from io import BytesIO import os import pickle import sys import traceback from infer.lib import jit from infer.lib.jit.get_synthesizer import get_synthesizer 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() def printt(strr, *args): if len(args) == 0: print(strr) else: print(strr % args) # 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, config: Config, last_rvc=None, ) -> None: """ 初始化 """ try: 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 # global config self.config = config self.inp_q = inp_q self.opt_q = opt_q # device="cpu"########强制cpu测试 self.device = config.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 self.use_jit = self.config.use_jit self.is_half = config.is_half if index_rate != 0: self.index = faiss.read_index(index_path) self.big_npy = self.index.reconstruct_n(0, self.index.ntotal) printt("Index search enabled") self.pth_path: str = pth_path self.index_path = index_path self.index_rate = index_rate if last_rvc is None: models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( ["assets/hubert/hubert_base.pt"], suffix="", ) hubert_model = models[0] hubert_model = hubert_model.to(self.device) if self.is_half: hubert_model = hubert_model.half() else: hubert_model = hubert_model.float() hubert_model.eval() self.model = hubert_model else: self.model = last_rvc.model self.net_g: nn.Module = None def set_default_model(): self.net_g, cpt = get_synthesizer(self.pth_path, self.device) 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.is_half: self.net_g = self.net_g.half() else: self.net_g = self.net_g.float() def set_jit_model(): jit_pth_path = self.pth_path.rstrip(".pth") jit_pth_path += ".half.jit" if self.is_half else ".jit" reload = False if str(self.device) == "cuda": self.device = torch.device("cuda:0") if os.path.exists(jit_pth_path): cpt = jit.load(jit_pth_path) model_device = cpt["device"] if model_device != str(self.device): reload = True else: reload = True if reload: cpt = jit.synthesizer_jit_export( self.pth_path, "script", None, device=self.device, is_half=self.is_half, ) self.tgt_sr = cpt["config"][-1] self.if_f0 = cpt.get("f0", 1) self.version = cpt.get("version", "v1") self.net_g = torch.jit.load( BytesIO(cpt["model"]), map_location=self.device ) self.net_g.infer = self.net_g.forward self.net_g.eval().to(self.device) def set_synthesizer(): if self.use_jit and not config.dml: if self.is_half and "cpu" in str(self.device): printt( "Use default Synthesizer model. \ Jit is not supported on the CPU for half floating point" ) set_default_model() else: set_jit_model() else: set_default_model() if last_rvc is None or last_rvc.pth_path != self.pth_path: set_synthesizer() else: self.tgt_sr = last_rvc.tgt_sr self.if_f0 = last_rvc.if_f0 self.version = last_rvc.version self.is_half = last_rvc.is_half if last_rvc.use_jit != self.use_jit: set_synthesizer() else: self.net_g = last_rvc.net_g if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"): self.model_rmvpe = last_rvc.model_rmvpe except: printt(traceback.format_exc()) def change_key(self, new_key): self.f0_up_key = new_key def change_index_rate(self, new_index_rate): if new_index_rate != 0 and self.index_rate == 0: self.index = faiss.read_index(self.index_path) self.big_npy = self.index.reconstruct_n(0, self.index.ntotal) printt("Index search enabled") self.index_rate = new_index_rate 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 + 1 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: # printt(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 + 1, dtype=np.float64) length = len(x) part_length = 160 * ((length // 160 - 1) // n_cpu + 1) n_cpu = (length // 160 - 1) // (part_length // 160) + 1 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:] 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() # printt("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 printt("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 use_jit=self.config.use_jit, ) # 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, block_frame_16k, rate, cache_pitch, cache_pitchf, f0method, ) -> np.ndarray: feats = feats.view(1, -1) if self.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] ) feats = F.pad(feats, (0, 0, 1, 0)) t2 = ttime() try: if hasattr(self, "index") and self.index_rate != 0: leng_replace_head = int(rate * 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 self.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: printt("Index search FAILED or disabled") except: traceback.print_exc() printt("Index search FAILED") 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) start_frame = block_frame_16k // 160 end_frame = len(cache_pitch) - (pitch.shape[0] - 4) + start_frame cache_pitch[:] = np.append(cache_pitch[start_frame:end_frame], pitch[3:-1]) cache_pitchf[:] = np.append( cache_pitchf[start_frame:end_frame], pitchf[3:-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: # printt(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, torch.FloatTensor([rate]), )[0][0, 0].data.float() else: infered_audio = self.net_g.infer( feats, p_len, sid, torch.FloatTensor([rate]) )[0][0, 0].data.float() t5 = ttime() printt( "Spent time: fea = %.2fs, index = %.2fs, f0 = %.2fs, model = %.2fs", t2 - t1, t3 - t2, t4 - t3, t5 - t4, ) return infered_audio