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: Compensate the word timestamps based on the VAD output.
  The length of each word is capped by self.max_word_ts_length_in_sec.
  
  Args:
      audio_file_list (list):
          List containing audio file paths.
      word_ts_dict (dict):
          Dictionary containing timestamps of words.
  
  Returns:
      enhanced_word_ts_dict (dict):
          Dictionary containing the enhanced word timestamp values indexed by unique-IDs.
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code: def _compensate_word_ts_list(
      self,
      audio_file_list: List[str],
      word_ts_dict: Dict[str, List[float]],
  ) -> Dict[str, List[List[float]]]:
      """
      Compensate the word timestamps based on the VAD output.
      The length of each word is capped by self.max_word_ts_length_in_sec.
  
      Args:
          audio_file_list (list):
              List containing audio file paths.
          word_ts_dict (dict):
              Dictionary containing timestamps of words.
  
      Returns:
          enhanced_word_ts_dict (dict):
              Dictionary containing the enhanced word timestamp values indexed by unique-IDs.
      """
      enhanced_word_ts_dict = {}
      for idx, (uniq_id, word_ts_seq_list) in enumerate(
          word_ts_dict.items()
      ):
          N = len(word_ts_seq_list)
          enhanced_word_ts_buffer = []
          for k, word_ts in enumerate(word_ts_seq_list):
              if k < N - 1:
                  word_len = round(word_ts[1] - word_ts[0], 2)
                  len_to_next_word = round(
                      word_ts_seq_list[k + 1][0] - word_ts[0] - 0.01, 2
                  )
                  if uniq_id in self.frame_VAD:
                      vad_index_word_end = int(100 * word_ts[1])
                      closest_sil_stt = (
                          self._get_the_closest_silence_start(
                              vad_index_word_end,
                              self.frame_VAD[uniq_id],
                          )
                      )
                      vad_est_len = round(
                          closest_sil_stt - word_ts[0], 2
                      )
                  else:
                      vad_est_len = len_to_next_word
                  min_candidate = min(vad_est_len, len_to_next_word)
                  fixed_word_len = max(
                      min(
                          self.max_word_ts_length_in_sec, min_candidate
                      ),
                      word_len,
                  )
                  enhanced_word_ts_buffer.append(
                      [word_ts[0], word_ts[0] + fixed_word_len]
                  )
              else:
                  enhanced_word_ts_buffer.append(
                      [word_ts[0], word_ts[1]]
                  )
  
          enhanced_word_ts_dict[uniq_id] = enhanced_word_ts_buffer
      return enhanced_word_ts_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 14 for calling the OfflineDiarWithASR obj's underlying member method code with example usage: obj._compensate_word_ts_list(audio_file_list, word_ts_dict) and returns: enhanced_word_ts_dict