class_docstr: A class designed for extracting word timestamps while the ASR decoding process.
  This class contains a few setups for a slew of NeMo ASR models such as QuartzNet, CitriNet and ConformerCTC models.
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docstr: Launch pyctcdecode with the loaded pretrained language model.
  
  Args:
      logprob (np.ndarray):
          The log probability from the ASR model inference in numpy array format.
      onset_delay_in_sec (float):
          The amount of delay that needs to be compensated for the timestamp outputs froM pyctcdecode.
      beam_width (int):
          The beam width parameter for beam search decodring.
  Returns:
      hyp_words (list):
          List containing the words in the hypothesis.
      word_ts (list):
          List containing the word timestamps from the decoder.
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code: def run_pyctcdecode(
      self,
      logprob: np.ndarray,
      onset_delay_in_sec: float = 0,
      beam_width: int = 32,
  ) -> Tuple[List[str], List[str]]:
      """
      Launch pyctcdecode with the loaded pretrained language model.
  
      Args:
          logprob (np.ndarray):
              The log probability from the ASR model inference in numpy array format.
          onset_delay_in_sec (float):
              The amount of delay that needs to be compensated for the timestamp outputs froM pyctcdecode.
          beam_width (int):
              The beam width parameter for beam search decodring.
      Returns:
          hyp_words (list):
              List containing the words in the hypothesis.
          word_ts (list):
              List containing the word timestamps from the decoder.
      """
      beams = self.beam_search_decoder.decode_beams(
          logprob, beam_width=self.ctc_decoder_params["beam_width"]
      )
      word_ts_beam, words_beam = [], []
      for idx, (word, _) in enumerate(beams[0][2]):
          ts = self.get_word_ts_from_wordframes(
              idx,
              beams[0][2],
              self.model_stride_in_secs,
              onset_delay_in_sec,
          )
          word_ts_beam.append(ts)
          words_beam.append(word)
      hyp_words, word_ts = words_beam, word_ts_beam
      return hyp_words, word_ts
  
imports: import copy
  import math
  from typing import Dict, List, Tuple, Type, Union
  import numpy
  import torch
  from omegaconf import OmegaConf
  import nemo.collections.asr
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.models import EncDecCTCModel, EncDecCTCModelBPE
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig, CTCDecoding, CTCDecodingConfig
  from nemo.collections.asr.parts.utils.audio_utils import get_samples
  from nemo.collections.asr.parts.utils.speaker_utils import audio_rttm_map, get_uniqname_from_filepath
  from nemo.collections.asr.parts.utils.streaming_utils import AudioFeatureIterator, FrameBatchASR
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.utils import logging
label_desc: Use image node_id 11 for calling the ASRDecoderTimeStamps obj's underlying member method code with example usage: obj.run_pyctcdecode(logprob, onset_delay_in_sec, beam_width) and returns: hyp_words,  word_ts