diff --git a/lib/model_utils.py b/lib/model_utils.py
index 905ae5b..c2e6566 100644
--- a/lib/model_utils.py
+++ b/lib/model_utils.py
@@ -21,16 +21,17 @@ def load_hubert(model_path: str, config):
         if model_path.endswith(".safetensors"):
             return HubertModelWithFinalProj.from_safetensors(model_path, device=config.device)
         else:
-            from fairseq import checkpoint_utils
-            models, _, _ = checkpoint_utils.load_model_ensemble_and_task([model_path],suffix="",)
-            hubert_model = models[0]
-            hubert_model = hubert_model.to(config.device)
-            if config.is_half:
-                hubert_model = hubert_model.half()
-            else:
-                hubert_model = hubert_model.float()
-            hubert_model.eval()
-            return hubert_model
+            raise NotImplementedError("Please use content-vec-best.safetensors!")
+            # from fairseq import checkpoint_utils
+            # models, _, _ = checkpoint_utils.load_model_ensemble_and_task([model_path],suffix="",)
+            # hubert_model = models[0]
+            # hubert_model = hubert_model.to(config.device)
+            # if config.is_half:
+            #     hubert_model = hubert_model.half()
+            # else:
+            #     hubert_model = hubert_model.float()
+            # hubert_model.eval()
+            # return hubert_model
     except Exception as e:
         print(e)
         return None
diff --git a/pyproject.toml b/pyproject.toml
index 978bdf7..a7f3493 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,33 @@ name = "comfy-rvc"
 description = ""
 version = "1.0.0"
 license = "{file = \"LICENSE\"}"
-dependencies = ["librosa~=0.10.2", "numpy", "fairseq==0.12.2", "faiss-cpu", "ffmpeg-python", "tqdm", "audioread", "audio-separator[gpu]", "onnxruntime-gpu", "ffmpy", "samplerate", "pyaudio", "spacy", "monotonic_align", "textacy", "pytube", "spacytextblob"]
+dependencies = [
+    "librosa~=0.10.2",
+    "numpy",
+    "faiss-cpu==1.7.4",
+    "ffmpeg-python",
+    "tqdm",
+    "audioread",
+    "audio-separator[gpu]",
+    "onnxruntime-gpu",
+    "ffmpy",
+    "samplerate",
+    "pyaudio",
+    "spacy",
+    "monotonic_align",
+    "textacy",
+    "pytube",
+    "spacytextblob",
+    "accelerate",
+    "yt-dlp",
+    "deep-translator",
+    "openmim",
+    "opencv-python",
+    "soundfile",
+    "diffusers",
+    "scikit-learn",
+    "torchcrepe"
+    ]
 
 [project.urls]
 Repository = "https://github.com/SayanoAI/Comfy-RVC"
diff --git a/requirements.txt b/requirements.txt
index 396455a..fef6df5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
 librosa~=0.10.2
 numpy
-fairseq==0.12.2
 faiss-cpu==1.7.4
 ffmpeg-python
 tqdm
@@ -20,7 +19,6 @@ deep-translator
 openmim
 opencv-python
 soundfile
-openmim
 diffusers
 scikit-learn
 torchcrepe
\ No newline at end of file
diff --git a/rvc_for_realtime.py b/rvc_for_realtime.py
deleted file mode 100644
index 5122707..0000000
--- a/rvc_for_realtime.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import os
-import logging
-from .lib import BASE_MODELS_DIR
-from .lib.model_utils import change_rms, load_hubert
-from .config import config
-from .pitch_extraction import FeatureExtractor
-from .lib.utils import gc_collect, get_filenames
-
-logger = logging.getLogger(__name__)
-
-import fairseq
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-if config.dml == True:
-
-    def forward_dml(ctx, x, scale):
-        ctx.scale = scale
-        res = x.clone().detach()
-        return res
-
-    fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
-
-
-# config.device=torch.device("cpu")########强制cpu测试
-# config.is_half=False########强制cpu测试
-class RVC(FeatureExtractor):
-    def __init__(self, model_path, config, onnx=False, device=None):
-
-        cpt = torch.load(model_path, map_location="cpu")
-        tgt_sr = cpt["config"][-1]
-        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-        if_f0 = cpt.get("f0", 1)
-        version = cpt.get("version", "v1")
-        self.sid = 0
-        
-        if version == "v1":
-            if if_f0 == 1:
-                from .lib.infer_pack.models import SynthesizerTrnMs256NSFsid
-                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-            else:
-                from .lib.infer_pack.models import SynthesizerTrnMs256NSFsid_nono
-                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-        elif version == "v2":
