webui.py

import os,shutil,sys,pdb,re
now_dir = os.getcwd()
sys.path.append(now_dir)
import json,yaml,warnings,torch
import platform
import psutil
import signal

warnings.filterwarnings("ignore")
torch.manual_seed(233333)
tmp = os.path.join(now_dir, "TEMP")
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
if(os.path.exists(tmp)):
    for name in os.listdir(tmp):
        if(name=="jieba.cache"):continue
        path="%s/%s"%(tmp,name)
        delete=os.remove if os.path.isfile(path) else shutil.rmtree
        delete(path)
import site
site_packages_roots = []
for path in site.getsitepackages():
    if "packages" in path:
        site_packages_roots.append(path)
if(site_packages_roots==[]):site_packages_roots=["%s/runtime/Lib/site-packages" % now_dir]
#os.environ["OPENBLAS_NUM_THREADS"] = "4"
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
for site_packages_root in site_packages_roots:
    if os.path.exists(site_packages_root):
        with open("%s/users.pth" % (site_packages_root), "w") as f:
            f.write(
                "%s\n%s/tools\n%s/tools/damo_asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
                % (now_dir, now_dir, now_dir, now_dir, now_dir)
            )
from tools import my_utils
import traceback
import shutil
import pdb
import gradio as gr
from subprocess import Popen
import signal
from config import python_exec,infer_device,is_half,exp_root,webui_port_main,webui_port_infer_tts,webui_port_uvr5,webui_port_subfix,is_share
from tools.i18n.i18n import I18nAuto
i18n = I18nAuto()
from scipy.io import wavfile
from tools.my_utils import load_audio
from multiprocessing import cpu_count
n_cpu=cpu_count()
           
# 判断是否有能用来训练和加速推理的N卡
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if_gpu_ok = False

if torch.cuda.is_available() or ngpu != 0:
    for i in range(ngpu):
        gpu_name = torch.cuda.get_device_name(i)
        if any(value in gpu_name.upper()for value in ["10","16","20","30","40","A2","A3","A4","P4","A50","500","A60","70","80","90","M4","T4","TITAN","L"]):
            # A10#A100#V100#A40#P40#M40#K80#A4500
            if_gpu_ok = True  # 至少有一张能用的N卡
            gpu_infos.append("%s\t%s" % (i, gpu_name))
            mem.append(int(torch.cuda.get_device_properties(i).total_memory/ 1024/ 1024/ 1024+ 0.4))
if if_gpu_ok and len(gpu_infos) > 0:
    gpu_info = "\n".join(gpu_infos)
    default_batch_size = min(mem) // 2
else:
    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
    default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])

pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"
pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
def get_weights_names():
    SoVITS_names = [pretrained_sovits_name]
    for name in os.listdir(SoVITS_weight_root):
        if name.endswith(".pth"):SoVITS_names.append(name)
    GPT_names = [pretrained_gpt_name]
    for name in os.listdir(GPT_weight_root):
        if name.endswith(".ckpt"): GPT_names.append(name)
    return SoVITS_names,GPT_names
SoVITS_weight_root="SoVITS_weights"
GPT_weight_root="GPT_weights"
os.makedirs(SoVITS_weight_root,exist_ok=True)
os.makedirs(GPT_weight_root,exist_ok=True)
SoVITS_names,GPT_names = get_weights_names()

def custom_sort_key(s):
    # 使用正则表达式提取字符串中的数字部分和非数字部分
    parts = re.split('(\d+)', s)
    # 将数字部分转换为整数，非数字部分保持不变
    parts = [int(part) if part.isdigit() else part for part in parts]
    return parts

def change_choices():
    SoVITS_names, GPT_names = get_weights_names()
    return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"}

p_label=None
p_uvr5=None
p_asr=None
p_tts_inference=None

def kill_proc_tree(pid, including_parent=True):  
    try:
        parent = psutil.Process(pid)
    except psutil.NoSuchProcess:
        # Process already terminated
        return

    children = parent.children(recursive=True)
    for child in children:
        try:
            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
        except OSError:
            pass
    if including_parent:
        try:
            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
        except OSError:
            pass

system=platform.system()
def kill_process(pid):
    if(system=="Windows"):
        cmd = "taskkill /t /f /pid %s" % pid
        os.system(cmd)
    else:
        kill_proc_tree(pid)
    

