bsroformer support fp16 inference

bsroformer support fp16 inference

tools/uvr5/bsroformer.py

@@ -1,4 +1,5 @@
 # This code is modified from https://github.com/ZFTurbo/
+import pdb
 
 import librosa
 from tqdm import tqdm
@@ -10,6 +11,7 @@ import torch.nn as nn
 
 import warnings
 warnings.filterwarnings("ignore")
+from bs_roformer.bs_roformer import BSRoformer
 
 class BsRoformer_Loader:
     def get_model_from_config(self):
@@ -40,7 +42,7 @@ class BsRoformer_Loader:
 
         }
 
-        from bs_roformer.bs_roformer import BSRoformer
+
         model = BSRoformer(
             **dict(config)
         )
@@ -95,6 +97,8 @@ class BsRoformer_Loader:
                             part = nn.functional.pad(input=part, pad=(0, C - length), mode='reflect')
                         else:
                             part = nn.functional.pad(input=part, pad=(0, C - length, 0, 0), mode='constant', value=0)
+                    if(self.is_half==True):
+                        part=part.half()
                     batch_data.append(part)
                     batch_locations.append((i, length))
                     i += step
@@ -102,6 +106,7 @@ class BsRoformer_Loader:
 
                     if len(batch_data) >= batch_size or (i >= mix.shape[1]):
                         arr = torch.stack(batch_data, dim=0)
+                        # print(23333333,arr.dtype)
                         x = model(arr)
 
                         window = window_middle
@@ -192,14 +197,18 @@ class BsRoformer_Loader:
         # print("Elapsed time: {:.2f} sec".format(time.time() - start_time))
 
 
-    def __init__(self, model_path, device):
+    def __init__(self, model_path, device,is_half):
         self.device = device
         self.extract_instrumental=True
 
         model = self.get_model_from_config()
-        state_dict = torch.load(model_path)
+        state_dict = torch.load(model_path,map_location="cpu")
         model.load_state_dict(state_dict)
+        self.is_half=is_half
+        if(is_half==False):
             self.model = model.to(device)
+        else:
+            self.model = model.half().to(device)
 
 
     def _path_audio_(self, input, others_root, vocal_root, format, is_hp3=False):

tools/uvr5/webui.py

@@ -17,8 +17,8 @@ from bsroformer import BsRoformer_Loader
 weight_uvr5_root = "tools/uvr5/uvr5_weights"
 uvr5_names = []
 for name in os.listdir(weight_uvr5_root):
-    if name.endswith(".pth") or "onnx" in name:
-        uvr5_names.append(name.replace(".pth", ""))
+    if name.endswith(".pth") or name.endswith(".ckpt") or "onnx" in name:
+        uvr5_names.append(name.replace(".pth", "").replace(".ckpt", ""))
 
 device=sys.argv[1]
 is_half=eval(sys.argv[2])
@@ -37,8 +37,9 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
         elif model_name == "Bs_Roformer" or "bs_roformer" in model_name.lower():
             func = BsRoformer_Loader
             pre_fun = func(
-                model_path = os.path.join(weight_uvr5_root, model_name + ".pth"),
+                model_path = os.path.join(weight_uvr5_root, model_name + ".ckpt"),
                 device = device,
+                is_half=is_half
             )
         else:
             func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho