diff --git "a/EasierGUI.py" "b/EasierGUI.py" --- "a/EasierGUI.py" +++ "b/EasierGUI.py" @@ -1,2347 +1,2347 @@ -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, -): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 - global tgt_sr, net_g, vc, hubert_model, version - if input_audio_path is None: - return "You need to upload an audio", None - f0_up_key = int(f0_up_key) - try: - audio = load_audio(input_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." - ) - 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) - 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) - 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() - 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) - 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)) - 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')): - audio_files.append(os.path.join('./audios',filename)) - return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} - -audio_files=[] -for filename in os.listdir("./audios"): - if filename.endswith(('.wav','.mp3')): - audio_files.append(os.path.join('./audios',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 new_path - -def save_to_wav2(dropbox): - file_path=dropbox.name - shutil.move(file_path,'./audios') - return os.path.join('./audios',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): - 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 - 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." - -with gr.Blocks(theme=gr.themes.Base()) as app: - with gr.Tabs(): - with gr.TabItem("Inference"): - gr.HTML("

Easy GUI v2 (rejekts) - adapted to Mangio-RVC-Fork 💻

") - # 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.Choose your Model.", choices=sorted(names), value=check_for_name()) - refresh_button = gr.Button("Refresh", variant="primary") - if check_for_name() != '': - get_vc(sorted(names)[0]) - vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 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("Convert", 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="2.Choose your audio.", - value="./audios/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("Refresh", 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('Text To Speech', open=False): - with gr.Column(): - lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en') - api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='') - elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices) - with gr.Column(): - tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.") - tts_button = gr.Button(value="Speak") - 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='Resolution:',choices=['Half','Full']) - face = gr.UploadButton("Upload A Character",type='file') - faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate']) - with gr.Row(): - preview = gr.Textbox(label="Status:",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("Index Settings", open=False): - file_index1 = gr.Dropdown( - label="3. Path to your added.index file (if it didn't automatically find it.)", - 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="Output Audio (Click on the Three Dots in the Right Corner to Download)",type='filepath') - animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) - with gr.Accordion("Advanced Settings", open=False): - f0method0 = gr.Radio( - label="Optional: Change the Pitch Extraction Algorithm.", - choices=["pm", "dio", "mangio-crepe-tiny", "crepe-tiny", "crepe", "mangio-crepe", "harvest"], # Fork Feature. Add Crepe-Tiny - value="mangio-crepe", - interactive=True, - ) - crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy.", - 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.Row(): - vc_output1 = gr.Textbox("") - 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) - - 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.Accordion("Batch Conversion",open=False): - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") - f0method1 = gr.Radio( - label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "crepe"], - value="pm", - interactive=True, - ) - 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, - ) - with gr.Column(): - 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, - ) - 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, - ) - refresh_button.click( - fn=lambda: change_choices()[1], - inputs=[], - outputs=file_index4, - ) - # file_big_npy2 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate2 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("检索特征占比"), - value=1, - interactive=True, - ) - with gr.Column(): - 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, - ) - 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=1, - 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.Column(): - dir_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), - value="E:\codes\py39\\test-20230416b\\todo-songs", - ) - inputs = gr.File( - file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") - ) - with gr.Row(): - format1 = gr.Radio( - label=i18n("导出文件格式"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", - interactive=True, - ) - but1 = gr.Button(i18n("转换"), variant="primary") - vc_output3 = gr.Textbox(label=i18n("输出信息")) - but1.click( - vc_multi, - [ - spk_item, - dir_input, - opt_input, - inputs, - vc_transform1, - f0method1, - file_index3, - file_index4, - # file_big_npy2, - index_rate2, - filter_radius1, - resample_sr1, - rms_mix_rate1, - protect1, - format1, - crepe_hop_length, - ], - [vc_output3], - ) - but1.click(fn=lambda: easy_uploader.clear()) - 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( - """ - Original RVC:https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI - Mangio's RVC Fork:https://github.com/Mangio621/Mangio-RVC-Fork - ❤️ If you like the EasyGUI, help me keep it.