Update data loader tests

TTS/tts/datasets/dataset.py

@@ -38,7 +38,7 @@ class TTSDataset(Dataset):
         outputs_per_step: int,
         compute_linear_spec: bool,
         ap: AudioProcessor,
-        meta_data: List[Dict],
+        samples: List[Dict],
         tokenizer: "TTSTokenizer" = None,
         compute_f0: bool = False,
         f0_cache_path: str = None,
@@ -67,7 +67,7 @@ class TTSDataset(Dataset):
 
             ap (TTS.tts.utils.AudioProcessor): Audio processor object.
 
-            meta_data (list): List of dataset samples.
+            samples (list): List of dataset samples.
 
             tokenizer (TTSTokenizer): tokenizer to convert text to sequence IDs. If None init internally else
                 use the given. Defaults to None.
@@ -111,7 +111,7 @@ class TTSDataset(Dataset):
         """
         super().__init__()
         self.batch_group_size = batch_group_size
-        self._samples = meta_data
+        self._samples = samples
         self.outputs_per_step = outputs_per_step
         self.sample_rate = ap.sample_rate
         self.compute_linear_spec = compute_linear_spec
@@ -200,7 +200,7 @@ class TTSDataset(Dataset):
             token_ids = self.get_phonemes(idx, text)["token_ids"]
         else:
             token_ids = self.tokenizer.text_to_ids(text)
-        return token_ids
+        return np.array(token_ids, dtype=np.int32)
 
     def load_data(self, idx):
         item = self.samples[idx]
@@ -258,7 +258,7 @@ class TTSDataset(Dataset):
         return audio_lengths, text_lengths
 
     @staticmethod
-    def sort_and_filter_by_length(lengths: List[int], min_len: int, max_len: int):
+    def filter_by_length(lengths: List[int], min_len: int, max_len: int):
         idxs = np.argsort(lengths)  # ascending order
         ignore_idx = []
         keep_idx = []
@@ -270,6 +270,11 @@ class TTSDataset(Dataset):
                 keep_idx.append(idx)
         return ignore_idx, keep_idx
 
+    @staticmethod
+    def sort_by_length(lengths: List[int]):
+        idxs = np.argsort(lengths)  # ascending order
+        return idxs
+
     @staticmethod
     def create_buckets(samples, batch_group_size: int):
         for i in range(len(samples) // batch_group_size):
@@ -280,24 +285,33 @@ class TTSDataset(Dataset):
             samples[offset:end_offset] = temp_items
         return samples
 
+    def select_samples_by_idx(self, idxs):
+        samples = []
+        audio_lengths = []
+        text_lengths = []
+        for idx in idxs:
+            samples.append(self.samples[idx])
+            audio_lengths.append(self.audio_lengths[idx])
+            text_lengths.append(self.text_lengths[idx])
+        return samples, audio_lengths, text_lengths
+
     def preprocess_samples(self):
         r"""Sort `items` based on text length or audio length in ascending order. Filter out samples out or the length
         range.
         """
 
         # sort items based on the sequence length in ascending order
-        text_ignore_idx, text_keep_idx = self.sort_and_filter_by_length(
+        text_ignore_idx, text_keep_idx = self.filter_by_length(self.text_lengths, self.min_text_len, self.max_text_len)
-            self.text_lengths, self.min_text_len, self.max_text_len
+        audio_ignore_idx, audio_keep_idx = self.filter_by_length(
-        )
-        audio_ignore_idx, audio_keep_idx = self.sort_and_filter_by_length(
             self.audio_lengths, self.min_audio_len, self.max_audio_len
         )
         keep_idx = list(set(audio_keep_idx) | set(text_keep_idx))
         ignore_idx = list(set(audio_ignore_idx) | set(text_ignore_idx))
 
-        samples = []
+        samples, audio_lengths, _ = self.select_samples_by_idx(keep_idx)
-        for idx in keep_idx:
+
-            samples.append(self.samples[idx])
+        sorted_idxs = self.sort_by_length(audio_lengths)
+        samples, audio_lengths, text_lengtsh = self.select_samples_by_idx(sorted_idxs)
 
         if len(samples) == 0:
             raise RuntimeError(" [!] No samples left")
@@ -309,6 +323,8 @@ class TTSDataset(Dataset):
 