def change_label(if_label,path_list):
    global p_label
    if(if_label==True and p_label==None):
        cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s'%(python_exec,path_list,webui_port_subfix,is_share)
        yield i18n("打标工具WebUI已开启")
        print(cmd)
        p_label = Popen(cmd, shell=True)
    elif(if_label==False and p_label!=None):
        kill_process(p_label.pid)
        p_label=None
        yield i18n("打标工具WebUI已关闭")

def change_uvr5(if_uvr5):
    global p_uvr5
    if(if_uvr5==True and p_uvr5==None):
        cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s'%(python_exec,infer_device,is_half,webui_port_uvr5,is_share)
        yield i18n("UVR5已开启")
        print(cmd)
        p_uvr5 = Popen(cmd, shell=True)
    elif(if_uvr5==False and p_uvr5!=None):
        kill_process(p_uvr5.pid)
        p_uvr5=None
        yield i18n("UVR5已关闭")

def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path):
    global p_tts_inference
    if(if_tts==True and p_tts_inference==None):
        os.environ["gpt_path"]=gpt_path if "/" in gpt_path else "%s/%s"%(GPT_weight_root,gpt_path)
        os.environ["sovits_path"]=sovits_path if "/"in sovits_path else "%s/%s"%(SoVITS_weight_root,sovits_path)
        os.environ["cnhubert_base_path"]=cnhubert_base_path
        os.environ["bert_path"]=bert_path
        os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_number
        os.environ["is_half"]=str(is_half)
        os.environ["infer_ttswebui"]=str(webui_port_infer_tts)
        os.environ["is_share"]=str(is_share)
        cmd = '"%s" GPT_SoVITS/inference_webui.py'%(python_exec)
        yield i18n("TTS推理进程已开启")
        print(cmd)
        p_tts_inference = Popen(cmd, shell=True)
    elif(if_tts==False and p_tts_inference!=None):
        kill_process(p_tts_inference.pid)
        p_tts_inference=None
        yield i18n("TTS推理进程已关闭")


def open_asr(asr_inp_dir):
    global p_asr
    if(p_asr==None):
        cmd = '"%s" tools/damo_asr/cmd-asr.py "%s"'%(python_exec,asr_inp_dir)
        yield "ASR任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
        print(cmd)
        p_asr = Popen(cmd, shell=True)
        p_asr.wait()
        p_asr=None
        yield "ASR任务完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}

def close_asr():
    global p_asr
    if(p_asr!=None):
        kill_process(p_asr.pid)
        p_asr=None
    return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}

p_train_SoVITS=None
def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
    global p_train_SoVITS
    if(p_train_SoVITS==None):
        with open("GPT_SoVITS/configs/s2.json")as f:
            data=f.read()
            data=json.loads(data)
        s2_dir="%s/%s"%(exp_root,exp_name)
        os.makedirs("%s/logs_s2"%(s2_dir),exist_ok=True)
        data["train"]["batch_size"]=batch_size
        data["train"]["epochs"]=total_epoch
        data["train"]["text_low_lr_rate"]=text_low_lr_rate
        data["train"]["pretrained_s2G"]=pretrained_s2G
        data["train"]["pretrained_s2D"]=pretrained_s2D
        data["train"]["if_save_latest"]=if_save_latest
        data["train"]["if_save_every_weights"]=if_save_every_weights
        data["train"]["save_every_epoch"]=save_every_epoch
        data["train"]["gpu_numbers"]=gpu_numbers1Ba
        data["data"]["exp_dir"]=data["s2_ckpt_dir"]=s2_dir
        data["save_weight_dir"]=SoVITS_weight_root
        data["name"]=exp_name
        tmp_config_path="TEMP/tmp_s2.json"
        with open(tmp_config_path,"w")as f:f.write(json.dumps(data))

        cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path)
        yield "SoVITS训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
        print(cmd)
        p_train_SoVITS = Popen(cmd, shell=True)
        p_train_SoVITS.wait()
        p_train_SoVITS=None
        yield "SoVITS训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的SoVITS训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}

def close1Ba():
    global p_train_SoVITS
    if(p_train_SoVITS!=None):
        kill_process(p_train_SoVITS.pid)
        p_train_SoVITS=None
    return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}

