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	import 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)
	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 signal

	import math

	from utils import load_audio, CSVutil

	global DoFormant, Quefrency, Timbre

	if not os.path.isdir('csvdb/'):
	os.makedirs('csvdb')
	frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
	frmnt.close()
	stp.close()

	try:
	DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
	DoFormant = (
	lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant)
	)(DoFormant)
	except (ValueError, TypeError, IndexError):
	DoFormant, Quefrency, Timbre = False, 1.0, 1.0
	CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre)

	def download_models():
	# Download hubert base model if not present
	if not os.path.isfile('./hubert_base.pt'):
	response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')

	if response.status_code == 200:
	with open('./hubert_base.pt', 'wb') as f:
	f.write(response.content)
	print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.")
	else:
	raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".")

	# Download rmvpe model if not present
	if not os.path.isfile('./rmvpe.pt'):
	response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058')

	if response.status_code == 200:
	with open('./rmvpe.pt', 'wb') as f:
	f.write(response.content)
	print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.")
	else:
	raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".")

	download_models()

	print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n")

	def formant_apply(qfrency, tmbre):
	Quefrency = qfrency
	Timbre = tmbre
	DoFormant = True
	CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)

	return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"})

	def get_fshift_presets():
	fshift_presets_list = []
	for dirpath, _, filenames in os.walk("./formantshiftcfg/"):
	for filename in filenames:
	if filename.endswith(".txt"):
	fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/'))

	if len(fshift_presets_list) > 0:
	return fshift_presets_list
	else:
	return ''



	def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button):

	if (cbox):

	DoFormant = True
	CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
	#print(f"is checked? - {cbox}\ngot {DoFormant}")

	return (
	{"value": True, "__type__": "update"},
	{"visible": True, "__type__": "update"},
	{"visible": True, "__type__": "update"},
	{"visible": True, "__type__": "update"},
	{"visible": True, "__type__": "update"},
	{"visible": True, "__type__": "update"},
	)


	else:

	DoFormant = False
	CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)

	#print(f"is checked? - {cbox}\ngot {DoFormant}")
	return (
	{"value": False, "__type__": "update"},
	{"visible": False, "__type__": "update"},
	{"visible": False, "__type__": "update"},
	{"visible": False, "__type__": "update"},
	{"visible": False, "__type__": "update"},
	{"visible": False, "__type__": "update"},
	{"visible": False, "__type__": "update"},
	)



	def preset_apply(preset, qfer, tmbr):
	if str(preset) != '':
	with open(str(preset), 'r') as p:
	content = p.readlines()
	qfer, tmbr = content[0].split('\n')[0], content[1]

	formant_apply(qfer, tmbr)
	else:
	pass
	return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"})

	def update_fshift_presets(preset, qfrency, tmbre):

	qfrency, tmbre = preset_apply(preset, qfrency, tmbre)

	if (str(preset) != ''):
	with open(str(preset), 'r') as p:
	content = p.readlines()
	qfrency, tmbre = content[0].split('\n')[0], content[1]

	formant_apply(qfrency, tmbre)
	else:
	pass
	return (
	{"choices": get_fshift_presets(), "__type__": "update"},
	{"value": qfrency, "__type__": "update"},
	{"value": tmbre, "__type__": "update"},
	)

	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 lib.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

	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"
	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))



	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, DoFormant, Quefrency, Timbre)
	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_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()

	# 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 "",
	)


	global log_interval


	def set_log_interval(exp_dir, batch_size12):
	log_interval = 1

	folder_path = os.path.join(exp_dir, "1_16k_wavs")

	if os.path.exists(folder_path) and os.path.isdir(folder_path):
	wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")]
	if wav_files:
	sample_size = len(wav_files)
	log_interval = math.ceil(sample_size / batch_size12)
	if log_interval > 1:
	log_interval += 1
	return log_interval

	# 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,
	):
	CSVutil('csvdb/stop.csv', 'w+', 'formanting', False)
	# 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)
	)

	log_interval = set_log_interval(exp_dir, batch_size12)

	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 -li %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 == True else 0,
	1 if if_cache_gpu17 == True else 0,
	1 if if_save_every_weights18 == True else 0,
	version19,
	log_interval,
	)
	)
	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 -li %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 == True else 0,
	1 if if_cache_gpu17 == True else 0,
	1 if if_save_every_weights18 == True else 0,
	version19,
	log_interval,
	)
	)
	print(cmd)
	p = Popen(cmd, shell=True, cwd=now_dir)
	global PID
	PID = p.pid
	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", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"})


	# 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 == True else 0,
	1 if if_cache_gpu17 == True else 0,
	1 if if_save_every_weights18 == True 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 == True else 0,
	1 if if_cache_gpu17 == True else 0,
	1 if if_save_every_weights18 == True 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！"))


	def whethercrepeornah(radio):
	mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False
	return ({"visible": mango, "__type__": "update"})

