Format code (#989)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

MDXNet.py

@@ -83,6 +83,7 @@ def get_models(device, dim_f, dim_t, n_fft):
 
 warnings.filterwarnings("ignore")
 import sys
+
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 from config import Config
@@ -104,10 +105,15 @@ class Predictor:
             device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
         )
         import onnxruntime as ort
+
         print(ort.get_available_providers())
         self.model = ort.InferenceSession(
             os.path.join(args.onnx, self.model_.target_name + ".onnx"),
-            providers=["CUDAExecutionProvider", "DmlExecutionProvider","CPUExecutionProvider"],
+            providers=[
+                "CUDAExecutionProvider",
+                "DmlExecutionProvider",
+                "CPUExecutionProvider",
+            ],
         )
         print("onnx load done")
 

config.py

@@ -36,7 +36,7 @@ class Config:
             self.iscolab,
             self.noparallel,
             self.noautoopen,
-            self.dml
+            self.dml,
         ) = self.arg_parse()
         self.instead = ""
         self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
@@ -71,7 +71,7 @@ class Config:
             cmd_opts.colab,
             cmd_opts.noparallel,
             cmd_opts.noautoopen,
-            cmd_opts.dml
+            cmd_opts.dml,
         )
 
     # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
@@ -149,26 +149,38 @@ class Config:
         if self.dml:
             print("use DirectML instead")
             try:
-                os.rename("runtime\Lib\site-packages\onnxruntime","runtime\Lib\site-packages\onnxruntime-cuda")
+                os.rename(
+                    "runtime\Lib\site-packages\onnxruntime",
+                    "runtime\Lib\site-packages\onnxruntime-cuda",
+                )
             except:
                 pass
             try:
-                os.rename("runtime\Lib\site-packages\onnxruntime-dml","runtime\Lib\site-packages\onnxruntime")
+                os.rename(
+                    "runtime\Lib\site-packages\onnxruntime-dml",
+                    "runtime\Lib\site-packages\onnxruntime",
+                )
             except:
-
                 pass
             import torch_directml
+
             self.device = torch_directml.device(torch_directml.default_device())
             self.is_half = False
         else:
             if self.instead:
                 print(f"use {self.instead} instead")
             try:
-                os.rename("runtime\Lib\site-packages\onnxruntime","runtime\Lib\site-packages\onnxruntime-cuda")
+                os.rename(
+                    "runtime\Lib\site-packages\onnxruntime",
+                    "runtime\Lib\site-packages\onnxruntime-cuda",
+                )
             except:
                 pass
             try:
-                os.rename("runtime\Lib\site-packages\onnxruntime-dml","runtime\Lib\site-packages\onnxruntime")
+                os.rename(
+                    "runtime\Lib\site-packages\onnxruntime-dml",
+                    "runtime\Lib\site-packages\onnxruntime",
+                )
             except:
                 pass
         return x_pad, x_query, x_center, x_max

extract_f0_rmvpe_dml.py

@@ -10,6 +10,7 @@ logging.getLogger("numba").setLevel(logging.WARNING)
 
 exp_dir = sys.argv[1]
 import torch_directml
+
 device = torch_directml.device(torch_directml.default_device())
 f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
 

extract_feature_print.py

@@ -20,7 +20,7 @@ import soundfile as sf
 import numpy as np
 import fairseq
 
-if("privateuseone"not in device):
+if "privateuseone" not in device:
     device = "cpu"
     if torch.cuda.is_available():
         device = "cuda"
@@ -28,11 +28,14 @@ if("privateuseone"not in device):
         device = "mps"
 else:
     import torch_directml
+
     device = torch_directml.device(torch_directml.default_device())
+
     def forward_dml(ctx, x, scale):
         ctx.scale = scale
         res = x.clone().detach()
         return res
+
     fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
 
 f = open("%s/extract_f0_feature.log" % exp_dir, "a+")

gui_v1.py

@@ -1,4 +1,5 @@
 import os, sys, pdb
+
 os.environ["OMP_NUM_THREADS"] = "2"
 if sys.platform == "darwin":
     os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
@@ -47,6 +48,7 @@ if __name__ == "__main__":
     import torchaudio.transforms as tat
     from i18n import I18nAuto
     import rvc_for_realtime
+
     i18n = I18nAuto()
     device = rvc_for_realtime.config.device
     # device = torch.device(
@@ -61,7 +63,6 @@ if __name__ == "__main__":
     for _ in range(n_cpu):
         Harvest(inp_q, opt_q).start()
 