         # update items to the new sorted items
         self.samples = samples
+        self.audio_lengths = audio_lengths
+        self.text_lengths = text_lengtsh
 
         if self.verbose:
             print(" | > Preprocessing samples")
@@ -391,7 +407,7 @@ class TTSDataset(Dataset):
             stop_targets = prepare_stop_target(stop_targets, self.outputs_per_step)
 
             # PAD sequences with longest instance in the batch
-            text = prepare_data(batch["token_ids"]).astype(np.int32)
+            token_ids = prepare_data(batch["token_ids"]).astype(np.int32)
 
             # PAD features with longest instance
             mel = prepare_tensor(mel, self.outputs_per_step)
@@ -401,7 +417,7 @@ class TTSDataset(Dataset):
 
             # convert things to pytorch
             token_ids_lengths = torch.LongTensor(token_ids_lengths)
-            text = torch.LongTensor(text)
+            token_ids = torch.LongTensor(token_ids)
             mel = torch.FloatTensor(mel).contiguous()
             mel_lengths = torch.LongTensor(mel_lengths)
             stop_targets = torch.FloatTensor(stop_targets)
@@ -453,7 +469,7 @@ class TTSDataset(Dataset):
                 attns = [batch["attn"][idx].T for idx in ids_sorted_decreasing]
                 for idx, attn in enumerate(attns):
                     pad2 = mel.shape[1] - attn.shape[1]
-                    pad1 = text.shape[1] - attn.shape[0]
+                    pad1 = token_ids.shape[1] - attn.shape[0]
                     assert pad1 >= 0 and pad2 >= 0, f"[!] Negative padding - {pad1} and {pad2}"
                     attn = np.pad(attn, [[0, pad1], [0, pad2]])
                     attns[idx] = attn
@@ -461,7 +477,7 @@ class TTSDataset(Dataset):
                 attns = torch.FloatTensor(attns).unsqueeze(1)
 
             return {
-                "token_id": text,
+                "token_id": token_ids,
                 "token_id_lengths": token_ids_lengths,
                 "speaker_names": batch["speaker_name"],
                 "linear": linear,
@@ -786,7 +802,7 @@ if __name__ == "__main__":
     dataset = TTSDataset(
         outputs_per_step=1,
         compute_linear_spec=False,
-        meta_data=samples,
+        samples=samples,
         ap=ap,
         return_wav=False,
         batch_group_size=0,

TTS/tts/utils/text/tokenizer.py

@@ -147,6 +147,7 @@ class TTSTokenizer:
         if isinstance(config.text_cleaner, (str, list)):
             text_cleaner = getattr(cleaners, config.text_cleaner)
 
+        phonemizer = None
         if config.use_phonemes:
             # init phoneme set
             characters = IPAPhonemes().init_from_config(config)

tests/data_tests/test_loader.py

@@ -7,9 +7,9 @@ import torch
 from torch.utils.data import DataLoader
 
 from tests import get_tests_output_path
-from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.configs.shared_configs import BaseTTSConfig, BaseDatasetConfig
 from TTS.tts.datasets import TTSDataset, load_tts_samples
-from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 # pylint: disable=unused-variable
@@ -50,18 +50,19 @@ class TestTTSDataset(unittest.TestCase):
         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(
-            r,
+            outputs_per_step=r,
-            c.text_cleaner,
             compute_linear_spec=True,
             return_wav=True,
+            tokenizer=tokenizer,
             ap=self.ap,
-            meta_data=items,
+            samples=items,
-            characters=c.characters,
             batch_group_size=bgs,
-            min_seq_len=c.min_seq_len,
+            min_text_len=c.min_text_len,
-            max_seq_len=float("inf"),
+            max_text_len=c.max_text_len,
-            use_phonemes=False,
+            min_audio_len=c.min_audio_len,
+            max_audio_len=c.max_audio_len,
         )
         dataloader = DataLoader(
             dataset,
@@ -80,27 +81,26 @@ class TestTTSDataset(unittest.TestCase):
             for i, data in enumerate(dataloader):
                 if i == self.max_loader_iter:
                     break
-                text_input = data["text"]
+                text_input = data["token_id"]
-                text_lengths = data["text_lengths"]
+                _ = data["token_id_lengths"]
                 speaker_name = data["speaker_names"]
                 linear_input = data["linear"]
                 mel_input = data["mel"]
                 mel_lengths = data["mel_lengths"]
-                stop_target = data["stop_targets"]
+                _ = data["stop_targets"]
-                item_idx = data["item_idxs"]
+                _ = data["item_idxs"]
                 wavs = data["waveform"]
 
