import os import shutil import unittest import numpy as np 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 # pylint: disable=unused-variable OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/") os.makedirs(OUTPATH, exist_ok=True) # create a dummy config for testing data loaders. c = BaseTTSConfig(text_cleaner="english_cleaners", num_loader_workers=0, batch_size=2, use_noise_augment=False) c.r = 5 c.data_path = os.path.join(get_tests_data_path(), "ljspeech/") ok_ljspeech = os.path.exists(c.data_path) dataset_config = BaseDatasetConfig( formatter="ljspeech_test", # ljspeech_test to multi-speaker meta_file_train="metadata.csv", meta_file_val=None, path=c.data_path, language="en", ) DATA_EXIST = True if not os.path.exists(c.data_path): DATA_EXIST = False print(" > Dynamic data loader test: {}".format(DATA_EXIST)) class TestTTSDataset(unittest.TestCase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.max_loader_iter = 4 self.ap = AudioProcessor(**c.audio) def _create_dataloader(self, batch_size, r, bgs, start_by_longest=False): # load dataset meta_data_train, meta_data_eval = load_tts_samples(dataset_config, eval_split=True, eval_split_size=0.2) items = meta_data_train + meta_data_eval tokenizer, _ = TTSTokenizer.init_from_config(c) dataset = TTSDataset( outputs_per_step=r, compute_linear_spec=True, return_wav=True, tokenizer=tokenizer, ap=self.ap, samples=items, batch_group_size=bgs, min_text_len=c.min_text_len, max_text_len=c.max_text_len, min_audio_len=c.min_audio_len, max_audio_len=c.max_audio_len, start_by_longest=start_by_longest, ) dataloader = DataLoader( dataset, batch_size=batch_size, shuffle=False, collate_fn=dataset.collate_fn, drop_last=True, num_workers=c.num_loader_workers, ) return dataloader, dataset def test_loader(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(1, 1, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break text_input = data["token_id"] _ = data["token_id_lengths"] speaker_name = data["speaker_names"] linear_input = data["linear"] mel_input = data["mel"] mel_lengths = data["mel_lengths"] _ = data["stop_targets"] _ = data["item_idxs"] wavs = data["waveform"] neg_values = text_input[text_input < 0] check_count = len(neg_values) # check basic conditions self.assertEqual(check_count, 0) self.assertEqual(linear_input.shape[0], mel_input.shape[0], c.batch_size) self.assertEqual(linear_input.shape[2], self.ap.fft_size // 2 + 1) self.assertEqual(mel_input.shape[2], c.audio["num_mels"]) self.assertEqual(wavs.shape[1], mel_input.shape[1] * c.audio.hop_length) self.assertIsInstance(speaker_name[0], str) # make sure that the computed mels and the waveform match and correctly computed mel_new = self.ap.melspectrogram(wavs[0].squeeze().numpy()) # remove padding in mel-spectrogram mel_dataloader = mel_input[0].T.numpy()[:, : mel_lengths[0]] # guarantee that both mel-spectrograms have the same size and that we will remove waveform padding mel_new = mel_new[:, : mel_lengths[0]] ignore_seg = -(1 + c.audio.win_length // c.audio.hop_length) mel_diff = (mel_new[:, : mel_input.shape[1]] - mel_input[0].T.numpy())[:, 0:ignore_seg] self.assertLess(abs(mel_diff.sum()), 1e-5) # check normalization ranges if self.ap.symmetric_norm: self.assertLessEqual(mel_input.max(), self.ap.max_norm) self.assertGreaterEqual( mel_input.min(), -self.ap.max_norm # pylint: disable=invalid-unary-operand-type ) self.assertLess(mel_input.min(), 0) else: self.assertLessEqual(mel_input.max(), self.ap.max_norm) self.assertGreaterEqual(mel_input.min(), 0) def test_batch_group_shuffle(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(2, c.r, 16) last_length = 0 frames = dataset.samples for i, data in enumerate(dataloader): if i == self.max_loader_iter: break mel_lengths = data["mel_lengths"] avg_length = mel_lengths.numpy().mean() dataloader.dataset.preprocess_samples() is_items_reordered = False for idx, item in enumerate(dataloader.dataset.samples): if item != frames[idx]: is_items_reordered = True break self.assertGreaterEqual(avg_length, last_length) self.assertTrue(is_items_reordered) def test_start_by_longest(self): """Test start_by_longest option. Ther first item of the fist batch must be longer than all the other items. """ if ok_ljspeech: dataloader, _ = self._create_dataloader(2, c.r, 0, True) dataloader.dataset.preprocess_samples() for i, data in enumerate(dataloader): if i == self.max_loader_iter: break mel_lengths = data["mel_lengths"] if i == 0: max_len = mel_lengths[0] print(mel_lengths) self.assertTrue(all(max_len >= mel_lengths)) 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]) if ok_ljspeech: dataloader, _ = self._create_dataloader(1, 1, 0) 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) 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) # check batch zero-frame conditions (zero-frame disabled) # assert (linear_input * stop_target.unsqueeze(2)).sum() == 0 # assert (mel_input * stop_target.unsqueeze(2)).sum() == 0