import zipfile, glob, subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np from mega import Mega os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" import threading from time import sleep from subprocess import Popen import faiss from random import shuffle import json, datetime, requests from gtts import gTTS now_dir = os.getcwd() sys.path.append(now_dir) 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, "weights"), exist_ok=True) os.environ["TEMP"] = tmp warnings.filterwarnings("ignore") torch.manual_seed(114514) from i18n import I18nAuto import ffmpeg #from MDXNet import MDXNetDereverb i18n = I18nAuto() #i18n.print() # 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 (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 ( "10" in gpu_name or "16" 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 "A60" in gpu_name.upper() or "70" in gpu_name or "80" in gpu_name or "90" in gpu_name or "M4" in gpu_name.upper() or "T4" in gpu_name.upper() or "TITAN" in gpu_name.upper() ): # 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 == True 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]) from infer_pack.models import (SynthesizerTrnMs256NSFsid,SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid,SynthesizerTrnMs768NSFsid_nono) import soundfile as sf from fairseq import checkpoint_utils import gradio as gr import logging from vc_infer_pipeline import VC from config import Config from infer_uvr5 import _audio_pre_, _audio_pre_new from my_utils import load_audio from train.process_ckpt import show_info, change_info, merge, extract_small_model config = Config() # from trainset_preprocess_pipeline import PreProcess logging.getLogger("numba").setLevel(logging.WARNING) 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(config.device) if config.is_half: hubert_model = hubert_model.half() else: hubert_model = hubert_model.float() hubert_model.eval() weight_root = "weights" weight_uvr5_root = "uvr5_weights" index_root = "logs" 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 vc_single( sid, input_audio_path, f0_up_key, f0_file, f0_method, file_index, #file_index2, # file_big_npy, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, crepe_hop_length, root_location='./audios' ): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 global tgt_sr, net_g, vc, hubert_model, version if input_audio_path is None: gr.Warning("You need to provide the path to an audio file") return "You need to provide the path to an audio file", None full_audio_path = root_location + '/' + input_audio_path if not os.path.exists(full_audio_path): gr.Warning(f"Could not find that file in audios/{input_audio_path}") return f"Could not find that file in audios/{input_audio_path}", None f0_up_key = int(f0_up_key) try: audio = load_audio(full_audio_path, 16000) audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max 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, input_audio_path, times, f0_up_key, f0_method, file_index, # file_big_npy, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, crepe_hop_length, f0_file=f0_file, ) if resample_sr >= 16000 and tgt_sr != resample_sr: tgt_sr = resample_sr index_info = ( "Using index:%s." % file_index if os.path.exists(file_index) else "Index not used." ) gr.Info('Success.') return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( index_info, times[0], times[1], times[2], ), (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_index2, # file_big_npy, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, format1, crepe_hop_length, ): 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_index2, # file_big_npy, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, crepe_hop_length ) if "Success" in info: try: tgt_sr, audio_opt = opt if format1 in ["wav", "flac"]: sf.write( "%s/%s.%s" % (opt_root, os.path.basename(path), format1), audio_opt, tgt_sr, ) else: path = "%s/%s.wav" % (opt_root, os.path.basename(path)) sf.write( path, audio_opt, tgt_sr, ) if os.path.exists(path): os.system( "ffmpeg -i %s -vn %s -q:a 2 -y" % (path, path[:-4] + ".%s" % format1) ) 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, agg, format0): 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(" ") ) if model_name == "onnx_dereverb_By_FoxJoy": pre_fun = MDXNetDereverb(15) else: func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new pre_fun = func( agg=int(agg), model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), device=config.device, is_half=config.