import os import sys import traceback from time import time as ttime import fairseq import faiss import numpy as np import parselmouth import pyworld import scipy.signal as signal import torch import torch.nn as nn import torch.nn.functional as F import torchcrepe from infer.lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) now_dir = os.getcwd() sys.path.append(now_dir) from multiprocessing import Manager as M from configs.config import Config config = Config() mm = M() if config.dml == True: def forward_dml(ctx, x, scale): ctx.scale = scale res = x.clone().detach() return res fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml # config.device=torch.device("cpu")########强制cpu测试 # config.is_half=False########强制cpu测试 class RVC: def __init__( self, key, pth_path, index_path, index_rate, n_cpu, inp_q, opt_q, device ) -> None: """ 初始化 """ try: global config self.inp_q = inp_q self.opt_q = opt_q # device="cpu"########强制cpu测试 self.device = device self.f0_up_key = key self.time_step = 160 / 16000 * 1000 self.f0_min = 50 self.f0_max = 1100 self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) self.sr = 16000 self.window = 160 self.n_cpu = n_cpu if index_rate != 0: self.index = faiss.read_index(index_path) self.big_npy = self.index.reconstruct_n(0, self.index.ntotal) print("index search enabled") self.index_rate = index_rate models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( ["assets/hubert/hubert_base.pt"], suffix="", ) hubert_model = models[0] hubert_model = hubert_model.to(config.device) if config.is_half: hubert_model = hubert_model.half() else: hubert_model = hubert_model.float() hubert_model.eval() self.model = hubert_model cpt = torch.load(pth_path, map_location="cpu") self.tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] self.if_f0 = cpt.get("f0", 1) self.version = cpt.get("version", "v1") if self.version == "v1": if self.if_f0 == 1: self.net_g = SynthesizerTrnMs256NSFsid( *cpt["config"], is_half=config.is_half ) else: self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif self.version == "v2": if self.if_f0 == 1: self.net_g = SynthesizerTrnMs768NSFsid( *cpt["config"], is_half=config.is_half ) else: self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del self.net_g.enc_q print(self.net_g.load_state_dict(cpt["weight"], strict=False)) self.net_g.eval().to(device) # print(2333333333,device,config.device,self.device)#net_g是device,hubert是config.device if config.is_half: self.net_g = self.net_g.half() else: self.net_g = self.net_g.float() self.is_half = config.is_half except: print(traceback.format_exc()) def get_f0_post(self, f0): f0_min = self.f0_min f0_max = self.f0_max f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) 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.int32) return f0_coarse, f0bak def get_f0(self, x, f0_up_key, n_cpu, method="harvest"): n_cpu = int(n_cpu) if method == "crepe": return self.get_f0_crepe(x, f0_up_key) if method == "rmvpe": return self.get_f0_rmvpe(x, f0_up_key) if method == "pm": p_len = x.shape[0] // 160 f0 = ( parselmouth.Sound(x, 16000) .to_pitch_ac( time_step=0.01, voicing_threshold=0.6, pitch_floor=50, pitch_ceiling=1100, ) .selected_array["frequency"] ) pad_size = (p_len - len(f0) + 1) // 2 if pad_size > 0 or p_len - len(f0) - pad_size > 0: # print(pad_size, p_len - len(f0) - pad_size) f0 = np.pad( f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" ) f0 *= pow(2, f0_up_key / 12) return self.get_f0_post(f0) if n_cpu == 1: f0, t = pyworld.harvest( x.astype(np.double), fs=16000, f0_ceil=1100, f0_floor=50, frame_period=10, ) f0 = signal.medfilt(f0, 3) f0 *= pow(2, f0_up_key / 12) return self.get_f0_post(f0) f0bak = np.zeros(x.shape[0] // 160, dtype=np.float64) length = len(x) part_length = int(length / n_cpu / 160) * 160 ts = ttime() res_f0 = mm.dict() for idx in range(n_cpu): tail = part_length * (idx + 1) + 320 if idx == 0: self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts)) else: self.inp_q.put( (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts) ) while 1: res_ts = self.opt_q.get() if res_ts == ts: break f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])] for idx, f0 in