                 neg_values = text_input[text_input < 0]
                 check_count = len(neg_values)
-                assert check_count == 0, " !! Negative values in text_input: {}".format(check_count)
+
-                assert isinstance(speaker_name[0], str)
+                # check basic conditions
-                assert linear_input.shape[0] == c.batch_size
+                self.assertEqual(check_count, 0)
-                assert linear_input.shape[2] == self.ap.fft_size // 2 + 1
+                self.assertEqual(linear_input.shape[0], mel_input.shape[0], c.batch_size)
-                assert mel_input.shape[0] == c.batch_size
+                self.assertEqual(linear_input.shape[2], self.ap.fft_size // 2 + 1)
-                assert mel_input.shape[2] == c.audio["num_mels"]
+                self.assertEqual(mel_input.shape[2], c.audio["num_mels"])
-                assert (
+                self.assertEqual(wavs.shape[1], mel_input.shape[1] * c.audio.hop_length)
-                    wavs.shape[1] == mel_input.shape[1] * c.audio.hop_length
+                self.assertIsInstance(speaker_name[0], str)
-                ), f"wavs.shape: {wavs.shape[1]}, mel_input.shape: {mel_input.shape[1] * c.audio.hop_length}"
 
                 # make sure that the computed mels and the waveform match and correctly computed
                 mel_new = self.ap.melspectrogram(wavs[0].squeeze().numpy())
@@ -109,55 +109,58 @@ class TestTTSDataset(unittest.TestCase):
                 # 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_dataloader)[:, 0:ignore_seg]
+                mel_diff = (mel_new[:, : mel_input.shape[1]] - mel_input[0].T.numpy())[:, 0:ignore_seg]
-                assert abs(mel_diff.sum()) < 1e-5
+                self.assertLess(abs(mel_diff.sum()), 1e-5)
 
                 # check normalization ranges
                 if self.ap.symmetric_norm:
-                    assert mel_input.max() <= self.ap.max_norm
+                    self.assertLessEqual(mel_input.max(), self.ap.max_norm)
-                    assert mel_input.min() >= -self.ap.max_norm  # pylint: disable=invalid-unary-operand-type
+                    self.assertGreaterEqual(
-                    assert mel_input.min() < 0
+                        mel_input.min(), -self.ap.max_norm
+                    )  # pylint: disable=invalid-unary-operand-type
+                    self.assertLess(mel_input.min(), 0)
                 else:
-                    assert mel_input.max() <= self.ap.max_norm
+                    self.assertLessEqual(mel_input.max(), self.ap.max_norm)
-                    assert mel_input.min() >= 0
+                    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.items
+            frames = dataset.samples
             for i, data in enumerate(dataloader):
                 if i == self.max_loader_iter:
                     break
-                text_input = data["text"]
-                text_lengths = data["text_lengths"]
-                speaker_name = data["speaker_names"]
-                linear_input = data["linear"]
-                mel_input = data["mel"]
                 mel_lengths = data["mel_lengths"]
-                stop_target = data["stop_targets"]
-                item_idx = data["item_idxs"]
-
                 avg_length = mel_lengths.numpy().mean()
-                assert avg_length >= last_length
+            dataloader.dataset.preprocess_samples()
-            dataloader.dataset.sort_and_filter_items()
             is_items_reordered = False
-            for idx, item in enumerate(dataloader.dataset.items):
+            for idx, item in enumerate(dataloader.dataset.samples):
                 if item != frames[idx]:
                     is_items_reordered = True
                     break
-            assert is_items_reordered
+            self.assertGreaterEqual(avg_length, last_length)
+            self.assertTrue(is_items_reordered)
+
+    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])
 