-
     class GUIConfig:
         def __init__(self) -> None:
             self.pth_path: str = ""

infer-web.py

@@ -43,9 +43,7 @@ logging.getLogger("numba").setLevel(logging.WARNING)
 now_dir = os.getcwd()
 tmp = os.path.join(now_dir, "TEMP")
 shutil.rmtree(tmp, ignore_errors=True)
-shutil.rmtree(
-    "%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True
-)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
 shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
 os.makedirs(tmp, exist_ok=True)
 os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
@@ -56,11 +54,13 @@ torch.manual_seed(114514)
 
 
 config = Config()
-if(config.dml==True):
+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
 i18n = I18nAuto()
 i18n.print()
@@ -451,15 +451,21 @@ def get_vc(sid, to_return_protect0, to_return_protect1):
             del net_g, cpt
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
-        return {"visible": False, "__type__": "update"},{
+        return (
+            {"visible": False, "__type__": "update"},
+            {
                 "visible": True,
                 "value": to_return_protect0,
                 "__type__": "update",
-        },{
+            },
+            {
                 "visible": True,
                 "value": to_return_protect1,
                 "__type__": "update",
-        },"",""
+            },
+            "",
+            "",
+        )
     person = "%s/%s" % (weight_root, sid)
     print("loading %s" % person)
 
@@ -504,15 +510,13 @@ def get_vc(sid, to_return_protect0, to_return_protect1):
         net_g = net_g.float()
     vc = VC(tgt_sr, config)
     n_spk = cpt["config"][-3]
-    index={
-        "value":get_index_path_from_model(sid),
-        "__type__": "update"
-    }
+    index = {"value": get_index_path_from_model(sid), "__type__": "update"}
     return (
         {"visible": True, "maximum": n_spk, "__type__": "update"},
         to_return_protect0,
         to_return_protect1,
-        index,index
+        index,
+        index,
     )
 
 
@@ -629,7 +633,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
                 ),
             ).start()
         else:
-            if(gpus_rmvpe!="-"):
+            if gpus_rmvpe != "-":
                 gpus_rmvpe = gpus_rmvpe.split("-")
                 leng = len(gpus_rmvpe)
                 ps = []
@@ -655,7 +659,8 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
                 ).start()
             else:
                 cmd = config.python_cmd + ' extract_f0_rmvpe_dml.py "%s/logs/%s" ' % (
-                    now_dir, exp_dir
+                    now_dir,
+                    exp_dir,
                 )
                 print(cmd)
                 p = Popen(
@@ -671,9 +676,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
             sleep(1)
             if done[0]:
                 break
-        with open(
-            "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
-        ) as f:
+        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
             log = f.read()
         print(log)
         yield log
@@ -1115,18 +1118,22 @@ def train1key(
             p = Popen(cmd, shell=True, cwd=now_dir)
             p.wait()
         else:
-            if(gpus_rmvpe!="-"):
+            if gpus_rmvpe != "-":
                 gpus_rmvpe = gpus_rmvpe.split("-")
                 leng = len(gpus_rmvpe)
                 ps = []
                 for idx, n_g in enumerate(gpus_rmvpe):
-                    cmd = config.python_cmd + ' extract_f0_rmvpe.py %s %s %s "%s" %s ' % (
+                    cmd = (
+                        config.python_cmd
+                        + ' extract_f0_rmvpe.py %s %s %s "%s" %s '
+                        % (
                             leng,
                             idx,
                             n_g,
                             model_log_dir,
                             config.is_half,
                         )
+                    )
                     yield get_info_str(cmd)
                     p = Popen(
                         cmd, shell=True, cwd=now_dir
@@ -1316,7 +1323,17 @@ def train1key(
     index_ivf = faiss.extract_index_ivf(index)  #
     index_ivf.nprobe = 1
     index.train(big_npy)
-    faiss.write_index(index,"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"% (model_log_dir.replace(now_dir+"/",""), n_ivf, index_ivf.nprobe, exp_dir1, version19))
+    faiss.write_index(
+        index,
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (
+            model_log_dir.replace(now_dir + "/", ""),
+            n_ivf,
+            index_ivf.nprobe,
+            exp_dir1,
+            version19,
+        ),
+    )
     yield get_info_str("adding index")
     batch_size_add = 8192
     for i in range(0, big_npy.shape[0], batch_size_add):
@@ -1324,7 +1341,13 @@ def train1key(
     faiss.write_index(
         index,
         "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (model_log_dir.replace(now_dir+"/",""), n_ivf, index_ivf.nprobe, exp_dir1, version19),
+        % (
+            model_log_dir.replace(now_dir + "/", ""),
+            n_ivf,
+            index_ivf.nprobe,
+            exp_dir1,
+            version19,
+        ),
     )
     yield get_info_str(
         "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
@@ -1351,6 +1374,8 @@ def change_info_(ckpt_path):
 
