make it optional to load linear specs in dataloader and fix tests

respectively

datasets/TTSDataset.py

@@ -13,6 +13,7 @@ class MyDataset(Dataset):
     def __init__(self,
                  outputs_per_step,
                  text_cleaner,
+                 compute_linear_spec,
                  ap,
                  meta_data,
                  tp=None,
@@ -28,6 +29,7 @@ class MyDataset(Dataset):
         Args:
             outputs_per_step (int): number of time frames predicted per step.
             text_cleaner (str): text cleaner used for the dataset.
+            compute_linear_spec (bool): compute linear spectrogram if True.
             ap (TTS.utils.AudioProcessor): audio processor object.
             meta_data (list): list of dataset instances.
             batch_group_size (int): (0) range of batch randomization after sorting
@@ -47,6 +49,7 @@ class MyDataset(Dataset):
         self.outputs_per_step = outputs_per_step
         self.sample_rate = ap.sample_rate
         self.cleaners = text_cleaner
+        self.compute_linear_spec = compute_linear_spec
         self.min_seq_len = min_seq_len
         self.max_seq_len = max_seq_len
         self.ap = ap
@@ -193,7 +196,6 @@ class MyDataset(Dataset):
 
             # compute features
             mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
-            linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
 
             mel_lengths = [m.shape[1] for m in mel]
 
@@ -208,25 +210,29 @@ class MyDataset(Dataset):
 
             # PAD sequences with longest instance in the batch
             text = prepare_data(text).astype(np.int32)
-            wav = prepare_data(wav)
 
             # PAD features with longest instance
-            linear = prepare_tensor(linear, self.outputs_per_step)
             mel = prepare_tensor(mel, self.outputs_per_step)
-            assert mel.shape[2] == linear.shape[2]
 
             # B x D x T --> B x T x D
-            linear = linear.transpose(0, 2, 1)
             mel = mel.transpose(0, 2, 1)
 
             # convert things to pytorch
             text_lenghts = torch.LongTensor(text_lenghts)
             text = torch.LongTensor(text)
-            linear = torch.FloatTensor(linear).contiguous()
             mel = torch.FloatTensor(mel).contiguous()
             mel_lengths = torch.LongTensor(mel_lengths)
             stop_targets = torch.FloatTensor(stop_targets)
 
+            # compute linear spectrogram 
+            if self.compute_linear_spec:
+                linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
+                linear = prepare_tensor(linear, self.outputs_per_step)
+                linear = linear.transpose(0, 2, 1)
+                assert mel.shape[1] == linear.shape[1]
+                linear = torch.FloatTensor(linear).contiguous()
+            else:
+                linear = None
             return text, text_lenghts, speaker_name, linear, mel, mel_lengths, \
                    stop_targets, item_idxs
 

tests/test_loader.py

@@ -36,6 +36,7 @@ class TestTTSDataset(unittest.TestCase):
         dataset = TTSDataset.MyDataset(
             r,
             c.text_cleaner,
+            compute_linear_spec=True,
             ap=self.ap,
             meta_data=items,
             tp=c.characters if 'characters' in c.keys() else None,

train.py

@@ -47,6 +47,7 @@ def setup_loader(ap, r, is_val=False, verbose=False):
         dataset = MyDataset(
             r,
             c.text_cleaner,
+            compute_linear_spec=True if c.model.lower() is 'tacotron' else False
             meta_data=meta_data_eval if is_val else meta_data_train,
             ap=ap,
             tp=c.characters if 'characters' in c.keys() else None,