from multiprocessing import cpu_count import threading from time import sleep from subprocess import Popen from time import sleep import torch, os,traceback,sys,warnings,shutil,numpy as np import faiss from webui_locale import I18nAuto i18n = I18nAuto() #判断是否有能用来训练和加速推理的N卡 ncpu=cpu_count() ngpu=torch.cuda.device_count() gpu_infos=[] if((not torch.cuda.is_available()) or ngpu==0):if_gpu_ok=False else: if_gpu_ok = False for i in range(ngpu): gpu_name=torch.cuda.get_device_name(i) if("16"in gpu_name or "MX"in gpu_name):continue if("10"in gpu_name or "20"in gpu_name or "30"in gpu_name or "40"in gpu_name or "A50"in gpu_name.upper() or "70"in gpu_name or "80"in gpu_name or "90"in gpu_name or "M4"in gpu_name or "T4"in gpu_name or "TITAN"in gpu_name.upper()):#A10#A100#V100#A40#P40#M40#K80 if_gpu_ok=True#至少有一张能用的N卡 gpu_infos.append("%s\t%s"%(i,gpu_name)) gpu_info="\n".join(gpu_infos)if if_gpu_ok==True and len(gpu_infos)>0 else "很遗憾您这没有能用的显卡来支持您训练" gpus="-".join([i[0]for i in gpu_infos]) now_dir=os.getcwd() sys.path.append(now_dir) tmp=os.path.join(now_dir,"TEMP") shutil.rmtree(tmp,ignore_errors=True) os.makedirs(tmp,exist_ok=True) os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True) os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True) os.environ["TEMP"]=tmp warnings.filterwarnings("ignore") torch.manual_seed(114514) from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono from scipy.io import wavfile from fairseq import checkpoint_utils import gradio as gr import logging from vc_infer_pipeline import VC from config import is_half,device,python_cmd,listen_port,iscolab,noparallel from infer_uvr5 import _audio_pre_ from my_utils import load_audio from train.process_ckpt import show_info,change_info,merge,extract_small_model # from trainset_preprocess_pipeline import PreProcess logging.getLogger('numba').setLevel(logging.WARNING) class ToolButton(gr.Button, gr.components.FormComponent): """Small button with single emoji as text, fits inside gradio forms""" def __init__(self, **kwargs): super().__init__(variant="tool", **kwargs) def get_block_name(self): return "button" hubert_model=None def load_hubert(): global hubert_model models, _, _ = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",) hubert_model = models[0] hubert_model = hubert_model.to(device) if(is_half):hubert_model = hubert_model.half() else:hubert_model = hubert_model.float() hubert_model.eval() weight_root="weights" weight_uvr5_root="uvr5_weights" names=[] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) uvr5_names=[] for name in os.listdir(weight_uvr5_root): if name.endswith(".pth"): uvr5_names.append(name.replace(".pth","")) def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_npy,index_rate):#spk_item, input_audio0, vc_transform0,f0_file,f0method0 global tgt_sr,net_g,vc,hubert_model if input_audio is None:return "You need to upload an audio", None f0_up_key = int(f0_up_key) try: audio=load_audio(input_audio,16000) times = [0, 0, 0] if(hubert_model==None):load_hubert() if_f0 = cpt.get("f0", 1) audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file) print("npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep='') return "Success", (tgt_sr, audio_opt) except: info=traceback.format_exc() print(info) return info,(None,None) def vc_multi(sid,dir_path,opt_root,paths,f0_up_key,f0_method,file_index,file_big_npy,index_rate): try: dir_path=dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")#防止小白拷路径头尾带了空格和"和回车 opt_root=opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") os.makedirs(opt_root, exist_ok=True) try: if(dir_path!