Add dataloader test wit hall supported audio formats

tests/data/ljspeech/metadata_flac.csv

@@ -0,0 +1,9 @@
+audio_file|text|transcription|speaker_name
+wavs/LJ001-0001.flac|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|ljspeech-0
+wavs/LJ001-0002.flac|in being comparatively modern.|in being comparatively modern.|ljspeech-0
+wavs/LJ001-0003.flac|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|ljspeech-1
+wavs/LJ001-0004.flac|produced the block books, which were the immediate predecessors of the true printed book,|produced the block books, which were the immediate predecessors of the true printed book,|ljspeech-1
+wavs/LJ001-0005.flac|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|ljspeech-2
+wavs/LJ001-0006.flac|And it is worth mention in passing that, as an example of fine typography,|And it is worth mention in passing that, as an example of fine typography,|ljspeech-2
+wavs/LJ001-0007.flac|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about 1455,|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about fourteen fifty-five,|ljspeech-3
+wavs/LJ001-0008.flac|has never been surpassed.|has never been surpassed.|ljspeech-3

tests/data/ljspeech/metadata_mp3.csv

@@ -0,0 +1,9 @@
+audio_file|text|transcription|speaker_name
+wavs/LJ001-0001.mp3|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|ljspeech-0
+wavs/LJ001-0002.mp3|in being comparatively modern.|in being comparatively modern.|ljspeech-0
+wavs/LJ001-0003.mp3|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|ljspeech-1
+wavs/LJ001-0004.mp3|produced the block books, which were the immediate predecessors of the true printed book,|produced the block books, which were the immediate predecessors of the true printed book,|ljspeech-1
+wavs/LJ001-0005.mp3|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|ljspeech-2
+wavs/LJ001-0006.mp3|And it is worth mention in passing that, as an example of fine typography,|And it is worth mention in passing that, as an example of fine typography,|ljspeech-2
+wavs/LJ001-0007.mp3|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about 1455,|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about fourteen fifty-five,|ljspeech-3
+wavs/LJ001-0008.mp3|has never been surpassed.|has never been surpassed.|ljspeech-3

tests/data/ljspeech/metadata_wav.csv

@@ -0,0 +1,9 @@
+audio_file|text|transcription|speaker_name
+wavs/LJ001-0001.wav|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|ljspeech-0
+wavs/LJ001-0002.wav|in being comparatively modern.|in being comparatively modern.|ljspeech-0
+wavs/LJ001-0003.wav|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|ljspeech-1
+wavs/LJ001-0004.wav|produced the block books, which were the immediate predecessors of the true printed book,|produced the block books, which were the immediate predecessors of the true printed book,|ljspeech-1
+wavs/LJ001-0005.wav|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|ljspeech-2
+wavs/LJ001-0006.wav|And it is worth mention in passing that, as an example of fine typography,|And it is worth mention in passing that, as an example of fine typography,|ljspeech-2
+wavs/LJ001-0007.wav|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about 1455,|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about fourteen fifty-five,|ljspeech-3
+wavs/LJ001-0008.wav|has never been surpassed.|has never been surpassed.|ljspeech-3

tests/data_tests/test_loader.py

@@ -21,16 +21,31 @@
 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",
+dataset_config_wav = BaseDatasetConfig(
+    formatter="coqui",  # ljspeech_test to multi-speaker
+    meta_file_train="metadata_wav.csv",
+    meta_file_val=None,
+    path=c.data_path,
+    language="en",
+)
+dataset_config_mp3 = BaseDatasetConfig(
+    formatter="coqui",  # ljspeech_test to multi-speaker
+    meta_file_train="metadata_mp3.csv",
+    meta_file_val=None,
+    path=c.data_path,
+    language="en",
+)
+dataset_config_flac = BaseDatasetConfig(
+    formatter="coqui",  # ljspeech_test to multi-speaker
+    meta_file_train="metadata_flac.csv",
     meta_file_val=None,
     path=c.data_path,
     language="en",
 )
 
+dataset_configs = [dataset_config_wav, dataset_config_mp3, dataset_config_flac]
+
 DATA_EXIST = True
 if not os.path.exists(c.data_path):
     DATA_EXIST = False
@@ -44,11 +59,10 @@ def __init__(self, *args, **kwargs):
         self.max_loader_iter = 4
         self.ap = AudioProcessor(**c.audio)
 
-    def _create_dataloader(self, batch_size, r, bgs, start_by_longest=False):
+    def _create_dataloader(self, batch_size, r, bgs, dataset_config, 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,
@@ -75,9 +89,8 @@ def _create_dataloader(self, batch_size, r, bgs, start_by_longest=False):
         return dataloader, dataset
 
     def test_loader(self):
-        if ok_ljspeech:
-            dataloader, dataset = self._create_dataloader(1, 1, 0)
-
+        for dataset_config in dataset_configs:
+            dataloader, _ = self._create_dataloader(1, 1, 0, dataset_config)
             for i, data in enumerate(dataloader):
                 if i == self.max_loader_iter:
                     break
@@ -104,8 +117,6 @@ def test_loader(self):
 
                 # 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)
@@ -124,40 +135,38 @@ def test_loader(self):
                     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)
+        dataloader, dataset = self._create_dataloader(2, c.r, 16, dataset_config_wav)
+        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))
+        dataloader, _ = self._create_dataloader(2, c.r, 0, dataset_config_wav, start_by_longest=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):
@@ -172,71 +181,70 @@ def check_conditions(idx, linear_input, mel_input, stop_target, mel_lengths):
             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)
+        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)
 
-                # 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
+            # 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