p_train_GPT=None
def open1Bb(batch_size,total_epoch,exp_name,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
    global p_train_GPT
    if(p_train_GPT==None):
        with open("GPT_SoVITS/configs/s1longer.yaml")as f:
            data=f.read()
            data=yaml.load(data, Loader=yaml.FullLoader)
        s1_dir="%s/%s"%(exp_root,exp_name)
        os.makedirs("%s/logs_s1"%(s1_dir),exist_ok=True)
        data["train"]["batch_size"]=batch_size
        data["train"]["epochs"]=total_epoch
        data["pretrained_s1"]=pretrained_s1
        data["train"]["save_every_n_epoch"]=save_every_epoch
        data["train"]["if_save_every_weights"]=if_save_every_weights
        data["train"]["if_save_latest"]=if_save_latest
        data["train"]["half_weights_save_dir"]=GPT_weight_root
        data["train"]["exp_name"]=exp_name
        data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
        data["train_phoneme_path"]="%s/2-name2text.txt"%s1_dir
        data["output_dir"]="%s/logs_s1"%s1_dir

        os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_numbers.replace("-",",")
        os.environ["hz"]="25hz"
        tmp_config_path="TEMP/tmp_s1.yaml"
        with open(tmp_config_path, "w") as f:f.write(yaml.dump(data, default_flow_style=False))
        # cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
        cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" '%(python_exec,tmp_config_path)
        yield "GPT训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
        print(cmd)
        p_train_GPT = Popen(cmd, shell=True)
        p_train_GPT.wait()
        p_train_GPT=None
        yield "GPT训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的GPT训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}

def close1Bb():
    global p_train_GPT
    if(p_train_GPT!=None):
        kill_process(p_train_GPT.pid)
        p_train_GPT=None
    return "已终止GPT训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}

ps_slice=[]
def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_parts):
    global ps_slice
    inp = my_utils.clean_path(inp)
    opt_root = my_utils.clean_path(opt_root)
    if(os.path.exists(inp)==False):
        yield "输入路径不存在",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
        return
    if os.path.isfile(inp):n_parts=1
    elif os.path.isdir(inp):pass
    else:
        yield "输入路径存在但既不是文件也不是文件夹",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
        return
    if (ps_slice == []):
        for i_part in range(n_parts):
            cmd = '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s''' % (python_exec,inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, i_part, n_parts)
            print(cmd)
            p = Popen(cmd, shell=True)
            ps_slice.append(p)
        yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
        for p in ps_slice:
            p.wait()
        ps_slice=[]
        yield "切割结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}

def close_slice():
    global ps_slice
    if (ps_slice != []):
        for p_slice in ps_slice:
            try:
                kill_process(p_slice.pid)
            except:
                traceback.print_exc()
        ps_slice=[]
    return "已终止所有切割进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}

ps1a=[]
def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir):
    global ps1a
    if (ps1a == []):
        opt_dir="%s/%s"%(exp_root,exp_name)
        config={
            "inp_text":inp_text,
            "inp_wav_dir":inp_wav_dir,
            "exp_name":exp_name,
            "opt_dir":opt_dir,
            "bert_pretrained_dir":bert_pretrained_dir,
        }
        gpu_names=gpu_numbers.split("-")
        all_parts=len(gpu_names)
        for i_part in range(all_parts):
            config.update(
                {
                    "i_part": str(i_part),
                    "all_parts": str(all_parts),
                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                    "is_half": str(is_half)
                }
            )
            os.environ.update(config)#
            cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
            print(cmd)
            p = Popen(cmd, shell=True)
            ps1a.append(p)
        yield "文本进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
        for p in ps1a:
            p.wait()
        opt = []
        for i_part in range(all_parts):
            txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
            with open(txt_path, "r", encoding="utf8") as f:
                opt += f.read().strip("\n").split("\n")
            os.remove(txt_path)
        path_text = "%s/2-name2text.txt" % opt_dir
        with open(path_text, "w", encoding="utf8") as f:
            f.write("\n".join(opt) + "\n")
        ps1a=[]
        yield "文本进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的文本任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}

def close1a():
    global ps1a
    if (ps1a != []):
        for p1a in ps1a:
            try:
                kill_process(p1a.pid)
            except:
                traceback.print_exc()
        ps1a=[]
    return "已终止所有1a进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}

ps1b=[]
def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
    global ps1b
    if (ps1b == []):
        config={
            "inp_text":inp_text,
            "inp_wav_dir":inp_wav_dir,
            "exp_name":exp_name,
            "opt_dir":"%s/%s"%(exp_root,exp_name),
            "cnhubert_base_dir":ssl_pretrained_dir,
            "is_half": str(is_half)
        }
        gpu_names=gpu_numbers.split("-")
        all_parts=len(gpu_names)
        for i_part in range(all_parts):
            config.update(
                {
                    "i_part": str(i_part),
                    "all_parts": str(all_parts),
                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                }
            )
            os.environ.update(config)
            cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
            print(cmd)
            p = Popen(cmd, shell=True)
            ps1b.append(p)
        yield "SSL提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
        for p in ps1b:
            p.wait()
        ps1b=[]
        yield "SSL提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}

