class_docstr: Speech2Understand class
  
  Examples:
      >>> import soundfile
      >>> speech2understand = Speech2Understand("slu_config.yml", "slu.pth")
      >>> audio, rate = soundfile.read("speech.wav")
      >>> speech2understand(audio)
      [(text, token, token_int, hypothesis object), ...]
docstr: Inference
  
  Args:
      data: Input speech data
  Returns:
      text, token, token_int, hyp
docstr_tok: ["Inference","Args",":","data",":","Input","speech","data","Returns",":","text",",","token",",","token_int",",","hyp"]
code: def __call__(
      self,
      speech: Union[torch.Tensor, np.ndarray],
      transcript: torch.Tensor = None,
  ) -> List[
      Tuple[
          Optional[str],
          List[str],
          List[int],
          Union[Hypothesis, ExtTransHypothesis, TransHypothesis],
      ]
  ]:
      """Inference
  
      Args:
          data: Input speech data
      Returns:
          text, token, token_int, hyp
  
      """
      assert check_argument_types()
  
      # Input as audio signal
      if isinstance(speech, np.ndarray):
          speech = torch.tensor(speech)
  
      # data: (Nsamples,) -> (1, Nsamples)
      speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
      # lengths: (1,)
      lengths = speech.new_full(
          [1], dtype=torch.long, fill_value=speech.size(1)
      )
      if transcript is None:
          batch = {"speech": speech, "speech_lengths": lengths}
          logging.info("speech length: " + str(speech.size(1)))
      else:
          transcript = transcript.unsqueeze(0).to(
              getattr(torch, self.dtype)
          )
          # lengths: (1,)
          transcript_lengths = transcript.new_full(
              [1], dtype=torch.long, fill_value=transcript.size(1)
          )
          # print(text)
          # print(text_lengths)
          batch = {
              "speech": speech,
              "speech_lengths": lengths,
              "transcript_pad": transcript,
              "transcript_pad_lens": transcript_lengths,
          }
  
      # a. To device
      batch = to_device(batch, device=self.device)
  
      # b. Forward Encoder
      enc, _ = self.asr_model.encode(**batch)
      if isinstance(enc, tuple):
          enc = enc[0]
      assert len(enc) == 1, len(enc)
  
      # c. Passed the encoder result and the beam search
      if self.beam_search_transducer:
          logging.info("encoder output length: " + str(enc[0].shape[0]))
          nbest_hyps = self.beam_search_transducer(enc[0])
  
          best = nbest_hyps[0]
          logging.info(f"total log probability: {best.score:.2f}")
          logging.info(
              f"normalized log probability: {best.score / len(best.yseq):.2f}"
          )
          logging.info(
              "best hypo: "
              + "".join(self.converter.ids2tokens(best.yseq[1:]))
              + "\n"
          )
      else:
          nbest_hyps = self.beam_search(
              x=enc[0],
              maxlenratio=self.maxlenratio,
              minlenratio=self.minlenratio,
          )
      nbest_hyps = nbest_hyps[: self.nbest]
  
      results = []
      for hyp in nbest_hyps:
          assert isinstance(hyp, (Hypothesis, TransHypothesis)), type(
              hyp
          )
  
          # remove sos/eos and get results
          last_pos = (
              None if self.asr_model.use_transducer_decoder else -1
          )
          if isinstance(hyp.yseq, list):
              token_int = hyp.yseq[1:last_pos]
          else:
              token_int = hyp.yseq[1:last_pos].tolist()
  
          # remove blank symbol id, which is assumed to be 0
          token_int = list(filter(lambda x: x != 0, token_int))
  
          # Change integer-ids to tokens
          token = self.converter.ids2tokens(token_int)
  
          if self.tokenizer is not None:
              text = self.tokenizer.tokens2text(token)
          else:
              text = None
          results.append((text, token, token_int, hyp))
  
      assert check_return_type(results)
      return results
  
imports: import argparse
  import logging
  import sys
  from distutils.version import LooseVersion
  from pathlib import Path
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.transducer.beam_search_transducer import BeamSearchTransducer
  from espnet2.asr.transducer.beam_search_transducer import ExtendedHypothesis
  from espnet2.asr.transducer.beam_search_transducer import Hypothesis
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  from espnet2.tasks.slu import SLUTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.batch_beam_search_online_sim import BatchBeamSearchOnlineSim
  from espnet.nets.beam_search import BeamSearch, Hypothesis
  from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.nets.scorers.length_bonus import LengthBonus
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 2 for calling the Speech2Understand obj's underlying member method code with example usage: obj.__call__(speech, transcript) and returns: results