 
 F0GPUVisible = config.dml == False
+
+
 def change_f0_method(f0method8):
     if f0method8 == "rmvpe_gpu":
         visible = F0GPUVisible
@@ -1450,7 +1475,9 @@ with gr.Blocks(title="RVC WebUI") as app:
                             label=i18n(
                                 "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
                             ),
-                            choices=["pm", "harvest", "crepe", "rmvpe"]if config.dml==False else ["pm", "harvest", "rmvpe"],
+                            choices=["pm", "harvest", "crepe", "rmvpe"]
+                            if config.dml == False
+                            else ["pm", "harvest", "rmvpe"],
                             value="pm",
                             interactive=True,
                         )
@@ -1556,7 +1583,9 @@ with gr.Blocks(title="RVC WebUI") as app:
                             label=i18n(
                                 "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
                             ),
-                            choices=["pm", "harvest", "crepe", "rmvpe"]if config.dml==False else ["pm", "harvest", "rmvpe"],
+                            choices=["pm", "harvest", "crepe", "rmvpe"]
+                            if config.dml == False
+                            else ["pm", "harvest", "rmvpe"],
                             value="pm",
                             interactive=True,
                         )
@@ -1790,9 +1819,12 @@ with gr.Blocks(title="RVC WebUI") as app:
                         gpus6 = gr.Textbox(
                             label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
                             value=gpus,
-                            interactive=True,visible=F0GPUVisible
+                            interactive=True,
+                            visible=F0GPUVisible,
+                        )
+                        gpu_info9 = gr.Textbox(
+                            label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible
                         )
-                        gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info,visible=F0GPUVisible)
                     with gr.Column():
                         f0method8 = gr.Radio(
                             label=i18n(

lib/infer_pack/models.py

@@ -315,7 +315,7 @@ class SineGen(torch.nn.Module):
         # generate uv signal
         uv = torch.ones_like(f0)
         uv = uv * (f0 > self.voiced_threshold)
-        if(uv.device.type=="privateuseone"):#for DirectML
+        if uv.device.type == "privateuseone":  # for DirectML
             uv = uv.float()
         return uv
 

lib/rmvpe.py

@@ -6,9 +6,18 @@ import numpy as np
 import torch.nn.functional as F
 from scipy.signal import get_window
 from librosa.util import pad_center, tiny, normalize
+
+
 ###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
-def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
-                     n_fft=800, dtype=np.float32, norm=None):
+def window_sumsquare(
+    window,
+    n_frames,
+    hop_length=200,
+    win_length=800,
+    n_fft=800,
+    dtype=np.float32,
+    norm=None,
+):
     """
     # from librosa 0.6
     Compute the sum-square envelope of a window function at a given hop length.
@@ -50,9 +59,11 @@ def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
         x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
     return x
 
+
 class STFT(torch.nn.Module):
-    def __init__(self, filter_length=1024, hop_length=512, win_length=None,
-                 window='hann'):
+    def __init__(
+        self, filter_length=1024, hop_length=512, win_length=None, window="hann"
+    ):
         """
         This module implements an STFT using 1D convolution and 1D transpose convolutions.
         This is a bit tricky so there are some cases that probably won't work as working
@@ -79,12 +90,15 @@ class STFT(torch.nn.Module):
         fourier_basis = np.fft.fft(np.eye(self.filter_length))
 
         cutoff = int((self.filter_length / 2 + 1))
-        fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),np.imag(fourier_basis[:cutoff, :])])
+        fourier_basis = np.vstack(
+            [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
+        )
         forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
         inverse_basis = torch.FloatTensor(
-            np.linalg.pinv(scale * fourier_basis).T[:, None, :])
+            np.linalg.pinv(scale * fourier_basis).T[:, None, :]
+        )
 
