import os, sys import datetime, subprocess from mega import Mega now_dir = os.getcwd() sys.path.append(now_dir) import logging import shutil import threading import traceback import warnings from random import shuffle from subprocess import Popen from time import sleep import json import pathlib import fairseq import faiss import gradio as gr import numpy as np import torch from dotenv import load_dotenv from sklearn.cluster import MiniBatchKMeans from configs.config import Config from i18n.i18n import I18nAuto from infer.lib.train.process_ckpt import ( change_info, extract_small_model, merge, show_info, ) from infer.modules.uvr5.modules import uvr from infer.modules.vc.modules import VC logging.getLogger("numba").setLevel(logging.WARNING) logger = logging.getLogger(__name__) tmp = os.path.join(now_dir, "TEMP") shutil.rmtree(tmp, ignore_errors=True) shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), 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, "assets/weights"), exist_ok=True) os.environ["TEMP"] = tmp warnings.filterwarnings("ignore") torch.manual_seed(114514) load_dotenv() config = Config() vc = VC(config) if config.dml == True: def forward_dml(ctx, x, scale): ctx.scale = scale res = x.clone().detach() return res fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml i18n = I18nAuto() logger.info(i18n) # 判断是否有能用来训练和加速推理的N卡 ngpu = torch.cuda.device_count() gpu_infos = [] mem = [] if_gpu_ok = False if torch.cuda.is_available() or ngpu != 0: for i in range(ngpu): gpu_name = torch.cuda.get_device_name(i) if any( value in gpu_name.upper() for value in [ "10", "16", "20", "30", "40", "A2", "A3", "A4", "P4", "A50", "500", "A60", "70", "80", "90", "M4", "T4", "TITAN", ] ): # A10#A100#V100#A40#P40#M40#K80#A4500 if_gpu_ok = True # 至少有一张能用的N卡 gpu_infos.append("%s\t%s" % (i, gpu_name)) mem.append( int( torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4 ) ) if if_gpu_ok and len(gpu_infos) > 0: gpu_info = "\n".join(gpu_infos) default_batch_size = min(mem) // 2 else: gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") default_batch_size = 1 gpus = "-".join([i[0] for i in gpu_infos]) 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" weight_root = os.getenv("weight_root") weight_uvr5_root = os.getenv("weight_uvr5_root") index_root = os.getenv("index_root") names = [] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) index_paths = [] for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: index_paths.append("%s/%s" % (root, name)) uvr5_names = [] for name in os.listdir(weight_uvr5_root): if name.endswith(".pth") or "onnx" in name: uvr5_names.append(name.replace(".pth", "")) def change_choices(): names = [] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) index_paths = [] for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: index_paths.append("%s/%s" % (root, name)) audio_files=[] for filename in os.listdir("./audios"): if filename.endswith(('.wav','.mp3','.ogg')): audio_files.append('./audios/'+filename) return {"choices": sorted(names), "__type__": "update"}, { "choices": sorted(index_paths), "__type__": "update", }, {"choices": sorted(audio_files), "__type__": "update"} def clean(): return {"value": "", "__type__": "update"} def export_onnx(): from infer.modules.onnx.export import export_onnx as eo eo() sr_dict = { "32k": 32000, "40k": 40000, "48k": 48000, } def if_done(done, p): while 1: if p.poll() is 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() is None: flag = 0 sleep(0.5) break if flag == 1: break done[0] = True def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): 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() per = 3.0 if config.is_half else 3.7 cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % ( config.python_cmd, trainset_dir, sr, n_p, now_dir, exp_dir, config.noparallel, per, ) logger.info(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]: break with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: log = f.read() logger.info(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, version19, gpus_rmvpe): 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: if f0method != "rmvpe_gpu": cmd = ( '"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s' % ( config.python_cmd, now_dir, exp_dir, n_p, f0method, ) ) logger.info(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() else: if gpus_rmvpe != "-": gpus_rmvpe = gpus_rmvpe.split("-") leng = len(gpus_rmvpe) ps = [] for idx, n_g in enumerate(gpus_rmvpe): cmd = ( '"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s ' % ( config.python_cmd, leng, idx, n_g, now_dir, exp_dir, config.is_half, ) ) logger.info(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() else: cmd = ( config.python_cmd + ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" ' % ( now_dir, exp_dir, ) ) logger.info(cmd) p = Popen( cmd, shell=True, cwd=now_dir ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir p.wait() done = [True] 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]: break with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: log = f.read() logger.info(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 = ( '"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s' % ( config.python_cmd, config.device, leng, idx, n_g, now_dir, exp_dir, version19, ) ) logger.info(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]: break with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: log = f.read() logger.info(log) yield log def get_pretrained_models(path_str, f0_str, sr2): if_pretrained_generator_exist = os.access( "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK ) if_pretrained_discriminator_exist = os.access( "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK ) if not if_pretrained_generator_exist: logger.warn( "assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model", path_str, f0_str, sr2, ) if not if_pretrained_discriminator_exist: logger.warn( "assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model", path_str, f0_str, sr2, ) return ( "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2) if if_pretrained_generator_exist else "", "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) if if_pretrained_discriminator_exist else "", ) def change_sr2(sr2, if_f0_3, version19): path_str = "" if version19 == "v1" else "_v2" f0_str = "f0" if if_f0_3 else "" return get_pretrained_models(path_str, f0_str, sr2) def change_version19(sr2, if_f0_3, version19): path_str = "" if version19 == "v1" else "_v2" if sr2 == "32k" and version19 == "v1": sr2 = "40k" to_return_sr2 = ( {"choices": ["40k", "48k"], "__type__": "update", "value": sr2} if version19 == "v1" else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2} ) f0_str = "f0" if if_f0_3 else "" return ( *get_pretrained_models(path_str, f0_str, sr2), to_return_sr2, ) def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 path_str = "" if version19 == "v1" else "_v2" return ( {"visible": if_f0_3, "__type__": "update"}, *get_pretrained_models(path_str, "f0", 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, if_save_every_weights18, version19, ): # 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) feature_dir = ( "%s/3_feature256" % (exp_dir) if version19 == "v1" else "%s/3_feature768" % (exp_dir) ) if if_f0_3: 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(feature_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(feature_dir)] ) opt = [] for name in names: if if_f0_3: opt.append( "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" % ( gt_wavs_dir.replace("\\", "\\\\"), name, feature_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, feature_dir.replace("\\", "\\\\"), name, spk_id5, ) ) fea_dim = 256 if version19 == "v1" else 768 if if_f0_3: for _ in range(2): opt.append( "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) ) else: for _ in range(2): opt.append( "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" % (now_dir, sr2, now_dir, fea_dim, spk_id5) ) shuffle(opt) with open("%s/filelist.txt" % exp_dir, "w") as f: f.write("\n".join(opt)) logger.debug("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" logger.info("Use gpus: %s", str(gpus16)) if pretrained_G14 == "": logger.info("No pretrained Generator") if pretrained_D15 == "": logger.info("No pretrained Discriminator") if version19 == "v1" or sr2 == "40k": config_path = "v1/%s.json" % sr2 else: config_path = "v2/%s.json" % sr2 config_save_path = os.path.join(exp_dir, "config.json") if not pathlib.Path(config_save_path).exists(): with open(config_save_path, "w", encoding="utf-8") as f: json.dump( config.json_config[config_path], f, ensure_ascii=False, indent=4, sort_keys=True, ) f.write("\n") if gpus16: cmd = ( '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' % ( config.python_cmd, exp_dir1, sr2, 1 if if_f0_3 else 0, batch_size12, gpus16, total_epoch11, save_epoch10, "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "", "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "", 1 if if_save_latest13 == i18n("是") else 0, 1 if if_cache_gpu17 == i18n("是") else 0, 1 if if_save_every_weights18 == i18n("是") else 0, version19, ) ) else: cmd = ( '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' % ( config.python_cmd, exp_dir1, sr2, 1 if if_f0_3 else 0, batch_size12, total_epoch11, save_epoch10, "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "", "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "", 1 if if_save_latest13 == i18n("是") else 0, 1 if if_cache_gpu17 == i18n("是") else 0, 1 if if_save_every_weights18 == i18n("是") else 0, version19, ) ) logger.info(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, version19): # exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) exp_dir = "logs/%s" % (exp_dir1) os.makedirs(exp_dir, exist_ok=True) feature_dir = ( "%s/3_feature256" % (exp_dir) if version19 == "v1" else "%s/3_feature768" % (exp_dir) ) if not os.path.exists(feature_dir): 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！" infos = [] npys = [] for name in sorted(listdir_res): phone = np.load("%s/%s" % (feature_dir, name)) npys.append(phone) big_npy = np.concatenate(npys, 0) big_npy_idx = np.arange(big_npy.shape[0]) np.random.shuffle(big_npy_idx) big_npy = big_npy[big_npy_idx] if big_npy.shape[0] > 2e5: infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) yield "\n".join(infos) try: big_npy = ( MiniBatchKMeans( n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random", ) .fit(big_npy) .cluster_centers_ ) except: info = traceback.format_exc() logger.info(info) infos.append(info) yield "\n".join(infos) np.save("%s/total_fea.npy" % exp_dir, big_npy) n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) infos.append("%s,%s" % (big_npy.shape, n_ivf)) yield "\n".join(infos) index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf) infos.append("training") yield "\n".join(infos) index_ivf = faiss.extract_index_ivf(index) # index_ivf.nprobe = 1 index.train(big_npy) faiss.write_index( index, "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), ) infos.append("adding") yield "\n".join(infos) batch_size_add = 8192 for i in range(0, big_npy.shape[0], batch_size_add): index.add(big_npy[i : i + batch_size_add]) faiss.write_index( index, "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), ) infos.append( "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index" % (n_ivf, index_ivf.nprobe, exp_dir1, version19) ) # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19)) 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, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17, if_save_every_weights18, version19, gpus_rmvpe, ): infos = [] def get_info_str(strr): infos.append(strr) return "\n".join(infos) ####### step1:处理数据 yield get_info_str(i18n("step1:正在处理数据")) [get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)] ####### step2a:提取音高 yield get_info_str(i18n("step2:正在提取音高&正在提取特征")) [ get_info_str(_) for _ in extract_f0_feature( gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe ) ] ####### step3a:训练模型 yield get_info_str(i18n("step3a:正在训练模型")) 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, if_save_every_weights18, version19, ) yield get_info_str(i18n("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 [get_info_str(_) for _ in train_index(exp_dir1, version19)] yield get_info_str(i18n("全流程结束！")) # ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) def change_info_(ckpt_path): if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")): return {"__type__": "update"}, {"__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"] version = "v2" if ("version" in info and info["version"] == "v2") else "v1" return sr, str(f0), version except: traceback.print_exc() return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} F0GPUVisible = config.dml == False def change_f0_method(f0method8): if f0method8 == "rmvpe_gpu": visible = F0GPUVisible else: visible = False return {"visible": visible, "__type__": "update"} def find_model(): if len(names) > 0: vc.get_vc(sorted(names)[0],None,None) return sorted(names)[0] else: try: gr.Info("Do not forget to choose a model.") except: pass return '' def find_audios(index=False): audio_files=[] if not os.path.exists('./audios'): os.mkdir("./audios") for filename in os.listdir("./audios"): if filename.endswith(('.wav','.mp3','.ogg')): audio_files.append("./audios/"+filename) if index: if len(audio_files) > 0: return sorted(audio_files)[0] else: return "" elif len(audio_files) > 0: return sorted(audio_files) else: return [] def get_index(): if find_model() != '': chosen_model=sorted(names)[0].split(".")[0] logs_path="./logs/"+chosen_model if os.path.exists(logs_path): for file in os.listdir(logs_path): if file.endswith(".index"): return os.path.join(logs_path, file) return '' else: return '' def get_indexes(): indexes_list=[] for dirpath, dirnames, filenames in os.walk("./logs/"): for filename in filenames: if filename.endswith(".index"): indexes_list.append(os.path.join(dirpath,filename)) if len(indexes_list) > 0: return indexes_list else: return '' def save_wav(file): try: file_path=file.name shutil.move(file_path,'./audios') return './audios/'+os.path.basename(file_path) except AttributeError: try: new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' new_path='./audios/'+new_name shutil.move(file,new_path) return new_path except TypeError: return None def download_from_url(url, model): if url == '': return "URL cannot be left empty." if model =='': return "You need to name your model. For example: My-Model" url = url.strip() zip_dirs = ["zips", "unzips"] for directory in zip_dirs: if os.path.exists(directory): shutil.rmtree(directory) os.makedirs("zips", exist_ok=True) os.makedirs("unzips", exist_ok=True) zipfile = model + '.zip' zipfile_path = './zips/' + zipfile try: if "drive.google.com" in url: subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) elif "mega.nz" in url: m = Mega() m.download_url(url, './zips') else: subprocess.run(["wget", url, "-O", zipfile_path]) for filename in os.listdir("./zips"): if filename.endswith(".zip"): zipfile_path = os.path.join("./zips/",filename) shutil.unpack_archive(zipfile_path, "./unzips", 'zip') else: return "No zipfile found." for root, dirs, files in os.walk('./unzips'): for file in files: file_path = os.path.join(root, file) if file.endswith(".index"): os.mkdir(f'./logs/{model}') shutil.copy2(file_path,f'./logs/{model}') elif "G_" not in file and "D_" not in file and file.endswith(".pth"): shutil.copy(file_path,f'./assets/weights/{model}.pth') shutil.rmtree("zips") shutil.rmtree("unzips") return "Success." except: return "There's been an error." def upload_to_dataset(files, dir): if dir == '': dir = './dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") if not os.path.exists(dir): os.makedirs(dir) for file in files: path=file.name shutil.copy2(path,dir) try: gr.Info(i18n("处理数据")) except: pass return i18n("处理数据"), {"value":dir,"__type__":"update"} def download_model_files(model): model_found = False index_found = False if os.path.exists(f'./assets/weights/{model}.pth'): model_found = True if os.path.exists(f'./logs/{model}'): for file in os.listdir(f'./logs/{model}'): if file.endswith('.index') and 'added' in file: log_file = file index_found = True if model_found and index_found: return [f'./assets/weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" elif model_found and not index_found: return f'./assets/weights/{model}.pth', "Could not find Index file." elif index_found and not model_found: return f'./logs/{model}/{log_file}', f'Make sure the Voice Name is correct. I could not find {model}.pth' else: return None, f'Could not find {model}.pth or corresponding Index file.' with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app: with gr.Row(): gr.HTML("image") 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), value=find_model()) refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary") #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], api_name="infer_clean" #) vc_transform0 = gr.Number( label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 ) but0 = gr.Button(i18n("转换"), variant="primary") with gr.Row(): with gr.Column(): with gr.Row(): dropbox = gr.File(label="Drop your audio here & hit the Reload button.") with gr.Row(): record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") with gr.Row(): input_audio0 = gr.Dropdown( label=i18n("输入待处理音频文件路径(默认是正确格式示例)"), value=find_audios(True), choices=find_audios() ) record_button.change(fn=save_wav, inputs=[record_button], outputs=[input_audio0]) dropbox.upload(fn=save_wav, inputs=[dropbox], outputs=[input_audio0]) with gr.Column(): with gr.Accordion(label=i18n("自动检测index路径,下拉式选择(dropdown)"), open=False): file_index2 = gr.Dropdown( label=i18n("自动检测index路径,下拉式选择(dropdown)"), choices=get_indexes(), interactive=True, value=get_index() ) index_rate1 = gr.Slider( minimum=0, maximum=1, label=i18n("检索特征占比"), value=0.66, interactive=True, ) vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) with