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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
实时GUI支持rmvpe
实时GUI支持rmvpe
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parent
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commit
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18
gui_v1.py
18
gui_v1.py
@ -81,6 +81,7 @@ if __name__ == '__main__':
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data["pm"]=data["f0method"]=="pm"
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data["harvest"]=data["f0method"]=="harvest"
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data["crepe"]=data["f0method"]=="crepe"
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data["rmvpe"]=data["f0method"]=="rmvpe"
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except:
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with open("values1.json", "w") as j:
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data = {
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@ -94,7 +95,7 @@ if __name__ == '__main__':
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"block_time": "1",
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"crossfade_length": "0.04",
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"extra_time": "1",
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"f0method": "harvest",
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"f0method": "rmvpe",
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}
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return data
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@ -193,6 +194,7 @@ if __name__ == '__main__':
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sg.Radio("pm","f0method",key="pm",default=data.get("pm","")==True),
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sg.Radio("harvest","f0method",key="harvest",default=data.get("harvest","")==True),
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sg.Radio("crepe","f0method",key="crepe",default=data.get("crepe","")==True),
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sg.Radio("rmvpe","f0method",key="rmvpe",default=data.get("rmvpe","")==True),
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],
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],
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title=i18n("常规设置"),
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@ -279,7 +281,7 @@ if __name__ == '__main__':
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"crossfade_length": values["crossfade_length"],
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"extra_time": values["extra_time"],
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"n_cpu": values["n_cpu"],
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"f0method": ["pm","harvest","crepe"][[values["pm"],values["harvest"],values["crepe"]].index(True)],
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"f0method": ["pm","harvest","crepe","rmvpe"][[values["pm"],values["harvest"],values["crepe"],values["rmvpe"]].index(True)],
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}
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with open("values1.json", "w") as j:
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json.dump(settings, j)
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@ -312,7 +314,7 @@ if __name__ == '__main__':
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self.config.O_noise_reduce = values["O_noise_reduce"]
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self.config.index_rate = values["index_rate"]
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self.config.n_cpu = values["n_cpu"]
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self.config.f0method = ["pm","harvest","crepe"][[values["pm"],values["harvest"],values["crepe"]].index(True)]
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self.config.f0method = ["pm","harvest","crepe","rmvpe"][[values["pm"],values["harvest"],values["crepe"],values["rmvpe"]].index(True)]
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return True
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def start_vc(self):
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@ -346,7 +348,7 @@ if __name__ == '__main__':
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self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
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self.resampler = tat.Resample(
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orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
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)
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).to(device)
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thread_vc = threading.Thread(target=self.soundinput)
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thread_vc.start()
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@ -389,13 +391,15 @@ if __name__ == '__main__':
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indata[i * hop_length : (i + 1) * hop_length] = 0
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self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
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# infer
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inp=torch.from_numpy(self.input_wav)
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inp=torch.from_numpy(self.input_wav).to(device)
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##0
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res1=self.resampler(inp)
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###55%
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rate1=self.block_frame/(self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)
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rate2=(self.crossfade_frame + self.sola_search_frame + self.block_frame)/(self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)
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res2=self.rvc.infer(res1,res1[-self.block_frame:].numpy(),rate1,rate2,self.pitch,self.pitchf,self.config.f0method)
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res2=self.rvc.infer(res1,res1[-self.block_frame:].cpu().numpy(),rate1,rate2,self.pitch,self.pitchf,self.config.f0method)
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self.output_wav_cache[-res2.shape[0]:]=res2
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infer_wav = self.output_wav_cache[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(device)
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infer_wav = self.output_wav_cache[-self.crossfade_frame - self.sola_search_frame - self.block_frame :]
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
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cor_nom = F.conv1d(
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infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
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@ -81,6 +81,7 @@ class RVC:
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self.net_g = self.net_g.half()
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else:
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self.net_g = self.net_g.float()
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self.is_half=config.is_half
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except:
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print(traceback.format_exc())
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@ -102,6 +103,7 @@ class RVC:
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def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
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n_cpu = int(n_cpu)
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if (method == "crepe"): return self.get_f0_crepe(x, f0_up_key)
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if (method == "rmvpe"): return self.get_f0_rmvpe(x, f0_up_key)
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if (method == "pm"):
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p_len = x.shape[0] // 160
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f0 = (
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@ -181,6 +183,16 @@ class RVC:
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f0 *= pow(2, f0_up_key / 12)
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return self.get_f0_post(f0)
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def get_f0_rmvpe(self, x, f0_up_key):
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if (hasattr(self, "model_rmvpe") == False):
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from rmvpe import RMVPE
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print("loading rmvpe model")
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# self.model_rmvpe = RMVPE("rmvpe.pt", is_half=self.is_half, device=self.device)
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self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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f0 *= pow(2, f0_up_key / 12)
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return self.get_f0_post(f0)
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def infer(self, feats: torch.Tensor, indata: np.ndarray, rate1, rate2, cache_pitch, cache_pitchf, f0method) -> np.ndarray:
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feats = feats.view(1, -1)
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if config.is_half:
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