Revert "change g2p and other fix" (#40)

data_utils.py

@@ -154,13 +154,13 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
 
         if language_str == "ZH":
             bert = bert
-            ja_bert = torch.zeros(1024, len(phone))
+            ja_bert = torch.zeros(768, len(phone))
         elif language_str == "JA":
             ja_bert = bert
             bert = torch.zeros(1024, len(phone))
         else:
             bert = torch.zeros(1024, len(phone))
-            ja_bert = torch.zeros(1024, len(phone))
+            ja_bert = torch.zeros(768, len(phone))
         assert bert.shape[-1] == len(phone), (
             bert.shape,
             len(phone),
@@ -208,13 +208,7 @@ class TextAudioSpeakerCollate:
             torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
         )
 
-        max_text_len = max(
-            [
-                batch[ids_sorted_decreasing[i]][7].size(1)
-                for i in range(len(ids_sorted_decreasing))
-            ]
-            + [len(x[0]) for x in batch]
-        )
+        max_text_len = max([len(x[0]) for x in batch])
         max_spec_len = max([x[1].size(1) for x in batch])
         max_wav_len = max([x[2].size(1) for x in batch])
 
@@ -227,7 +221,7 @@ class TextAudioSpeakerCollate:
         tone_padded = torch.LongTensor(len(batch), max_text_len)
         language_padded = torch.LongTensor(len(batch), max_text_len)
         bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-        ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
+        ja_bert_padded = torch.FloatTensor(len(batch), 768, max_text_len)
 
         spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
         wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)