gr.Accordion(label=i18n("常规设置"), open=False): 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,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" ), choices=["pm", "harvest", "crepe", "rmvpe"] if config.dml == False else ["pm", "harvest", "rmvpe"], value="rmvpe", interactive=True, ) filter_radius0 = gr.Slider( minimum=0, maximum=7, label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), value=3, step=1, interactive=True, ) resample_sr0 = gr.Slider( minimum=0, maximum=48000, label=i18n("后处理重采样至最终采样率，0为不进行重采样"), value=0, step=1, interactive=True, visible=False ) rms_mix_rate0 = gr.Slider( minimum=0, maximum=1, label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), value=0.21, interactive=True, ) protect0 = gr.Slider( minimum=0, maximum=0.5, label=i18n( "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" ), value=0.33, step=0.01, interactive=True, ) file_index1 = gr.Textbox( label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), value="", interactive=True, visible=False ) refresh_button.click( fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio0], api_name="infer_refresh", ) # 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, # ) with gr.Row(): 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"), visible=False) with gr.Row(): vc_output1 = gr.Textbox(label=i18n("输出信息")) but0.click( vc.vc_single, [ spk_item, input_audio0, vc_transform0, f0_file, f0method0, file_index1, file_index2, # file_big_npy1, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, ], [vc_output1, vc_output2], api_name="infer_convert", ) with gr.Row(): with gr.Accordion(open=False, label=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(): opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") vc_transform1 = gr.Number( label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 ) 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,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" ), choices=["pm", "harvest", "crepe", "rmvpe"] if config.dml == False else ["pm", "harvest", "rmvpe"], value="pm", interactive=True, ) with gr.Row(): filter_radius1 = gr.Slider( minimum=0, maximum=7, label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), value=3, step=1, interactive=True, visible=False ) with gr.Row(): file_index3 = gr.Textbox( label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), value="", interactive=True, visible=False ) file_index4 = gr.Dropdown( label=i18n("自动检测index路径,下拉式选择(dropdown)"), choices=sorted(index_paths), interactive=True, visible=False ) refresh_button.click( fn=lambda: change_choices()[1], inputs=[], outputs=file_index4, api_name="infer_refresh_batch", ) # 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, visible=False ) with gr.Row(): resample_sr1 = gr.Slider( minimum=0, maximum=48000, label=i18n("后处理重采样至最终采样率，0为不进行重采样"), value=0, step=1, interactive=True, visible=False ) rms_mix_rate1 = gr.Slider( minimum=0, maximum=1, label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), value=0.21, interactive=True, ) protect1 = gr.Slider( minimum=0, maximum=0.5, label=i18n( "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" ), value=0.33, step=0.01, interactive=True, ) with gr.Row(): 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="./audios", ) 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.Row(): format1 = gr.Radio( label=i18n("导出文件格式"), choices=["wav", "flac", "mp3", "m4a"], value="wav", interactive=True, ) but1 = gr.Button(i18n("转换"), variant="primary") vc_output3 = gr.Textbox(label=i18n("输出信息")) but1.click( vc.vc_multi, [ spk_item, dir_input, opt_input, inputs, vc_transform1, f0method1, file_index1, file_index2, # file_big_npy2, index_rate1, filter_radius1, resample_sr1, rms_mix_rate1, protect1, format1, ], [vc_output3], api_name="infer_convert_batch", ) sid0.change( fn=vc.get_vc, inputs=[sid0, protect0, protect1], outputs=[spk_item, protect0, protect1, file_index2, file_index4], ) with gr.TabItem("Download Model"): with gr.Row(): url=gr.Textbox(label="Enter the URL to the Model:") with gr.Row(): model = gr.Textbox(label="Name your model:") download_button=gr.Button("Download") with gr.Row(): status_bar=gr.Textbox(label="") download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) with gr.Row(): gr.Markdown( """ ❤️ If you use this and like it, help me keep it.