❤️ - https://paypal.me/lesantillan - """ - ) - - with gr.TabItem("Train", visible=False): - with gr.Row(): - with gr.Column(): - exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice") - 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 to use (Leave it unless you know what you're doing!)", - value=config.n_cpu, - interactive=True, - visible=False - ) - trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset") - easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio']) - but1 = gr.Button("1.Process The Dataset", variant="primary") - info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", 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.Pitch Extraction", variant="primary") - info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", 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="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):", - value=250, - interactive=True, - ) - but3 = gr.Button("3.Train Model", variant="primary") - but4 = gr.Button("4.Train Index", variant="primary") - info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10) - with gr.Accordion("Training Preferences (You can leave these as they are)", 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="Backup every # of epochs:", - value=25, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label="Batch Size (LEAVE IT unless you know what you're doing!):", - 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.Download Model') - zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:') - zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) - with gr.Group(): - with gr.Accordion("Base Model Locations:", 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) - - - def on_preset_changed(preset_name): - print("Changed Preset to %s!" % preset_name) - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - - print("Searching for " + preset_name) - returning_preset = None - for preset in data['presets']: - if(preset['name'] == preset_name): - print("Found a preset") - returning_preset = preset - # return all new input values - return ( - # returning_preset['model'], - # returning_preset['transpose'], - # returning_preset['audio_file'], - # returning_preset['f0_method'], - # returning_preset['crepe_hop_length'], - # returning_preset['median_filtering'], - # returning_preset['feature_path'], - # returning_preset['auto_feature_path'], - # returning_preset['search_feature_ratio'], - # returning_preset['resample'], - # returning_preset['volume_envelope'], - # returning_preset['protect_voiceless'], - # returning_preset['f0_file_path'] - ) - - # Preset State Changes - - # This click calls save_preset that saves the preset into inference-presets.json with the preset name - # mangio_preset_save_btn.click( - # fn=save_preset, - # inputs=[ - # mangio_preset_name_save, - # sid0, - # vc_transform0, - # input_audio0, - # f0method0, - # crepe_hop_length, - # filter_radius0, - # file_index1, - # file_index2, - # index_rate1, - # resample_sr0, - # rms_mix_rate0, - # protect0, - # f0_file - # ], - # outputs=[] - # ) - - # mangio_preset.change( - # on_preset_changed, - # inputs=[ - # # Pass inputs here - # mangio_preset - # ], - # outputs=[ - # # Pass Outputs here. These refer to the gradio elements that we want to directly change - # # sid0, - # # vc_transform0, - # # input_audio0, - # # f0method0, - # # crepe_hop_length, - # # filter_radius0, - # # file_index1, - # # file_index2, - # # index_rate1, - # # resample_sr0, - # # rms_mix_rate0, - # # protect0, - # # f0_file - # ] - # ) - - - # with gr.TabItem(i18n("招募音高曲线前端编辑器")): - # gr.Markdown(value=i18n("加开发群联系我xxxxx")) - # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): - # gr.Markdown(value=i18n("xxxxx")) - - - if 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) - 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, - ) +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() +# No supported Nvidia card found +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 # At least one usable Nvidia card + 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("Unfortunately you don't have a working graphics card to support your training.") + 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, +): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 + global tgt_sr, net_g, vc, hubert_model, version + if input_audio_path is None: + return "You need to upload an audio", None + f0_up_key = int(f0_up_key) + try: + audio = load_audio(input_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") + ) + ) # Prevent newbies from making mistakes and automatically replace them for them + # 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." + ) + 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(" ") + ) # Prevent newbies from copying the path with spaces, " and carriage returns at the beginning and end + 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) + + +# A tab can only have one sound globally. +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: # Considering polling, we need to add a check to see if the sid is switched from a model to a model-free state. + 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() + ###The downstairs won’t be clean without this much trouble + 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 Indicates that the process has not ended + # As long as there is a process that has not ended, it will not stop. + 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 + ###Shabi gr, popen read All have to be run through and then read at once, without gr, just read and output one sentence at a time; you can only make an additional text stream to read at a fixed time + done = [False] + threading.Thread( + target=if_done, + args=( + done, + p, + ), + ).start() + while 1: + with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: + yield (f.read()) + sleep(1) + if done[0] == True: + break + with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: + log = f.read() + print(log) + yield log + + +# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) +def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, 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 + ###Shabi gr, popen read All have to be run through and then read at once, without gr, just read and output one sentence at a time; you can only make an additional text stream to read at a fixed time + done = [False] + threading.Thread( + target=if_done, + args=( + done, + p, + ), + ).start() + while 1: + with open( + "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" + ) as f: + yield (f.read()) + sleep(1) + if done[0] == True: + break + with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: + log = f.read() + print(log) + yield log + ####to different part Open multiple processes separately + """ + 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) + ### Shabi gr, popen read All have to be run through and then read at once, without gr, just read and output one sentence at a time; you can only make an additional text stream to read at a fixed time + 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, +): + # generate 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") + # generate config No need to generate 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() + return "Training is over, You can view the console training log or the experiment folder under train.log" + + +# 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 "Please perform feature extraction first!" + listdir_res = list(os.listdir(feature_dir)) + if len(listdir_res) == 0: + return "Please perform feature extraction first!" + 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( + "Index built successfully，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("Index built successfully，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, + 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:Process data + 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:Processing data")) + yield get_info_str(cmd) + p = Popen(cmd, shell=True) + p.wait() + with open(preprocess_log_path, "r") as f: + print(f.read()) + #########step2a:extract pitch + open(extract_f0_feature_log_path, "w") + if if_f0_3: + yield get_info_str("step2a:Extracting pitch") + 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:No need to extract pitch")) + #######step2b:Extract features + yield get_info_str(i18n("step2b:Extracting features")) + 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:Training model + yield get_info_str(i18n("step3a:Training model")) + # 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("After the training is finished, you can view the console training log or the experimental folder train.log")) + #######step3b: training index + 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( + "Index built successfully, added_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (n_ivf, index_ivf.nprobe, exp_dir1, version19) + ) + yield get_info_str(i18n("The whole process ends！")) + + +# 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，for 768Vec prepare + + test_phone = torch.rand(1, 200, hidden_channels) # hidden unit + test_phone_lengths = torch.tensor([200]).long() # hidden unit Length (seemingly useless) + test_pitch = torch.randint(size=(1, 200), low=5, high=255) # Fundamental frequency (in Hz) + test_pitchf = torch.rand(1, 