Test updates for loaders

tests/layers_tests.py

@@ -2,7 +2,8 @@ import unittest
 import torch as T
 
 from TTS.layers.tacotron import Prenet, CBHG, Decoder, Encoder
-from TTS.layers.losses import L1LossMasked, _sequence_mask
+from TTS.layers.losses import L1LossMasked
+from TTS.utils.generic_utils import sequence_mask
 
 
 class PrenetTests(unittest.TestCase):
@@ -79,7 +80,7 @@ class L1LossMaskedTests(unittest.TestCase):
         dummy_input = T.ones(4, 8, 128).float()
         dummy_target = T.zeros(4, 8, 128).float()
         dummy_length = (T.arange(5, 9)).long()
-        mask = ((_sequence_mask(dummy_length).float() - 1.0)
+        mask = ((sequence_mask(dummy_length).float() - 1.0)
                 * 100.0).unsqueeze(2)
         output = layer(dummy_input + mask, dummy_target, dummy_length)
         assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])

tests/loader_tests.py

@@ -4,7 +4,8 @@ import numpy as np
 
 from torch.utils.data import DataLoader
 from TTS.utils.generic_utils import load_config
-from TTS.datasets.LJSpeech import LJSpeechDataset
+from TTS.utils.audio import AudioProcessor
+from TTS.datasets import LJSpeech, Kusal
 
 file_path = os.path.dirname(os.path.realpath(__file__))
 c = load_config(os.path.join(file_path, 'test_config.json'))
@@ -15,21 +16,25 @@ class TestLJSpeechDataset(unittest.TestCase):
     def __init__(self, *args, **kwargs):
         super(TestLJSpeechDataset, self).__init__(*args, **kwargs)
         self.max_loader_iter = 4
+        self.ap = AudioProcessor(sample_rate=c.sample_rate,
+                                 num_mels=c.num_mels, 
+                                 min_level_db=c.min_level_db, 
+                                 frame_shift_ms=c.frame_shift_ms,
+                                 frame_length_ms=c.frame_length_ms,
+                                 ref_level_db=c.ref_level_db,
+                                 num_freq=c.num_freq,
+                                 power=c.power,
+                                 preemphasis=c.preemphasis,
+                                 min_mel_freq=c.min_mel_freq,
+                                 max_mel_freq=c.max_mel_freq)
 
     def test_loader(self):
-        dataset = LJSpeechDataset(os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
-                                  os.path.join(c.data_path_LJSpeech, 'wavs'),
+        dataset = LJSpeech.MyDataset(os.path.join(c.data_path_LJSpeech),
+                                  os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
                                   c.r,
-                                  c.sample_rate,
                                   c.text_cleaner,
-                                  c.num_mels,
-                                  c.min_level_db,
-                                  c.frame_shift_ms,
-                                  c.frame_length_ms,
-                                  c.preemphasis,
-                                  c.ref_level_db,
-                                  c.num_freq,
-                                  c.power
+                                  ap = self.ap,
+                                  min_seq_len=c.min_seq_len
                                   )
 
         dataloader = DataLoader(dataset, batch_size=2,
@@ -57,19 +62,12 @@ class TestLJSpeechDataset(unittest.TestCase):
             assert mel_input.shape[2] == c.num_mels
 
     def test_padding(self):
-        dataset = LJSpeechDataset(os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
-                                  os.path.join(c.data_path_LJSpeech, 'wavs'),
+        dataset = LJSpeech.MyDataset(os.path.join(c.data_path_LJSpeech),
+                                  os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
                                   1,
-                                  c.sample_rate,
                                   c.text_cleaner,
-                                  c.num_mels,
-                                  c.min_level_db,
-                                  c.frame_shift_ms,
-                                  c.frame_length_ms,
-                                  c.preemphasis,
-                                  c.ref_level_db,
-                                  c.num_freq,
-                                  c.power
+                                  ap = self.ap,
+                                  min_seq_len=c.min_seq_len
                                   )
 
         # Test for batch size 1
@@ -141,6 +139,135 @@ class TestLJSpeechDataset(unittest.TestCase):
             assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
             assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
 
