Refactor and add merge

text/__init__.py

@@ -18,13 +18,15 @@ def cleaned_text_to_sequence(cleaned_text, tones, language):
     return phones, tones, lang_ids
 
 
-def get_bert(norm_text, word2ph, language, device, style_text=None, style_weight=0.7):
+def get_bert(
+    norm_text, word2ph, language, device, assist_text=None, assist_text_weight=0.7
+):
     from .chinese_bert import get_bert_feature as zh_bert
     from .english_bert_mock import get_bert_feature as en_bert
     from .japanese_bert import get_bert_feature as jp_bert
 
     lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
     bert = lang_bert_func_map[language](
-        norm_text, word2ph, device, style_text, style_weight
+        norm_text, word2ph, device, assist_text, assist_text_weight
     )
     return bert

text/chinese_bert.py

@@ -16,8 +16,8 @@ def get_bert_feature(
     text,
     word2ph,
     device=config.bert_gen_config.device,
-    style_text=None,
-    style_weight=0.7,
+    assist_text=None,
+    assist_text_weight=0.7,
 ):
     if (
         sys.platform == "darwin"
@@ -37,8 +37,8 @@ def get_bert_feature(
             inputs[i] = inputs[i].to(device)
         res = models[device](**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-        if style_text:
-            style_inputs = tokenizer(style_text, return_tensors="pt")
+        if assist_text:
+            style_inputs = tokenizer(assist_text, return_tensors="pt")
             for i in style_inputs:
                 style_inputs[i] = style_inputs[i].to(device)
             style_res = models[device](**style_inputs, output_hidden_states=True)
@@ -48,10 +48,10 @@ def get_bert_feature(
     word2phone = word2ph
     phone_level_feature = []
     for i in range(len(word2phone)):
-        if style_text:
+        if assist_text:
             repeat_feature = (
-                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
-                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
             )
         else:
             repeat_feature = res[i].repeat(word2phone[i], 1)

text/english_bert_mock.py

@@ -17,8 +17,8 @@ def get_bert_feature(
     text,
     word2ph,
     device=config.bert_gen_config.device,
-    style_text=None,
-    style_weight=0.7,
+    assist_text=None,
+    assist_text_weight=0.7,
 ):
     if (
         sys.platform == "darwin"
@@ -38,8 +38,8 @@ def get_bert_feature(
             inputs[i] = inputs[i].to(device)
         res = models[device](**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-        if style_text:
-            style_inputs = tokenizer(style_text, return_tensors="pt")
+        if assist_text:
+            style_inputs = tokenizer(assist_text, return_tensors="pt")
             for i in style_inputs:
                 style_inputs[i] = style_inputs[i].to(device)
             style_res = models[device](**style_inputs, output_hidden_states=True)
@@ -49,10 +49,10 @@ def get_bert_feature(
     word2phone = word2ph
     phone_level_feature = []
     for i in range(len(word2phone)):
-        if style_text:
+        if assist_text:
             repeat_feature = (
-                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
-                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
             )
         else:
             repeat_feature = res[i].repeat(word2phone[i], 1)

text/japanese_bert.py

@@ -17,12 +17,12 @@ def get_bert_feature(
     text,
     word2ph,
     device=config.bert_gen_config.device,
-    style_text=None,
-    style_weight=0.7,
+    assist_text=None,
+    assist_text_weight=0.7,
 ):
     text = "".join(text2sep_kata(text)[0])
-    if style_text:
-        style_text = "".join(text2sep_kata(style_text)[0])
+    if assist_text:
+        assist_text = "".join(text2sep_kata(assist_text)[0])
     if (
         sys.platform == "darwin"
         and torch.backends.mps.is_available()
@@ -41,8 +41,8 @@ def get_bert_feature(
             inputs[i] = inputs[i].to(device)
         res = models[device](**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-        if style_text:
-            style_inputs = tokenizer(style_text, return_tensors="pt")
+        if assist_text:
+            style_inputs = tokenizer(assist_text, return_tensors="pt")
             for i in style_inputs:
                 style_inputs[i] = style_inputs[i].to(device)
             style_res = models[device](**style_inputs, output_hidden_states=True)
@@ -53,10 +53,10 @@ def get_bert_feature(
     word2phone = word2ph
     phone_level_feature = []
     for i in range(len(word2phone)):
-        if style_text:
+        if assist_text:
             repeat_feature = (
-                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
-                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
             )
         else:
             repeat_feature = res[i].repeat(word2phone[i], 1)