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 from scipy import signal bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) 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(np.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()] score, ix = index.search(npy, k=8) weight = np.square(1 / score) weight /= weight.sum(axis=1, keepdims=True) npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) 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] < p_len: p_len = feats.shape[1] if pitch != None and pitchf != None: pitch = pitch[:, :p_len] pitchf = pitchf[:, :p_len] p_len = torch.tensor([p_len], device=self.device).long() with torch.no_grad(): if pitch != None and pitchf != None: audio1 = ( (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768) .data.cpu() .float() .numpy() .astype(np.int16) ) else: audio1 = ( (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768) .data.cpu() .float() .numpy() .astype(np.int16) ) del feats, p_len, padding_mask if torch.cuda.is_available(): torch.cuda.empty_cache() t2 = ttime() times[0] += t1 - t0 times[2] += t2 - t1 return audio1 def pipeline( self, model, net_g, sid, audio, times, f0_up_key, f0_method, file_index, # file_big_npy, index_rate, if_f0, f0_file=None, ): if ( file_index != "" # and file_big_npy != "" # and os.path.exists(file_big_npy) == True and os.path.exists(file_index) == True and index_rate != 0 ): try: index = faiss.read_index(file_index) # big_npy = np.load(file_big_npy) big_npy = index.reconstruct_n(0, index.ntotal) except: traceback.print_exc() index = big_npy = None else: index = big_npy = None audio = signal.filtfilt(bh, ah, audio) audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") opt_ts = [] if audio_pad.shape[0] > 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