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 path in paths: inp_path = os.path.join(inp_root, path) need_reformat = 1 done = 0 try: info = ffmpeg.probe(inp_path, cmd="ffprobe") if ( info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100" ): need_reformat = 0 pre_fun._path_audio_( inp_path, save_root_ins, save_root_vocal, format0 ) done = 1 except: need_reformat = 1 traceback.print_exc() if need_reformat == 1: tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path)) os.system( "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" % (inp_path, tmp_path) ) inp_path = tmp_path try: if done == 0: pre_fun._path_audio_( inp_path, save_root_ins, save_root_vocal, format0 ) 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: if model_name == "onnx_dereverb_By_FoxJoy": del pre_fun.pred.model del pre_fun.pred.model_ else: 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, version if sid == "" or 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) version = cpt.get("version", "v1") if version == "v1": if if_f0 == 1: net_g = SynthesizerTrnMs256NSFsid( *cpt["config"], is_half=config.is_half ) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid( *cpt["config"], is_half=config.is_half ) else: net_g = SynthesizerTrnMs768NSFsid_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) version = cpt.get("version", "v1") if version == "v1": if if_f0 == 1: net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del net_g.enc_q print(net_g.load_state_dict(cpt["weight"], strict=False)) net_g.eval().to(config.device) if config.is_half: net_g = net_g.half() else: net_g = net_g.float() vc = VC(tgt_sr, config) n_spk = cpt["config"][-3] return {"visible": False, "maximum": n_spk, "__type__": "update"} 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)) return {"choices": sorted(names), "__type__": "update"}, { "choices": sorted(index_paths), "__type__": "update", } def clean(): return {"value": "", "__type__": "update"} 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): 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 = ( config.python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " % (trainset_dir, sr, n_p, now_dir, exp_dir) + str(config.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) gr.Info("End Preprocess means you're done with this step. Go to step 2.") 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, echl): 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: cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( now_dir, exp_dir, n_p, f0method, echl, ) 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) gr.Info('Wait to see "all feature done" in the status box to know it finished.') 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 = ( config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" % ( config.device, leng, idx, n_g, now_dir, exp_dir, version19, ) ) 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, version19): path_str = "" if version19 == "v1" else "_v2" f0_str = "f0" if if_f0_3 else "" if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) if (if_pretrained_generator_exist == False): print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") if (if_pretrained_discriminator_exist == False): print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") return ( ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", {"visible": True, "__type__": "update"} ) def change_version19(sr2, if_f0_3, version19): path_str = "" if version19 == "v1" else "_v2" f0_str = "f0" if if_f0_3 else "" if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) if (if_pretrained_generator_exist == False): print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") if (if_pretrained_discriminator_exist == False): print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") return ( ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", ) def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 path_str = "" if version19 == "v1" else "_v2" if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK) if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK) if (if_pretrained_generator_exist == False): print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") if (if_pretrained_discriminator_exist == False): print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") if if_f0_3: return ( {"visible": True, "__type__": "update"}, "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "", "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "", ) return ( {"visible": False, "__type__": "update"}, ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "", ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "", ) # 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)) 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 pretrained_G14 == "": print("no pretrained Generator") if pretrained_D15 == "": print("no pretrained Discriminator") if gpus16: cmd = ( config.python_cmd + " train_nsf_sim_cache_sid_load_pretrain.