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 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 i18n 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 and "V100" not 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 "A2" in gpu_name.upper() or "A3" in gpu_name.upper() or "A4" in gpu_name.upper() or "P4" in gpu_name.upper() 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]) 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, noautoopen, ) 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) file_index = ( file_index.strip(" ") .strip('"') .strip("\n") .strip('"') .strip(" ") .replace("trained", "added") ) # 防止小白写错,自动帮他替换掉 file_big_npy = ( file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") ) 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/3_feature256/mute.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/3_feature256/mute.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=0.6, 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="是", 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=not noautoopen, server_port=listen_port, quiet=True, )