code: def read_audio_from_buffer(
      audio_manifest: dict,
      buffer_dict: dict,
      offset_index: int,
      device: torch.device,
      max_audio_read_sec: float = 2.5,
      min_alignment_count: int = 2,
      read_subset: bool = True,
  ) -> Tuple[torch.Tensor, int, dict]:
      """
      Read from the provided file path while maintaining a hash-table that saves loading time.
      Also, this function only reads a subset of the audio file if `read_subset` is True for faster audio-file reading.
  
      Args:
          audio_manifest (dict): Audio manifest dictionary.
              keys: 'audio_filepath', 'duration', 'alignments', 'words'
          buffer_dict (dict): Hash-table that saves loaded audio files.
          offset_index (int): Index of the offset for the audio file.
          device (torch.device): Device to load the audio file.
          max_audio_read_sec (float): Maximum audio read duration.
          min_alignment_count (int): Minimum number of alignments to read.
          read_subset (bool): If True, read a subset of the audio file.
                              To control the length of the audio file, use data_simulator.session_params.max_audio_read_sec.
                              Note that using large value (greater than 3~4 sec) for `max_audio_read_sec` will slow down the generation process.
                              If False, read the entire audio file.
  
      Returns:
          audio_file (torch.Tensor): Time-series audio data in a tensor.
          sr (int): Sample rate of the audio file.
          audio_manifest (dict): (modified) audio manifest dictionary.
      """
      audio_file_id = (
          f"{audio_manifest['audio_filepath']}#{offset_index}"
      )
      if audio_file_id in buffer_dict:
          audio_file, sr, audio_manifest = buffer_dict[audio_file_id]
      else:
          if read_subset:
              audio_manifest = get_subset_of_audio_manifest(
                  audio_manifest=audio_manifest,
                  offset_index=offset_index,
                  max_audio_read_sec=max_audio_read_sec,
                  min_alignment_count=min_alignment_count,
              )
              segment = AudioSegment.from_file(
                  audio_file=audio_manifest["audio_filepath"],
                  offset=audio_manifest["offset"],
                  duration=audio_manifest["duration"],
              )
          else:
              segment = AudioSegment.from_file(
                  audio_file=audio_manifest["audio_filepath"]
              )
          audio_file, sr = (
              torch.from_numpy(segment.samples).to(device),
              segment.sample_rate,
          )
          if read_subset and segment.duration < (
              audio_manifest["alignments"][-1]
              - audio_manifest["alignments"][0]
          ):
              audio_manifest["alignments"][-1] = min(
                  segment.duration, audio_manifest["alignments"][-1]
              )
          if audio_file.ndim > 1:
              audio_file = torch.mean(audio_file, 1, False).to(device)
          buffer_dict[audio_file_id] = (audio_file, sr, audio_manifest)
      return audio_file, sr, audio_manifest
  
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 4 for calling a global function with example usage: read_audio_from_buffer(audio_manifest, buffer_dict, offset_index, device, max_audio_read_sec, min_alignment_count, read_subset) and returns: audio_file,  sr,  audio_manifest