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

This reverts commit 2f687e4.

bert/bert-base-japanese-v3/config.json → bert/bert-large-japanese-v2/config.json

@@ -5,14 +5,14 @@
     "attention_probs_dropout_prob": 0.1,
     "hidden_act": "gelu",
     "hidden_dropout_prob": 0.1,
-    "hidden_size": 768,
+    "hidden_size": 1024,
     "initializer_range": 0.02,
-    "intermediate_size": 3072,
+    "intermediate_size": 4096,
     "layer_norm_eps": 1e-12,
     "max_position_embeddings": 512,
     "model_type": "bert",
-    "num_attention_heads": 12,
-    "num_hidden_layers": 12,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
     "pad_token_id": 0,
     "type_vocab_size": 2,
     "vocab_size": 32768

bert/bert-large-japanese-v2/tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+    "tokenizer_class": "BertJapaneseTokenizer",
+    "model_max_length": 512,
+    "do_lower_case": false,
+    "word_tokenizer_type": "mecab",
+    "subword_tokenizer_type": "wordpiece",
+    "mecab_kwargs": {
+        "mecab_dic": "unidic_lite"
+    }
+}

data_utils.py

@@ -154,13 +154,13 @@ def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
 
         if language_str == "ZH":
             bert = bert
-            ja_bert = torch.zeros(768, len(phone))
+            ja_bert = torch.zeros(1024, 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(768, len(phone))
+            ja_bert = torch.zeros(1024, len(phone))
         assert bert.shape[-1] == len(phone), (
             bert.shape,
             len(phone),
@@ -208,7 +208,13 @@ def __call__(self, batch):
             torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
         )
 
-        max_text_len = max([len(x[0]) for x in batch])
+        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_spec_len = max([x[1].size(1) for x in batch])
         max_wav_len = max([x[2].size(1) for x in batch])
 
@@ -221,7 +227,7 @@ def __call__(self, batch):
         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), 768, max_text_len)
+        ja_bert_padded = torch.FloatTensor(len(batch), 1024, 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)

models.py

@@ -340,7 +340,7 @@ def __init__(
         self.language_emb = nn.Embedding(num_languages, hidden_channels)
         nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
         self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
-        self.ja_bert_proj = nn.Conv1d(768, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
 
         self.encoder = attentions.Encoder(
             hidden_channels,

requirements.txt

@@ -15,7 +15,7 @@ pypinyin
 cn2an
 gradio
 av
-mecab-python3
+pyopenjtalk
 loguru
 unidic-lite
 cmudict

text/__init__.py

@@ -11,7 +11,12 @@ def cleaned_text_to_sequence(cleaned_text, tones, language):
     Returns:
       List of integers corresponding to the symbols in the text
     """
-    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    phones = []  # _symbol_to_id[symbol] for symbol in cleaned_text
+    for symbol in cleaned_text:
+        try:
+            phones.append(_symbol_to_id[symbol])
+        except KeyError:
+            phones.append(0)  # symbol not found in ID map, use 0('_') by default
     tone_start = language_tone_start_map[language]
     tones = [i + tone_start for i in tones]
     lang_id = language_id_map[language]