fix(web): unload model error

IndexError: tuple index out of range

i18n/locale/en_US.json

@@ -53,7 +53,7 @@
     "加载预训练底模D路径": "Load pre-trained base model D path",
     "加载预训练底模G路径": "Load pre-trained base model G path",
     "单次推理": "Single Inference",
-    "卸载音色省显存": "Unload voice to save GPU memory",
+    "卸载音色省显存": "Unload model to save GPU memory",
     "变调(整数, 半音数量, 升八度12降八度-12)": "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)",
     "后处理重采样至最终采样率，0为不进行重采样": "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
     "否": "No",

infer-web.py

@@ -1093,7 +1093,7 @@ with gr.Blocks(title="RVC WebUI") as app:
                 )
                 sid0.change(
                     fn=vc.get_vc,
-                    inputs=[sid0, protect0, protect1],
+                    inputs=[sid0, protect0, protect1, file_index2, file_index4],
                     outputs=[
                         spk_item,
                         protect0,

infer/modules/vc/hash.py

@@ -190,27 +190,30 @@ def _extend_difference(n, a, b):
 
 
 def hash_similarity(h1: str, h2: str) -> float:
-    h1b, h2b = decode_from_string(h1), decode_from_string(h2)
-    if len(h1b) != half_hash_len * 2 or len(h2b) != half_hash_len * 2:
-        raise Exception("invalid hash length")
-    h1n, h2n = np.frombuffer(h1b, dtype=">i2"), np.frombuffer(h2b, dtype=">i2")
-    d = 0
-    for i in range(half_hash_len // 4):
-        a = i * 2
-        b = a + 1
-        ax = complex(h1n[a], h1n[b])
-        bx = complex(h2n[a], h2n[b])
-        if abs(ax) == 0 or abs(bx) == 0:
-            continue
-        d += np.abs(ax - bx)
-    frac = np.linalg.norm(h1n) * np.linalg.norm(h2n)
-    cosine = (
-        np.dot(h1n.astype(np.float32), h2n.astype(np.float32)) / frac
-        if frac != 0
-        else 1.0
-    )
-    distance = _extend_difference(np.exp(-d / expand_factor), 0.5, 1.0)
-    return round((abs(cosine) + distance) / 2, 6)
+    try:
+        h1b, h2b = decode_from_string(h1), decode_from_string(h2)
+        if len(h1b) != half_hash_len * 2 or len(h2b) != half_hash_len * 2:
+            raise Exception("invalid hash length")
+        h1n, h2n = np.frombuffer(h1b, dtype=">i2"), np.frombuffer(h2b, dtype=">i2")
+        d = 0
+        for i in range(half_hash_len // 4):
+            a = i * 2
+            b = a + 1
+            ax = complex(h1n[a], h1n[b])
+            bx = complex(h2n[a], h2n[b])
+            if abs(ax) == 0 or abs(bx) == 0:
+                continue
+            d += np.abs(ax - bx)
+        frac = np.linalg.norm(h1n) * np.linalg.norm(h2n)
+        cosine = (
+            np.dot(h1n.astype(np.float32), h2n.astype(np.float32)) / frac
+            if frac != 0
+            else 1.0
+        )
+        distance = _extend_difference(np.exp(-d / expand_factor), 0.5, 1.0)
+        return round((abs(cosine) + distance) / 2, 6)
+    except Exception as e:
+        return str(e)
 
 
 def hash_id(h: str) -> str:

infer/modules/vc/modules.py

@@ -89,11 +89,15 @@ class VC:
                 elif torch.backends.mps.is_available():
                     torch.mps.empty_cache()
             return (
-                {"visible": False, "__type__": "update"},
-                to_return_protect0,
-                to_return_protect1,
-                "",
-                "",
+                (
+                    {"visible": False, "__type__": "update"},
+                    to_return_protect0,
+                    to_return_protect1,
+                    {"value": to_return_protect[2], "__type__": "update"},
+                    {"value": to_return_protect[3], "__type__": "update"},
+                    {"value": "", "__type__": "update"},
+                ) if to_return_protect
+                else {"visible": True, "maximum": 0, "__type__": "update"}
             )
         person = f'{os.getenv("weight_root")}/{sid}'
         logger.info(f"Loading: {person}")
@@ -221,10 +225,10 @@ class VC:
                 % (index_info, *times),
                 (tgt_sr, audio_opt),
             )
-        except:
+        except Exception as e:
             info = traceback.format_exc()
             logger.warning(info)
-            return info, (None, None)
+            return str(e), None
 
     def vc_multi(
         self,

infer/modules/vc/pipeline.py

@@ -253,7 +253,10 @@ class Pipeline(object):
             # _, I = index.search(npy, 1)
             # npy = big_npy[I.squeeze()]
 
-            score, ix = index.search(npy, k=8)
+            try:
+                score, ix = index.search(npy, k=8)
+            except:
+                raise Exception("index mistatch")
             weight = np.square(1 / score)
             weight /= weight.sum(axis=1, keepdims=True)
             npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)