code: def load_wav_feat_data(data_path, feat_path, eval_split_size):
      wav_paths = find_wav_files(data_path)
      feat_paths = find_feat_files(feat_path)
  
      wav_paths.sort(key=lambda x: Path(x).stem)
      feat_paths.sort(key=lambda x: Path(x).stem)
  
      assert len(wav_paths) == len(feat_paths)
      for wav, feat in zip(wav_paths, feat_paths):
          wav_name = Path(wav).stem
          feat_name = Path(feat).stem
          assert wav_name == feat_name
  
      items = list(zip(wav_paths, feat_paths))
      np.random.seed(0)
      np.random.shuffle(items)
      return items[:eval_split_size], items[eval_split_size:]
  
file_path: TTS/TTS/vocoder/datasets/preprocess.py
imports: import glob
  import os
  from pathlib import Path
  from tqdm import tqdm
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: load_wav_feat_data(data_path, feat_path, eval_split_size) and returns: items,  items