Upload infer-web.py

infer-web.py

@@ -0,0 +1,631 @@
+from multiprocessing import cpu_count
+import threading
+from time import sleep
+from subprocess import Popen,PIPE,run as runn
+from time import sleep
+import torch, pdb, os,traceback,sys,warnings,shutil,numpy as np,faiss
+#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(torch.cuda.is_available()==False 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 librosa
+import logging
+from vc_infer_pipeline import VC
+import soundfile as sf
+from config import is_half,device,is_half
+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, saved_cfg, task = 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):names.append(name)
+uvr5_names=[]
+for name in os.listdir(weight_uvr5_root):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(times)
+        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(" ")#防止小白拷路径头尾带了空格
+        opt_root=opt_root.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("\n")
+        save_root_vocal = save_root_vocal.strip(" ").strip("\n")
+        save_root_ins = save_root_ins.strip(" ").strip("\n")
+        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")
+        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
+        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
+        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
+        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():return {"choices": sorted(list(os.listdir(weight_root))), "__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="runtime\python.exe trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir,sr,n_p,now_dir,exp_dir)
+    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="runtime\python.exe 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="runtime\python.exe extract_feature_print.py %s %s %s %s/logs/%s"%(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))
+    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 = "runtime\python.exe 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"
+    cmd = "runtime\python.exe 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)
+    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="runtime\python.exe trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)
+    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="runtime\python.exe 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="runtime\python.exe extract_feature_print.py %s %s %s %s/logs/%s"%(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))
+    with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
+    yield get_info_str("write filelist done")
+    cmd = "runtime\python.exe 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)
+    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="""
+        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。<br>
+        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("模型推理"):
+            with gr.Row():
+                sid0 = gr.Dropdown(label="推理音色", choices=names)
+                refresh_button = gr.Button("刷新音色列表", variant="primary")
+                refresh_button.click(
+                    fn=change_choices,
+                    inputs=[],
+                    outputs=[sid0]
+                )
+                clean_button = gr.Button("卸载音色省显存", variant="primary")
+                spk_item = gr.Slider(minimum=0, maximum=2333, step=1, label='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="""
+                    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="变调（整数，半音数量，升八度12降八度-12）", value=0)
+                        input_audio0 = gr.Textbox(label="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="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="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="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="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("转换", variant="primary")
+                    with gr.Column():
+                        vc_output1 = gr.Textbox(label="输出信息")
+                        vc_output2 = gr.Audio(label="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="""
+                    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="变调（整数，半音数量，升八度12降八度-12）", value=0)
+                        opt_input = gr.Textbox(label="指定输出文件夹",value="opt")
+                        f0method1=gr.Radio(label="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="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="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='检索特征占比', value=1,interactive=True)
+                    with gr.Column():
+                        dir_input = gr.Textbox(label="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="也可批量输入音频文件，二选一，优先读文件夹")
+                    but1=gr.Button("转换", variant="primary")
+                    vc_output3 = gr.Textbox(label="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("伴奏人声分离"):
+            with gr.Group():
+                gr.Markdown(value="""
+                    人声伴奏分离批量处理，使用UVR5模型。<br>
+                    不带和声用HP2，带和声且提取的人声不需要和声用HP5<br>
+                    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="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="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="模型", choices=uvr5_names)
+                        opt_vocal_root = gr.Textbox(label="指定输出人声文件夹",value="opt")
+                        opt_ins_root = gr.Textbox(label="指定输出乐器文件夹",value="opt")
