feat:Added .voice loader

GPT_SoVITS/VoiceSave/__init__.py

@@ -73,7 +73,10 @@ class ZIP_File:
             raise FileNotFoundError(f"File {file_path} does not exist.")
         return file_path
     
-    def get_file_obj(self, file_name:str,location:str,mode:str='r'):
+    def get_file_obj(self, file_name:str,location:str='',mode:str='r'):
+        if location == '':
+            file_path = fl.merge_dir_txt2(self.temp_write,file_name)
+        else:
             file_path = fl.merge_dir_txt2(self.temp_write, location, file_name)
         if not os.path.exists(file_path):
             raise FileNotFoundError(f"File {file_path} does not exist.")
@@ -125,14 +128,14 @@ def save_tensor(path: str, tensors: Union[torch.Tensor, list],name:str,MySet:set
     zf.close()
     del zf
 
-def load_tensor(path: str,name:str,find_func,MySet:set=set()) -> list:
+def load_tensor(path: str,name:str,find_func,MySet:set=set()) -> list[torch.Tensor]:
     zf = ZIP_File(path, name, MySet=MySet)
     zf.release()
     voice_path = find_func(zf,il)
     tensors = []
     for i in range(len(voice_path)):
         v = voice_path[i]
-        np_array = np.load(v)
+        np_array = np.load(v,allow_pickle=True)
         tensor = torch.from_numpy(np_array)
         tensors.append(tensor)
     zf.close()

GPT_SoVITS/inference_webui.py

@@ -39,6 +39,21 @@ def _get_unique_name(name,MySet:set=set()):
     POOL.add(name)
     return name
 
+def find_func(zf,il):
+    f = zf.get_file_path("voice.json")
+    info = il.load_info(f)
+    if info is None:
+        return None
+    list_names = info["access_list"]
+    ret = []
+    for name in list_names:
+        try:
+            a = zf.get_file_path(name)
+            ret.append(a)
+        except FileNotFoundError:
+            continue
+    return ret
+
 def set_high_priority():
     """把当前 Python 进程设为 HIGH_PRIORITY_CLASS"""
     if os.name != "nt":
@@ -798,8 +813,14 @@ def get_tts_wav(
 
     SaveSvEmb=False,
     SaveRefers=False,
-    SaveSvEmbName=None,
-    SaveRefersName=None
+    SaveSvEmbName="sv_emb.voice",
+    SaveRefersName="refers.voice",
+
+    InjectSvEmb=False,
+    InjectRefers=False,
+    InjectSvEmbName="sv_emb.voice",
+    InjectRefersName="refers.voice",
+
 ):
     global cache
     if ref_wav_path:
@@ -952,7 +973,7 @@ def get_tts_wav(
                 if SaveSvEmb and is_v2pro:
                     names = []
                     for i in sv_emb:
-                        names.append(_get_unique_name(str(i.shape)))
+                        names.append(_get_unique_name(str(i.shape))+".npy")
                     sv_path = merge_dir_txt2(ROOT_DIR,"output","sv_emb_opt")
                     if not os.path.exists(sv_path):
                         os.makedirs(sv_path,exist_ok=True)
@@ -964,7 +985,7 @@ def get_tts_wav(
                 if SaveRefers:
                     names = []
                     for i in refers:
-                        names.append(_get_unique_name(str(i.shape)))
+                        names.append(_get_unique_name(str(i.shape))+".npy")
                     refers_path = merge_dir_txt2(ROOT_DIR,"output","refers_opt")
                     if not os.path.exists(refers_path):
                         os.makedirs(refers_path,exist_ok=True)
@@ -972,6 +993,28 @@ def get_tts_wav(
             except:
                 traceback.print_exc()
             
+            #print("refers数量:", len(refers))
+            #print("sv_emb数量:", len(sv_emb) if is_v2pro else "无sv_emb")
+            try:
+                if InjectSvEmb and is_v2pro:
+                    _sv_emb = VoiceSave.load_tensor(str(merge_dir_txt2(ROOT_DIR,"output","sv_emb_opt",InjectSvEmbName)),InjectSvEmbName,find_func)
+                    for i in range(len(_sv_emb)):
+                        sv_emb.append(_sv_emb[i].to(device))
+            except:
+                traceback.print_exc()
+
+            try:
+                if InjectRefers:
+                    _refers = VoiceSave.load_tensor(str(merge_dir_txt2(ROOT_DIR,"output","refers_opt",InjectRefersName)),InjectRefersName,find_func)
+                    for i in range(len(_refers)):
+                        refers.append(_refers[i].to(device))
+            except:
+                traceback.print_exc()
+
+            #print("注入后refers数量:", len(refers))
+            #print("注入后sv_emb数量:", len(sv_emb) if is_v2pro else "无sv_emb")
+
+
             if is_v2pro:
                 audio = vq_model.decode(
                     pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed, sv_emb=sv_emb