=""):paths=[os.path.join(dir_path,name)for name in os.listdir(dir_path)] else:paths=[path.name for path in paths] except: traceback.print_exc() paths = [path.name for path in paths] infos=[] for path in paths: info,opt=vc_single(sid,path,f0_up_key,None,f0_method,file_index,file_big_npy,index_rate) if(info=="Success"): try: tgt_sr,audio_opt=opt wavfile.write("%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt) except: info=traceback.format_exc() infos.append("%s->%s"%(os.path.basename(path),info)) yield "\n".join(infos) yield "\n".join(infos) except: yield traceback.format_exc() def uvr(model_name,inp_root,save_root_vocal,paths,save_root_ins): infos = [] try: inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") save_root_vocal = save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") save_root_ins = save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") pre_fun = _audio_pre_(model_path=os.path.join(weight_uvr5_root,model_name+".pth"), device=device, is_half=is_half) if (inp_root != ""):paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] else:paths = [path.name for path in paths] for name in paths: inp_path=os.path.join(inp_root,name) try: pre_fun._path_audio_(inp_path , save_root_ins,save_root_vocal) infos.append("%s->Success"%(os.path.basename(inp_path))) yield "\n".join(infos) except: infos.append("%s->%s" % (os.path.basename(inp_path),traceback.format_exc())) yield "\n".join(infos) except: infos.append(traceback.format_exc()) yield "\n".join(infos) finally: try: del pre_fun.model del pre_fun except: traceback.print_exc() print("clean_empty_cache") if torch.cuda.is_available(): torch.cuda.empty_cache() yield "\n".join(infos) #一个选项卡全局只能有一个音色 def get_vc(sid): global n_spk,tgt_sr,net_g,vc,cpt if(sid==[]): global hubert_model if (hubert_model != None): # 考虑到轮询,需要加个判断看是否 sid 是由有模型切换到无模型的 print("clean_empty_cache") del net_g, n_spk, vc, hubert_model,tgt_sr#,cpt hubert_model = net_g=n_spk=vc=hubert_model=tgt_sr=None if torch.cuda.is_available(): torch.cuda.empty_cache() ###楼下不这么折腾清理不干净 if_f0 = cpt.get("f0", 1) if (if_f0 == 1): net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) del net_g,cpt if torch.cuda.is_available(): torch.cuda.empty_cache() cpt=None return {"visible": False, "__type__": "update"} person = "%s/%s" % (weight_root, sid) print("loading %s"%person) cpt = torch.load(person, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk if_f0=cpt.get("f0",1) if(if_f0==1): net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) del net_g.enc_q print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净,真奇葩 net_g.eval().to(device) if (is_half):net_g = net_g.half() else:net_g = net_g.float() vc = VC(tgt_sr, device, is_half) n_spk=cpt["config"][-3] return {"visible": True,"maximum": n_spk, "__type__": "update"} def change_choices(): names=[] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) return {"choices": sorted(names), "__type__": "update"} def clean():return {"value": "", "__type__": "update"} def change_f0(if_f0_3,sr2):#np7, f0method8,pretrained_G14,pretrained_D15 if(if_f0_3=="是"):return {"visible": True, "__type__": "update"},{"visible": True, "__type__": "update"},"pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2 return {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"},"pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2 sr_dict={ "32k":32000, "40k":40000, "48k":48000, } def if_done(done,p): while 1: if(p.poll()==None):sleep(0.5) else:break done[0]=True def if_done_multi(done,ps): while 1: #poll==None代表进程未结束 #只要有一个进程未结束都不停 flag=1 for p in ps: if(p.poll()==None): flag = 0 sleep(0.5) break if(flag==1):break done[0]=True def preprocess_dataset(trainset_dir,exp_dir,sr,n_p=ncpu): sr=sr_dict[sr] os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True) f = open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "w") f.close() cmd=python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "%(trainset_dir,sr,n_p,now_dir,exp_dir)+str(noparallel) print(cmd) p = Popen(cmd, shell=True)#, stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir ###煞笔gr,popen