+
+class TestKusalDataset(unittest.TestCase):
+
+    def __init__(self, *args, **kwargs):
+        super(TestKusalDataset, self).__init__(*args, **kwargs)
+        self.max_loader_iter = 4
+        self.ap = AudioProcessor(sample_rate=c.sample_rate,
+                                 num_mels=c.num_mels, 
+                                 min_level_db=c.min_level_db, 
+                                 frame_shift_ms=c.frame_shift_ms,
+                                 frame_length_ms=c.frame_length_ms,
+                                 ref_level_db=c.ref_level_db,
+                                 num_freq=c.num_freq,
+                                 power=c.power,
+                                 preemphasis=c.preemphasis,
+                                 min_mel_freq=c.min_mel_freq,
+                                 max_mel_freq=c.max_mel_freq)
+
+    def test_loader(self):
+        dataset = Kusal.MyDataset(os.path.join(c.data_path_Kusal),
+                                  os.path.join(c.data_path_Kusal, 'prompts.txt'),
+                                  c.r,
+                                  c.text_cleaner,
+                                  ap = self.ap,
+                                  min_seq_len=c.min_seq_len
+                                  )
+
+        dataloader = DataLoader(dataset, batch_size=2,
+                                shuffle=True, collate_fn=dataset.collate_fn,
+                                drop_last=True, num_workers=c.num_loader_workers)
+
+        for i, data in enumerate(dataloader):
+            if i == self.max_loader_iter:
+                break
+            text_input = data[0]
+            text_lengths = data[1]
+            linear_input = data[2]
+            mel_input = data[3]
+            mel_lengths = data[4]
+            stop_target = data[5]
+            item_idx = data[6]
+
+            neg_values = text_input[text_input < 0]
+            check_count = len(neg_values)
+            assert check_count == 0, \
+                " !! Negative values in text_input: {}".format(check_count)
+            # TODO: more assertion here
+            assert linear_input.shape[0] == c.batch_size
+            assert mel_input.shape[0] == c.batch_size
+            assert mel_input.shape[2] == c.num_mels
+
+    def test_padding(self):
+        dataset = Kusal.MyDataset(os.path.join(c.data_path_Kusal),
+                                  os.path.join(c.data_path_Kusal, 'prompts.txt'),
+                                  1,
+                                  c.text_cleaner,
+                                  ap = self.ap,
+                                  min_seq_len=c.min_seq_len
+                                  )
+
+        # Test for batch size 1
+        dataloader = DataLoader(dataset, batch_size=1,
+                                shuffle=False, collate_fn=dataset.collate_fn,
+                                drop_last=True, num_workers=c.num_loader_workers)
+
+        for i, data in enumerate(dataloader):
+            if i == self.max_loader_iter:
+                break
+            text_input = data[0]
+            text_lengths = data[1]
+            linear_input = data[2]
+            mel_input = data[3]
+            mel_lengths = data[4]
+            stop_target = data[5]
+            item_idx = data[6]
+
+            # check the last time step to be zero padded
+            assert mel_input[0, -1].sum() == 0
+            # assert mel_input[0, -2].sum() != 0
+            assert linear_input[0, -1].sum() == 0
+            # assert linear_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] == mel_input[0].shape[0]
+
+        # Test for batch size 2
+        dataloader = DataLoader(dataset, batch_size=2,
+                                shuffle=False, collate_fn=dataset.collate_fn,
+                                drop_last=False, num_workers=c.num_loader_workers)
+
+        for i, data in enumerate(dataloader):
+            if i == self.max_loader_iter:
+                break
+            text_input = data[0]
+            text_lengths = data[1]
+            linear_input = data[2]
+            mel_input = data[3]
+            mel_lengths = data[4]
+            stop_target = data[5]
+            item_idx = data[6]
+
+            if mel_lengths[0] > mel_lengths[1]:
+                idx = 0
+            else:
+                idx = 1
+
+            # check the first item in the batch
+            assert mel_input[idx, -1].sum() == 0
+            assert mel_input[idx, -2].sum() != 0, mel_input
+            assert linear_input[idx, -1].sum() == 0
+            assert linear_input[idx, -2].sum() != 0
+            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]
+
+            # check the second itme in the batch
+            assert mel_input[1-idx, -1].sum() == 0
+            assert linear_input[1-idx, -1].sum() == 0
+            assert stop_target[1-idx, -1] == 1
+            assert len(mel_lengths.shape) == 1
+
+            # check batch conditions
+            assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
+            assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
+
             
 # class TestTWEBDataset(unittest.TestCase):
 

tests/test_config.json

@@ -9,6 +9,8 @@
     "ref_level_db": 20,
     "hidden_size": 128,
     "embedding_size": 256,
+    "min_mel_freq": null,
+    "max_mel_freq": null,
     "text_cleaner": "english_cleaners",
 
     "epochs": 2000,
@@ -27,8 +29,9 @@
     "num_loader_workers": 4,
 
     "save_step": 200,
-    "data_path_LJSpeech": "/data/shared/KeithIto/LJSpeech-1.0",
-    "data_path_TWEB": "/data/shared/BibleSpeech",
+    "data_path_LJSpeech": "C:/Users/erogol/Data/LJSpeech-1.1",
+    "data_path_Kusal": "C:/Users/erogol/Data/Kusal",
     "output_path": "result",
+    "min_seq_len": 0,
     "log_dir": "/home/erogol/projects/TTS/logs/"
     }