Add configurable phoneme cache path

datasets/TTSDataset.py

@@ -22,7 +22,8 @@ class MyDataset(Dataset):
                  batch_group_size=0,
                  min_seq_len=0,
                  max_seq_len=float("inf"),
-                 cached=False):
+                 cached=False,
+                 phoneme_cache_path=None):
         """
         Args:
             root_path (str): root path for the data folder.
@@ -40,6 +41,7 @@ class MyDataset(Dataset):
             max_seq_len (int): (float("inf")) maximum sequence length.
             cached (bool): (false) true if the given data path is created 
                 by extract_features.py.
+            phoneme_cache_path (str): path to cache phoneme features. 
         """
         self.root_path = root_path
         self.batch_group_size = batch_group_size
@@ -51,6 +53,7 @@ class MyDataset(Dataset):
         self.max_seq_len = max_seq_len
         self.ap = ap
         self.cached = cached
+        self.phoneme_cache_path = phoneme_cache_path
         print(" > DataLoader initialization")
         print(" | > Data path: {}".format(root_path))
         print(" | > Cached dataset: {}".format(self.cached))
@@ -87,7 +90,7 @@ class MyDataset(Dataset):
         else:
             text, wav_file = self.items[idx]
             file_name = os.path.basename(wav_file).split('.')[0]
-            tmp_path = os.path.join("tmp/",file_name+'_phoneme.npy')
+            tmp_path = os.path.join(self.phoneme_cache_path, file_name+'_phoneme.npy')
             if os.path.isfile(tmp_path):
                 text = np.load(tmp_path)
             else:

train.py

@@ -46,7 +46,9 @@ def setup_loader(is_val=False):
             batch_group_size=0 if is_val else 8 * c.batch_size,
             min_seq_len=0 if is_val else c.min_seq_len,
             max_seq_len=float("inf") if is_val else c.max_seq_len,
-            cached=False if c.dataset != "tts_cache" else True)
+            cached=False if c.dataset != "tts_cache" else True,
+            phoneme_cache_path=c.phoneme_cache_path
+            )
         loader = DataLoader(
             dataset,
             batch_size=c.eval_batch_size if is_val else c.batch_size,