-    def test_padding_and_spec(self):
         if ok_ljspeech:
-            dataloader, dataset = self._create_dataloader(1, 1, 0)
+            dataloader, _ = self._create_dataloader(1, 1, 0)
 
             for i, data in enumerate(dataloader):
                 if i == self.max_loader_iter:
                     break
-                text_input = data["text"]
-                text_lengths = data["text_lengths"]
-                speaker_name = data["speaker_names"]
                 linear_input = data["linear"]
                 mel_input = data["mel"]
                 mel_lengths = data["mel_lengths"]
@@ -172,7 +175,7 @@ class TestTTSDataset(unittest.TestCase):
                 # 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.
-                assert abs(mel.T - mel_dl).max() < 1e-5, abs(mel.T - mel_dl).max()
+                self.assertLess(abs(mel.T - mel_dl).max(), 1e-5)
 
                 # check mel-spec correctness
                 mel_spec = mel_input[0].cpu().numpy()
@@ -186,56 +189,36 @@ class TestTTSDataset(unittest.TestCase):
                 self.ap.save_wav(wav, OUTPATH + "/linear_inv_dataloader.wav")
                 shutil.copy(item_idx[0], OUTPATH + "/linear_target_dataloader.wav")
 
-                # check the last time step to be zero padded
+                # check the outputs
-                assert linear_input[0, -1].sum() != 0
+                check_conditions(0, linear_input, mel_input, stop_target, mel_lengths)
-                assert linear_input[0, -2].sum() != 0
-                assert mel_input[0, -1].sum() != 0
-                assert mel_input[0, -2].sum() != 0
-                assert stop_target[0, -1] == 1
-                assert stop_target[0, -2] == 0
-                assert stop_target.sum() == 1
-                assert len(mel_lengths.shape) == 1
-                assert mel_lengths[0] == linear_input[0].shape[0]
-                assert mel_lengths[0] == mel_input[0].shape[0]
 
             # Test for batch size 2
-            dataloader, dataset = self._create_dataloader(2, 1, 0)
+            dataloader, _ = self._create_dataloader(2, 1, 0)
 
             for i, data in enumerate(dataloader):
                 if i == self.max_loader_iter:
                     break
-                text_input = data["text"]
-                text_lengths = data["text_lengths"]
-                speaker_name = data["speaker_names"]
                 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 first item in the batch
+                # check the longer item in the batch
-                assert linear_input[idx, -1].sum() != 0
+                check_conditions(idx, linear_input, mel_input, stop_target, mel_lengths)
-                assert linear_input[idx, -2].sum() != 0, linear_input
-                assert mel_input[idx, -1].sum() != 0
-                assert mel_input[idx, -2].sum() != 0, mel_input
-                assert stop_target[idx, -1] == 1
-                assert stop_target[idx, -2] == 0
-                assert stop_target[idx].sum() == 1
-                assert len(mel_lengths.shape) == 1
-                assert mel_lengths[idx] == mel_input[idx].shape[0]
-                assert mel_lengths[idx] == linear_input[idx].shape[0]
 
-                # check the second itme in the batch
+                # check the other item in the batch
-                assert linear_input[1 - idx, -1].sum() == 0
+                self.assertEqual(linear_input[1 - idx, -1].sum(), 0)
-                assert mel_input[1 - idx, -1].sum() == 0
+                self.assertEqual(mel_input[1 - idx, -1].sum(), 0)
-                assert stop_target[1, mel_lengths[1] - 1] == 1
+                self.assertEqual(stop_target[1, mel_lengths[1] - 1], 1)
-                assert stop_target[1, mel_lengths[1] :].sum() == stop_target.shape[1] - mel_lengths[1]
+                self.assertEqual(stop_target[1, mel_lengths[1] :].sum(), stop_target.shape[1] - mel_lengths[1])
-                assert len(mel_lengths.shape) == 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