class_docstr: A class designed for performing ASR and diarization together.
  
  Attributes:
      cfg_diarizer (OmegaConf):
          Hydra config for diarizer key
      params (OmegaConf):
          Parameters config in diarizer.asr
      ctc_decoder_params (OmegaConf)
          Hydra config for beam search decoder
      realigning_lm_params (OmegaConf):
          Hydra config for realigning language model
      manifest_filepath (str):
          Path to the input manifest path
      nonspeech_threshold (float):
          Threshold for VAD logits that are used for creating speech segments
      fix_word_ts_with_VAD (bool):
          Choose whether to fix word timestamps by using VAD results
      root_path (str):
          Path to the folder where diarization results are saved
      vad_threshold_for_word_ts (float):
          Threshold used for compensating word timestamps with VAD output
      max_word_ts_length_in_sec (float):
          Maximum limit for the duration of each word timestamp
      word_ts_anchor_offset (float):
          Offset for word timestamps from ASR decoders
      run_ASR:
          Placeholder variable for an ASR launcher function
      realigning_lm:
          Placeholder variable for a loaded ARPA Language model
      ctm_exists (bool):
          Boolean that indicates whether all files have the corresponding reference CTM file
      frame_VAD (dict):
          Dictionary containing frame-level VAD logits
      AUDIO_RTTM_MAP:
          Dictionary containing the input manifest information
      color_palette (dict):
          Dictionary containing the ANSI color escape codes for each speaker label (speaker index)
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docstr: Match the diarization result with the ASR output.
  The words and the timestamps for the corresponding words are matched in a for loop.
  
  Args:
      diar_hyp (dict):
          Dictionary of the Diarization output labels in str. Indexed by unique IDs.
  
          Example:
          >>>  diar_hyp['my_audio_01'] = ['0.0 4.375 speaker_1', '4.375 5.125 speaker_0', ...]
  
      word_hyp (dict):
          Dictionary of words from ASR inference. Indexed by unique IDs.
  
          Example:
          >>> word_hyp['my_audio_01'] = ['hi', 'how', 'are', ...]
  
      word_ts_hyp (dict):
          Dictionary containing the start time and the end time of each word.
          Indexed by unique IDs.
  
          Example:
          >>> word_ts_hyp['my_audio_01'] = [[0.0, 0.04], [0.64, 0.68], [0.84, 0.88], ...]
  
  Returns:
      trans_info_dict (dict):
          Dictionary containing word timestamps, speaker labels and words from all sessions.
          Each session is indexed by a unique ID.
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code: def get_transcript_with_speaker_labels(
      self,
      diar_hyp: Dict[str, List[str]],
      word_hyp: Dict[str, List[str]],
      word_ts_hyp: Dict[str, List[float]],
  ) -> Dict[str, Dict[str, float]]:
      """
      Match the diarization result with the ASR output.
      The words and the timestamps for the corresponding words are matched in a for loop.
  
      Args:
          diar_hyp (dict):
              Dictionary of the Diarization output labels in str. Indexed by unique IDs.
  
              Example:
              >>>  diar_hyp['my_audio_01'] = ['0.0 4.375 speaker_1', '4.375 5.125 speaker_0', ...]
  
          word_hyp (dict):
              Dictionary of words from ASR inference. Indexed by unique IDs.
  
              Example:
              >>> word_hyp['my_audio_01'] = ['hi', 'how', 'are', ...]
  
          word_ts_hyp (dict):
              Dictionary containing the start time and the end time of each word.
              Indexed by unique IDs.
  
              Example:
              >>> word_ts_hyp['my_audio_01'] = [[0.0, 0.04], [0.64, 0.68], [0.84, 0.88], ...]
  
      Returns:
          trans_info_dict (dict):
              Dictionary containing word timestamps, speaker labels and words from all sessions.
              Each session is indexed by a unique ID.
      """
      trans_info_dict = {}
      if self.fix_word_ts_with_VAD:
          if self.frame_VAD == {}:
              logging.warning(
                  f"VAD timestamps are not provided. Fixing word timestamps without VAD. Please check the hydra configurations."
              )
          word_ts_refined = self._compensate_word_ts_list(
              self.audio_file_list, word_ts_hyp
          )
      else:
          word_ts_refined = word_ts_hyp
  
      if self.realigning_lm_params["arpa_language_model"]:
          if not ARPA:
              raise ImportError(
                  "LM for realigning is provided but arpa is not installed. Install arpa using PyPI: pip install arpa"
              )
          else:
              self.realigning_lm = self._load_realigning_LM()
  
      word_dict_seq_list = []
      for k, audio_file_path in enumerate(self.audio_file_list):
          uniq_id = get_uniqname_from_filepath(audio_file_path)
          words, diar_labels = word_hyp[uniq_id], diar_hyp[uniq_id]
          word_ts, word_rfnd_ts = (
              word_ts_hyp[uniq_id],
              word_ts_refined[uniq_id],
          )
  
          # Assign speaker labels to words
          word_dict_seq_list = self.get_word_level_json_list(
              words=words,
              word_ts=word_ts,
              word_rfnd_ts=word_rfnd_ts,
              diar_labels=diar_labels,
          )
          if self.realigning_lm:
              word_dict_seq_list = self.realign_words_with_lm(
                  word_dict_seq_list
              )
  
          # Create a transscript information json dictionary from the output variables
          trans_info_dict[uniq_id] = self._make_json_output(
              uniq_id, diar_labels, word_dict_seq_list
          )
      logging.info(
          f"Diarization with ASR output files are saved in: {self.root_path}/pred_rttms"
      )
      return trans_info_dict
  
imports: import copy
  import csv
  import json
  import os
  from collections import OrderedDict
  from datetime import datetime
  from typing import Dict, List, Tuple
  import numpy
  from nemo.collections.asr.metrics.der import concat_perm_word_error_rate
  from nemo.collections.asr.metrics.wer import word_error_rate
  from nemo.collections.asr.models import ClusteringDiarizer
  from nemo.collections.asr.parts.utils.speaker_utils import audio_rttm_map, get_uniqname_from_filepath, labels_to_rttmfile, rttm_to_labels, write_rttm2manifest
  from nemo.utils import logging
label_desc: Use image node_id 15 for calling the OfflineDiarWithASR obj's underlying member method code with example usage: obj.get_transcript_with_speaker_labels(diar_hyp, word_hyp, word_ts_hyp) and returns: trans_info_dict