diff --git a/.gitignore b/.gitignore
index ac0e8ec5..2e3ea7c3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -41,5 +41,6 @@ client/lib/worklet/dist
 docker/cudnn/
 
 server/weights/
+server/weights_/
 
 start_trainer.sh
diff --git a/server/MMVCServerSIO.py b/server/MMVCServerSIO.py
index 906ec22a..f024dec9 100755
--- a/server/MMVCServerSIO.py
+++ b/server/MMVCServerSIO.py
@@ -169,8 +169,11 @@ if __name__ == "MMVCServerSIO":
                 "position": 1,
             }
         )
-    with ThreadPoolExecutor() as pool:
-        pool.map(download, downloadParams)
+
+    # with ThreadPoolExecutor() as pool:
+    #     pool.map(download, downloadParams)
+    for param in downloadParams:
+        download(param)
 
     if (
         os.path.exists(voiceChangerParams.hubert_base) is False
diff --git a/server/voice_changer/RVC/_ModelWrapper.py b/server/voice_changer/RVC/_ModelWrapper.py
deleted file mode 100644
index a659a05a..00000000
--- a/server/voice_changer/RVC/_ModelWrapper.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import onnxruntime
-import torch
-import numpy as np
-import json
-
-# providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
-providers = ["CPUExecutionProvider"]
-
-
-class ModelWrapper:
-    def __init__(self, onnx_model):
-        self.onnx_model = onnx_model
-
-        # ort_options = onnxruntime.SessionOptions()
-        # ort_options.intra_op_num_threads = 8
-        self.onnx_session = onnxruntime.InferenceSession(
-            self.onnx_model, providers=providers
-        )
-        # input_info = s
-        first_input_type = self.onnx_session.get_inputs()[0].type
-        if first_input_type == "tensor(float)":
-            self.is_half = False
-        else:
-            self.is_half = True
-        modelmeta = self.onnx_session.get_modelmeta()
-        try:
-            metadata = json.loads(modelmeta.custom_metadata_map["metadata"])
-            self.samplingRate = metadata["samplingRate"]
-            self.f0 = metadata["f0"]
-            self.embChannels = metadata["embChannels"]
-            self.modelType = metadata["modelType"]
-            self.deprecated = False
-            self.embedder = (
-                metadata["embedder"] if "embedder" in metadata else "hubert_base"
-            )
-            print(
-                f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}, embedder:{self.embedder}"
-            )
-        except:
-            self.samplingRate = 48000
-            self.f0 = True
-            self.embChannels = 256
-            self.modelType = 0
-            self.deprecated = True
-            self.embedder = "hubert_base"
-            print(
-                "[Voice Changer] ############## !!!! CAUTION !!!! ####################"
-            )
-            print(
-                "[Voice Changer] This onnx's version is depricated. Please regenerate onnxfile. Fallback to default"
-            )
-            print(
-                f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}"
-            )
-            print(
-                "[Voice Changer] ############## !!!! CAUTION !!!! ####################"
-            )
-
-    def getSamplingRate(self):
-        return self.samplingRate
-
-    def getF0(self):
-        return self.f0
-
-    def getEmbChannels(self):
-        return self.embChannels
-
-    def getModelType(self):
-        return self.modelType
-
-    def getDeprecated(self):
-        return self.deprecated
-
-    def getEmbedder(self):
-        return self.embedder
-
-    def set_providers(self, providers, provider_options=[{}]):
-        self.onnx_session.set_providers(
-            providers=providers, provider_options=provider_options
-        )
-
-    def get_providers(self):
-        return self.onnx_session.get_providers()
-
-    def infer_pitchless(self, feats, p_len, sid):
-        if self.is_half:
-            audio1 = self.onnx_session.run(
-                ["audio"],
-                {
-                    "feats": feats.cpu().numpy().astype(np.float16),
-                    "p_len": p_len.cpu().numpy().astype(np.int64),
-                    "sid": sid.cpu().numpy().astype(np.int64),
-                },
-            )
-        else:
-            audio1 = self.onnx_session.run(
-                ["audio"],
-                {
-                    "feats": feats.cpu().numpy().astype(np.float32),
-                    "p_len": p_len.cpu().numpy().astype(np.int64),
-                    "sid": sid.cpu().numpy().astype(np.int64),
-                },
-            )
-        return torch.tensor(np.array(audio1))
-
-    def infer(self, feats, p_len, pitch, pitchf, sid):
-        if self.is_half:
-            audio1 = self.onnx_session.run(
-                ["audio"],
-                {
-                    "feats": feats.cpu().numpy().astype(np.float16),
-                    "p_len": p_len.cpu().numpy().astype(np.int64),
-                    "pitch": pitch.cpu().numpy().astype(np.int64),
-                    "pitchf": pitchf.cpu().numpy().astype(np.float32),
-                    "sid": sid.cpu().numpy().astype(np.int64),
-                },
-            )
-        else:
-            audio1 = self.onnx_session.run(
-                ["audio"],
-                {
-                    "feats": feats.cpu().numpy().astype(np.float32),
-                    "p_len": p_len.cpu().numpy().astype(np.int64),
-                    "pitch": pitch.cpu().numpy().astype(np.int64),
-                    "pitchf": pitchf.cpu().numpy().astype(np.float32),
-                    "sid": sid.cpu().numpy().astype(np.int64),
-                },
-            )
-
-        return torch.tensor(np.array(audio1))