200) # NSF base frequency + test_ds = torch.LongTensor([0]) # Speaker ID + test_rnd = torch.rand(1, 192, 200) # Noise (adding random factors) + + device = "cpu" # Device when exporting (does not affect the use of the model) + + + net_g = SynthesizerTrnMsNSFsidM( + *cpt["config"], is_half=False,version=cpt.get("version","v1") + ) # fp32 export (C++ needs to manually rearrange memory to support fp16, so fp16 is not used for the time being) + 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) Export of multiple role mix tracks + 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("yes") if (int(com[8]) == 1) else i18n("no") + if_cache_gpu = i18n("yes") if (int(com[9]) == 1) else i18n("no") + if_save_every_weight = i18n("yes") if (int(com[10]) == 1) else i18n("no") + 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')): + audio_files.append(os.path.join('./audios',filename)) + return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} + +audio_files=[] +for filename in os.listdir("./audios"): + if filename.endswith(('.wav','.mp3')): + audio_files.append(os.path.join('./audios',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 new_path + +def save_to_wav2(dropbox): + file_path=dropbox.name + shutil.move(file_path,'./audios') + return os.path.join('./audios',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): + 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 + 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." + +with gr.Blocks(theme=gr.themes.Base()) as app: + with gr.Tabs(): + with gr.TabItem("Inference"): + gr.HTML("

Easy GUI v2 (rejekts) - adapted to Mangio-RVC-Fork 💻

") + # 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.Choose your Model.", choices=sorted(names), value=check_for_name()) + refresh_button = gr.Button("Refresh", variant="primary") + if check_for_name() != '': + get_vc(sorted(names)[0]) + vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0) + #clean_button = gr.Button(i18n("Uninstalling sounds saves video memory"), variant="primary") + spk_item = gr.Slider( + minimum=0, + maximum=2333, + step=1, + label=i18n("Please select the speaker 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("Convert", 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="2.Choose your audio.", + value="./audios/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("Refresh", 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('Text To Speech', open=False): + with gr.Column(): + lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en') + api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='') + elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices) + with gr.Column(): + tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.") + tts_button = gr.Button(value="Speak") + 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='Resolution:',choices=['Half','Full']) + face = gr.UploadButton("Upload A Character",type='file') + faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate']) + with gr.Row(): + preview = gr.Textbox(label="Status:",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("Index Settings", open=False): + file_index1 = gr.Dropdown( + label="3. Path to your added.index file (if it didn't automatically find it.)", + 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("Feature file path"), + # 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("Search feature proportion"), + value=0.66, + interactive=True, + ) + vc_output2 = gr.Audio(label="Output Audio (Click on the Three Dots in the Right Corner to Download)",type='filepath') + animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) + with gr.Accordion("Advanced Settings", open=False): + f0method0 = gr.Radio( + label="Optional: Change the Pitch Extraction Algorithm.", + choices=["pm", "dio", "mangio-crepe-tiny", "crepe-tiny", "crepe", "mangio-crepe", "harvest"], # Fork Feature. Add Crepe-Tiny + value="mangio-crepe", + interactive=True, + ) + crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy.", + value=120, + interactive=True + ) + filter_radius0 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3, use median filtering on the result of harvest pitch recognition, the value is the filter radius, which can reduce mute"), + value=3, + step=1, + interactive=True, + ) + resample_sr0 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("Post-processing resampling to the final sampling rate, 0 means no resampling"), + value=0, + step=1, + interactive=True, + visible=False + ) + rms_mix_rate0 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("The input source volume envelope replaces the output volume envelope blending ratio. The closer it is to 1, the more the output envelope is used."), + value=0.21, + interactive=True, + ) + protect0 = gr.Slider( + minimum=0, + maximum=0.5, + label=i18n("Protects clear consonants and breathing sounds, and prevents electronic music tearing and other artifacts. It is not enabled when it is set to 0.5. It is more effective when it is lowered, but the indexing effect may be