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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-19 02:20:45 +00:00
Format code (#1193)
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72a18e66b6
commit
a6456f6d46
150
gui_v1.py
150
gui_v1.py
@@ -478,15 +478,28 @@ if __name__ == "__main__":
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inp_q,
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opt_q,
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device,
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self.rvc if hasattr(self, "rvc") else None
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self.rvc if hasattr(self, "rvc") else None,
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)
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self.config.samplerate = self.rvc.tgt_sr
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self.zc = self.rvc.tgt_sr // 100
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self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc
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self.block_frame = (
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int(np.round(self.config.block_time * self.config.samplerate / self.zc))
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* self.zc
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)
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self.block_frame_16k = 160 * self.block_frame // self.zc
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self.crossfade_frame = int(np.round(self.config.crossfade_time * self.config.samplerate / self.zc)) * self.zc
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self.crossfade_frame = (
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int(
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np.round(
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self.config.crossfade_time * self.config.samplerate / self.zc
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)
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)
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* self.zc
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)
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self.sola_search_frame = self.zc
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self.extra_frame = int(np.round(self.config.extra_time * self.config.samplerate / self.zc)) * self.zc
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self.extra_frame = (
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int(np.round(self.config.extra_time * self.config.samplerate / self.zc))
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* self.zc
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)
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self.input_wav: torch.Tensor = torch.zeros(
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self.extra_frame
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+ self.crossfade_frame
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@@ -495,7 +508,11 @@ if __name__ == "__main__":
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device=device,
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dtype=torch.float32,
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)
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self.input_wav_res: torch.Tensor= torch.zeros(160 * self.input_wav.shape[0] // self.zc, device=device,dtype=torch.float32)
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self.input_wav_res: torch.Tensor = torch.zeros(
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160 * self.input_wav.shape[0] // self.zc,
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device=device,
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dtype=torch.float32,
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)
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self.pitch: np.ndarray = np.zeros(
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self.input_wav.shape[0] // self.zc,
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dtype="int32",
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@@ -509,7 +526,9 @@ if __name__ == "__main__":
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)
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self.nr_buffer: torch.Tensor = self.sola_buffer.clone()
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self.output_buffer: torch.Tensor = self.input_wav.clone()
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self.res_buffer: torch.Tensor = torch.zeros(2 * self.zc, device=device,dtype=torch.float32)
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self.res_buffer: torch.Tensor = torch.zeros(
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2 * self.zc, device=device, dtype=torch.float32
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)
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self.valid_rate = 1 - (self.extra_frame - 1) / self.input_wav.shape[0]
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self.fade_in_window: torch.Tensor = (
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torch.sin(
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@@ -529,7 +548,9 @@ if __name__ == "__main__":
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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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).to(device)
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self.tg = TorchGate(sr=self.config.samplerate, n_fft=4*self.zc, prop_decrease=0.9).to(device)
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self.tg = TorchGate(
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sr=self.config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9
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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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@@ -560,7 +581,7 @@ if __name__ == "__main__":
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indata = librosa.to_mono(indata.T)
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if self.config.threhold > -60:
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rms = librosa.feature.rms(
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y=indata, frame_length=4*self.zc, hop_length=self.zc
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y=indata, frame_length=4 * self.zc, hop_length=self.zc
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)
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db_threhold = (
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librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
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@@ -568,28 +589,44 @@ if __name__ == "__main__":
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for i in range(db_threhold.shape[0]):
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if db_threhold[i]:
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indata[i * self.zc : (i + 1) * self.zc] = 0
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self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
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self.input_wav[-self.block_frame: ] = torch.from_numpy(indata).to(device)
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self.input_wav_res[ : -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
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self.input_wav[: -self.block_frame] = self.input_wav[
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self.block_frame :
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].clone()
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self.input_wav[-self.block_frame :] = torch.from_numpy(indata).to(device)
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self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[
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self.block_frame_16k :
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].clone()
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# input noise reduction and resampling
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if self.config.I_noise_reduce:
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input_wav = self.input_wav[-self.crossfade_frame -self.block_frame-2*self.zc: ]
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input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0))[0, 2*self.zc:]
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input_wav = self.input_wav[
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-self.crossfade_frame - self.block_frame - 2 * self.zc :
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]
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input_wav = self.tg(
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input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)
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)[0, 2 * self.zc :]
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input_wav[: self.crossfade_frame] *= self.fade_in_window
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input_wav[: self.crossfade_frame] += self.nr_buffer * self.fade_out_window
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self.nr_buffer[:] = input_wav[-self.crossfade_frame: ]
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input_wav = torch.cat((self.res_buffer[:], input_wav[: self.block_frame]))
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self.res_buffer[:] = input_wav[-2*self.zc: ]
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self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(input_wav)[160: ]
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input_wav[: self.crossfade_frame] += (
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self.nr_buffer * self.fade_out_window
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)
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self.nr_buffer[:] = input_wav[-self.crossfade_frame :]
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input_wav = torch.cat(
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(self.res_buffer[:], input_wav[: self.block_frame])
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)
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self.res_buffer[:] = input_wav[-2 * self.zc :]
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self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
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input_wav
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)[160:]
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else:
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self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(self.input_wav[-self.block_frame-2*self.zc: ])[160: ]
