WIP: refactor, commonalize crossfade process
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wataru
parent
b9741fe577
commit
49291a1de2
@ -292,7 +292,7 @@ class VoiceChanger():
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return self.get_info()
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def _generate_strength(self, dataLength):
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def _generate_strength(self, dataLength: int):
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if self.unpackedData_length != dataLength or \
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self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate or \
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@ -369,17 +369,13 @@ class VoiceChanger():
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return data
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def _onnx_inference(self, data, inputSize):
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def _onnx_inference(self, data):
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if hasattr(self, "onnx_session") == False or self.onnx_session == None:
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print("[Voice Changer] No ONNX session.")
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return np.zeros(1).astype(np.int16)
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# x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
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# sid_tgt1 = torch.LongTensor([self.settings.dstId])
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spec, spec_lengths, sid_src, sin, d = data
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sid_tgt1 = torch.LongTensor([self.settings.dstId])
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# if spec.size()[2] >= 8:
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audio1 = self.onnx_session.run(
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["audio"],
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{
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@ -393,109 +389,29 @@ class VoiceChanger():
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy()
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})[0][0, 0] * self.hps.data.max_wav_value
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return audio1
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if hasattr(self, 'np_prev_audio1') == True:
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overlapSize = min(self.settings.crossFadeOverlapSize, inputSize)
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prev_overlap = self.np_prev_audio1[-1 * overlapSize:]
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cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
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# print(prev_overlap.shape, self.np_prev_strength.shape, cur_overlap.shape, self.np_cur_strength.shape)
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# print(">>>>>>>>>>>", -1*(inputSize + overlapSize) , -1*inputSize)
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powered_prev = prev_overlap * self.np_prev_strength
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powered_cur = cur_overlap * self.np_cur_strength
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powered_result = powered_prev + powered_cur
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cur = audio1[-1 * inputSize:-1 * overlapSize]
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result = np.concatenate([powered_result, cur], axis=0)
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else:
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result = np.zeros(1).astype(np.int16)
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self.np_prev_audio1 = audio1
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return result
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def _pyTorch_inference(self, data, inputSize):
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def _pyTorch_inference(self, data):
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if hasattr(self, "net_g") == False or self.net_g == None:
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print("[Voice Changer] No pyTorch session.")
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return np.zeros(1).astype(np.int16)
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if self.settings.gpu < 0 or self.gpu_num == 0:
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with torch.no_grad():
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spec, spec_lengths, sid_src, sin, d = data
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spec = spec.cpu()
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spec_lengths = spec_lengths.cpu()
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sid_src = sid_src.cpu()
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sin = sin.cpu()
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d = tuple([d[:1].cpu() for d in d])
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sid_target = torch.LongTensor([self.settings.dstId]).cpu()
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audio1 = self.net_g.cpu().voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value
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if self.prev_strength.device != torch.device('cpu'):
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print(f"prev_strength move from {self.prev_strength.device} to cpu")
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self.prev_strength = self.prev_strength.cpu()
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if self.cur_strength.device != torch.device('cpu'):
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print(f"cur_strength move from {self.cur_strength.device} to cpu")
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self.cur_strength = self.cur_strength.cpu()
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'): # prev_audio1が所望のデバイスに無い場合は一回休み。
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overlapSize = min(self.settings.crossFadeOverlapSize, inputSize)
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prev_overlap = self.prev_audio1[-1 * overlapSize:]
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cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
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powered_prev = prev_overlap * self.prev_strength
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powered_cur = cur_overlap * self.cur_strength
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powered_result = powered_prev + powered_cur
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cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
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result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合
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else:
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cur = audio1[-2 * inputSize:-1 * inputSize]
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result = cur
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self.prev_audio1 = audio1
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result = result.cpu().float().numpy()
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dev = torch.device("cpu")
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else:
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with torch.no_grad():
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spec, spec_lengths, sid_src, sin, d = data
