class_docstr: This is WER class that is modified for generating timestamps with logits.
  The functions in WER class is modified to save the timestamps whenever character
  is being saved into a list.
  This class is designed to support ASR models based on CTC and Character-level tokens.
  Please refer to the definition of WER class for more information.
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code: def decode_ids_to_tokens_with_ts(
      self, tokens: List[int], timestamps: List[int]
  ) -> List[str]:
      token_list, timestamp_list = [], []
      for i, c in enumerate(tokens):
          if c != self.decoding.blank_id:
              token_list.append(self.decoding.labels_map[c])
              timestamp_list.append(timestamps[i])
      return token_list, timestamp_list
  
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 3 for calling the WER_TS obj's underlying member method code with example usage: obj.decode_ids_to_tokens_with_ts(tokens, timestamps) and returns: token_list,  timestamp_list