-        assert (filter_length >= self.win_length)
+        assert filter_length >= self.win_length
         # get window and zero center pad it to filter_length
         fft_window = get_window(window, self.win_length, fftbins=True)
         fft_window = pad_center(fft_window, size=filter_length)
@@ -94,8 +108,8 @@ class STFT(torch.nn.Module):
         forward_basis *= fft_window
         inverse_basis *= fft_window
 
-        self.register_buffer('forward_basis', forward_basis.float())
-        self.register_buffer('inverse_basis', inverse_basis.float())
+        self.register_buffer("forward_basis", forward_basis.float())
+        self.register_buffer("inverse_basis", inverse_basis.float())
 
     def transform(self, input_data):
         """Take input data (audio) to STFT domain.
@@ -117,14 +131,16 @@ class STFT(torch.nn.Module):
         # similar to librosa, reflect-pad the input
         input_data = input_data.view(num_batches, 1, num_samples)
         # print(1234,input_data.shape)
-        input_data = F.pad(input_data.unsqueeze(1),(self.pad_amount, self.pad_amount, 0, 0,0,0),mode='reflect').squeeze(1)
+        input_data = F.pad(
+            input_data.unsqueeze(1),
+            (self.pad_amount, self.pad_amount, 0, 0, 0, 0),
+            mode="reflect",
+        ).squeeze(1)
         # print(2333,input_data.shape,self.forward_basis.shape,self.hop_length)
         # pdb.set_trace()
         forward_transform = F.conv1d(
-            input_data,
-            self.forward_basis,
-            stride=self.hop_length,
-            padding=0)
+            input_data, self.forward_basis, stride=self.hop_length, padding=0
+        )
 
         cutoff = int((self.filter_length / 2) + 1)
         real_part = forward_transform[:, :cutoff, :]
@@ -150,24 +166,33 @@ class STFT(torch.nn.Module):
                 shape (num_batch, num_samples)
         """
         recombine_magnitude_phase = torch.cat(
-            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1)
+            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
+        )
 
         inverse_transform = F.conv_transpose1d(
             recombine_magnitude_phase,
             self.inverse_basis,
             stride=self.hop_length,
-            padding=0)
+            padding=0,
+        )
 
         if self.window is not None:
             window_sum = window_sumsquare(
-                self.window, magnitude.size(-1), hop_length=self.hop_length,
-                win_length=self.win_length, n_fft=self.filter_length,
-                dtype=np.float32)
+                self.window,
+                magnitude.size(-1),
+                hop_length=self.hop_length,
+                win_length=self.win_length,
+                n_fft=self.filter_length,
+                dtype=np.float32,
+            )
             # remove modulation effects
             approx_nonzero_indices = torch.from_numpy(
-                np.where(window_sum > tiny(window_sum))[0])
+                np.where(window_sum > tiny(window_sum))[0]
+            )
             window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
-            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
+            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
+                approx_nonzero_indices
+            ]
 