❤️ https://paypal.me/lesantillan """ ) with gr.TabItem(i18n("训练")): with gr.Row(): with gr.Column(): exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="My-Voice") np7 = gr.Slider( minimum=0, maximum=config.n_cpu, step=1, label=i18n("提取音高和处理数据使用的CPU进程数"), value=int(np.ceil(config.n_cpu / 1.5)), interactive=True, ) sr2 = gr.Radio( label=i18n("目标采样率"), choices=["40k", "48k"], value="40k", interactive=True, visible=False ) 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=[True, False], value=True, interactive=True, visible=False ) version19 = gr.Radio( label=i18n("版本"), choices=["v1", "v2"], value="v2", interactive=True, visible=False, ) trainset_dir4 = gr.Textbox( label=i18n("输入训练文件夹路径"), value='./dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") ) easy_uploader = gr.Files(label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),file_types=['audio']) but1 = gr.Button(i18n("处理数据"), variant="primary") info1 = gr.Textbox(label=i18n("输出信息"), value="") easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1, trainset_dir4]) 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, visible=F0GPUVisible, ) gpu_info9 = gr.Textbox( label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible ) spk_id5 = gr.Slider( minimum=0, maximum=4, step=1, label=i18n("请指定说话人id"), value=0, interactive=True, visible=False ) but1.click( preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1], api_name="train_preprocess", ) with gr.Column(): 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,rmvpe效果最好且微吃CPU/GPU" ), choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], value="rmvpe_gpu", interactive=True, ) gpus_rmvpe = gr.Textbox( label=i18n( "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程" ), value="%s-%s" % (gpus, gpus), interactive=True, visible=F0GPUVisible, ) but2 = gr.Button(i18n("特征提取"), variant="primary") info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) f0method8.change( fn=change_f0_method, inputs=[f0method8], outputs=[gpus_rmvpe], ) but2.click( extract_f0_feature, [ gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe, ], [info2], api_name="train_extract_f0_feature", ) with gr.Column(): total_epoch11 = gr.Slider( minimum=2, maximum=1000, step=1, label=i18n("总训练轮数total_epoch"), value=150, interactive=True, ) 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="0", interactive=True, visible=True ) but3 = gr.Button(i18n("训练模型"), variant="primary") but4 = gr.Button(i18n("训练特征索引"), variant="primary") info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) with gr.Accordion(label=i18n("常规设置"), open=False): save_epoch10 = gr.Slider( minimum=1, maximum=50, step=1, label=i18n("保存频率save_every_epoch"), value=25, interactive=True, ) batch_size12 = gr.Slider( minimum=1, maximum=40, step=1, label=i18n("每张显卡的batch_size"), value=default_batch_size, 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=[i18n("是"), i18n("否")], value=i18n("是"), interactive=True, visible=False ) 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=[i18n("是"), i18n("否")], value=i18n("否"), interactive=True, ) if_save_every_weights18 = gr.Radio( label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), choices=[i18n("是"), i18n("否")], value=i18n("是"), interactive=True, ) with gr.Row(): download_model = gr.Button('5.Download Model') with gr.Row(): model_files = gr.Files(label='Your Model and Index file can be downloaded here:') download_model.click(fn=download_model_files, inputs=[exp_dir1], outputs=[model_files, info3]) with gr.Row(): pretrained_G14 = gr.Textbox( label=i18n("加载预训练底模G路径"), value="assets/pretrained_v2/f0G40k.pth", interactive=True, visible=False ) pretrained_D15 = gr.Textbox( label=i18n("加载预训练底模D路径"), value="assets/pretrained_v2/f0D40k.pth", interactive=True, visible=False ) sr2.change( change_sr2, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15], ) version19.change( change_version19, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15, sr2], ) if_f0_3.change( change_f0, [if_f0_3, sr2, version19], [f0method8, pretrained_G14, pretrained_D15], ) with gr.Row(): but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) 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, if_save_every_weights18, version19, ], info3, api_name="train_start", ) but4.click(train_index, [exp_dir1, version19], info3) but5.click( train1key, [ exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17, if_save_every_weights18, version19, gpus_rmvpe, ], info3, api_name="train_start_all", ) if config.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 config.noautoopen, server_port=config.listen_port, quiet=True, )