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",")",",","...","]"]
code: def __init__(
      self,
      s2t_train_config: Union[Path, str] = None,
      s2t_model_file: Union[Path, str] = None,
      lm_train_config: Union[Path, str] = None,
      lm_file: Union[Path, str] = None,
      ngram_scorer: str = "full",
      ngram_file: Union[Path, str] = None,
      token_type: str = None,
      bpemodel: str = None,
      device: str = "cpu",
      maxlenratio: float = 0.0,
      minlenratio: float = 0.0,
      batch_size: int = 1,
      dtype: str = "float32",
      beam_size: int = 20,
      ctc_weight: float = 0.0,
      lm_weight: float = 0.0,
      ngram_weight: float = 0.0,
      penalty: float = 0.0,
      nbest: int = 1,
      normalize_length: bool = False,
      streaming: bool = False,
      quantize_s2t_model: bool = False,
      quantize_lm: bool = False,
      quantize_modules: List[str] = ["Linear"],
      quantize_dtype: str = "qint8",
      category_sym: str = "<en>",
      task_sym: str = "<asr>",
      time_sym: Optional[str] = "<notimestamps>",
  ):
      assert check_argument_types()
  
      quantize_modules = set(
          [getattr(torch.nn, q) for q in quantize_modules]
      )
      quantize_dtype = getattr(torch, quantize_dtype)
  
      # 1. Build S2T model
      s2t_model, s2t_train_args = S2TTask.build_model_from_file(
          s2t_train_config, s2t_model_file, device
      )
      s2t_model.to(dtype=getattr(torch, dtype)).eval()
  
      if quantize_s2t_model:
          logging.info("Use quantized s2t model for decoding.")
  
          s2t_model = torch.quantization.quantize_dynamic(
              s2t_model,
              qconfig_spec=quantize_modules,
              dtype=quantize_dtype,
          )
  
      decoder = s2t_model.decoder
      ctc = CTCPrefixScorer(ctc=s2t_model.ctc, eos=s2t_model.eos)
      token_list = s2t_model.token_list
      scorers = dict(
          decoder=decoder,
          ctc=ctc,
          length_bonus=LengthBonus(len(token_list)),
      )
  
      # 2. Build language model
      if lm_train_config is not None:
          lm, lm_train_args = LMTask.build_model_from_file(
              lm_train_config, lm_file, device
          )
  
          if quantize_lm:
              logging.info("Use quantized lm for decoding.")
  
              lm = torch.quantization.quantize_dynamic(
                  lm,
                  qconfig_spec=quantize_modules,
                  dtype=quantize_dtype,
              )
  
          scorers["lm"] = lm.lm
  
      # 3. Build ngram model
      if ngram_file is not None:
          if ngram_scorer == "full":
              from espnet.nets.scorers.ngram import NgramFullScorer
  
              ngram = NgramFullScorer(ngram_file, token_list)
          else:
              from espnet.nets.scorers.ngram import NgramPartScorer
  
              ngram = NgramPartScorer(ngram_file, token_list)
          scorers["ngram"] = ngram
  
      # 4. Build BeamSearch object
      weights = dict(
          decoder=1.0 - ctc_weight,
          ctc=ctc_weight,
          lm=lm_weight,
          ngram=ngram_weight,
          length_bonus=penalty,
      )
      beam_search = BeamSearch(
          beam_size=beam_size,
          weights=weights,
          scorers=scorers,
          sos=s2t_model.sos,
          eos=s2t_model.eos,
          vocab_size=len(token_list),
          token_list=token_list,
          pre_beam_score_key=None if ctc_weight == 1.0 else "full",
          normalize_length=normalize_length,
      )
  
      # TODO(karita): make all scorers batchfied
      if batch_size == 1:
          non_batch = [
              k
              for k, v in beam_search.full_scorers.items()
              if not isinstance(v, BatchScorerInterface)
          ]
          if len(non_batch) == 0:
              if streaming:
                  beam_search.__class__ = BatchBeamSearchOnlineSim
                  beam_search.set_streaming_config(s2t_train_config)
                  logging.info(
                      "BatchBeamSearchOnlineSim implementation is selected."
                  )
              else:
                  beam_search.__class__ = BatchBeamSearch
                  logging.info(
                      "BatchBeamSearch implementation is selected."
                  )
          else:
              logging.warning(
                  f"As non-batch scorers {non_batch} are found, "
                  f"fall back to non-batch implementation."
              )
  
          beam_search.to(
              device=device, dtype=getattr(torch, dtype)
          ).eval()
          for scorer in scorers.values():
              if isinstance(scorer, torch.nn.Module):
                  scorer.to(
                      device=device, dtype=getattr(torch, dtype)
                  ).eval()
          logging.info(f"Beam_search: {beam_search}")
          logging.info(f"Decoding device={device}, dtype={dtype}")
  
      # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
      if token_type is None:
          token_type = s2t_train_args.token_type
      if bpemodel is None:
          bpemodel = s2t_train_args.bpemodel
  
      if token_type is None:
          tokenizer = None
      elif (
          token_type == "bpe"
          or token_type == "hugging_face"
          or "whisper" in token_type
      ):
          if bpemodel is not None:
              tokenizer = build_tokenizer(
                  token_type=token_type, bpemodel=bpemodel
              )
          else:
              tokenizer = None
      else:
          tokenizer = build_tokenizer(token_type=token_type)
  
      if bpemodel not in ["whisper_en", "whisper_multilingual"]:
          converter = TokenIDConverter(token_list=token_list)
      else:
          converter = OpenAIWhisperTokenIDConverter(model_type=bpemodel)
          beam_search.set_hyp_primer(
              list(
                  converter.tokenizer.sot_sequence_including_notimestamps
              )
          )
      logging.info(f"Text tokenizer: {tokenizer}")
  
      self.s2t_model = s2t_model
      self.s2t_train_args = s2t_train_args
      self.converter = converter
      self.tokenizer = tokenizer
      self.beam_search = beam_search
      self.maxlenratio = maxlenratio
      self.minlenratio = minlenratio
      self.device = device
      self.dtype = dtype
      self.nbest = nbest
  
      self.category_id = converter.token2id[category_sym]
      self.task_id = converter.token2id[task_sym]
      self.time_id = (
          converter.token2id[time_sym] if time_sym is not None else None
      )
  
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 1 to create a new Speech2Text object  with example: obj = Speech2Text(s2t_train_config, s2t_model_file, lm_train_config, lm_file, ngram_scorer, ngram_file, token_type, bpemodel, device, maxlenratio, minlenratio, batch_size, dtype, beam_size, ctc_weight, lm_weight, ngram_weight, penalty, nbest, normalize_length, streaming, quantize_s2t_model, quantize_lm, quantize_modules, quantize_dtype, category_sym, task_sym, time_sym)