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" % ( 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 = ( config.python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" % ( exp_dir1, sr2, 1 if if_f0_3 else 0, batch_size12, total_epoch11, save_epoch10, ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b", ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b", 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, ) ) print(cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() gr.Warning('Done! Check your console in Colab to see if it trained successfully.') return 'Done! Check your console in Colab to see if it trained successfully.' # but4.click(train_index, [exp_dir1], info3) def train_index(exp_dir1, version19): exp_dir = "%s/logs/%s" % (now_dir, 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 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) big_npy_idx = np.arange(big_npy.shape[0]) np.random.shuffle(big_npy_idx) big_npy = big_npy[big_npy_idx] np.save("%s/total_fea.npy" % exp_dir, big_npy) # n_ivf = big_npy.shape[0] // 39 n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) infos = [] 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), ) # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,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)) gr.Info('Successfully trained the index file!') 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, echl ): infos = [] def get_info_str(strr): infos.append(strr) return "\n".join(infos) model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1) preprocess_log_path = "%s/preprocess.log" % model_log_dir extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir feature_dir = ( "%s/3_feature256" % model_log_dir if version19 == "v1" else "%s/3_feature768" % model_log_dir ) os.makedirs(model_log_dir, exist_ok=True) #########step1:处理数据 open(preprocess_log_path, "w").close() cmd = ( config.python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s " % (trainset_dir4, sr_dict[sr2], np7, model_log_dir) + str(config.noparallel) ) yield get_info_str(i18n("step1:正在处理数据")) yield get_info_str(cmd) p = Popen(cmd, shell=True) p.wait() with open(preprocess_log_path, "r") as f: print(f.read()) #########step2a:提取音高 open(extract_f0_feature_log_path, "w") if if_f0_3: yield get_info_str("step2a:正在提取音高") cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % ( model_log_dir, np7, f0method8, echl ) yield get_info_str(cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() with open(extract_f0_feature_log_path, "r") as f: print(f.read()) else: yield get_info_str(i18n("step2a:无需提取音高")) #######step2b:提取特征 yield get_info_str(i18n("step2b:正在提取特征")) gpus = gpus16.split("-") leng = len(gpus) ps = [] for idx, n_g in enumerate(gpus): cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % ( config.device, leng, idx, n_g, model_log_dir, version19, ) 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(extract_f0_feature_log_path, "r") as f: print(f.read()) #######step3a:训练模型 yield get_info_str(i18n("step3a:正在训练模型")) # 生成filelist if if_f0_3: f0_dir = "%s/2a_f0" % model_log_dir f0nsf_dir = "%s/2b-f0nsf" % model_log_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" % model_log_dir, "w") as f: f.write("\n".join(opt)) yield get_info_str("write filelist done") if gpus16: cmd = ( config.python_cmd +" train_nsf_sim_cache_sid_load_pretrain.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" % ( 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 = ( config.python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" % ( 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, ) ) yield get_info_str(cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() 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 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) big_npy_idx = np.arange(big_npy.shape[0]) np.random.shuffle(big_npy_idx) big_npy = big_npy[big_npy_idx] np.save("%s/total_fea.npy" % model_log_dir, big_npy) # n_ivf = big_npy.shape[0] // 39 n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) yield get_info_str("training index") 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" % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), ) yield get_info_str("adding index") 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" % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), ) yield get_info_str( "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" % (n_ivf, index_ivf.nprobe, 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 ( os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")) == False ): 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"} from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM def export_onnx(ModelPath, ExportedPath, MoeVS=True): cpt = torch.load(ModelPath, map_location="cpu") cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels，为768Vec做准备 test_phone = torch.rand(1, 200, hidden_channels) # hidden unit test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用） test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹） test_pitchf = torch.rand(1, 200) # nsf基频 test_ds = torch.LongTensor([0]) # 说话人ID test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子） device = "cpu" # 导出时设备（不影响使用模型） net_g = SynthesizerTrnMsNSFsidM( *cpt["config"], is_half=False,version=cpt.get("version","v1") ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16） net_g.load_state_dict(cpt["weight"], strict=False) input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] output_names = [ "audio", ] # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 torch.onnx.export( net_g, ( test_phone.to(device), test_phone_lengths.to(device), test_pitch.to(device), test_pitchf.to(device), test_ds.to(device), test_rnd.to(device), ), ExportedPath, dynamic_axes={ "phone": [1], "pitch": [1], "pitchf": [1], "rnd": [2], }, do_constant_folding=False, opset_version=16, verbose=False, input_names=input_names, output_names=output_names, ) return "Finished" #region Mangio-RVC-Fork CLI App import re as regex import scipy.io.wavfile as wavfile cli_current_page = "HOME" def cli_split_command(com): exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)' split_array = regex.findall(exp, com) split_array = [group[0] if group[0] else group[1] for group in split_array] return split_array def execute_generator_function(genObject): for _ in genObject: pass def cli_infer(com): # get VC first com = cli_split_command(com) model_name = com[0] source_audio_path = com[1] output_file_name = com[2] feature_index_path = com[3] f0_file = None # Not Implemented Yet # Get parameters for inference speaker_id = int(com[4]) transposition = float(com[5]) f0_method = com[6] crepe_hop_length = int(com[7]) harvest_median_filter = int(com[8]) resample = int(com[9]) mix = float(com[10]) feature_ratio = float(com[11]) protection_amnt = float(com[12]) print("Mangio-RVC-Fork Infer-CLI: Starting the inference...") vc_data = get_vc(model_name) print(vc_data) print("Mangio-RVC-Fork Infer-CLI: Performing inference...") conversion_data = vc_single( speaker_id, source_audio_path, transposition, f0_file, f0_method, feature_index_path, #feature_index_path, feature_ratio, harvest_median_filter, resample, mix, protection_amnt, crepe_hop_length, ) if "Success." in conversion_data[0]: print("Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..." % ('audio-outputs', output_file_name)) wavfile.write('%s/%s' % ('audio-outputs', output_file_name), conversion_data[1][0], conversion_data[1][1]) print("Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s" % ('audio-outputs', output_file_name)) else: print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ") print(conversion_data[0]) def cli_pre_process(com): com = cli_split_command(com) model_name = com[0] trainset_directory = com[1] sample_rate = com[2] num_processes = int(com[3]) print("Mangio-RVC-Fork Pre-process: Starting...") generator = preprocess_dataset( trainset_directory, model_name, sample_rate, num_processes ) execute_generator_function(generator) print("Mangio-RVC-Fork Pre-process: Finished") def cli_extract_feature(com): com = cli_split_command(com) model_name = com[0] gpus = com[1] num_processes = int(com[2]) has_pitch_guidance = True if (int(com[3]) == 1) else False f0_method = com[4] crepe_hop_length = int(com[5]) version = com[6] # v1 or v2 print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance)) print("Mangio-RVC-CLI: Extract Feature Version: " + str(version)) print("Mangio-RVC-Fork Feature Extraction: Starting...") generator = extract_f0_feature( gpus, num_processes, f0_method, has_pitch_guidance, model_name, version, crepe_hop_length ) execute_generator_function(generator) print("Mangio-RVC-Fork Feature Extraction: Finished") def cli_train(com): com = cli_split_command(com) model_name = com[0] sample_rate = com[1] has_pitch_guidance = True if (int(com[2]) == 1) else False speaker_id = int(com[3]) save_epoch_iteration = int(com[4]) total_epoch = int(com[5]) # 10000 batch_size = int(com[6]) gpu_card_slot_numbers = com[7] if_save_latest = i18n("是") if (int(com[8]) == 1) else i18n("否") if_cache_gpu = i18n("是") if (int(com[9]) == 1) else i18n("否") if_save_every_weight = i18n("是") if (int(com[10]) == 1) else i18n("否") version = com[11] pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/" g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate) d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate) print("Mangio-RVC-Fork Train-CLI: Training...") click_train( model_name, sample_rate, has_pitch_guidance, speaker_id, save_epoch_iteration, total_epoch, batch_size, if_save_latest, g_pretrained_path, d_pretrained_path, gpu_card_slot_numbers, if_cache_gpu, if_save_every_weight, version ) def cli_train_feature(com): com = cli_split_command(com) model_name = com[0] version = com[1] print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait") generator = train_index( model_name, version ) execute_generator_function(generator) print("Mangio-RVC-Fork Train Feature Index-CLI: Done!") def cli_extract_model(com): com = cli_split_command(com) model_path = com[0] save_name = com[1] sample_rate = com[2] has_pitch_guidance = com[3] info = com[4] version = com[5] extract_small_model_process = extract_small_model( model_path, save_name, sample_rate, has_pitch_guidance, info, version ) if extract_small_model_process == "Success.": print("Mangio-RVC-Fork Extract Small Model: Success!") else: print(str(extract_small_model_process)) print("Mangio-RVC-Fork Extract Small Model: Failed!") def print_page_details(): if cli_current_page == "HOME": print(" go home : Takes you back to home with a navigation list.") print(" go infer : Takes you to inference command execution.