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self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
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self.input_wav[-self.block_frame - 2 * self.zc :]
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)[160:]
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# infer
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f0_extractor_frame = self.block_frame_16k + 800
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if self.config.f0method == 'rmvpe':
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if self.config.f0method == "rmvpe":
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f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
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infer_wav = self.rvc.infer(
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self.input_wav_res,
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self.input_wav_res[-f0_extractor_frame :].cpu().numpy(),
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self.input_wav_res[-f0_extractor_frame:].cpu().numpy(),
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self.block_frame_16k,
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self.valid_rate,
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self.pitch,
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@@ -601,48 +638,77 @@ if __name__ == "__main__":
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]
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# output noise reduction
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if self.config.O_noise_reduce:
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self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone()
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self.output_buffer[-self.block_frame: ] = infer_wav[-self.block_frame:]
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infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0)
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self.output_buffer[: -self.block_frame] = self.output_buffer[
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self.block_frame :
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].clone()
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self.output_buffer[-self.block_frame :] = infer_wav[-self.block_frame :]
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infer_wav = self.tg(
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infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)
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).squeeze(0)
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# volume envelop mixing
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if self.config.rms_mix_rate < 1:
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rms1 = librosa.feature.rms(
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y=self.input_wav_res[-160*infer_wav.shape[0]//self.zc :].cpu().numpy(),
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frame_length=640,
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hop_length=160,
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y=self.input_wav_res[-160 * infer_wav.shape[0] // self.zc :]
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.cpu()
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.numpy(),
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frame_length=640,
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hop_length=160,
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)
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rms1 = torch.from_numpy(rms1).to(device)
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rms1 = F.interpolate(
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rms1.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear",align_corners=True,
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)[0,0,:-1]
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rms1.unsqueeze(0),
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size=infer_wav.shape[0] + 1,
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mode="linear",
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align_corners=True,
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)[0, 0, :-1]
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rms2 = librosa.feature.rms(
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y=infer_wav[:].cpu().numpy(), frame_length=4*self.zc, hop_length=self.zc
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y=infer_wav[:].cpu().numpy(),
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frame_length=4 * self.zc,
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hop_length=self.zc,
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)
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rms2 = torch.from_numpy(rms2).to(device)
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rms2 = F.interpolate(
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rms2.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear",align_corners=True,
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)[0,0,:-1]
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rms2.unsqueeze(0),
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size=infer_wav.shape[0] + 1,
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mode="linear",
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align_corners=True,
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)[0, 0, :-1]
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rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3)
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infer_wav *= torch.pow(rms1 / rms2, torch.tensor(1 - self.config.rms_mix_rate))
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infer_wav *= torch.pow(
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rms1 / rms2, torch.tensor(1 - self.config.rms_mix_rate)
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)
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
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conv_input = infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
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conv_input = infer_wav[
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None, None, : self.crossfade_frame + self.sola_search_frame
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]
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cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :])
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cor_den = torch.sqrt(
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F.conv1d(conv_input ** 2, torch.ones(1, 1, self.crossfade_frame, device=device)) + 1e-8)
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F.conv1d(
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conv_input**2,
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torch.ones(1, 1, self.crossfade_frame, device=device),
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)
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+ 1e-8
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)
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if sys.platform == "darwin":
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_, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0])
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sola_offset = sola_offset.item()
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else:
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sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
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logger.debug("sola_offset = %d", int(sola_offset))
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infer_wav = infer_wav[sola_offset: sola_offset + self.block_frame + self.crossfade_frame]
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infer_wav = infer_wav[
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sola_offset : sola_offset + self.block_frame + self.crossfade_frame
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]
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infer_wav[: self.crossfade_frame] *= self.fade_in_window
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infer_wav[: self.crossfade_frame] += self.sola_buffer *self.fade_out_window
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self.sola_buffer[:] = infer_wav[-self.crossfade_frame:]
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infer_wav[: self.crossfade_frame] += self.sola_buffer * self.fade_out_window
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self.sola_buffer[:] = infer_wav[-self.crossfade_frame :]
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if sys.platform == "darwin":
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outdata[:] = infer_wav[:-self.crossfade_frame].cpu().numpy()[:, np.newaxis]
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outdata[:] = (
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infer_wav[: -self.crossfade_frame].cpu().numpy()[:, np.newaxis]
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)
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else:
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outdata[:] = infer_wav[:-self.crossfade_frame].repeat(2, 1).t().cpu().numpy()
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outdata[:] = (
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infer_wav[: -self.crossfade_frame].repeat(2, 1).t().cpu().numpy()
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)
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total_time = time.perf_counter() - start_time
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self.window["infer_time"].update(int(total_time * 1000))
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logger.info("Infer time: %.2f", total_time)
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@@ -698,9 +764,7 @@ if __name__ == "__main__":
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sd.default.device[1] = output_device_indices[
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output_devices.index(output_device)
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]
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logger.info(
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"Input device: %s:%s", str(sd.default.device[0]), input_device
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)
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logger.info("Input device: %s:%s", str(sd.default.device[0]), input_device)
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logger.info(
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"Output device: %s:%s", str(sd.default.device[1]), output_device
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)
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