diff --git a/tools/rvc_for_realtime.py b/tools/rvc_for_realtime.py
index 8e16e87..f36ffb3 100644
--- a/tools/rvc_for_realtime.py
+++ b/tools/rvc_for_realtime.py
@@ -46,22 +46,23 @@ def printt(strr, *args):
 # config.is_half=False########强制cpu测试
 class RVC:
     def __init__(
-            self,
-            key,
-            pth_path,
-            index_path,
-            index_rate,
-            n_cpu,
-            inp_q,
-            opt_q,
-            config: Config,
-            last_rvc=None,
+        self,
+        key,
+        pth_path,
+        index_path,
+        index_rate,
+        n_cpu,
+        inp_q,
+        opt_q,
+        config: Config,
+        last_rvc=None,
     ) -> None:
         """
         初始化
         """
         try:
             if config.dml == True:
+
                 def forward_dml(ctx, x, scale):
                     ctx.scale = scale
                     res = x.clone().detach()
@@ -205,7 +206,7 @@ class RVC:
         f0bak = f0.copy()
         f0_mel = 1127 * np.log(1 + f0 / 700)
         f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-                f0_mel_max - f0_mel_min
+            f0_mel_max - f0_mel_min
         ) + 1
         f0_mel[f0_mel <= 1] = 1
         f0_mel[f0_mel > 255] = 255
@@ -261,7 +262,7 @@ class RVC:
                 self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
             else:
                 self.inp_q.put(
-                    (idx, x[part_length * idx - 320: tail], res_f0, n_cpu, ts)
+                    (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
                 )
         while 1:
             res_ts = self.opt_q.get()
@@ -276,7 +277,7 @@ class RVC:
             else:
                 f0 = f0[2:]
             f0bak[
-            part_length * idx // 160: part_length * idx // 160 + f0.shape[0]
+                part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
             ] = f0
         f0bak = signal.medfilt(f0bak, 3)
         f0bak *= pow(2, f0_up_key / 12)
@@ -328,26 +329,32 @@ class RVC:
     def get_f0_fcpe(self, x, f0_up_key):
         if hasattr(self, "model_fcpe") == False:
             from torchfcpe import spawn_bundled_infer_model
+
             printt("Loading fcpe model")
             self.model_fcpe = spawn_bundled_infer_model(self.device)
-        f0 = self.model_fcpe.infer(
-            torch.from_numpy(x).to(self.device).unsqueeze(0).float(),
-            sr=16000,
-            decoder_mode='local_argmax',
-            threshold=0.006,
-        ).squeeze().cpu().numpy()
+        f0 = (
+            self.model_fcpe.infer(
+                torch.from_numpy(x).to(self.device).unsqueeze(0).float(),
+                sr=16000,
+                decoder_mode="local_argmax",
+                threshold=0.006,
+            )
+            .squeeze()
+            .cpu()
+            .numpy()
+        )
         f0 *= pow(2, f0_up_key / 12)
         return self.get_f0_post(f0)
 
     def infer(
-            self,
-            feats: torch.Tensor,
-            indata: np.ndarray,
-            block_frame_16k,
-            rate,
-            cache_pitch,
-            cache_pitchf,
-            f0method,
+        self,
+        feats: torch.Tensor,
+        indata: np.ndarray,
+        block_frame_16k,
+        rate,
+        cache_pitch,
+        cache_pitchf,
+        f0method,
     ) -> np.ndarray:
         feats = feats.view(1, -1)
         if self.config.is_half:
@@ -380,8 +387,8 @@ class RVC:
                 if self.config.is_half:
                     npy = npy.astype("float16")
                 feats[0][-leng_replace_head:] = (
-                        torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
-                        + (1 - self.index_rate) * feats[0][-leng_replace_head:]
+                    torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
+                    + (1 - self.index_rate) * feats[0][-leng_replace_head:]
                 )
             else:
                 printt("Index search FAILED or disabled")