of inputs.
  
      :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.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
      :param batch_size: Batch size.
      :return: Transcription as a numpy array of characters. A possible example of a transcription return
               is `np.array(['SIXTY ONE', 'HELLO'])`.
      """
  
      def get_symbols_to_strip_from_output(generator):
          if hasattr(generator, "symbols_to_strip_from_output"):
              return generator.symbols_to_strip_from_output
  
          return {generator.eos, generator.pad}
  
      x_in = np.empty(len(x), dtype=object)
      x_in[:] = list(x)
  
      # Put the model in the eval mode
      self.model.eval()
  
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(
          x_in, y=None, fit=False
      )
  
      # Run prediction with batch processing
      decoded_output = []
      # result_output_sizes = np.zeros(x_preprocessed.shape[0], dtype=int)
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
  
      for m in range(num_batch):
          # Batch indexes
          begin, end = (
              m * batch_size,
              min((m + 1) * batch_size, x_preprocessed.shape[0]),
          )
  
          # Transform x into the model input space
          # Note that batch is re-ordered during transformation
          batch, batch_idx = self._transform_model_input(
              x=x_preprocessed[begin:end]
          )
  
          hypos = self.task.inference_step(
              self.generator, self._models, batch
          )
  
          decoded_output_batch = []
  
          for _, hypos_i in enumerate(hypos):
              # Process top predictions
              for _, hypo in enumerate(hypos_i[: self.esp_args.nbest]):
                  hypo_str = self.dictionary.string(
                      hypo["tokens"].int().cpu(),
                      bpe_symbol=None,
                      extra_symbols_to_ignore=get_symbols_to_strip_from_output(
                          self.generator
                      ),
                  )  # not removing bpe at this point
                  detok_hypo_str = self.bpe.decode(hypo_str)
                  decoded_output_batch.append(detok_hypo_str)
  
          decoded_output_array = np.array(decoded_output_batch)
  
          decoded_output_copy = decoded_output_array.copy()
          decoded_output_array[
              batch_idx
          ] = decoded_output_copy  # revert decoded output to its original order
          decoded_output.append(decoded_output_array)
  
      return np.concatenate(decoded_output)
  
imports: import ast
  from argparse import Namespace
  import logging
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 2 for calling the PyTorchEspresso obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: np, unknown, generator