	# 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 lib.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 RVC WebUI App

	def get_presets():
	data = None
	with open('../inference-presets.json', 'r') as file:
	data = json.load(file)
	preset_names = []
	for preset in data['presets']:
	preset_names.append(preset['name'])

	return preset_names

	def change_choices2():
	audio_files=[]
	for filename in os.listdir("./audios"):
	if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
	audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
	return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}

	audio_files=[]
	for filename in os.listdir("./audios"):
	if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
	audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))

	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 stoptraining(mim):
	if int(mim) == 1:
	try:
	CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True')
	os.kill(PID, signal.SIGTERM)
	except Exception as e:
	print(f"Couldn't click due to {e}")
	return (
	{"visible": False, "__type__": "update"},
	{"visible": True, "__type__": "update"},
	)


	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(), title='Mangio-RVC-Web 💻') as app:
	with gr.Tabs():
	with gr.TabItem("Inference"):
	gr.HTML("<h1> RVC V2 Huggingface Version </h1>")
	gr.HTML("<h4> Inference may take time because this space does not use GPU :( </h4>")
	gr.HTML("<h10> Huggingface version made by Clebersla </h10>")
	gr.HTML("<h10> Easy GUI coded by Rejekt's </h10>")
	gr.HTML("<h4> If you want to use this space privately, I recommend you duplicate the space. </h4>")

	# 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',
	interactive=False,
	)
	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.\nExtraction methods are sorted from 'worst quality' to 'best quality'.\nmangio-crepe may or may not be better than rmvpe in cases where 'smoothness' is more important, but rmvpe is the best overall.",
	choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny
	value="rmvpe",
	interactive=True,
	)

	crepe_hop_length = gr.Slider(
	minimum=1,
	maximum=512,
	step=1,
	label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.",
	value=120,
	interactive=True,
	visible=False,
	)
	f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length])
	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,
	)
	formanting = gr.Checkbox(
	value=bool(DoFormant),
	label="[EXPERIMENTAL] Formant shift inference audio",
	info="Used for male to female and vice-versa conversions",
	interactive=True,
	visible=True,
	)

	formant_preset = gr.Dropdown(
	value='',
	choices=get_fshift_presets(),
	label="browse presets for formanting",
	visible=bool(DoFormant),
	)
	formant_refresh_button = gr.Button(
	value='\U0001f504',
	visible=bool(DoFormant),
	variant='primary',
	)
	#formant_refresh_button = ToolButton( elem_id='1')
	#create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets")

	qfrency = gr.Slider(
	value=Quefrency,
	info="Default value is 1.0",
	label="Quefrency for formant shifting",
	minimum=0.0,
	maximum=16.0,
	step=0.1,
	visible=bool(DoFormant),
	interactive=True,
	)
	tmbre = gr.Slider(
	value=Timbre,
	info="Default value is 1.0",
	label="Timbre for formant shifting",
	minimum=0.0,
	maximum=16.0,
	step=0.1,
	visible=bool(DoFormant),
	interactive=True,
	)

	formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre])
	frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant))
	formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button])
	frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre])
	formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre])
	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", "rmvpe"],
	value="rmvpe",
	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
	"""
	)

	def has_two_files_in_pretrained_folder():
	pretrained_folder = "./pretrained/"
	if not os.path.exists(pretrained_folder):
	return False

	files_in_folder = os.listdir(pretrained_folder)
	num_files = len(files_in_folder)
	return num_files >= 2

	if has_two_files_in_pretrained_folder():
	print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------")
	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 for data processing (Leave as it is)",
	value=config.n_cpu,
	interactive=True,
	visible=True
	)
	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", "rmvpe"], # Fork feature: Crepe on f0 extraction for training.
	value="rmvpe",
	interactive=True,
	)

	extraction_crepe_hop_length = gr.Slider(
	minimum=1,
	maximum=512,
	step=1,
	label=i18n("crepe_hop_length"),
	value=128,
	interactive=True,
	visible=False,
	)
	f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length])
	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=1,
	maximum=5000,
	step=10,
	label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):",
	value=250,
	interactive=True,
	)
	butstop = gr.Button(
	"Stop Training",
	variant='primary',
	visible=False,
	)
	but3 = gr.Button("3. Train Model", variant="primary", visible=True)

	but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop])
	butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3])


	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=1,
	maximum=200,
	step=1,
	label="Backup every X amount of epochs:",
	value=10,
	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.Checkbox(
	label="Save only the latest '.ckpt' file to save disk space.",
	value=True,
	interactive=True,
	)
	if_cache_gpu17 = gr.Checkbox(
	label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.",
	value=False,
	interactive=True,
	)
	if_save_every_weights18 = gr.Checkbox(
	label="Save a small final model to the 'weights' folder at each save point.",
	value=True,
	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,
	butstop,
	but3,
	],
	)
	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,
	)

	else:
	print(
	"Pretrained weights not downloaded. Disabling training tab.\n"
	"Wondering how to train a voice? Visit here for the RVC model training guide: https://t.ly/RVC_Training_Guide\n"
	"-------------------------------\n"
	)

	app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True)
	#endregion