\n") print(" go pre-process : Takes you to training step.1) pre-process command execution.") print(" go extract-feature : Takes you to training step.2) extract-feature command execution.") print(" go train : Takes you to training step.3) being or continue training command execution.") print(" go train-feature : Takes you to the train feature index command execution.\n") print(" go extract-model : Takes you to the extract small model command execution.") elif cli_current_page == "INFER": print(" arg 1) model name with .pth in ./weights: mi-test.pth") print(" arg 2) source audio path: myFolder\\MySource.wav") print(" arg 3) output file name to be placed in './audio-outputs': MyTest.wav") print(" arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index") print(" arg 5) speaker id: 0") print(" arg 6) transposition: 0") print(" arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny)") print(" arg 8) crepe hop length: 160") print(" arg 9) harvest median filter radius: 3 (0-7)") print(" arg 10) post resample rate: 0") print(" arg 11) mix volume envelope: 1") print(" arg 12) feature index ratio: 0.78 (0-1)") print(" arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.) \n") print("Example: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33") elif cli_current_page == "PRE-PROCESS": print(" arg 1) Model folder name in ./logs: mi-test") print(" arg 2) Trainset directory: mydataset (or) E:\\my-data-set") print(" arg 3) Sample rate: 40k (32k, 40k, 48k)") print(" arg 4) Number of CPU threads to use: 8 \n") print("Example: mi-test mydataset 40k 24") elif cli_current_page == "EXTRACT-FEATURE": print(" arg 1) Model folder name in ./logs: mi-test") print(" arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)") print(" arg 3) Number of CPU threads to use: 8") print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") print(" arg 5) f0 Method: harvest (pm, harvest, dio, crepe)") print(" arg 6) Crepe hop length: 128") print(" arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n") print("Example: mi-test 0 24 1 harvest 128 v2") elif cli_current_page == "TRAIN": print(" arg 1) Model folder name in ./logs: mi-test") print(" arg 2) Sample rate: 40k (32k, 40k, 48k)") print(" arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") print(" arg 4) speaker id: 0") print(" arg 5) Save epoch iteration: 50") print(" arg 6) Total epochs: 10000") print(" arg 7) Batch size: 8") print(" arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)") print(" arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)") print(" arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)") print(" arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)") print(" arg 12) Model architecture version: v2 (use either v1 or v2)\n") print("Example: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2") elif cli_current_page == "TRAIN-FEATURE": print(" arg 1) Model folder name in ./logs: mi-test") print(" arg 2) Model architecture version: v2 (use either v1 or v2)\n") print("Example: mi-test v2") elif cli_current_page == "EXTRACT-MODEL": print(" arg 1) Model Path: logs/mi-test/G_168000.pth") print(" arg 2) Model save name: MyModel") print(" arg 3) Sample rate: 40k (32k, 40k, 48k)") print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") print(' arg 5) Model information: "My Model"') print(" arg 6) Model architecture version: v2 (use either v1 or v2)\n") print('Example: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2') print("") def change_page(page): global cli_current_page cli_current_page = page return 0 def execute_command(com): if com == "go home": return change_page("HOME") elif com == "go infer": return change_page("INFER") elif com == "go pre-process": return change_page("PRE-PROCESS") elif com == "go extract-feature": return change_page("EXTRACT-FEATURE") elif com == "go train": return change_page("TRAIN") elif com == "go train-feature": return change_page("TRAIN-FEATURE") elif com == "go extract-model": return change_page("EXTRACT-MODEL") else: if com[:3] == "go ": print("page '%s' does not exist!" % com[3:]) return 0 if cli_current_page == "INFER": cli_infer(com) elif cli_current_page == "PRE-PROCESS": cli_pre_process(com) elif cli_current_page == "EXTRACT-FEATURE": cli_extract_feature(com) elif cli_current_page == "TRAIN": cli_train(com) elif cli_current_page == "TRAIN-FEATURE": cli_train_feature(com) elif cli_current_page == "EXTRACT-MODEL": cli_extract_model(com) def cli_navigation_loop(): while True: print("You are currently in '%s':" % cli_current_page) print_page_details() command = input("%s: " % cli_current_page) try: execute_command(command) except: print(traceback.format_exc()) if(config.is_cli): print("\n\nMangio-RVC-Fork v2 CLI App!