def close1b():
    global ps1b
    if (ps1b != []):
        for p1b in ps1b:
            try:
                kill_process(p1b.pid)
            except:
                traceback.print_exc()
        ps1b=[]
    return "已终止所有1b进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}

ps1c=[]
def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
    global ps1c
    if (ps1c == []):
        opt_dir="%s/%s"%(exp_root,exp_name)
        config={
            "inp_text":inp_text,
            "exp_name":exp_name,
            "opt_dir":opt_dir,
            "pretrained_s2G":pretrained_s2G_path,
            "s2config_path":"GPT_SoVITS/configs/s2.json",
            "is_half": str(is_half)
        }
        gpu_names=gpu_numbers.split("-")
        all_parts=len(gpu_names)
        for i_part in range(all_parts):
            config.update(
                {
                    "i_part": str(i_part),
                    "all_parts": str(all_parts),
                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                }
            )
            os.environ.update(config)
            cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
            print(cmd)
            p = Popen(cmd, shell=True)
            ps1c.append(p)
        yield "语义token提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
        for p in ps1c:
            p.wait()
        opt = ["item_name\tsemantic_audio"]
        path_semantic = "%s/6-name2semantic.tsv" % opt_dir
        for i_part in range(all_parts):
            semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
            with open(semantic_path, "r", encoding="utf8") as f:
                opt += f.read().strip("\n").split("\n")
            os.remove(semantic_path)
        with open(path_semantic, "w", encoding="utf8") as f:
            f.write("\n".join(opt) + "\n")
        ps1c=[]
        yield "语义token提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
    else:
        yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}

def close1c():
    global ps1c
    if (ps1c != []):
        for p1c in ps1c:
            try:
                kill_process(p1c.pid)
            except:
                traceback.print_exc()
        ps1c=[]
    return "已终止所有语义token进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
#####inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G
ps1abc=[]
def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,ssl_pretrained_dir,pretrained_s2G_path):
    global ps1abc
    if (ps1abc == []):
        opt_dir="%s/%s"%(exp_root,exp_name)
        try:
            #############################1a
            path_text="%s/2-name2text.txt" % opt_dir
            if(os.path.exists(path_text)==False or (os.path.exists(path_text)==True and len(open(path_text,"r",encoding="utf8").read().strip("\n").split("\n"))<2)):
                config={
                    "inp_text":inp_text,
                    "inp_wav_dir":inp_wav_dir,
                    "exp_name":exp_name,
                    "opt_dir":opt_dir,
                    "bert_pretrained_dir":bert_pretrained_dir,
                    "is_half": str(is_half)
                }
                gpu_names=gpu_numbers1a.split("-")
                all_parts=len(gpu_names)
                for i_part in range(all_parts):
                    config.update(
                        {
                            "i_part": str(i_part),
                            "all_parts": str(all_parts),
                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                        }
                    )
                    os.environ.update(config)
                    cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
                    print(cmd)
                    p = Popen(cmd, shell=True)
                    ps1abc.append(p)
                yield "进度：1a-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
                for p in ps1abc:p.wait()

                opt = []
                for i_part in range(all_parts):#txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
                    txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
                    with open(txt_path, "r",encoding="utf8") as f:
                        opt += f.read().strip("\n").split("\n")
                    os.remove(txt_path)
                with open(path_text, "w",encoding="utf8") as f:
                    f.write("\n".join(opt) + "\n")

            yield "进度：1a-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
            ps1abc=[]
            #############################1b
            config={
                "inp_text":inp_text,
                "inp_wav_dir":inp_wav_dir,
                "exp_name":exp_name,
                "opt_dir":opt_dir,
                "cnhubert_base_dir":ssl_pretrained_dir,
            }
            gpu_names=gpu_numbers1Ba.split("-")
            all_parts=len(gpu_names)
            for i_part in range(all_parts):
                config.update(
                    {
                        "i_part": str(i_part),
                        "all_parts": str(all_parts),
                        "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                    }
                )
                os.environ.update(config)
                cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
                print(cmd)
                p = Popen(cmd, shell=True)
                ps1abc.append(p)
            yield "进度：1a-done, 1b-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
            for p in ps1abc:p.wait()
            yield "进度：1a1b-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
            ps1abc=[]
            #############################1c
            path_semantic = "%s/6-name2semantic.tsv" % opt_dir
            if(os.path.exists(path_semantic)==False or (os.path.exists(path_semantic)==True and os.path.getsize(path_semantic)<31)):
                config={
                    "inp_text":inp_text,
                    "exp_name":exp_name,
                    "opt_dir":opt_dir,
                    "pretrained_s2G":pretrained_s2G_path,
                    "s2config_path":"GPT_SoVITS/configs/s2.json",
                }
                gpu_names=gpu_numbers1c.split("-")
                all_parts=len(gpu_names)
                for i_part in range(all_parts):
                    config.update(
                        {
                            "i_part": str(i_part),
                            "all_parts": str(all_parts),
                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
                        }
                    )
                    os.environ.update(config)
                    cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
                    print(cmd)
                    p = Popen(cmd, shell=True)
                    ps1abc.append(p)
                yield "进度：1a1b-done, 1cing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
                for p in ps1abc:p.wait()

