fix: index_root searching

close #1147

tools/app.py

@@ -23,7 +23,7 @@ vc = VC(config)
 
 weight_root = os.getenv("weight_root")
 weight_uvr5_root = os.getenv("weight_uvr5_root")
-index_root = "logs"
+index_root = os.getenv("index_root")
 names = []
 hubert_model = None
 for name in os.listdir(weight_root):

tools/calc_rvc_model_similarity.py

@@ -55,7 +55,7 @@ def main(path, root):
     torch.manual_seed(114514)
     model_a = torch.load(path, map_location="cpu")["weight"]
 
-    print("query:\t\t%s\t%s" % (path, model_hash(path)))
+    print("Query:\t\t%s\t%s" % (path, model_hash(path)))
 
     map_attn_a = {}
     map_rand_input = {}
@@ -83,7 +83,7 @@ def main(path, root):
             sims.append(sim)
 
         print(
-            "reference:\t%s\t%s\t%s"
+            "Reference:\t%s\t%s\t%s"
             % (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%")
         )
 

tools/infer/infer-pm-index256.py

@@ -34,7 +34,7 @@ from scipy.io import wavfile
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model_path = r"E:\codes\py39\vits_vc_gpu_train\assets\hubert\hubert_base.pt"  #
-print("load model(s) from {}".format(model_path))
+print("Load model(s) from {}".format(model_path))
 models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
     [model_path],
     suffix="",

tools/infer/train-index-v2.py

@@ -50,14 +50,14 @@ np.save("tools/infer/big_src_feature_mi.npy", big_npy)
 # big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
 n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
 index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf)  # mi
-print("training")
+print("Training...")
 index_ivf = faiss.extract_index_ivf(index)  #
 index_ivf.nprobe = 1
 index.train(big_npy)
 faiss.write_index(
     index, "tools/infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
 )
-print("adding")
+print("Adding...")
 batch_size_add = 8192
 for i in range(0, big_npy.shape[0], batch_size_add):
     index.add(big_npy[i : i + batch_size_add])

tools/infer/train-index.py

@@ -20,12 +20,12 @@ np.save("infer/big_src_feature_mi.npy", big_npy)
 # big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
 print(big_npy.shape)
 index = faiss.index_factory(256, "IVF512,Flat")  # mi
-print("training")
+print("Training...")
 index_ivf = faiss.extract_index_ivf(index)  #
 index_ivf.nprobe = 9
 index.train(big_npy)
 faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
-print("adding")
+print("Adding...")
 index.add(big_npy)
 faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
 """

tools/rvc_for_realtime.py

@@ -67,7 +67,7 @@ class RVC:
             if index_rate != 0:
                 self.index = faiss.read_index(index_path)
                 self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                print("index search enabled")
+                print("Index search enabled")
             self.index_path = index_path
             self.index_rate = index_rate
             models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
@@ -120,7 +120,7 @@ class RVC:
         if new_index_rate != 0 and self.index_rate == 0:
             self.index = faiss.read_index(self.index_path)
             self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-            print("index search enabled")
+            print("Index search enabled")
         self.index_rate = new_index_rate
 
     def get_f0_post(self, f0):
@@ -237,7 +237,7 @@ class RVC:
         if hasattr(self, "model_rmvpe") == False:
             from infer.lib.rmvpe import RMVPE
 
-            print("loading rmvpe model")
+            print("Loading rmvpe model")
             self.model_rmvpe = RMVPE(
                 # "rmvpe.pt", is_half=self.is_half if self.device.type!="privateuseone" else False, device=self.device if self.device.type!="privateuseone"else "cpu"####dml时强制对rmvpe用cpu跑
                 #  "rmvpe.pt", is_half=False, device=self.device####dml配置
@@ -295,10 +295,10 @@ class RVC:
                     + (1 - self.index_rate) * feats[0][-leng_replace_head:]
                 )
             else:
-                print("index search FAIL or disabled")
+                print("Index search FAILED or disabled")
         except:
             traceback.print_exc()
-            print("index search FAIL")
+            print("Index search FAILED")
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         t3 = ttime()
         if self.if_f0 == 1:
@@ -338,5 +338,5 @@ class RVC:
                     .float()
                 )
         t5 = ttime()
-        print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
+        print("Spent time: fea =", t2 - t1, ", index =", t3 - t2, ", f0 =", t4 - t3, ", model =", t5 - t4)
         return infered_audio