code: def get_random_offset_index(
      audio_manifest: dict,
      audio_read_buffer_dict: dict,
      offset_min: int = 0,
      max_audio_read_sec: float = 2.5,
      min_alignment_count: int = 2,
  ) -> int:
      """
      Get an index for randomly accessing the silence in alignment timestamps.
  
      Args:
          audio_manifest (dict): Audio manifest dictionary.
              keys: 'audio_filepath', 'duration', 'alignments', 'words'
          audio_read_buffer_dict (dict): Dictionary containing audio read buffer.
          offset_min (int): Minimum offset index. (Default: 0)
          max_audio_read_sec (float): Maximum audio read duration in seconds. (Default: 2.5)
          min_alignment_count (int): Minimum number of alignment timestamps. (Default: 2)
  
      Returns:
          (int): Random offset index smaller than `offset_count`.
      """
      if len(audio_manifest["alignments"]) <= min_alignment_count:
          raise ValueError(
              f"Audio file {audio_manifest['audio_filepath']} has less than {min_alignment_count} alignment timestamps."
          )
      index_file_id = f"{audio_manifest['audio_filepath']}#index"
  
      # Avoid multiple indexings of the same audio file by using a hash-table.
      if index_file_id in audio_read_buffer_dict:
          (sil_inds, offset_max) = audio_read_buffer_dict[index_file_id]
      else:
          # Find all silence indices
          sil_inds = np.where(
              (np.array(audio_manifest["words"]) == "") == True
          )[0]
          if (
              audio_manifest["alignments"][-1]
              - audio_manifest["alignments"][0]
              < max_audio_read_sec
          ):
              # The total duration is already short, therefore skip range search.
              offset_max = 1
          else:
              # Find the range that satisfies `max_audio_read_sec` duration.
              offset_max = binary_search_alignments(
                  inds=sil_inds,
                  max_audio_read_sec=max_audio_read_sec,
                  min_alignment_count=min_alignment_count,
                  alignments=audio_manifest["alignments"],
              )
  
          audio_read_buffer_dict[index_file_id] = (sil_inds, offset_max)
  
      # If the audio file is shorter than the max_audio_read_sec, then we don't need to read a subset of the audio file.
      if (
          len(sil_inds) <= min_alignment_count
          or (
              audio_manifest["alignments"][-1]
              - audio_manifest["alignments"][0]
          )
          < max_audio_read_sec
      ):
          return offset_min
      else:
          offset_index = np.random.randint(offset_min, offset_max)
          return sil_inds[offset_index]
  
imports: import copy
  import os
  import shutil
  from collections import defaultdict
  from typing import IO, Dict, List, Optional, Tuple
  import numpy
  import torch
  from scipy.stats import beta, gamma
  from tqdm import tqdm
  from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.collections.asr.parts.utils.manifest_utils import get_ctm_line, read_manifest, write_ctm, write_manifest, write_text
  from nemo.collections.asr.parts.utils.speaker_utils import labels_to_rttmfile
  from nemo.utils import logging
label_desc: Use image node_id 11 for calling a global function with example usage: get_random_offset_index(audio_manifest, audio_read_buffer_dict, offset_min, max_audio_read_sec, min_alignment_count) and returns: offset_min, sil_inds