+                    but2=gr.Button("转换", variant="primary")
+                    vc_output4 = gr.Textbox(label="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("训练"):
+            gr.Markdown(value="""
+                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="输入实验名",value="xxxx")
+                sr2 = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
+                if_f0_3 = gr.Radio(label="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="""
+                    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="输入训练文件夹路径",value="E:\\xxx\\xxxxx")
+                    spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label='请指定说话人id', value=0,interactive=True)
+                    but1=gr.Button("处理数据", variant="primary")
+                    info1=gr.Textbox(label="输出信息",value="")
+                    but1.click(preprocess_dataset,[trainset_dir4,exp_dir1,sr2],[info1])
+            with gr.Group():
+                gr.Markdown(value="""
+                    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="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="显卡信息",value=gpu_info)
+                    with gr.Column():
+                        np7 = gr.Slider(minimum=0, maximum=ncpu, step=1, label='提取音高使用的CPU进程数', value=ncpu,interactive=True)
+                        f0method8 = gr.Radio(label="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("特征提取", variant="primary")
+                    info2=gr.Textbox(label="���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="""
+                    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='保存频率save_every_epoch', value=5,interactive=True)
+                    total_epoch11 = gr.Slider(minimum=0, maximum=100, step=1, label='总训练轮数total_epoch', value=10,interactive=True)
+                    batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='batch_size', value=4,interactive=True)
+                    if_save_latest13 = gr.Radio(label="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="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="加载预训练底模G路径", value="pretrained/f0G40k.pth",interactive=True)
+                    pretrained_D15 = gr.Textbox(label="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="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("训练模型", variant="primary")
+                    but4 = gr.Button("训练特征索引", variant="primary")
+                    but5 = gr.Button("一键训练", variant="primary")
+                    info3 = gr.Textbox(label="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("ckpt处理"):
+            with gr.Group():
+                gr.Markdown(value="""模型融合，可用于测试音色融合""")
+                with gr.Row():
+                    ckpt_a = gr.Textbox(label="A模型路径", value="", interactive=True)
+                    ckpt_b = gr.Textbox(label="B模型路径", value="", interactive=True)
+                    alpha_a = gr.Slider(minimum=0, maximum=1, label='A模型权重', value=0.5, interactive=True)
+                with gr.Row():
+                    sr_ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
+                    if_f0_ = gr.Radio(label="模型是否带音高指导", choices=["是","否"],value="是", interactive=True)
+                    info__ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True)
+                    name_to_save0=gr.Textbox(label="保存的模型名不带后缀", value="", max_lines=1, interactive=True)
+                with gr.Row():
+                    but6 = gr.Button("融合", variant="primary")
+                    info4 = gr.Textbox(label="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="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="模型路径", value="", interactive=True)
+                    info_=gr.Textbox(label="要改的模型信息", value="", max_lines=8, interactive=True)
+                    name_to_save1=gr.Textbox(label="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("修改", variant="primary")
+                    info5 = gr.Textbox(label="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="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="模型路径", value="", interactive=True)
+                    but8 = gr.Button("查看", variant="primary")
+                    info6 = gr.Textbox(label="���出信息", value="", max_lines=8)
+                but8.click(show_info, [ckpt_path1], info6)
+            with gr.Group():
+                gr.Markdown(value="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="模型路径", value="E:\codes\py39\logs\mi-test_f0_48k\\G_23333.pth", interactive=True)
+                    save_name = gr.Textbox(label="保存名", value="", interactive=True)
+                    sr__ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
+                    if_f0__ = gr.Radio(label="模型是否带音高指导,1是0否", choices=["1","0"],value="1", interactive=True)
+                    info___ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True)
+                    but9 = gr.Button("提取", variant="primary")
+                    info7 = gr.Textbox(label="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("招募音高曲线前端编辑器"):
+            gr.Markdown(value="""加开发群联系我647947694""")
+        with gr.TabItem("招募实时变声插件开发"):
+            gr.Markdown(value="""加开发群联系我647947694""")
+        with gr.TabItem("点击查看交流、问题反馈群号"):
+            gr.Markdown(value="""259421308""")
+
+    # app.launch(server_name="0.0.0.0",server_port=7860)
+    app.queue(concurrency_count=511, max_size=1022).launch(server_name="127.0.0.1",inbrowser=True,server_port=7865,quiet=True,share=True)