code: def test_padding_and_spectrograms(self):
      def check_conditions(
          idx, linear_input, mel_input, stop_target, mel_lengths
      ):
          self.assertNotEqual(
              linear_input[idx, -1].sum(), 0
          )  # check padding
          self.assertNotEqual(linear_input[idx, -2].sum(), 0)
          self.assertNotEqual(mel_input[idx, -1].sum(), 0)
          self.assertNotEqual(mel_input[idx, -2].sum(), 0)
          self.assertEqual(stop_target[idx, -1], 1)
          self.assertEqual(stop_target[idx, -2], 0)
          self.assertEqual(stop_target[idx].sum(), 1)
          self.assertEqual(len(mel_lengths.shape), 1)
          self.assertEqual(mel_lengths[idx], linear_input[idx].shape[0])
          self.assertEqual(mel_lengths[idx], mel_input[idx].shape[0])
  
      dataloader, _ = self._create_dataloader(
          1, 1, 0, dataset_config_wav
      )
  
      for i, data in enumerate(dataloader):
          if i == self.max_loader_iter:
              break
          linear_input = data["linear"]
          mel_input = data["mel"]
          mel_lengths = data["mel_lengths"]
          stop_target = data["stop_targets"]
          item_idx = data["item_idxs"]
  
          # check mel_spec consistency
          wav = np.asarray(
              self.ap.load_wav(item_idx[0]), dtype=np.float32
          )
          mel = self.ap.melspectrogram(wav).astype("float32")
          mel = torch.FloatTensor(mel).contiguous()
          mel_dl = mel_input[0]
          # NOTE: Below needs to check == 0 but due to an unknown reason
          # there is a slight difference between two matrices.
          # TODO: Check this assert cond more in detail.
          self.assertLess(abs(mel.T - mel_dl).max(), 1e-5)
  
          # check mel-spec correctness
          mel_spec = mel_input[0].cpu().numpy()
          wav = self.ap.inv_melspectrogram(mel_spec.T)
          self.ap.save_wav(wav, OUTPATH + "/mel_inv_dataloader.wav")
          shutil.copy(
              item_idx[0], OUTPATH + "/mel_target_dataloader.wav"
          )
  
          # check linear-spec
          linear_spec = linear_input[0].cpu().numpy()
          wav = self.ap.inv_spectrogram(linear_spec.T)
          self.ap.save_wav(wav, OUTPATH + "/linear_inv_dataloader.wav")
          shutil.copy(
              item_idx[0], OUTPATH + "/linear_target_dataloader.wav"
          )
  
          # check the outputs
          check_conditions(
              0, linear_input, mel_input, stop_target, mel_lengths
          )
  
      # Test for batch size 2
      dataloader, _ = self._create_dataloader(
          2, 1, 0, dataset_config_wav
      )
  
      for i, data in enumerate(dataloader):
          if i == self.max_loader_iter:
              break
          linear_input = data["linear"]
          mel_input = data["mel"]
          mel_lengths = data["mel_lengths"]
          stop_target = data["stop_targets"]
          item_idx = data["item_idxs"]
  
          # set id to the longest sequence in the batch
          if mel_lengths[0] > mel_lengths[1]:
              idx = 0
          else:
              idx = 1
  
          # check the longer item in the batch
          check_conditions(
              idx, linear_input, mel_input, stop_target, mel_lengths
          )
  
          # check the other item in the batch
          self.assertEqual(linear_input[1 - idx, -1].sum(), 0)
          self.assertEqual(mel_input[1 - idx, -1].sum(), 0)
          self.assertEqual(stop_target[1, mel_lengths[1] - 1], 1)
          self.assertEqual(
              stop_target[1, mel_lengths[1] :].sum(),
              stop_target.shape[1] - mel_lengths[1],
          )
          self.assertEqual(len(mel_lengths.shape), 1)
  
file_path: TTS/tests/data_tests/test_loader.py
imports: import os
  import shutil
  import unittest
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from tests import get_tests_data_path, get_tests_output_path
  from TTS.tts.configs.shared_configs import BaseDatasetConfig, BaseTTSConfig
  from TTS.tts.datasets import TTSDataset, load_tts_samples
  from TTS.tts.utils.text.tokenizer import TTSTokenizer
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 6 for calling the TestTTSDataset obj's underlying member method code with example usage: obj.test_padding_and_spectrograms() without return types