-            if if_f0 == 1:
-                from .lib.infer_pack.models import SynthesizerTrnMs768NSFsid
-                net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-            else:
-                from .lib.infer_pack.models import SynthesizerTrnMs768NSFsid_nono
-                net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-        del net_g.enc_q
-        
-        net_g.load_state_dict(cpt["weight"], strict=False)
-        net_g.eval().to(device if device else config.device)
-        if config.is_half:
-            net_g = net_g.half()
-        else:
-            net_g = net_g.float()
-        hubert_model = load_hubert(config)
-        model_name = os.path.basename(model_path).split(".")[0]
-        index_files = get_filenames(root=os.path.join(BASE_MODELS_DIR,"RVC"),folder=".index",exts=["index"],name_filters=[model_name])
-        file_index = index_files.pop() if len(index_files) else ""
-
-        self.cpt = cpt
-        self.net_g = net_g
-        self.hubert_model = hubert_model
-        self.model_name = model_name
-        self.index, self.big_npy = self.load_index(file_index)
-        self.tgt_sr = tgt_sr
-        self.if_f0 = if_f0
-        self.version = version
-        super().__init__(tgt_sr, config, onnx) # initiate Feature Extraction
-
-    def __del__(self):
-        super().__del__()
-        del self.cpt, self.net_g, self.hubert_model, self.index, self.big_npy
-        gc_collect()
-
-    # def process_input(self, x: np.ndarray, **kwargs) -> np.ndarray:
-    def vc(self, x: np.ndarray, **kwargs) -> np.ndarray:
-        index_rate = kwargs.pop("index_rate",.5)
-        protect = kwargs.pop("protect",.5)
-        rms_mix_rate = kwargs.pop("rms_mix_rate",1.)
-    
-        feats = torch.from_numpy(x.copy())
-        feats = feats.view(1, -1)
-        if config.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        feats = feats.to(self.device)
-
-        with torch.no_grad():
-            padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-            inputs = {
-                "source": feats,
-                "padding_mask": padding_mask,
-                "output_layer": 9 if self.version == "v1" else 12,
-            }
-            logits = self.hubert_model.extract_features(**inputs)
-            feats = (
-                self.hubert_model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-
-        if protect < 0.5 and self.if_f0:
-            feats0 = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-
-        if self.index is not None and self.big_npy is not None and index_rate != 0:
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float16")
-
-            score, ix = self.index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-
-        if self.if_f0 == 1:
-            pitch, pitchf = self.get_f0(x, **kwargs)
-            p_len = min(feats.shape[1], pitch.shape[0])
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.from_numpy(pitch).to(self.device).unsqueeze(0)
-            pitchf = torch.from_numpy(pitchf).to(self.device).unsqueeze(0)
-            if protect < 0.5:
-                pitchff = pitchf.clone()
-                pitchff[pitchf > 0] = 1
-                pitchff[pitchf < 1] = protect
-                pitchff = pitchff.unsqueeze(-1)
-                feats = feats * pitchff + feats0 * (1 - pitchff)
-                del pitchff
-        else:
-            pitch, pitchf = None, None
-            p_len = feats.shape[1]
-       
-        p_len = torch.LongTensor([p_len]).to(self.device)
-        sid = torch.LongTensor([self.sid]).to(self.device)
-        with torch.no_grad():
-            if self.is_half: feats = feats.to(torch.half)
-            if self.if_f0 == 1:
-                # print("process_output",feats,p_len,pitch,pitchf)
-                # print(12222222222,feats.dtype,pitch.dtype,pitchf.dtype,sid.dtype,self.is_half)
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0].data
-                )
-            else:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data
-                )
-
-            audio_opt = infered_audio.cpu().float().numpy()
-            if rms_mix_rate < 1.:
-                audio_opt = change_rms(x, self.sr, audio_opt, self.tgt_sr, rms_mix_rate)
-
-            del feats, p_len, sid, pitch, pitchf, infered_audio
-        return audio_opt
\ No newline at end of file