\n") print("Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n") cli_navigation_loop() #endregion #region RVC WebUI App def get_presets(): data = None with open('../inference-presets.json', 'r') as file: data = json.load(file) preset_names = [] for preset in data['presets']: preset_names.append(preset['name']) return preset_names def change_choices2(): audio_files=[] for filename in os.listdir("./audios"): if filename.endswith(('.wav','.mp3','.ogg')): audio_files.append(filename) return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} 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(filename) def get_index(): if check_for_name() != '': 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 get_name(): if len(audio_files) > 0: return sorted(audio_files)[0] else: return '' def save_to_wav(record_button): if record_button is None: pass else: path_to_file=record_button new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' new_path='./audios/'+new_name shutil.move(path_to_file,new_path) return os.path.basename(new_path) def save_to_wav2(dropbox): file_path=dropbox.name shutil.move(file_path,'./audios') return os.path.basename(file_path) def match_index(sid0): folder=sid0.split(".")[0] parent_dir="./logs/"+folder if os.path.exists(parent_dir): for filename in os.listdir(parent_dir): if filename.endswith(".index"): index_path=os.path.join(parent_dir,filename) return index_path else: return '' def check_for_name(): if len(names) > 0: return sorted(names)[0] else: return '' 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'./weights/{model}.pth') shutil.rmtree("zips") shutil.rmtree("unzips") return "Success." except: return "There's been an error." def success_message(face): return f'{face.name} has been uploaded.', 'None' def mouth(size, face, voice, faces): if size == 'Half': size = 2 else: size = 1 if faces == 'None': character = face.name else: if faces == 'Ben Shapiro': character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4' elif faces == 'Andrew Tate': character = '/content/wav2lip-HD/inputs/tate-7.mp4' command = "python inference.py " \ "--checkpoint_path checkpoints/wav2lip.pth " \ f"--face {character} " \ f"--audio {voice} " \ "--pads 0 20 0 0 " \ "--outfile /content/wav2lip-HD/outputs/result.mp4 " \ "--fps 24 " \ f"--resize_factor {size}" process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master') stdout, stderr = process.communicate() return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.' eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli'] eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O'] chosen_voice = dict(zip(eleven_voices, eleven_voices_ids)) def elevenTTS(xiapi, text, id, lang): if xiapi!= '' and id !='': choice = chosen_voice[id] CHUNK_SIZE = 1024 url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}" headers = { "Accept": "audio/mpeg", "Content-Type": "application/json", "xi-api-key": xiapi } if lang == 'en': data = { "text": text, "model_id": "eleven_monolingual_v1", "voice_settings": { "stability": 0.5, "similarity_boost": 0.5 } } else: data = { "text": text, "model_id": "eleven_multilingual_v1", "voice_settings": { "stability": 0.5, "similarity_boost": 0.5 } } response = requests.post(url, json=data, headers=headers) with open('./temp_eleven.mp3', 'wb') as f: for chunk in response.iter_content(chunk_size=CHUNK_SIZE): if chunk: f.write(chunk) aud_path = save_to_wav('./temp_eleven.mp3') return aud_path, aud_path else: tts = gTTS(text, lang=lang) tts.save('./temp_gTTS.mp3') aud_path = save_to_wav('./temp_gTTS.mp3') return aud_path, aud_path def upload_to_dataset(files, dir): gr.Warning('Wait until your data is uploaded...') if dir == '': dir = './dataset' if not os.path.exists(dir): os.makedirs(dir) count = 0 for file in files: path=file.name shutil.copy2(path,dir) count += 1 gr.Info(f'Done! {count} files were uploaded. Now click "1.Process The Dataset."') return f' {count} files uploaded to {dir}.' def zip_downloader(model): if not os.path.exists(f'./weights/{model}.pth'): return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth' index_found = False for file in os.listdir(f'./logs/{model}'): if file.endswith('.index') and 'added' in file: log_file = file index_found = True if index_found: return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" else: return f'./weights/{model}.pth', "Could not find Index file." def fast(filepath, spk_item, vc_transform0,f0method0,file_index1,index_rate1,filter_radius0, resample_sr0,rms_mix_rate0, protect0, hop): source_audio_path = filepath output_file_name = os.path.basename(filepath) conversion_data = vc_single( spk_item, source_audio_path, vc_transform0, f0_file, f0method0, file_index1, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, hop, "" ) if "Success." in conversion_data[0]: wavfile.write(f'audio-outputs/{output_file_name}', conversion_data[1][0], conversion_data[1][1]) return f"audio-outputs/{output_file_name}", None, conversion_data[0] else: return gr.update(visible=True), None, conversion_data[0] with gr.Blocks(theme=gr.themes.Base()) as app: with gr.Tabs(): with gr.TabItem("Работа с моделью"): gr.HTML("