read都非得全跑完了再一次性读取,不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 done=[False] threading.Thread(target=if_done,args=(done,p,)).start() while(1): with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir),"r")as f:yield(f.read()) sleep(1) if(done[0]==True):break with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "r")as f:log = f.read() print(log) yield log #but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir): gpus=gpus.split("-") os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True) f = open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "w") f.close() if(if_f0=="是"): cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method) print(cmd) p = Popen(cmd, shell=True,cwd=now_dir)#, stdin=PIPE, stdout=PIPE,stderr=PIPE ###煞笔gr,popen read都非得全跑完了再一次性读取,不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 done=[False] threading.Thread(target=if_done,args=(done,p,)).start() while(1): with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir),"r")as f:yield(f.read()) sleep(1) if(done[0]==True):break with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read() print(log) yield log ####对不同part分别开多进程 ''' n_part=int(sys.argv[1]) i_part=int(sys.argv[2]) i_gpu=sys.argv[3] exp_dir=sys.argv[4] os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) ''' leng=len(gpus) ps=[] for idx,n_g in enumerate(gpus): cmd=python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s"%(device,leng,idx,n_g,now_dir,exp_dir) print(cmd) p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir ps.append(p) ###煞笔gr,popen read都非得全跑完了再一次性读取,不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 done = [False] threading.Thread(target=if_done_multi, args=(done, ps,)).start() while (1): with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:yield (f.read()) sleep(1) if (done[0] == True): break with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read() print(log) yield log def change_sr2(sr2,if_f0_3): if(if_f0_3=="是"):return "pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2 else:return "pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2 #but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16]) def click_train(exp_dir1,sr2,if_f0_3,spk_id5,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16,if_cache_gpu17): #生成filelist exp_dir="%s/logs/%s"%(now_dir,exp_dir1) os.makedirs(exp_dir,exist_ok=True) gt_wavs_dir="%s/0_gt_wavs"%(exp_dir) co256_dir="%s/3_feature256"%(exp_dir) if(if_f0_3=="是"): f0_dir = "%s/2a_f0" % (exp_dir) f0nsf_dir="%s/2b-f0nsf"%(exp_dir) names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])&set([name.split(".")[0]for name in os.listdir(f0_dir)])&set([name.split(".")[0]for name in os.listdir(f0nsf_dir)]) else: names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)]) opt=[] for name in names: if (if_f0_3 == "是"): opt.append("%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,f0_dir.replace("\\","\\\\"),name,f0nsf_dir.replace("\\","\\\\"),name,spk_id5)) else: opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5)) if (if_f0_3 == "是"): opt.append("%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"%(now_dir,sr2,now_dir,now_dir,now_dir,spk_id5)) else: opt.append("%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"%(now_dir,sr2,now_dir,spk_id5)) with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt)) print("write filelist done") #生成config#无需生成config # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0" print("use gpus:",gpus16) if gpus16: cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0) else: cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0) print(cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() return "训练结束,您可查看控制台训练日志或实验文件夹下的train.log" # but4.click(train_index, [exp_dir1], info3) def train_index(exp_dir1): exp_dir="%s/logs/%s"%(now_dir,exp_dir1) os.makedirs(exp_dir,exist_ok=True) feature_dir="%s/3_feature256"%(exp_dir) if(os.path.exists(feature_dir)==False):return "请先进行特征提取!" listdir_res=list(os.listdir(feature_dir)) if(len(listdir_res)==0):return "请先进行特征提取!" npys = [] for name in sorted(listdir_res): phone = np.load("%s/%s" % (feature_dir, name)) npys.append(phone) big_npy = np.concatenate(npys, 0) np.save("%s/total_fea.npy"%exp_dir, big_npy) n_ivf = big_npy.shape[0] // 39 infos=[] infos.append("%s,%s"%(big_npy.shape,n_ivf)) yield "\n".join(infos) index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf) infos.append("training") yield "\n".join(infos) index_ivf = faiss.extract_index_ivf(index) # index_ivf.nprobe = int(np.power(n_ivf,0.3)) index.train(big_npy) faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) infos.append("adding") yield "\n".join(infos) index.add(big_npy) faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) infos.append("成功构建索引,added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe)) yield "\n".join(infos) #but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3) def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17): infos=[] def get_info_str(strr): infos.append(strr) return "\n".join(infos) os.makedirs("%s/logs/%s"%(now_dir,exp_dir1),exist_ok=True) #########step1:处理数据 open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir1), "w").close() cmd=python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)+str(noparallel) yield get_info_str("step1:正在处理数据") yield get_info_str(cmd) p = Popen(cmd, shell=True) p.wait() with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r")as f: print(f.read()) #########step2a:提取音高 open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w") if(if_f0_3=="是"): yield get_info_str("step2a:正在提取音高") cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8) yield get_info_str(cmd) p = Popen(cmd, shell=True,cwd=now_dir) p.wait() with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read()) else:yield get_info_str("step2a:无需提取音高") #######step2b:提取特征 yield get_info_str("step2b:正在提取特征") gpus=gpus16.split("-") leng=len(gpus) ps=[] for idx,n_g in enumerate(gpus): cmd=python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s"%(device,leng,idx,n_g,now_dir,exp_dir1) yield get_info_str(cmd) p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir ps.append(p) for p in ps:p.wait() with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read()) #######step3a:训练模型 yield get_info_str("step3a:正在训练模型") #生成filelist exp_dir="%s/logs/%s"%(now_dir,exp_dir1) gt_wavs_dir="%s/0_gt_wavs"%(exp_dir) co256_dir="%s/3_feature256"%(exp_dir) if(if_f0_3=="是"): f0_dir = "%s/2a_f0" % (exp_dir) f0nsf_dir="%s/2b-f0nsf"%(exp_dir) names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])&set([name.split(".")[0]for name in os.listdir(f0_dir)])&set([name.split(".")[0]for name in os.listdir(f0nsf_dir)]) else: names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)]) opt=[] for name in names: if (if_f0_3 == "是"): opt.append("%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,f0_dir.replace("\\","\\\\"),name,f0nsf_dir.replace("\\","\\\\"),name,spk_id5)) else: opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5)) if (if_f0_3 == "是"): opt.append("%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"%(now_dir,sr2,now_dir,now_dir,now_dir,spk_id5)) else: opt.append("%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"%(now_dir,sr2,now_dir,spk_id5)) with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt)) yield get_info_str("write filelist done") if gpus16: cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0) else: cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0) yield get_info_str(cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() yield get_info_str("训练结束,您可查看控制台训练日志或实验文件夹下的train.log") #######step3b:训练索引 feature_dir="%s/3_feature256"%(exp_dir) npys = [] listdir_res=list(os.listdir(feature_dir)) for name in sorted(listdir_res): phone = np.load("%s/%s" % (feature_dir, name)) npys.append(phone) big_npy = np.concatenate(npys, 0) np.save("%s/total_fea.npy"%exp_dir, big_npy) n_ivf = big_npy.shape[0] // 39 yield get_info_str("%s,%s"%(big_npy.shape,n_ivf)) index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf) yield get_info_str("training index") index_ivf = faiss.extract_index_ivf(index) # index_ivf.nprobe = int(np.power(n_ivf,0.3)) index.train(big_npy) faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) yield get_info_str("adding index") index.add(big_npy) faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) yield get_info_str("成功构建索引,added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe)) yield get_info_str("全流程结束!") # ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) def change_info_(ckpt_path): if(os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"))==False):return {"__type__": "update"},{"__type__": "update"} try: with open(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"),"r")as f: info=eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) sr,f0=info["sample_rate"],info["if_f0"] return sr,str(f0) except: traceback.print_exc() return {"__type__": "update"}, {"__type__": "update"} with gr.Blocks() as app: gr.Markdown(value=i18n(""" 本软件以MIT协议开源,作者不对软件具备任何控制力,使用软件者、传播软件导出的声音者自负全责。
如不认可该条款,则不能使用或引用软件包内任何代码和文件。详见根目录"使用需遵守的协议-LICENSE.txt"。 """)) with gr.Tabs(): with gr.TabItem(i18n("模型推理")): with gr.Row(): sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) refresh_button = gr.Button(i18n("刷新音色列表"), variant="primary") refresh_button.click( fn=change_choices, inputs=[], outputs=[sid0] ) clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") spk_item = gr.Slider(minimum=0, maximum=2333, step=1, label=i18n("请选择说话人id"), value=0, visible=False, interactive=True) clean_button.click( fn=clean, inputs=[], outputs=[sid0] ) sid0.change( fn=get_vc, inputs=[sid0], outputs=[spk_item], ) with gr.Group(): gr.Markdown(value=i18n(""" 男转女推荐+12key,女转男推荐-12key,如果音域爆炸导致音色失真也可以自己调整到合适音域。 """)) with gr.Row(): with gr.Column(): vc_transform0 = gr.Number(label=i18n("变调(整数,半音数量,升八度12降八度-12)"), value=0) input_audio0 = gr.Textbox(label=i18n("输入待处理音频文件路径(默认是正确格式示例)"),value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs\冬之花clip1.wav") f0method0=gr.Radio(label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"), choices=["pm","harvest"],value="pm", interactive=True) with gr.Column(): file_index1 = gr.Textbox(label=i18n("特征检索库文件路径"),value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\added_IVF677_Flat_nprobe_7.index", interactive=True) file_big_npy1 = gr.Textbox(label=i18n("特征文件路径"),value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\total_fea.npy", interactive=True) index_rate1 = gr.Slider(minimum=0, maximum=1,label='检索特征占比', value=1,interactive=True) f0_file = gr.File(label=i18n("F0曲线文件,可选,一行一个音高,代替默认F0及升降调")) but0=gr.Button(i18n("转换"), variant="primary") with gr.Column(): vc_output1 = gr.Textbox(label=i18n("输出信息")) vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) but0.click(vc_single, [spk_item, input_audio0, vc_transform0,f0_file,f0method0,file_index1,file_big_npy1,index_rate1], [vc_output1, vc_output2]) with gr.Group(): gr.Markdown(value=i18n(""" 批量转换,输入待转换音频文件夹,或上传多个音频文件,在指定文件夹(默认opt)下输出转换的音频。 """)) with gr.Row(): with gr.Column(): vc_transform1 = gr.Number(label=i18n("变调(整数,半音数量,升八度12降八度-12)"), value=0) opt_input = gr.Textbox(label=i18n("指定输出文件夹"),value="opt") f0method1=gr.Radio(label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"), choices=["pm","harvest"],value="pm", interactive=True) with gr.Column(): file_index2 = gr.Textbox(label=i18n("特征检索库文件路径"),value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\added_IVF677_Flat_nprobe_7.index", interactive=True) file_big_npy2 = gr.Textbox(label=i18n("特征文件路径"),value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\total_fea.npy", interactive=True) index_rate2 = gr.Slider(minimum=0, maximum=1,label=i18n("检索特征占比"), value=1,interactive=True) with gr.Column(): dir_input = gr.Textbox(label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs") inputs = gr.File(file_count="multiple", label=i18n("也可批量输入音频文件,二选一,优先读文件夹")) but1=gr.Button(i18n("转换"), variant="primary") vc_output3 = gr.Textbox(label=i18n("输出信息")) but1.click(vc_multi, [spk_item, dir_input,opt_input,inputs, vc_transform1,f0method1,file_index2,file_big_npy2,index_rate2], [vc_output3]) with gr.TabItem(i18n("伴奏人声分离")): with gr.Group(): gr.Markdown(value=i18n(""" 人声伴奏分离批量处理,使用UVR5模型。
不带和声用HP2,带和声且提取的人声不需要和声用HP5
合格的文件夹路径格式举例:E:\codes\py39\\vits_vc_gpu\白鹭霜华测试样例(去文件管理器地址栏拷就行了) """)) with gr.Row(): with gr.Column(): dir_wav_input = gr.Textbox(label=i18n("输入待处理音频文件夹路径"),value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs") wav_inputs = gr.File(file_count="multiple", label=i18n("也可批量输入音频文件,二选一,优先读文件夹")) with gr.Column(): model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) opt_vocal_root = gr.Textbox(label=i18n("指定输出人声文件夹"),value="opt") opt_ins_root = gr.Textbox(label=i18n("指定输出乐器文件夹"),value="opt") but2=gr.Button(i18n("转换"), variant="primary") vc_output4 = gr.Textbox(label=i18n("输出信息")) but2.click(uvr, [model_choose, dir_wav_input,opt_vocal_root,wav_inputs,opt_ins_root], [vc_output4]) with gr.TabItem(i18n("训练")): gr.Markdown(value=i18n(""" step1:填写实验配置。实验数据放在logs下,每个实验一个文件夹,需手工输入实验名路径,内含实验配置,日志,训练得到的模型文件。 """)) with gr.Row(): exp_dir1 = gr.Textbox(label=i18n("输入实验名"),value="mi-test") sr2 = gr.Radio(label=i18n("目标采样率"), choices=["32k","40k","48k"],value="40k", interactive=True) if_f0_3 = gr.Radio(label=i18n("模型是否带音高指导(唱歌一定要,语音可以不要)"), choices=["是","否"],value="是", interactive=True) with gr.Group():#暂时单人的,后面支持最多4人的#数据处理 gr.Markdown(value=i18n(""" step2a:自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化,在实验目录下生成2个wav文件夹;暂时只支持单人训练。 """)) with gr.Row(): trainset_dir4 = gr.Textbox(label=i18n("输入训练文件夹路径"),value="E:\语音音频+标注\米津玄师\src") spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label=i18n("请指定说话人id"), value=0,interactive=True) but1=gr.Button(i18n("处理数据"), variant="primary") info1=gr.Textbox(label=i18n("输出信息"),value="") but1.click(preprocess_dataset,[trainset_dir4,exp_dir1,sr2],[info1]) with gr.Group(): gr.Markdown(value=i18n(""" step2b:使用CPU提取音高(如果模型带音高),使用GPU提取特征(选择卡号) """)) with gr.Row(): with gr.Column(): gpus6 = gr.Textbox(label=i18n("以-分隔输入使用的卡号,例如 0-1-2 使用卡0和卡1和卡2"),value=gpus,interactive=True) gpu_info9 = gr.Textbox(label=i18n("显卡信息"),value=gpu_info) with gr.Column(): np7 = gr.Slider(minimum=0, maximum=ncpu, step=1, label=i18n("提取音高使用的CPU进程数"), value=ncpu,interactive=True) f0method8 = gr.Radio(label=i18n("选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"), choices=["pm", "harvest","dio"], value="harvest", interactive=True) but2=gr.Button(i18n("特征提取"), variant="primary") info2=gr.Textbox(label=i18n("输出信息"),value="",max_lines=8) but2.click(extract_f0_feature,[gpus6,np7,f0method8,if_f0_3,exp_dir1],[info2]) with gr.Group(): gr.Markdown(value=i18n(""" step3:填写训练设置,开始训练模型和索引 """)) with gr.Row(): save_epoch10 = gr.Slider(minimum=0, maximum=50, step=1, label=i18n("保存频率save_every_epoch"), value=5,interactive=True) total_epoch11 = gr.Slider(minimum=0, maximum=1000, step=1, label=i18n("总训练轮数total_epoch"), value=20,interactive=True) batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='每张显卡的batch_size', value=4,interactive=True) if_save_latest13 = gr.Radio(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), choices=["是", "否"], value="否", interactive=True) if_cache_gpu17 = gr.Radio(label=i18n("是否缓存所有训练集至显存。