Upload app.py

app.py

@@ -201,58 +201,109 @@ def create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, file_index):
             return info, None
     return vc_fn
 
+# def load_model():
+    # categories = []
+    # if os.path.isfile("weights/folder_info.json"):
+        # with open("weights/folder_info.json", "r", encoding="utf-8") as f:
+            # folder_info = json.load(f)
+        # for category_name, category_info in folder_info.items():
+            # if not category_info['enable']:
+                # continue
+            # category_title = category_info['title']
+            # category_folder = category_info['folder_path']
+            # description = category_info['description']
+            # models = []
+            # with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
+                # models_info = json.load(f)
+            # for character_name, info in models_info.items():
+                # if not info['enable']:
+                    # continue
+                # model_title = info['title']
+                # model_name = info['model_path']
+                # model_author = info.get("author", None)
+                # model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
+                # model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
+                # cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
+                # tgt_sr = cpt["config"][-1]
+                # cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+                # if_f0 = cpt.get("f0", 1)
+                # version = cpt.get("version", "v1")
+                # if version == "v1":
+                    # if if_f0 == 1:
+                        # net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+                    # else:
+                        # net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+                    # model_version = "V1"
+                # elif version == "v2":
+                    # if if_f0 == 1:
+                        # net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+                    # else:
+                        # net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+                    # model_version = "V2"
+                # del net_g.enc_q
+                # print(net_g.load_state_dict(cpt["weight"], strict=False))
+                # net_g.eval().to(config.device)
+                # if config.is_half:
+                    # net_g = net_g.half()
+                # else:
+                    # net_g = net_g.float()
+                # vc = VC(tgt_sr, config)
+                # print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
+                # models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
+            # categories.append([category_title, category_folder, description, models])
+    # else:
+        # categories = []
+    # return categories
+
 def load_model():
     categories = []
-    if os.path.isfile("weights/folder_info.json"):
-        with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-            folder_info = json.load(f)
-        for category_name, category_info in folder_info.items():
-            if not category_info['enable']:
+    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
+        folder_info = json.load(f)
+    for category_name, category_info in folder_info.items():
+        if not category_info['enable']:
+            continue
+        category_title = category_info['title']
+        category_folder = category_info['folder_path']
+        description = category_info['description']
+        models = []
+        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
+            models_info = json.load(f)
+        for character_name, info in models_info.items():
+            if not info['enable']:
                 continue
-            category_title = category_info['title']
-            category_folder = category_info['folder_path']
-            description = category_info['description']
-            models = []
-            with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-                models_info = json.load(f)
-            for character_name, info in models_info.items():
-                if not info['enable']:
-                    continue
-                model_title = info['title']
-                model_name = info['model_path']
-                model_author = info.get("author", None)
-                model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-                model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
-                cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
-                tgt_sr = cpt["config"][-1]
-                cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-                if_f0 = cpt.get("f0", 1)
-                version = cpt.get("version", "v1")
-                if version == "v1":
-                    if if_f0 == 1:
-                        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-                    else:
-                        net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-                    model_version = "V1"
-                elif version == "v2":
-                    if if_f0 == 1:
-                        net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-                    else:
-                        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-                    model_version = "V2"
-                del net_g.enc_q
-                print(net_g.load_state_dict(cpt["weight"], strict=False))
-                net_g.eval().to(config.device)
-                if config.is_half:
-                    net_g = net_g.half()
+            model_title = info['title']
+            model_name = info['model_path']
+            model_author = info.get("author", None)
+            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
+            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
+            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
+            tgt_sr = cpt["config"][-1]
+            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+            if_f0 = cpt.get("f0", 1)
+            version = cpt.get("version", "v1")
+            if version == "v1":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
                 else:
-                    net_g = net_g.float()
-                vc = VC(tgt_sr, config)
-                print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
-                models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
-            categories.append([category_title, category_folder, description, models])
-    else:
-        categories = []
+                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+                model_version = "V1"
+            elif version == "v2":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+                model_version = "V2"
+            del net_g.enc_q
+            print(net_g.load_state_dict(cpt["weight"], strict=False))
+            net_g.eval().to(config.device)
+            if config.is_half:
+                net_g = net_g.half()
+            else:
+                net_g = net_g.float()
+            vc = VC(tgt_sr, config)
+            print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
+            models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
+        categories.append([category_title, category_folder, description, models])
     return categories
 
 def download_audio(url, audio_provider):