add tf tacotron2 test and edit test utils imports after utils

refactoring

tests/test_demo_server.py

@@ -6,7 +6,8 @@ import torch as T
 from TTS.server.synthesizer import Synthesizer
 from TTS.tests import get_tests_input_path, get_tests_output_path
 from TTS.utils.text.symbols import make_symbols, phonemes, symbols
-from TTS.utils.generic_utils import load_config, save_checkpoint, setup_model
+from TTS.utils.generic_utils import setup_model
+from TTS.utils.io import load_config, save_checkpoint
 
 
 class DemoServerTest(unittest.TestCase):

tests/test_loader.py

@@ -5,7 +5,7 @@ import torch
 import numpy as np
 
 from torch.utils.data import DataLoader
-from TTS.utils.generic_utils import load_config
+from TTS.utils.io import load_config
 from TTS.utils.audio import AudioProcessor
 from TTS.datasets import TTSDataset
 from TTS.datasets.preprocess import ljspeech

tests/test_tacotron2_model.py

@@ -6,7 +6,7 @@ import numpy as np
 
 from torch import optim
 from torch import nn
-from TTS.utils.generic_utils import load_config
+from TTS.utils.io import load_config
 from TTS.layers.losses import MSELossMasked
 from TTS.models.tacotron2 import Tacotron2
 

tests/test_tacotron2_tf_model.py

@@ -0,0 +1,59 @@
+import os
+import copy
+import torch
+import unittest
+import numpy as np
+import tensorflow as tf
+
+from torch import optim
+from torch import nn
+from TTS.utils.io import load_config
+from TTS.layers.losses import MSELossMasked
+from TTS.tf.models.tacotron2 import Tacotron2
+
+#pylint: disable=unused-variable
+
+torch.manual_seed(1)
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+c = load_config(os.path.join(file_path, 'test_config.json'))
+
+
+class TacotronTFTrainTest(unittest.TestCase):
+    def test_train_step(self):
+        ''' test forward pass '''
+        input = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
+        input_lengths = torch.sort(input_lengths, descending=True)[0]
+        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
+
+        input = tf.convert_to_tensor(input.cpu().numpy())
+        input_lengths = tf.convert_to_tensor(input_lengths.cpu().numpy())
+        mel_spec = tf.convert_to_tensor(mel_spec.cpu().numpy())
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
+
+        model = Tacotron2(num_chars=24, r=c.r, num_speakers=5)
+        # training pass
+        output = model(input, input_lengths, mel_spec, training=True)
+
+        # check model output shapes
+        assert np.all(output[0].shape == mel_spec.shape)
+        assert np.all(output[1].shape == mel_spec.shape)
+        assert output[2].shape[2] == input.shape[1]
+        assert output[2].shape[1] == (mel_spec.shape[1] // model.decoder.r)
+        assert output[3].shape[1] == (mel_spec.shape[1] // model.decoder.r)
+
+        # inference pass
+        output = model(input, training=False)

tests/test_tacotron_model.py

@@ -5,7 +5,7 @@ import unittest
 
 from torch import optim
 from torch import nn
-from TTS.utils.generic_utils import load_config
+from TTS.utils.io import load_config
 from TTS.layers.losses import L1LossMasked
 from TTS.models.tacotron import Tacotron
 

tests/test_text_processing.py

@@ -5,7 +5,7 @@ import os
 import unittest
 from TTS.utils.text import *
 from TTS.tests import get_tests_path
-from TTS.utils.generic_utils import load_config
+from TTS.utils.io import load_config
 
 TESTS_PATH = get_tests_path()
 conf = load_config(os.path.join(TESTS_PATH, 'test_config.json'))