RVC V2 by https://www.youtube.com/@ba1yya 💻

") # Inference Preset Row # with gr.Row(): # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) # mangio_preset_name_save = gr.Textbox( # label="Your preset name" # ) # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") # Other RVC stuff with gr.Row(): sid0 = gr.Dropdown(label="1.Выберете модель.", choices=sorted(names), value=check_for_name()) refresh_button = gr.Button("Обновить", variant="primary") if check_for_name() != '': get_vc(sorted(names)[0]) vc_transform0 = gr.Number(label="Необязательно: здесь вы можете изменить высоту тона или оставить значение 0.", value=0) #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], ) but0 = gr.Button("Обработка", variant="primary") with gr.Row(): with gr.Column(): with gr.Row(): dropbox = gr.File(label="Перетащите сюда свой аудиофайл и нажмите кнопку «Обновить».") with gr.Row(): record_button=gr.Audio(source="microphone", label="ИЛИ Запись звука.", type="filepath") with gr.Row(): input_audio0 = gr.Dropdown( label="2.Выберите аудио.", value="someguy.mp3", choices=audio_files ) dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0]) refresh_button2 = gr.Button("Обновить", variant="primary", size='sm') record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0]) with gr.Row(): with gr.Accordion('Текст в речь', open=False): with gr.Column(): lang = gr.Radio(label='Китайский и японский языки в настоящее время не работают с ElevenLabs.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en') api_box = gr.Textbox(label="Введите свой ключ API для ElevenLabs или оставьте пустым, чтобы использовать GoogleTTS", value='') elevenid=gr.Dropdown(label="Голос:", choices=eleven_voices) with gr.Column(): tfs = gr.Textbox(label="Введите свой текст", interactive=True, value="This is a test.") tts_button = gr.Button(value="Говорить") tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0]) with gr.Row(): with gr.Accordion('Wav2Lip', open=False): with gr.Row(): size = gr.Radio(label='Разрешение:',choices=['Half','Full']) face = gr.UploadButton("Загрузите персонажа",type='file') faces = gr.Dropdown(label="ИЛИ Выберите один:", choices=['None','Ben Shapiro','Andrew Tate']) with gr.Row(): preview = gr.Textbox(label="Статус:",interactive=False) face.upload(fn=success_message,inputs=[face], outputs=[preview, faces]) with gr.Row(): animation = gr.Video(type='filepath') refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation]) with gr.Row(): animate_button = gr.Button('Animate') with gr.Column(): with gr.Accordion("Настройки индекса", open=False): file_index1 = gr.Dropdown( label="3. Путь к файлу add.index (если он не был найден автоматически)", choices=get_indexes(), value=get_index(), interactive=True, ) sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1]) refresh_button.click( fn=change_choices, inputs=[], outputs=[sid0, file_index1] ) # 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=i18n("检索特征占比"), value=0.66, interactive=True, ) vc_output2 = gr.Audio(label="Вывод аудио (нажмите три точки в правом углу, чтобы загрузить)",type='filepath') animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) with gr.Accordion("Расширенные настройки", open=False): f0method0 = gr.Radio( label="Необязательно: измените алгоритм извлечения высоты звука.", choices=["pm", "rmvpe", "dio", "mangio-crepe-tiny", "crepe-tiny", "crepe", "mangio-crepe", "harvest"], # Fork Feature. Add Crepe-Tiny value="rmvpe", interactive=True, ) crepe_hop_length = gr.Slider( minimum=1, maximum=512, step=1, label="Mangio-Crepe Hop Length. Более высокие числа уменьшат вероятность резких изменений шага, но меньшие числа повысят точность.", value=120, 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, ) with gr.Accordion("Fast-Mode (TESTING)", open=False): fast_audio = gr.Audio(label="As soon as you stop recording, inference will start.",type="filepath", source="microphone", autoplay=False) fast_result = gr.Audio(label="Result",type="filepath", autoplay=True) with gr.Row(): vc_output1 = gr.Textbox(label="Output Information:") 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) fast_audio.stop_recording( fn=fast, inputs=[ fast_audio, spk_item, vc_transform0, f0method0, file_index1, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, crepe_hop_length ], outputs=[ fast_result, fast_audio, vc_output1 ] ) but0.click( 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, crepe_hop_length ], [vc_output1, vc_output2], ) with gr.TabItem("Загрузка готовой модели"): with gr.Row(): url=gr.Textbox(label="Введите URL-адрес модели:") with gr.Row(): model = gr.Textbox(label="Назовите свою модель:") download_button=gr.Button("Загрузить") 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( """ Original RVC:https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI ❤️ Если вам нравится моя версия RVC, помогите мне сохранить ее.