class_docstr: Speech2Text class
  
  Examples:
      >>> import soundfile
      >>> speech2text = Speech2Text("s2t_config.yml", "s2t.pth")
      >>> audio, rate = soundfile.read("speech.wav")
      >>> speech2text(audio)
      [(text, token, token_int, hypothesis object), ...]
class_docstr_tok: ["Speech2Text","class","Examples",":",">",">",">","import","soundfile",">",">",">","speech2text","=","Speech2Text","(","``","s2t_config.yml","''",",","``","s2t.pth","''",")",">",">",">","audio",",","rate","=","soundfile.read","(","``","speech.wav","''",")",">",">",">","speech2text","(","audio",")","[","(","text",",","token",",","token_int",",","hypothesis","object",")",",","...","]"]
docstr: Inference for a short utterance.
  
  Args:
      speech: Input speech
      text_prev: Previous text used as condition (optional)
  Returns:
      text, token, token_int, hyp
docstr_tok: ["Inference","for","a","short","utterance",".","Args",":","speech",":","Input","speech","text_prev",":","Previous","text","used","as","condition","(","optional",")","Returns",":","text",",","token",",","token_int",",","hyp"]
code: def __call__(
      self,
      speech: Union[torch.Tensor, np.ndarray],
      text_prev: Optional[Union[torch.Tensor, np.ndarray, str]] = None,
  ) -> Union[
      ListOfHypothesis,
      Tuple[
          ListOfHypothesis,
          Optional[Dict[int, List[str]]],
      ],
  ]:
      """Inference for a short utterance.
  
      Args:
          speech: Input speech
          text_prev: Previous text used as condition (optional)
      Returns:
          text, token, token_int, hyp
  
      """
      assert check_argument_types()
  
      # Prepare hyp_primer
      if text_prev is not None:
          if isinstance(text_prev, str):
              text_prev = self.converter.tokens2ids(
                  self.tokenizer.text2tokens(text_prev)
              )
          else:
              text_prev = text_prev.tolist()
  
          # Check if text_prev is valid
          if self.s2t_model.na in text_prev:
              text_prev = None
  
      if text_prev is not None:
          hyp_primer = (
              [self.s2t_model.sop]
              + text_prev
              + [self.s2t_model.sos, self.category_id, self.task_id]
          )
      else:
          hyp_primer = [
              self.s2t_model.sos,
              self.category_id,
              self.task_id,
          ]
      if self.time_id is not None:
          hyp_primer.append(self.time_id)
      self.beam_search.set_hyp_primer(hyp_primer)
  
      # Preapre speech
      if isinstance(speech, np.ndarray):
          speech = torch.tensor(speech)
  
      # Batchify input
      # speech: (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)
      )
      batch = {"speech": speech, "speech_lengths": lengths}
      logging.info("speech length: " + str(speech.size(1)))
  
      # a. To device
      batch = to_device(batch, device=self.device)
  
      # b. Forward Encoder
      enc, enc_olens = self.s2t_model.encode(**batch)
  
      intermediate_outs = None
      if isinstance(enc, tuple):
          enc, intermediate_outs = enc
  
      assert len(enc) == 1, len(enc)
  
      # c. Pass the encoder result to the beam search
      results = self._decode_single_sample(enc[0])
  
      # Encoder intermediate CTC predictions
      if intermediate_outs is not None:
          encoder_interctc_res = self._decode_interctc(
              intermediate_outs
          )
          results = (results, encoder_interctc_res)
  
      assert check_return_type(results)
  
      return results
  
imports: import argparse
  import logging
  import sys
  from itertools import groupby
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  import torch.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  from espnet2.tasks.s2t import S2TTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.text.whisper_token_id_converter import OpenAIWhisperTokenIDConverter
  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 Speech2Text obj's underlying member method code with example usage: obj.__call__(speech, text_prev) and returns: results