class_docstr: Class for sampling speech samples for Multispeaker Audio Session Simulator 
  
  Args:
      cfg: OmegaConf configuration loaded from yaml file.
  
  Variables for sampling speech:
      self.running_speech_len_samples (int): Running total of speech samples in the current audio session.
      self.running_silence_len_samples (int): Running total of silence samples in the current audio session.
      self.running_overlap_len_samples (int): Running total of overlap samples in the current audio session.
  
      self.sess_silence_mean (int) : Targeted mean number of silence samples in the current audio session.
      self.per_silence_min_len (int): Minimum number of silence samples in the silence segment.
      self.per_silence_max_len (int): Maximum number of silence samples in the silence segment.
  
      self.sess_overlap_mean (int): Targeted mean number of overlap samples in the current audio session.
      self.per_overlap_min_len (int): Minimum number of overlap samples in the overlap segment.
      self.per_overlap_max_len (int): Maximum number of overlap samples in the overlap segment.
  
  data_simulator: 
      session_params: 
          mean_silence (float): Mean proportion of silence to speaking time in the audio session. Should be in range [0, 1).
          mean_silence_var (float): Variance for mean silence in all audio sessions. 
                                  This value should be 0 <= mean_silence_var < mean_silence * (1 - mean_silence).
          per_silence_var (float):  Variance for each silence in an audio session, set large values (e.g., 20) for de-correlation.
          per_silence_min (float): Minimum duration for each silence, default to 0.
          per_silence_max (float): Maximum duration for each silence, default to -1 for no maximum.
          
          mean_overlap (float): Mean proportion of overlap in the overall non-silence duration. Should be in range [0, 1) and 
                              recommend [0, 0.15] range for accurate results.
          mean_overlap_var (float): Variance for mean overlap in all audio sessions. 
                                  This value should be 0 <= mean_overlap_var < mean_overlap * (1 - mean_overlap).
          per_overlap_var (float): Variance for per overlap in each session, set large values to de-correlate silence lengths 
                                  with the latest speech segment lengths
          per_overlap_min (float): Minimum per overlap duration in seconds
          per_overlap_max (float): Maximum per overlap duration in seconds, set -1 for no maximum 
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docstr: Compare the current silence ratio to the current overlap ratio. Switch to either silence or overlap mode according 
  to the amount of the gap between current ratio and session mean in config.
  
  Args:
      running_len_samples (int): Length of the current session in samples.
      non_silence_len_samples (int): Length of the signal that is not silence in samples.
  
  Returns:
      add_overlap (bool): True if the current silence ratio is less than the current overlap ratio, False otherwise.
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code: def silence_vs_overlap_selector(
      self, running_len_samples: int, non_silence_len_samples: int
  ) -> bool:
      """
      Compare the current silence ratio to the current overlap ratio. Switch to either silence or overlap mode according
      to the amount of the gap between current ratio and session mean in config.
  
      Args:
          running_len_samples (int): Length of the current session in samples.
          non_silence_len_samples (int): Length of the signal that is not silence in samples.
  
      Returns:
          add_overlap (bool): True if the current silence ratio is less than the current overlap ratio, False otherwise.
      """
      if running_len_samples > 0:
          self.current_silence_ratio = (
              running_len_samples - self.running_speech_len_samples
          ) / running_len_samples
          self.current_overlap_ratio = (
              self.running_overlap_len_samples / non_silence_len_samples
          )
      else:
          self.current_silence_ratio, self.current_overlap_ratio = 0, 0
  
      # self.silence_discrepancy = max(0, self.sess_silence_mean - self.current_silence_ratio)
      # self.overlap_discrepancy = max(0, self.sess_overlap_mean - self.current_overlap_ratio)
      # threshold = self.silence_discrepancy / (self.overlap_discrepancy + self.silence_discrepancy + 1e-10)
      # add_overlap = np.random.rand() > threshold
      self.silence_discrepancy = (
          self.current_silence_ratio - self.sess_silence_mean
      )
      self.overlap_discrepancy = (
          self.current_overlap_ratio - self.sess_overlap_mean
      )
      add_overlap = bool(
          self.overlap_discrepancy < self.silence_discrepancy
      )
      return add_overlap
  
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 5 for calling the SpeechSampler obj's underlying member method code with example usage: obj.silence_vs_overlap_selector(running_len_samples, non_silence_len_samples) and returns: add_overlap