             # scale by hop ratio
             inverse_transform *= float(self.filter_length) / self.hop_length
@@ -191,7 +216,11 @@ class STFT(torch.nn.Module):
         self.magnitude, self.phase = self.transform(input_data)
         reconstruction = self.inverse(self.magnitude, self.phase)
         return reconstruction
+
+
 from time import time as ttime
+
+
 class BiGRU(nn.Module):
     def __init__(self, input_features, hidden_features, num_layers):
         super(BiGRU, self).__init__()
@@ -514,7 +543,7 @@ class MelSpectrogram(torch.nn.Module):
                 filter_length=n_fft_new,
                 hop_length=hop_length_new,
                 win_length=win_length_new,
-                window='hann'
+                window="hann",
             ).to(audio.device)
         magnitude = self.stft.transform(audio)  # phase
         # if (audio.device.type == "privateuseone"):
@@ -544,9 +573,12 @@ class RMVPE:
         self.mel_extractor = MelSpectrogram(
             is_half, 128, 16000, 1024, 160, None, 30, 8000
         ).to(device)
-        if ("privateuseone" in str(device)):
+        if "privateuseone" in str(device):
             import onnxruntime as ort
-            ort_session = ort.InferenceSession("rmvpe.onnx", providers=["DmlExecutionProvider"])
+
+            ort_session = ort.InferenceSession(
+                "rmvpe.onnx", providers=["DmlExecutionProvider"]
+            )
             self.model = ort_session
         else:
             model = E2E(4, 1, (2, 2))
@@ -566,10 +598,13 @@ class RMVPE:
             mel = F.pad(
                 mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
             )
-            if("privateuseone" in str(self.device) ):
+            if "privateuseone" in str(self.device):
                 onnx_input_name = self.model.get_inputs()[0].name
                 onnx_outputs_names = self.model.get_outputs()[0].name
-                hidden = self.model.run([onnx_outputs_names], input_feed={onnx_input_name: mel.cpu().numpy()})[0]
+                hidden = self.model.run(
+                    [onnx_outputs_names],
+                    input_feed={onnx_input_name: mel.cpu().numpy()},
+                )[0]
             else:
                 hidden = self.model(mel)
             return hidden[:, :n_frames]
@@ -584,7 +619,9 @@ class RMVPE:
     def infer_from_audio(self, audio, thred=0.03):
         # torch.cuda.synchronize()
         t0 = ttime()
-        mel = self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True)
+        mel = self.mel_extractor(
+            torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
+        )
         # print(123123123,mel.device.type)
         # torch.cuda.synchronize()
         t1 = ttime()
@@ -592,7 +629,7 @@ class RMVPE:
         # torch.cuda.synchronize()
         t2 = ttime()
         # print(234234,hidden.device.type)
-        if("privateuseone" not in str(self.device)):
+        if "privateuseone" not in str(self.device):
             hidden = hidden.squeeze(0).cpu().numpy()
         else:
             hidden = hidden[0]
@@ -632,8 +669,9 @@ class RMVPE:
         return devided
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import soundfile as sf, librosa
+
     audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
     if len(audio.shape) > 1:
         audio = librosa.to_mono(audio.transpose(1, 0))
@@ -642,7 +680,7 @@ if __name__ == '__main__':
         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
     model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
     thred = 0.03  # 0.01
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    device = "cuda" if torch.cuda.is_available() else "cpu"
     rmvpe = RMVPE(model_path, is_half=False, device=device)
     t0 = ttime()
     f0 = rmvpe.infer_from_audio(audio, thred=thred)

rvc_for_realtime.py

@@ -18,12 +18,16 @@ from multiprocessing import Manager as M
 
 mm = M()
 config = Config()
-if(config.dml==True):
+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:
@@ -209,12 +213,15 @@ class RVC:
     def get_f0_rmvpe(self, x, f0_up_key):
         if hasattr(self, "model_rmvpe") == False:
             from lib.rmvpe import RMVPE
+
             print("loading rmvpe model")
             self.model_rmvpe = RMVPE(
                 # "rmvpe.pt", is_half=self.is_half if self.device.type!="privateuseone" else False, device=self.device if self.device.type!="privateuseone"else "cpu"####dml时强制对rmvpe用cpu跑
                 #  "rmvpe.pt", is_half=False, device=self.device####dml配置
                 # "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置
-                 "rmvpe.pt", is_half=self.is_half, device=self.device####正常逻辑
+                "rmvpe.pt",
+                is_half=self.is_half,
+                device=self.device,  ####正常逻辑
             )
             # self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
         f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)

vc_infer_pipeline.py

@@ -130,13 +130,14 @@ class VC(object):
         elif f0_method == "rmvpe":
             if hasattr(self, "model_rmvpe") == False:
                 from lib.rmvpe import RMVPE
+
                 print("loading rmvpe model")
                 self.model_rmvpe = RMVPE(
                     "rmvpe.pt", is_half=self.is_half, device=self.device
                 )
 
             f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-            if("privateuseone"in str(self.device)):#clean ortruntime memory
+            if "privateuseone" in str(self.device):  # clean ortruntime memory
                 del self.model_rmvpe.model
                 del self.model_rmvpe
                 print("cleaning ortruntime memory")