enumerate(f0s): if idx == 0: f0 = f0[:-3] elif idx != n_cpu - 1: f0 = f0[2:-3] else: f0 = f0[2:-1] f0bak[ part_length * idx // 160 : part_length * idx // 160 + f0.shape[0] ] = f0 f0bak = signal.medfilt(f0bak, 3) f0bak *= pow(2, f0_up_key / 12) return self.get_f0_post(f0bak) def get_f0_crepe(self, x, f0_up_key): if "privateuseone" in str(self.device): ###不支持dml,cpu又太慢用不成,拿pm顶替 return self.get_f0(x, f0_up_key, 1, "pm") audio = torch.tensor(np.copy(x))[None].float() # print("using crepe,device:%s"%self.device) f0, pd = torchcrepe.predict( audio, self.sr, 160, self.f0_min, self.f0_max, "full", batch_size=512, # device=self.device if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用 device=self.device, return_periodicity=True, ) pd = torchcrepe.filter.median(pd, 3) f0 = torchcrepe.filter.mean(f0, 3) f0[pd < 0.1] = 0 f0 = f0[0].cpu().numpy() f0 *= pow(2, f0_up_key / 12) return self.get_f0_post(f0) def get_f0_rmvpe(self, x, f0_up_key): if hasattr(self, "model_rmvpe") == False: from infer.lib.rmvpe import RMVPE print("loading rmvpe model") self.model_rmvpe = RMVPE( # "rmvpe.pt", is_half=self.is_half if self.device.type!="privateuseone" else False, device=self.device if self.device.type!="privateuseone"else "cpu"####dml时强制对rmvpe用cpu跑 # "rmvpe.pt", is_half=False, device=self.device####dml配置 # "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置 "assets/rmvpe/rmvpe.pt", is_half=self.is_half, device=self.device, ####正常逻辑 ) # self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device) f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) f0 *= pow(2, f0_up_key / 12) return self.get_f0_post(f0) def infer( self, feats: torch.Tensor, indata: np.ndarray, rate1, rate2, cache_pitch, cache_pitchf, f0method, ) -> np.ndarray: feats = feats.view(1, -1) if config.is_half: feats = feats.half() else: feats = feats.float() feats = feats.to(self.device) t1 = ttime() with torch.no_grad(): padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) inputs = { "source": feats, "padding_mask": padding_mask, "output_layer": 9 if self.version == "v1" else 12, } logits = self.model.extract_features(**inputs) feats = ( self.model.final_proj(logits[0]) if self.version == "v1" else logits[0] ) t2 = ttime() try: if hasattr(self, "index") and self.index_rate != 0: leng_replace_head = int(rate1 * feats[0].shape[0]) npy = feats[0][-leng_replace_head:].cpu().numpy().astype("float32") score, ix = self.index.search(npy, k=8) weight = np.square(1 / score) weight /= weight.sum(axis=1, keepdims=True) npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) if config.is_half: npy = npy.astype("float16") feats[0][-leng_replace_head:] = ( torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate + (1 - self.index_rate) * feats[0][-leng_replace_head:] ) else: print("index search FAIL or disabled") except: traceback.print_exc() print("index search FAIL") feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) t3 = ttime() if self.if_f0 == 1: pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method) cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0] :], pitch[:-1]) cache_pitchf[:] = np.append( cache_pitchf[pitchf[:-1].shape[0] :], pitchf[:-1] ) p_len = min(feats.shape[1], 13000, cache_pitch.shape[0]) else: cache_pitch, cache_pitchf = None, None p_len = min(feats.shape[1], 13000) t4 = ttime() feats = feats[:, :p_len, :] if self.if_f0 == 1: cache_pitch = cache_pitch[:p_len] cache_pitchf = cache_pitchf[:p_len] cache_pitch = torch.LongTensor(cache_pitch).unsqueeze(0).to(self.device) cache_pitchf = torch.FloatTensor(cache_pitchf).unsqueeze(0).to(self.device) p_len = torch.LongTensor([p_len]).to(self.device) ii = 0 # sid sid = torch.LongTensor([ii]).to(self.device) with torch.no_grad(): if self.if_f0 == 1: # print(12222222222,feats.device,p_len.device,cache_pitch.device,cache_pitchf.device,sid.device,rate2) infered_audio = ( self.net_g.infer( feats, p_len, cache_pitch, cache_pitchf, sid, rate2 )[0][0, 0] .data.cpu() .float() ) else: infered_audio = ( self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0] .data.cpu() .float() ) t5 = ttime() print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4) return infered_audio