10min以下小数据可缓存以加速训练,大数据缓存会炸显存也加不了多少速"), choices=["是", "否"], value="否", interactive=True) with gr.Row(): pretrained_G14 = gr.Textbox(label=i18n("加载预训练底模G路径"), value="pretrained/f0G40k.pth",interactive=True) pretrained_D15 = gr.Textbox(label=i18n("加载预训练底模D路径"), value="pretrained/f0D40k.pth",interactive=True) sr2.change(change_sr2, [sr2,if_f0_3], [pretrained_G14,pretrained_D15]) if_f0_3.change(change_f0, [if_f0_3, sr2], [np7, f0method8, pretrained_G14, pretrained_D15]) gpus16 = gr.Textbox(label=i18n("以-分隔输入使用的卡号,例如 0-1-2 使用卡0和卡1和卡2"), value=gpus,interactive=True) but3 = gr.Button(i18n("训练模型"), variant="primary") but4 = gr.Button(i18n("训练特征索引"), variant="primary") but5 = gr.Button(i18n("一键训练"), variant="primary") info3 = gr.Textbox(label=i18n("输出信息"), value="",max_lines=10) but3.click(click_train,[exp_dir1,sr2,if_f0_3,spk_id5,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16,if_cache_gpu17],info3) but4.click(train_index,[exp_dir1],info3) but5.click(train1key,[exp_dir1,sr2,if_f0_3,trainset_dir4,spk_id5,gpus6,np7,f0method8,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16,if_cache_gpu17],info3) with gr.TabItem(i18n("ckpt处理")): with gr.Group(): gr.Markdown(value=i18n("""模型融合,可用于测试音色融合""")) with gr.Row(): ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True) ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True) alpha_a = gr.Slider(minimum=0, maximum=1, label=i18n("A模型权重"), value=0.5, interactive=True) with gr.Row(): sr_ = gr.Radio(label=i18n("目标采样率"), choices=["32k","40k","48k"],value="40k", interactive=True) if_f0_ = gr.Radio(label=i18n("模型是否带音高指导"), choices=["是","否"],value="是", interactive=True) info__ = gr.Textbox(label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True) name_to_save0=gr.Textbox(label=i18n("保存的模型名不带后缀"), value="", max_lines=1, interactive=True) with gr.Row(): but6 = gr.Button(i18n("融合"), variant="primary") info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but6.click(merge, [ckpt_a,ckpt_b,alpha_a,sr_,if_f0_,info__,name_to_save0], info4)#def merge(path1,path2,alpha1,sr,f0,info): with gr.Group(): gr.Markdown(value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")) with gr.Row(): ckpt_path0 = gr.Textbox(label=i18n("模型路径"), value="", interactive=True) info_=gr.Textbox(label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True) name_to_save1=gr.Textbox(label=i18n("保存的文件名,默认空为和源文件同名"), value="", max_lines=8, interactive=True) with gr.Row(): but7 = gr.Button(i18n("修改"), variant="primary") info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but7.click(change_info, [ckpt_path0,info_,name_to_save1], info5) with gr.Group(): gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")) with gr.Row(): ckpt_path1 = gr.Textbox(label=i18n("模型路径"), value="", interactive=True) but8 = gr.Button(i18n("查看"), variant="primary") info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but8.click(show_info, [ckpt_path1], info6) with gr.Group(): gr.Markdown(value=i18n("模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况")) with gr.Row(): ckpt_path2 = gr.Textbox(label=i18n("模型路径"), value="E:\codes\py39\logs\mi-test_f0_48k\\G_23333.pth", interactive=True) save_name = gr.Textbox(label=i18n("保存名"), value="", interactive=True) sr__ = gr.Radio(label=i18n("目标采样率"), choices=["32k","40k","48k"],value="40k", interactive=True) if_f0__ = gr.Radio(label=i18n("模型是否带音高指导,1是0否"), choices=["1","0"],value="1", interactive=True) info___ = gr.Textbox(label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True) but9 = gr.Button(i18n("提取"), variant="primary") info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) but9.click(extract_small_model, [ckpt_path2,save_name,sr__,if_f0__,info___], info7) with gr.TabItem(i18n("招募音高曲线前端编辑器")): gr.Markdown(value=i18n("""加开发群联系我xxxxx""")) with gr.TabItem(i18n("点击查看交流、问题反馈群号")): gr.Markdown(value=i18n("""xxxxx""")) if iscolab: app.queue(concurrency_count=511, max_size=1022).launch(share=True) else: app.queue(concurrency_count=511, max_size=1022).launch(server_name="0.0.0.0",inbrowser=True,server_port=listen_port,quiet=True)