❤️ https://boosty.to/ba1yya """ ) with gr.TabItem("Тренировка", visible=False): with gr.Row(): with gr.Column(): exp_dir1 = gr.Textbox(label="Голосовое имя:", value="Voice_1") 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="RVC version", choices=["v1", "v2"], value="v2", interactive=True, visible=False, ) np7 = gr.Slider( minimum=0, maximum=config.n_cpu, step=1, label="# of CPUs for data processing (Leave as it is)", value=config.n_cpu, interactive=True, visible=True ) trainset_dir4 = gr.Textbox(label="Путь к вашему набору данных (аудиофайлы, а не zip):", value="./dataset") easy_uploader = gr.Files(label='ИЛИ Перетащите сюда свои аудиозаписи. Они будут загружены по указанному выше пути к набору данных.',file_types=['audio']) but1 = gr.Button("1.Обработать набор данных", variant="primary") info1 = gr.Textbox(label="Статус:", value="") easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1]) but1.click( preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] ) with gr.Column(): 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 ) with gr.Accordion('GPU Settings', open=False, visible=False): 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=False ) gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) 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=["harvest","crepe", "mangio-crepe"], # Fork feature: Crepe on f0 extraction for training. value="mangio-crepe", interactive=True, ) extraction_crepe_hop_length = gr.Slider( minimum=1, maximum=512, step=1, label=i18n("crepe_hop_length"), value=128, interactive=True ) but2 = gr.Button("2.Извлечение высоты тона", variant="primary") info2 = gr.Textbox(label="Статус:", value="", max_lines=8) but2.click( extract_f0_feature, [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], [info2], ) with gr.Row(): with gr.Column(): total_epoch11 = gr.Slider( minimum=0, maximum=10000, step=10, label="Общее количество эпох обучения (много не вводите, возможна перетренировка):", value=250, interactive=True, ) but3 = gr.Button("3.Тренировать модель", variant="primary") but4 = gr.Button("4.Тренировать индекс", variant="primary") info3 = gr.Textbox(label="Статус:", value="", max_lines=10) with gr.Accordion("Настройки обучения (вы можете оставить их как есть)", open=False): #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) with gr.Column(): save_epoch10 = gr.Slider( minimum=0, maximum=100, step=5, label="Резервное копирование каждые # эпох:", value=25, interactive=True, ) batch_size12 = gr.Slider( minimum=1, maximum=40, step=1, label="Размер пакета (Оставьте его, если вы не знаете, что это!):", 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, ) 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, ) zip_model = gr.Button('5.Скачать модель') zipped_model = gr.Files(label='Файл вашей модели и индекса можно скачать здесь:') zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) with gr.Group(): with gr.Accordion("Расположение базовой модели:", open=False, visible=False): pretrained_G14 = gr.Textbox( label=i18n("加载预训练底模G路径"), value="pretrained_v2/f0G40k.pth", interactive=True, ) pretrained_D15 = gr.Textbox( label=i18n("加载预训练底模D路径"), value="pretrained_v2/f0D40k.pth", 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=gpus, interactive=True, ) sr2.change( change_sr2, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15, version19], ) version19.change( change_version19, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15], ) if_f0_3.change( change_f0, [if_f0_3, sr2, version19], [f0method8, pretrained_G14, pretrained_D15], ) 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, ) 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, extraction_crepe_hop_length ], info3, ) try: if tab_faq == "常见问题解答": with open("docs/faq.md", "r", encoding="utf8") as f: info = f.read() else: with open("docs/faq_en.md", "r", encoding="utf8") as f: info = f.read() gr.Markdown(value=info) except: gr.Markdown("") #region Mangio Preset Handler Region def save_preset(preset_name,sid0,vc_transform,input_audio,f0method,crepe_hop_length,filter_radius,file_index1,file_index2,index_rate,resample_sr,rms_mix_rate,protect,f0_file): data = None with open('../inference-presets.json', 'r') as file: data = json.load(file) preset_json = { 'name': preset_name, 'model': sid0, 'transpose': vc_transform, 'audio_file': input_audio, 'f0_method': f0method, 'crepe_hop_length': crepe_hop_length, 'median_filtering': filter_radius, 'feature_path': file_index1, 'auto_feature_path': file_index2, 'search_feature_ratio': index_rate, 'resample': resample_sr, 'volume_envelope': rms_mix_rate, 'protect_voiceless': protect, 'f0_file_path': f0_file } data['presets'].append(preset_json) with open('../inference-presets.json', 'w') as file: json.dump(data, file) file.flush() print("Saved Preset %s into inference-presets.json!" % preset_name) if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) app.queue(concurrency_count=511, max_size=1022).launch(share=True, quiet=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, )