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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
Streaming noise reduction and other optimizations for real-time gui (#1188)
* loudness factor control and gpu-accelerated noise reduction * loudness factor control and gpu-accelerated noise reduction * loudness factor control and gpu-accelerated noise reduction * streaming noise reduction and other optimizations * streaming noise reduction and other optimizations
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yxlllc
GitHub
parent
b09b6ad05c
commit
a669fee786
167
gui_v1.py
167
gui_v1.py
@ -5,7 +5,7 @@ from dotenv import load_dotenv
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load_dotenv()
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os.environ["OMP_NUM_THREADS"] = "2"
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os.environ["OMP_NUM_THREADS"] = "4"
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if sys.platform == "darwin":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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@ -481,49 +481,21 @@ if __name__ == "__main__":
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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.config.crossfade_time = min(
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self.config.crossfade_time, self.config.block_time
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)
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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_16k = 160 * self.block_frame // self.zc
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self.crossfade_frame = int(
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self.config.crossfade_time * self.config.samplerate
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)
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self.sola_search_frame = int(0.01 * self.config.samplerate)
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self.extra_frame = int(self.config.extra_time * self.config.samplerate)
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self.input_wav: np.ndarray = np.zeros(
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int(
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np.ceil(
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(
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self.extra_frame
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+ self.crossfade_frame
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+ self.sola_search_frame
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+ self.block_frame
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)
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/ self.zc
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)
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* self.zc
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),
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dtype="float32",
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)
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self.input_wav_res: torch.Tensor= torch.zeros(160 * len(self.input_wav) // self.zc, device=device,dtype=torch.float32)
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self.output_wav_cache: torch.Tensor = torch.zeros(
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int(
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np.ceil(
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(
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self.extra_frame
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+ self.crossfade_frame
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+ self.sola_search_frame
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+ self.block_frame
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)
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/ self.zc
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)
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* self.zc
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),
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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.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.input_wav: torch.Tensor = torch.zeros(
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self.extra_frame
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+ self.crossfade_frame
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+ self.sola_search_frame
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+ self.block_frame,
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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.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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@ -532,12 +504,13 @@ if __name__ == "__main__":
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self.input_wav.shape[0] // self.zc,
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dtype="float64",
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)
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self.output_wav: torch.Tensor = torch.zeros(
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self.block_frame, device=device, dtype=torch.float32
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)
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self.sola_buffer: torch.Tensor = torch.zeros(
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self.crossfade_frame, device=device, dtype=torch.float32
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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.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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0.5
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@ -556,8 +529,7 @@ 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.input_tg = TorchGate(sr=16000, nonstationary=True, n_fft=640).to(device)
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self.output_tg = TorchGate(sr=self.config.samplerate, nonstationary=True, n_fft=4*self.zc).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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thread_vc = threading.Thread(target=self.soundinput)
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thread_vc.start()
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@ -586,114 +558,91 @@ if __name__ == "__main__":
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"""
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start_time = time.perf_counter()
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indata = librosa.to_mono(indata.T)
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frame_length = 2048
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hop_length = 1024
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rms = librosa.feature.rms(
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y=indata, frame_length=frame_length, hop_length=hop_length
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)
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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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)
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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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)
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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 * hop_length : (i + 1) * hop_length] = 0
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self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :]
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self.input_wav[-self.block_frame: ] = indata
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# infer
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inp = torch.from_numpy(self.input_wav[-self.block_frame-2*self.zc :]).to(device)
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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_res[-self.block_frame_16k-160 :] = self.resampler(inp)[160 :]
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# input noise reduction and resampling
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if self.config.I_noise_reduce:
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self.input_wav_res[-self.block_frame_16k-320 :] = self.input_tg(self.input_wav_res[None, -self.block_frame_16k-800 :])[0, 480 : ]
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rate = (
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self.crossfade_frame + self.sola_search_frame + self.block_frame
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) / (
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self.extra_frame
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+ self.crossfade_frame
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+ self.sola_search_frame
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+ self.block_frame
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)
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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.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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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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# 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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f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
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res2 = self.rvc.infer(
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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.block_frame_16k,
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rate,
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self.valid_rate,
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self.pitch,
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self.pitchf,
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self.config.f0method,
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)
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self.output_wav_cache[-res2.shape[0] :] = res2
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infer_wav = self.output_wav_cache[
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infer_wav = infer_wav[
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-self.crossfade_frame - self.sola_search_frame - self.block_frame :
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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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infer_wav = self.output_tg(infer_wav.unsqueeze(0)).squeeze(0)
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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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# 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[-self.crossfade_frame - self.sola_search_frame - self.block_frame :],
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frame_length=frame_length,
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hop_length=hop_length
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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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)
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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], mode="linear"
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).squeeze()
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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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rms2 = librosa.feature.rms(
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y=infer_wav[:].cpu().numpy(), frame_length=frame_length, hop_length=hop_length
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y=infer_wav[:].cpu().numpy(), frame_length=4*self.zc, 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], mode="linear"
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).squeeze()
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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 = 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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# 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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self.sola_buffer[None, None, :],
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)
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conv_input = infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
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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(
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infer_wav[
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None, None, : self.crossfade_frame + self.sola_search_frame
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]
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** 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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F.conv1d(conv_input ** 2, torch.ones(1, 1, self.crossfade_frame, device=device)) + 1e-8)
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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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self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
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self.output_wav[: self.crossfade_frame] *= self.fade_in_window
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self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
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# crossfade
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if sola_offset < self.sola_search_frame:
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self.sola_buffer[:] = (
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infer_wav[
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-self.sola_search_frame
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- self.crossfade_frame
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+ sola_offset : -self.sola_search_frame
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+ sola_offset
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]
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* self.fade_out_window
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)
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else:
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self.sola_buffer[:] = (
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infer_wav[-self.crossfade_frame :] * self.fade_out_window
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)
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infer_wav = infer_wav[sola_offset: sola_offset + self.block_frame + self.crossfade_frame]
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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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if sys.platform == "darwin":
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outdata[:] = self.output_wav[:].cpu().numpy()[:, np.newaxis]
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outdata[:] = infer_wav[:-self.crossfade_frame].cpu().numpy()[:, np.newaxis]
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else:
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outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
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outdata[:] = infer_wav[:-self.crossfade_frame].repeat(2, 1).t().cpu().numpy()
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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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@ -91,7 +91,7 @@ class RVC:
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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hubert_model = hubert_model.to(device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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@ -309,6 +309,7 @@ class RVC:
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feats = (
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self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
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)
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feats = F.pad(feats, (0, 0, 1, 0))
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t2 = ttime()
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try:
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if hasattr(self, "index") and self.index_rate != 0:
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@ -360,13 +361,13 @@ class RVC:
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self.net_g.infer(
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feats, p_len, cache_pitch, cache_pitchf, sid, rate
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)[0][0, 0]
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.data.cpu()
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.data
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.float()
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)
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else:
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infered_audio = (
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self.net_g.infer(feats, p_len, sid, rate)[0][0, 0]
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.data.cpu()
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.data
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.float()
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)
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t5 = ttime()
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