reduced."), + value=0.33, + step=0.01, + interactive=True, + ) + with gr.Row(): + vc_output1 = gr.Textbox("") + f0_file = gr.File(label=i18n("F0 curve file, optional, one line per pitch, replaces the default F0 and sharp and flat tones"), visible=False) + + 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.Accordion("Batch Conversion",open=False): + with gr.Row(): + with gr.Column(): + vc_transform1 = gr.Number( + label=i18n("Transpose(integer, number of semitones, octave up 12 octave down -12)"), value=0 + ) + opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") + f0method1 = gr.Radio( + label=i18n( + "Select the pitch extraction algorithm. You can use pm to speed up the input singing voice. Harvest has good bass but is extremely slow. Crepe has good effect but consumes GPU." + ), + choices=["pm", "harvest", "crepe"], + value="pm", + interactive=True, + ) + filter_radius1 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3, use median filtering on the result of harvest pitch recognition, the value is the filter radius, which can reduce mute"), + value=3, + step=1, + interactive=True, + ) + with gr.Column(): + file_index3 = gr.Textbox( + label=i18n("Feature retrieval library file path, if empty, use the drop-down selection result"), + value="", + interactive=True, + ) + file_index4 = gr.Dropdown( + label=i18n("Automatically detect index path, drop-down selection"), + choices=sorted(index_paths), + interactive=True, + ) + refresh_button.click( + fn=lambda: change_choices()[1], + inputs=[], + outputs=file_index4, + ) + # file_big_npy2 = gr.Textbox( + # label=i18n("Feature file path"), + # 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("Search feature proportion"), + value=1, + interactive=True, + ) + with gr.Column(): + resample_sr1 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("Post-processing resampling to the final sampling rate, 0 means no resampling"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("The input source volume envelope replaces the output volume envelope blending ratio. The closer it is to 1, the more the output envelope is used."), + value=1, + interactive=True, + ) + protect1 = gr.Slider( + minimum=0, + maximum=0.5, + label=i18n( + "Protects clear consonants and breathing sounds, and prevents electronic music tearing and other artifacts. It is not enabled when it is set to 0.5. It is more effective when it is lowered, but the indexing effect may be reduced." + ), + value=0.33, + step=0.01, + interactive=True, + ) + with gr.Column(): + dir_input = gr.Textbox( + label=i18n("Enter the path of the audio folder to be processed (just copy it from the address bar of the file manager)"), + value="E:\codes\py39\\test-20230416b\\todo-songs", + ) + inputs = gr.File( + file_count="multiple", label=i18n("You can also batch import audio files, choose one of the two, and read the folder first") + ) + with gr.Row(): + format1 = gr.Radio( + label=i18n("Export file format"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + but1 = gr.Button(i18n("Convert"), variant="primary") + vc_output3 = gr.Textbox(label=i18n("Output information")) + but1.click( + vc_multi, + [ + spk_item, + dir_input, + opt_input, + inputs, + vc_transform1, + f0method1, + file_index3, + file_index4, + # file_big_npy2, + index_rate2, + filter_radius1, + resample_sr1, + rms_mix_rate1, + protect1, + format1, + crepe_hop_length, + ], + [vc_output3], + ) + but1.click(fn=lambda: easy_uploader.clear()) + 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( + """ + Original RVC:https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI + Mangio's RVC Fork:https://github.com/Mangio621/Mangio-RVC-Fork + ❤️ If you like the EasyGUI, help me keep it.❤️ + https://paypal.me/lesantillan + """ + ) + + with gr.TabItem("Train", visible=False): + with gr.Row(): + with gr.Column(): + exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice") + sr2 = gr.Radio( + label=i18n("target sampling rate"), + choices=["40k", "48k"], + value="40k", + interactive=True, + visible=False + ) + if_f0_3 = gr.Radio( + label=i18n("Does the model have pitch guidance (must be provided for singing, but not for voice)"), + 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 to use (Leave it unless you know what you're doing!)", + value=config.n_cpu, + interactive=True, + visible=False + ) + trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset") + easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio']) + but1 = gr.Button("1.Process The Dataset", variant="primary") + info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", 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("Please specify the speaker id"), + value=0, + interactive=True, + visible=False + ) + with gr.Accordion('GPU Settings', open=False, visible=False): + gpus6 = gr.Textbox( + label=i18n("Enter the card numbers to be used separated by -, for example 0-1-2 uses card 0, card 1 and card 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( + "Select the pitch extraction algorithm: input