code: def get_mock_dataset(root_dir):
      """
      root_dir: directory to the mocked dataset
      """
      mocked_data = []
      dataset_dir = os.path.join(
          root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL
      )
      os.makedirs(dataset_dir, exist_ok=True)
      sample_rate = 16000  # 16kHz
      seed = 0
  
      for speaker_id in range(5):
          speaker_path = os.path.join(dataset_dir, str(speaker_id))
          os.makedirs(speaker_path, exist_ok=True)
  
          for chapter_id in range(3):
              chapter_path = os.path.join(speaker_path, str(chapter_id))
              os.makedirs(chapter_path, exist_ok=True)
              trans_content = []
  
              for utterance_id in range(10):
                  filename = f"{speaker_id}-{chapter_id}-{utterance_id:04d}.wav"
                  path = os.path.join(chapter_path, filename)
  
                  transcript = " ".join(
                      [
                          _NUMBERS[x]
                          for x in [
                              speaker_id,
                              chapter_id,
                              utterance_id,
                          ]
                      ]
                  )
                  trans_content.append(
                      f"{speaker_id}-{chapter_id}-{utterance_id:04d} {transcript}"
                  )
  
                  data = get_whitenoise(
                      sample_rate=sample_rate,
                      duration=0.01,
                      n_channels=1,
                      dtype="float32",
                      seed=seed,
                  )
                  save_wav(path, data, sample_rate)
                  sample = (
                      normalize_wav(data),
                      sample_rate,
                      transcript,
                      speaker_id,
                      chapter_id,
                      utterance_id,
                  )
                  mocked_data.append(sample)
  
                  seed += 1
  
              trans_filename = f"{speaker_id}-{chapter_id}.trans.txt"
              trans_path = os.path.join(chapter_path, trans_filename)
              with open(trans_path, "w") as f:
                  f.write("\n".join(trans_content))
      return mocked_data
  
file_path: audio/test/torchaudio_unittest/datasets/librispeech_test_impl.py
imports: import os
  from pathlib import Path
  from torchaudio.datasets import librispeech
  from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin
label_desc: Use image node_id 1 for calling a global function with example usage: get_mock_dataset(root_dir) and returns: mocked_data