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: Decode unsegmented long-form speech.
  
  Args:
      speech: long-form speech of shape (nsamples,)
      segment_sec: segment length in seconds, default: 30
      fs: sampling rate, default: 16000
      condition_on_prev_text: whether to condition on previous text
      init_text: text used as condition for the first segment
      start_time: the start timestamp symbol of the first segment
      end_time_threshold: the last utterance is considered as incomplete
          if its end timestamp exceeds this threshold
      first_time_sym: first timestamp symbol
      last_time_sym: last timestamp symbol
      resolution: time resolution
docstr_tok: ["Decode","unsegmented","long-form","speech",".","Args",":","speech",":","long-form","speech","of","shape","(","nsamples",",",")","segment_sec",":","segment","length","in","seconds",",","default",":","30","fs",":","sampling","rate",",","default",":","16000","condition_on_prev_text",":","whether","to","condition","on","previous","text","init_text",":","text","used","as","condition","for","the","first","segment","start_time",":","the","start","timestamp","symbol","of","the","first","segment","end_time_threshold",":","the","last","utterance","is","considered","as","incomplete","if","its","end","timestamp","exceeds","this","threshold","first_time_sym",":","first","timestamp","symbol","last_time_sym",":","last","timestamp","symbol","resolution",":","time","resolution"]
code: def decode_long(
      self,
      speech: Union[torch.Tensor, np.ndarray],
      segment_sec: float = 30,
      fs: int = 16000,
      condition_on_prev_text: bool = False,
      init_text: Optional[str] = None,
      start_time: Optional[str] = "<0.00>",
      end_time_threshold: str = "<29.00>",
      first_time_sym: str = "<0.00>",
      last_time_sym: str = "<30.00>",
      resolution: float = 0.02,
  ):
      """Decode unsegmented long-form speech.
  
      Args:
          speech: long-form speech of shape (nsamples,)
          segment_sec: segment length in seconds, default: 30
          fs: sampling rate, default: 16000
          condition_on_prev_text: whether to condition on previous text
          init_text: text used as condition for the first segment
          start_time: the start timestamp symbol of the first segment
          end_time_threshold: the last utterance is considered as incomplete
              if its end timestamp exceeds this threshold
          first_time_sym: first timestamp symbol
          last_time_sym: last timestamp symbol
          resolution: time resolution
      """
  
      assert check_argument_types()
  
      if isinstance(speech, np.ndarray):
          speech = torch.tensor(speech)
  
      segment_len = int(segment_sec * fs)
      start_time_id = (
          self.converter.token2id[start_time]
          if start_time is not None
          else None
      )
      end_time_id_threshold = self.converter.token2id[
          end_time_threshold
      ]
      first_time_id = self.converter.token2id[first_time_sym]
      last_time_id = self.converter.token2id[last_time_sym]
  
      self.time_id = start_time_id
      logging.warning(
          f"Overwrite start time as: {start_time}, {start_time_id}"
      )
  
      utterances = []
      offset = 0
      text_prev = init_text
      while offset < len(speech):
          segment = speech[
              offset : min(offset + segment_len, len(speech))
          ]
          if len(segment) < segment_len:
              segment = F.pad(segment, (0, segment_len - len(segment)))
  
          result = self.__call__(
              speech=segment,
              text_prev=text_prev if condition_on_prev_text else None,
          )
          if isinstance(result, tuple):
              result = result[0]
  
          # NOTE(yifan): sos and eos have been removed
          text, token, token_int, text_nospecial, hyp = result[
              0
          ]  # best hyp
          token_int = token_int[2:]  # remove category and task
  
          # Find all timestamp positions
          time_pos = [
              idx
              for idx, tok in enumerate(token_int)
              if tok >= first_time_id and tok <= last_time_id
          ]
          # NOTE(yifan): this is an edge case with only a start time
          if len(time_pos) == 1:
              token_int.append(last_time_id)
              time_pos.append(len(token_int) - 1)
  
          if len(time_pos) % 2 == 0:  # Timestamps are all paired
              if (
                  len(time_pos) > 2
                  and token_int[time_pos[-1]] > end_time_id_threshold
              ):
                  # The last utterance is incomplete
                  new_start_time_id = token_int[time_pos[-2]]
                  time_pos = time_pos[:-2]
              else:
                  new_start_time_id = token_int[time_pos[-1]]
          else:  # The last utterance only has start time
              new_start_time_id = token_int[time_pos[-1]]
              time_pos = time_pos[:-1]
  
          # Get utterances in this segment
          text_prev = ""
          for i in range(0, len(time_pos), 2):
              utt = (
                  round(
                      (token_int[time_pos[i]] - first_time_id)
                      * resolution
                      + offset / fs,
                      2,
                  ),
                  round(
                      (token_int[time_pos[i + 1]] - first_time_id)
                      * resolution
                      + offset / fs,
                      2,
                  ),
                  self.tokenizer.tokens2text(
                      self.converter.ids2tokens(
                          token_int[time_pos[i] + 1 : time_pos[i + 1]]
                      )
                  ),
              )
              text_prev = text_prev + utt[-1]
              utterances.append(utt)
  
          offset += round(
              (new_start_time_id - first_time_id) * resolution * fs
          )
          self.time_id = first_time_id
  
      return utterances
  
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 5 for calling the Speech2Text obj's underlying member method code with example usage: obj.decode_long(speech, segment_sec, fs, condition_on_prev_text, init_text, start_time, end_time_threshold, first_time_sym, last_time_sym, resolution) and returns: utterances