Add non-search inference support. (#82)

Co-authored-by: 源文雨 <[email protected]>

Author: EntropyRiser

gui.py

@@ -16,7 +16,6 @@
 from i18n import I18nAuto
 
 i18n = I18nAuto()
-print(i18n.language_map)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
@@ -33,10 +32,11 @@ def __init__(
         self.f0_max = 1100
         self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
         self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-        self.index = faiss.read_index(index_path)
+        if index_rate !=0:
+            self.index = faiss.read_index(index_path)
+            self.big_npy = np.load(npy_path)
+            print('index search enabled')
         self.index_rate = index_rate
-        """NOT YET USED"""
-        self.big_npy = np.load(npy_path)
         model_path = hubert_path
         print("load model(s) from {}".format(model_path))
         models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
@@ -112,8 +112,8 @@ def infer(self, feats: torch.Tensor) -> np.ndarray:
 
         ####索引优化
         if (
-            isinstance(self.index, type(None)) == False
-            and isinstance(self.big_npy, type(None)) == False
+            hasattr(self,'index')
+            and hasattr(self,'big_npy')
             and self.index_rate != 0
         ):
             npy = feats[0].cpu().numpy().astype("float32")
@@ -123,6 +123,8 @@ def infer(self, feats: torch.Tensor) -> np.ndarray:
                 torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
                 + (1 - self.index_rate) * feats
             )
+        else:
+            print('index search FAIL or disabled')
 
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         torch.cuda.synchronize()