code: def transform_data(path_loadSession):
      """
      Create a dictionary that maps speaker id and corresponding wavs
  
      Arguments
      ---------
      path_loadSession : str
          Path to the folder where the original IEMOCAP dataset is stored.
  
      Example
      -------
      >>> data_original = '/path/to/iemocap/IEMOCAP_full_release/Session'
      >>> data_transformed = '/path/to/iemocap/IEMOCAP_ahsn_leave-two-speaker-out'
      >>> transform_data(data_original, data_transformed)
      """
  
      speaker_dict = {str(i + 1): [] for i in range(10)}
  
      speaker_count = 0
      for k in range(5):
          session = load_session("%s%s" % (path_loadSession, k + 1))
          for idx in range(len(session)):
              if session[idx][2] == "F":
                  speaker_dict[str(speaker_count + 1)].append(
                      session[idx]
                  )
              else:
                  speaker_dict[str(speaker_count + 2)].append(
                      session[idx]
                  )
          speaker_count += 2
  
      return speaker_dict
  
file_path: speechbrain/recipes/IEMOCAP/iemocap_prepare.py
imports: import os
  import re
  import json
  import random
  import logging
  from speechbrain.dataio.dataio import read_audio
label_desc: Use image node_id 6 for calling a global function with example usage: transform_data(path_loadSession) and returns: speaker_dict