fix: index_root searching

close #1147

infer/modules/train/extract/extract_f0_print.py

@@ -85,7 +85,7 @@ class FeatureInput(object):
             if hasattr(self, "model_rmvpe") == False:
                 from infer.lib.rmvpe import RMVPE
 
-                print("loading rmvpe model")
+                print("Loading rmvpe model")
                 self.model_rmvpe = RMVPE(
                     "assets/rmvpe/rmvpe.pt", is_half=False, device="cpu"
                 )

infer/modules/train/extract/extract_f0_rmvpe.py

@@ -48,7 +48,7 @@ class FeatureInput(object):
             if hasattr(self, "model_rmvpe") == False:
                 from infer.lib.rmvpe import RMVPE
 
-                print("loading rmvpe model")
+                print("Loading rmvpe model")
                 self.model_rmvpe = RMVPE(
                     "assets/rmvpe/rmvpe.pt", is_half=is_half, device="cuda"
                 )

infer/modules/train/extract/extract_f0_rmvpe_dml.py

@@ -46,7 +46,7 @@ class FeatureInput(object):
             if hasattr(self, "model_rmvpe") == False:
                 from infer.lib.rmvpe import RMVPE
 
-                print("loading rmvpe model")
+                print("Loading rmvpe model")
                 self.model_rmvpe = RMVPE(
                     "assets/rmvpe/rmvpe.pt", is_half=False, device=device
                 )

infer/modules/uvr5/mdxnet.py

@@ -101,7 +101,7 @@ class Predictor:
                 "CPUExecutionProvider",
             ],
         )
-        print("onnx load done")
+        print("ONNX load done")
 
     def demix(self, mix):
         samples = mix.shape[-1]

infer/modules/uvr5/modules.py

@@ -90,7 +90,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
                 del pre_fun
         except:
             traceback.print_exc()
-        print("clean_empty_cache")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
+            print("Executed torch.cuda.empty_cache()")
     yield "\n".join(infos)

infer/modules/vc/modules.py

@@ -31,7 +31,7 @@ class VC:
 
     def get_vc(self, sid, *to_return_protect):
         person = f'{os.getenv("weight_root")}/{sid}'
-        print(f"loading {person}")
+        print(f"Loading: {person}")
 
         self.cpt = torch.load(person, map_location="cpu")
         self.tgt_sr = self.cpt["config"][-1]
@@ -77,6 +77,7 @@ class VC:
         self.pipeline = Pipeline(self.tgt_sr, self.config)
         n_spk = self.cpt["config"][-3]
         index = {"value": get_index_path_from_model(sid), "__type__": "update"}
+        print("Select index:", index["value"])
 
         return (
             (

infer/modules/vc/pipeline.py

@@ -140,7 +140,7 @@ class Pipeline(object):
                 from infer.lib.rmvpe import RMVPE
 
                 print(
-                    "loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
+                    "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
                 )
                 self.model_rmvpe = RMVPE(
                     "%s/rmvpe.pt" % os.environ["rmvpe_root"],
@@ -152,7 +152,7 @@ class Pipeline(object):
         if "privateuseone" in str(self.device):  # clean ortruntime memory
             del self.model_rmvpe.model
             del self.model_rmvpe
-            print("cleaning ortruntime memory")
+            print("Cleaning ortruntime memory")
 
         f0 *= pow(2, f0_up_key / 12)
         # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
@@ -262,17 +262,12 @@ class Pipeline(object):
             feats = feats.to(feats0.dtype)
         p_len = torch.tensor([p_len], device=self.device).long()
         with torch.no_grad():
-            if pitch is not None and pitchf is not None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
+            hasp = pitch is not None and pitchf is not None
+            arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
+            audio1 = (
+                (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
+            )
+            del hasp, arg
         del feats, p_len, padding_mask
         if torch.cuda.is_available():
             torch.cuda.empty_cache()

infer/modules/vc/utils.py

@@ -2,33 +2,20 @@ import os
 
 from fairseq import checkpoint_utils
 
-### don't modify the code before you test it
-# def get_index_path_from_model(sid):
-#     return next(
-#         (
-#             f
-#             for f in [
-#                 os.path.join(root, name)
-#                 for root, dirs, files in os.walk(os.getenv("index_root"), topdown=False)
-#                 for name in files
-#                 if name.endswith(".index") and "trained" not in name
-#             ]
-#             if sid.split(".")[0] in f
-#         ),
-#         "",
-#     )
-
-
 def get_index_path_from_model(sid):
-    sel_index_path = ""
-    name = os.path.join("logs", sid.split(".")[0], "")
-    # print(name)
-    for f in index_paths:
-        if name in f:
-            # print("selected index path:", f)
-            sel_index_path = f
-            break
-    return sel_index_path
+    return next(
+        (
+            f
+            for f in [
+                os.path.join(root, name)
+                for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
+                for name in files
+                if name.endswith(".index") and "trained" not in name
+            ]
+            if sid.split(".")[0] in f
+        ),
+        "",
+    )
 
 
 def load_hubert(config):