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spec = spec.cuda(self.settings.gpu)
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spec_lengths = spec_lengths.cuda(self.settings.gpu)
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sid_src = sid_src.cuda(self.settings.gpu)
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sin = sin.cuda(self.settings.gpu)
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d = tuple([d[:1].cuda(self.settings.gpu) for d in d])
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sid_target = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu)
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dev = torch.device("cuda", index=self.settings.gpu)
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# audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src,
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# sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value
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with torch.no_grad():
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spec, spec_lengths, sid_src, sin, d = data
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spec = spec.to(dev)
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spec_lengths = spec_lengths.to(dev)
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sid_src = sid_src.to(dev)
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sin = sin.to(dev)
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d = tuple([d[:1].to(dev) for d in d])
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sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
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audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sin, d,
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sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value
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if self.prev_strength.device != torch.device('cuda', self.settings.gpu):
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print(f"prev_strength move from {self.prev_strength.device} to gpu{self.settings.gpu}")
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self.prev_strength = self.prev_strength.cuda(self.settings.gpu)
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if self.cur_strength.device != torch.device('cuda', self.settings.gpu):
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print(f"cur_strength move from {self.cur_strength.device} to gpu{self.settings.gpu}")
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self.cur_strength = self.cur_strength.cuda(self.settings.gpu)
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.settings.gpu):
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overlapSize = min(self.settings.crossFadeOverlapSize, inputSize)
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prev_overlap = self.prev_audio1[-1 * overlapSize:]
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cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
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powered_prev = prev_overlap * self.prev_strength
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powered_cur = cur_overlap * self.cur_strength
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powered_result = powered_prev + powered_cur
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# print(overlapSize, prev_overlap.shape, cur_overlap.shape, self.prev_strength.shape, self.cur_strength.shape)
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# print(self.prev_audio1.shape, audio1.shape, inputSize, overlapSize)
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cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
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result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合
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else:
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cur = audio1[-2 * inputSize:-1 * inputSize]
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result = cur
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self.prev_audio1 = audio1
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result = result.cpu().float().numpy()
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audio1 = self.net_g.to(dev).voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value
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result = audio1.float().cpu().numpy()
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return result
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def on_request(self, unpackedData: any):
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@ -516,12 +432,31 @@ class VoiceChanger():
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with Timer("main-process") as t:
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try:
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if self.settings.framework == "ONNX":
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result = self._onnx_inference(data, unpackedData.shape[0])
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audio = self._onnx_inference(data)
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# result = self.voiceChanger._onnx_inference(data, unpackedData.shape[0])
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else:
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result = self._pyTorch_inference(data, unpackedData.shape[0])
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audio = self._pyTorch_inference(data)
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# result = self.voiceChanger._pyTorch_inference(data, unpackedData.shape[0])
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inputSize = unpackedData.shape[0]
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if hasattr(self, 'np_prev_audio1') == True:
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np.set_printoptions(threshold=10000)
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overlapSize = min(self.settings.crossFadeOverlapSize, inputSize)
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prev_overlap = self.np_prev_audio1[-1 * overlapSize:]
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cur_overlap = audio[-1 * (inputSize + overlapSize):-1 * inputSize]
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# print(prev_overlap.shape, self.np_prev_strength.shape, cur_overlap.shape, self.np_cur_strength.shape)
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# print(">>>>>>>>>>>", -1 * (inputSize + overlapSize), -1 * inputSize, self.np_prev_audio1.shape, overlapSize)
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powered_prev = prev_overlap * self.np_prev_strength
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powered_cur = cur_overlap * self.np_cur_strength
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powered_result = powered_prev + powered_cur
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cur = audio[-1 * inputSize:-1 * overlapSize]
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result = np.concatenate([powered_result, cur], axis=0)
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else:
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result = np.zeros(1).astype(np.int16)
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self.np_prev_audio1 = audio
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except Exception as e:
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print("VC PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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