added support for mixed language API according to the current implementation in inference_webui.py

api.py

@@ -80,6 +80,23 @@ RESP:
 失败: json, 400
 
 
+### 动态更换底模 
+
+endpoint: `/set_model`
+
+GET:
+    `http://127.0.0.1:9880/set_model?gpt_model_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt&sovits_model_path=GPT_SoVITS/pretrained_models/s2G488k.pth`
+POST:
+```json
+{
+    "gpt_model_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+    "sovits_model_path": "GPT_SoVITS/pretrained_models/s2G488k.pth"
+}
+```
+
+RESP:
+成功: json, http code 200
+
 ### 命令控制
 
 endpoint: `/control`
@@ -126,6 +143,7 @@ from module.models import SynthesizerTrn
 from AR.models.t2s_lightning_module import Text2SemanticLightningModule
 from text import cleaned_text_to_sequence
 from text.cleaner import clean_text
+import LangSegment
 from module.mel_processing import spectrogram_torch
 from my_utils import load_audio
 import config as global_config
@@ -350,9 +368,84 @@ dict_language = {
     "JA": "ja",
     "zh": "zh",
     "en": "en",
-    "ja": "ja"
+    "ja": "ja",
+    "auto": "auto",
+    "中英混合": "zh",
+    "日英混合": "ja", 
+    "多语种混合": "auto"
 }
 
+dtype=torch.float16 if is_half == True else torch.float32
+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 clean_text_inf(text, language):
+    phones, word2ph, norm_text = clean_text(text, language)
+    phones = cleaned_text_to_sequence(phones)
+    return phones, word2ph, norm_text
+
+# Mixed language support 
+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"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                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(dtype),norm_text
 
 def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
     t0 = ttime()
@@ -380,22 +473,13 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
     phones1 = cleaned_text_to_sequence(phones1)
     texts = text.split("\n")
     audio_opt = []
+    phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
 
     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)
 
-        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
         bert = bert.to(device).unsqueeze(0)
         all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
         prompt = prompt_semantic.unsqueeze(0).to(device)