code: def load_speaker_sample(
      speaker_wav_align_map: List[dict],
      speaker_ids: List[str],
      speaker_turn: int,
      min_alignment_count: int,
  ) -> str:
      """
      Load a sample for the selected speaker ID.
      The first alignment and word must be silence that determines the start of the alignments.
  
      Args:
          speaker_wav_align_map (dict): Dictionary containing speaker IDs and their corresponding wav filepath and alignments.
          speaker_ids (list): LibriSpeech speaker IDs for each speaker in the current session.
          speaker_turn (int): Current speaker turn.
          output_precision (int): Precision of the output alignments in integer.
          min_alignment_count (int): Minimum number of alignments in the audio file.
  
      Returns:
          audio_manifest (dict): Audio manifest dictionary containing the wav filepath, words and alignments.
      """
      speaker_id = speaker_ids[speaker_turn]
      file_id = np.random.randint(
          0, max(len(speaker_wav_align_map[str(speaker_id)]) - 1, 1)
      )
      audio_manifest = speaker_wav_align_map[str(speaker_id)][file_id]
  
      # Check if the alignment file has at least 2 words.
      if len(audio_manifest["alignments"]) < min_alignment_count:
          raise ValueError(
              f"Alignment file {audio_manifest['audio_filepath']} has an inappropriate length of {len(audio_manifest['alignments'])} < 2."
          )
  
      # Check whether the first word is silence and insert a silence token if the first token is not silence.
      if audio_manifest["words"][0] != "":
          audio_manifest = add_silence_to_alignments(audio_manifest)
  
      audio_manifest = copy.deepcopy(audio_manifest)
      return 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 17 for calling a global function with example usage: load_speaker_sample(speaker_wav_align_map, speaker_ids, speaker_turn, min_alignment_count) and returns: audio_manifest