import numpy as np,parselmouth,torch,pdb from time import time as ttime import torch.nn.functional as F from config import x_pad,x_query,x_center,x_max import scipy.signal as signal import pyworld,os,traceback,faiss class VC(object): def __init__(self,tgt_sr,device,is_half): self.sr=16000#hubert输入采样率 self.window=160#每帧点数 self.t_pad=self.sr*x_pad#每条前后pad时间 self.t_pad_tgt=tgt_sr*x_pad self.t_pad2=self.t_pad*2 self.t_query=self.sr*x_query#查询切点前后查询时间 self.t_center=self.sr*x_center#查询切点位置 self.t_max=self.sr*x_max#免查询时长阈值 self.device=device self.is_half=is_half def get_f0(self,x, p_len,f0_up_key,f0_method,inp_f0=None): time_step = self.window / self.sr * 1000 f0_min = 50 f0_max = 1100 f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) if(f0_method=="pm"): f0 = parselmouth.Sound(x, self.sr).to_pitch_ac( time_step=time_step / 1000, voicing_threshold=0.6, pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] pad_size=(p_len - len(f0) + 1) // 2 if(pad_size>0 or p_len - len(f0) - pad_size>0): f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') elif(f0_method=="harvest"): f0, t = pyworld.harvest( x.astype(np.double), fs=self.sr, f0_ceil=f0_max, f0_floor=f0_min, frame_period=10, ) f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) f0 = signal.medfilt(f0, 3) f0 *= pow(2, f0_up_key / 12) # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) tf0=self.sr//self.window#每秒f0点数 if (inp_f0 is not None): delta_t=np.round((inp_f0[:,0].max()-inp_f0[:,0].min())*tf0+1).astype("int16") replace_f0=np.interp(list(range(delta_t)), inp_f0[:, 0]*100, inp_f0[:, 1]) shape=f0[x_pad*tf0:x_pad*tf0+len(replace_f0)].shape[0] f0[x_pad*tf0:x_pad*tf0+len(replace_f0)]=replace_f0[:shape] # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) f0bak = f0.copy() f0_mel = 1127 * np.log(1 + f0 / 700) f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1 f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > 255] = 255 f0_coarse = np.rint(f0_mel).astype(int) return f0_coarse, f0bak#1-0 def vc(self,model,net_g,sid,audio0,pitch,pitchf,times,index,big_npy,index_rate):#,file_index,file_big_npy feats = torch.from_numpy(audio0) if(self.is_half):feats=feats.half() else:feats=feats.float() if feats.dim() == 2: # double channels feats = feats.mean(-1) assert feats.dim() == 1, feats.dim() feats = feats.view(1, -1) padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) inputs = { "source": feats.to(self.device), "padding_mask": padding_mask, "output_layer": 9, # layer 9 } t0 = ttime() with torch.no_grad(): logits = model.extract_features(**inputs) feats = model.final_proj(logits[0]) if(isinstance(index,type(None))==False and isinstance(big_npy,type(None))==False and index_rate!=0): npy = feats[0].cpu().numpy() if(self.is_half):npy=npy.astype("float32") _, I = index.search(npy, 1) npy=big_npy[I.squeeze()] if(self.is_half):npy=npy.astype("float16") feats = torch.from_numpy(npy).unsqueeze(0).to(self.device)*index_rate + (1-index_rate)*feats feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) t1 = ttime() p_len = audio0.shape[0]//self.window if(feats.shape[1]self.t_max): audio_sum = np.zeros_like(audio) for i in range(self.window): audio_sum += audio_pad[i:i - self.window] for t in range(self.t_center, audio.shape[0],self.t_center):opt_ts.append(t - self.t_query + np.where(np.abs(audio_sum[t - self.t_query:t + self.t_query]) == np.abs(audio_sum[t - self.t_query:t + self.t_query]).min())[0][0]) s = 0 audio_opt=[] t=None t1=ttime() audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect') p_len=audio_pad.shape[0]//self.window inp_f0=None if(hasattr(f0_file,'name') ==True): try: with open(f0_file.name,"r")as f: lines=f.read().strip("\n").split("\n") inp_f0=[] for line in lines:inp_f0.append([float(i)for i in line.split(",")]) inp_f0=np.array(inp_f0,dtype="float32") except: traceback.print_exc() sid=torch.tensor(sid,device=self.device).unsqueeze(0).long() pitch, pitchf=None,None if(if_f0==1): pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key,f0_method,inp_f0) pitch = pitch[:p_len] pitchf = pitchf[:p_len] pitch = torch.tensor(pitch,device=self.device).unsqueeze(0).long() pitchf = torch.tensor(pitchf,device=self.device).unsqueeze(0).float() t2=ttime() times[1] += (t2 - t1) for t in opt_ts: t=t//self.window*self.window if (if_f0 == 1): audio_opt.append(self.vc(model,net_g,sid,audio_pad[s:t+self.t_pad2+self.window],pitch[:,s//self.window:(t+self.t_pad2)//self.window],pitchf[:,s//self.window:(t+self.t_pad2)//self.window],times,index,big_npy,index_rate)[self.t_pad_tgt:-self.t_pad_tgt]) else: audio_opt.append(self.vc(model,net_g,sid,audio_pad[s:t+self.t_pad2+self.window],None,None,times,index,big_npy,index_rate)[self.t_pad_tgt:-self.t_pad_tgt]) s = t if (if_f0 == 1): audio_opt.append(self.vc(model,net_g,sid,audio_pad[t:],pitch[:,t//self.window:]if t is not None else pitch,pitchf[:,t//self.window:]if t is not None else pitchf,times,index,big_npy,index_rate)[self.t_pad_tgt:-self.t_pad_tgt]) else: audio_opt.append(self.vc(model,net_g,sid,audio_pad[t:],None,None,times,index,big_npy,index_rate)[self.t_pad_tgt:-self.t_pad_tgt]) audio_opt=np.concatenate(audio_opt) del pitch,pitchf,sid if torch.cuda.is_available(): torch.cuda.empty_cache() return audio_opt