singing voice can be accelerated by pm, high-quality voice but poor CPU can be accelerated by dio, harvest has better quality but is slow" + ), + 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.Pitch Extraction", variant="primary") + info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", 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="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):", + value=250, + interactive=True, + ) + but3 = gr.Button("3.Train Model", variant="primary") + but4 = gr.Button("4.Train Index", variant="primary") + info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10) + with gr.Accordion("Training Preferences (You can leave these as they are)", open=False): + #gr.Markdown(value=i18n("step3: Fill in the training settings and start training the model and indexing")) + with gr.Column(): + save_epoch10 = gr.Slider( + minimum=0, + maximum=100, + step=5, + label="Backup every # of epochs:", + value=25, + interactive=True, + ) + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label="Batch Size (LEAVE IT unless you know what you're doing!):", + value=default_batch_size, + interactive=True, + ) + if_save_latest13 = gr.Radio( + label=i18n("Whether to save only the latest ckpt file to save hard disk space"), + choices=[i18n("是"), i18n("否")], + value=i18n("是"), + interactive=True, + ) + if_cache_gpu17 = gr.Radio( + label=i18n( + "Whether to cache all training sets to the video memory. Small data under 10 minutes can be cached to speed up training. Large data cache will explode the video memory and will not increase the speed much." + ), + choices=[i18n("是"), i18n("否")], + value=i18n("否"), + interactive=True, + ) + if_save_every_weights18 = gr.Radio( + label=i18n("Whether to save the final small model to the weights folder at each save time point"), + choices=[i18n("yes"), i18n("no")], + value=i18n("yes"), + interactive=True, + ) + zip_model = gr.Button('5.Download Model') + zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:') + zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) + with gr.Group(): + with gr.Accordion("Base Model Locations:", open=False, visible=False): + pretrained_G14 = gr.Textbox( + label=i18n("Load the pre-trained bottom model G path"), + value="pretrained_v2/f0G40k.pth", + interactive=True, + ) + pretrained_D15 = gr.Textbox( + label=i18n("Load the pre-trained bottom model D path"), + value="pretrained_v2/f0D40k.pth", + interactive=True, + ) + gpus16 = gr.Textbox( + label=i18n("Enter the card numbers to be used separated by -, for example 0-1-2 uses card 0, card 1 and card 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("One click training"), 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 == "FAQ": + 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) + + + def on_preset_changed(preset_name): + print("Changed Preset to %s!" % preset_name) + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + + print("Searching for " + preset_name) + returning_preset = None + for preset in data['presets']: + if(preset['name'] == preset_name): + print("Found a preset") + returning_preset = preset + # return all new input values + return ( + # returning_preset['model'], + # returning_preset['transpose'], + # returning_preset['audio_file'], + # returning_preset['f0_method'], + # returning_preset['crepe_hop_length'], + # returning_preset['median_filtering'], + # returning_preset['feature_path'], + # returning_preset['auto_feature_path'], + # returning_preset['search_feature_ratio'], + # returning_preset['resample'], + # returning_preset['volume_envelope'], + # returning_preset['protect_voiceless'], + # returning_preset['f0_file_path'] + ) + + # Preset State Changes + + # This click calls save_preset that saves the preset into inference-presets.json with the preset name + # mangio_preset_save_btn.click( + # fn=save_preset, + # inputs=[ + # mangio_preset_name_save, + # sid0, + # vc_transform0, + # input_audio0, + # f0method0, + # crepe_hop_length, + # filter_radius0, + # file_index1, + # file_index2, + # index_rate1, + # resample_sr0, + # rms_mix_rate0, + # protect0, + # f0_file + # ], + # outputs=[] + # ) + + # mangio_preset.change( + # on_preset_changed, + # inputs=[ + # # Pass inputs here + # mangio_preset + # ], + # outputs=[ + # # Pass Outputs here. These refer to the gradio elements that we want to directly change + # # sid0, + # # vc_transform0, + # # input_audio0, + # # f0method0, + # # crepe_hop_length, + # # filter_radius0, + # # file_index1, + # # file_index2, + # # index_rate1, + # # resample_sr0, + # # rms_mix_rate0, + # # protect0, + # # f0_file + # ] + # ) + + + # with gr.TabItem(i18n("Recruiting pitch curve front-end editor")): + # gr.Markdown(value=i18n("Add the development group to contact me xxxxx")) + # with gr.TabItem(i18n("Click to view the communication and problem feedback group number")): + # gr.Markdown(value=i18n("xxxxx")) + + + 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) + 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, + )