Add zh/jp/en mix

api.py

@@ -111,6 +111,7 @@ sys.path.append(now_dir)
 sys.path.append("%s/GPT_SoVITS" % (now_dir))
 
 import signal
+import LangSegment
 from time import time as ttime
 import torch
 import librosa
@@ -249,6 +250,8 @@ def change_sovits_weights(sovits_path):
     print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
     with open("./sweight.txt", "w", encoding="utf-8") as f:
         f.write(sovits_path)
+
+
 def change_gpt_weights(gpt_path):
     global hz, max_sec, t2s_model, config
     hz = 50
@@ -283,6 +286,83 @@ def get_bert_feature(text, word2ph):
     return phone_level_feature.T
 
 
+def clean_text_inf(text, language):
+    phones, word2ph, norm_text = clean_text(text, language)
+    phones = cleaned_text_to_sequence(phones)
+    return phones, word2ph, norm_text
+
+
+def get_bert_inf(phones, word2ph, norm_text, language):
+    language=language.replace("all_","")
+    if language == "zh":
+        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
+    else:
+        bert = torch.zeros(
+            (1024, len(phones)),
+            dtype=torch.float16 if is_half == True else torch.float32,
+        ).to(device)
+
+    return bert
+
+
+def get_phones_and_bert(text,language):
+    if language in {"en","all_zh","all_ja"}:
+        language = language.replace("all_","")
+        if language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+        else:
+            # 因无法区别中日文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        phones, word2ph, norm_text = clean_text_inf(formattext, language)
+        if language == "zh":
+            bert = get_bert_feature(norm_text, word2ph).to(device)
+        else:
+            bert = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float16 if is_half == True else torch.float32,
+            ).to(device)
+    elif language in {"zh", "ja","auto"}:
+        textlist=[]
+        langlist=[]
+        LangSegment.setfilters(["zh","ja","en","ko"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "ko":
+                    langlist.append("zh")
+                    textlist.append(tmp["text"])
+                else:
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
+        phones_list = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+            bert = get_bert_inf(phones, word2ph, norm_text, lang)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
+            bert_list.append(bert)
+        bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
+
+    return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
+
+
 n_semantic = 1024
 dict_s2 = torch.load(sovits_path, map_location="cpu")
 hps = dict_s2["config"]
@@ -342,15 +422,18 @@ def get_spepc(hps, filename):
 
 
 dict_language = {
-    "中文": "zh",
+    "中文": "all_zh",
     "英文": "en",
-    "日文": "ja",
-    "ZH": "zh",
-    "EN": "en",
-    "JA": "ja",
-    "zh": "zh",
+    "日文": "all_ja",
+    "中英混合": "zh",
+    "日英混合": "ja",
+    "多语种混合": "auto",    #多语种启动切分识别语种
+    "all_zh": "all_zh",
     "en": "en",
-    "ja": "ja"
+    "all_ja": "all_ja",
+    "zh": "zh",
+    "ja": "ja",
+    "auto": "auto",
 }
 
 
@@ -374,25 +457,14 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
         codes = vq_model.extract_latent(ssl_content)
         prompt_semantic = codes[0, 0]
     t1 = ttime()
-    prompt_language = dict_language[prompt_language]
-    text_language = dict_language[text_language]
-    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
-    phones1 = cleaned_text_to_sequence(phones1)
+    prompt_language = dict_language[prompt_language.lower()]
+    text_language = dict_language[text_language.lower()]
+    phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
     texts = text.split("\n")
     audio_opt = []
 
     for text in texts:
-        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
-        phones2 = cleaned_text_to_sequence(phones2)
-        if (prompt_language == "zh"):
-            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
-        else:
-            bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
-                device)
-        if (text_language == "zh"):
-            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
-        else:
-            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
+        phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
         bert = torch.cat([bert1, bert2], 1)
 
         all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)