                opt = ["item_name\tsemantic_audio"]
                for i_part in range(all_parts):
                    semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
                    with open(semantic_path, "r",encoding="utf8") as f:
                        opt += f.read().strip("\n").split("\n")
                    os.remove(semantic_path)
                with open(path_semantic, "w",encoding="utf8") as f:
                    f.write("\n".join(opt) + "\n")
                yield "进度：all-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
            ps1abc = []
            yield "一键三连进程结束", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
        except:
            traceback.print_exc()
            close1abc()
            yield "一键三连中途报错", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
    else:
        yield "已有正在进行的一键三连任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}

def close1abc():
    global ps1abc
    if (ps1abc != []):
        for p1abc in ps1abc:
            try:
                kill_process(p1abc.pid)
            except:
                traceback.print_exc()
        ps1abc=[]
    return "已终止所有一键三连进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}

with gr.Blocks(title="GPT-SoVITS WebUI") as app:
    gr.Markdown(
        value=
            i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
    )
    with gr.Tabs():
        with gr.TabItem(i18n("0-前置数据集获取工具")):#提前随机切片防止uvr5爆内存->uvr5->slicer->asr->打标
            gr.Markdown(value=i18n("0a-UVR5人声伴奏分离&去混响去延迟工具"))
            with gr.Row():
                if_uvr5 = gr.Checkbox(label=i18n("是否开启UVR5-WebUI"),show_label=True)
                uvr5_info = gr.Textbox(label=i18n("UVR5进程输出信息"))
            gr.Markdown(value=i18n("0b-语音切分工具"))
            with gr.Row():
                with gr.Row():
                    slice_inp_path=gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"),value="")
                    slice_opt_root=gr.Textbox(label=i18n("切分后的子音频的输出根目录"),value="output/slicer_opt")
                    threshold=gr.Textbox(label=i18n("threshold:音量小于这个值视作静音的备选切割点"),value="-34")
                    min_length=gr.Textbox(label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"),value="4000")
                    min_interval=gr.Textbox(label=i18n("min_interval:最短切割间隔"),value="300")
                    hop_size=gr.Textbox(label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"),value="10")
                    max_sil_kept=gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"),value="500")
                with gr.Row():
                    open_slicer_button=gr.Button(i18n("开启语音切割"), variant="primary",visible=True)
                    close_slicer_button=gr.Button(i18n("终止语音切割"), variant="primary",visible=False)
                    _max=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("max:归一化后最大值多少"),value=0.9,interactive=True)
                    alpha=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("alpha_mix:混多少比例归一化后音频进来"),value=0.25,interactive=True)
                    n_process=gr.Slider(minimum=1,maximum=n_cpu,step=1,label=i18n("切割使用的进程数"),value=4,interactive=True)
                    slicer_info = gr.Textbox(label=i18n("语音切割进程输出信息"))
            gr.Markdown(value=i18n("0c-中文批量离线ASR工具"))
            with gr.Row():
                open_asr_button = gr.Button(i18n("开启离线批量ASR"), variant="primary",visible=True)
                close_asr_button = gr.Button(i18n("终止ASR进程"), variant="primary",visible=False)
                asr_inp_dir = gr.Textbox(
                    label=i18n("批量ASR(中文only)输入文件夹路径"),
                    value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx",
                    interactive=True,
                )
                asr_info = gr.Textbox(label=i18n("ASR进程输出信息"))
            gr.Markdown(value=i18n("0d-语音文本校对标注工具"))
            with gr.Row():
                if_label = gr.Checkbox(label=i18n("是否开启打标WebUI"),show_label=True)
                path_list = gr.Textbox(
                    label=i18n("打标数据标注文件路径"),
                    value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list",
                    interactive=True,
                )
                label_info = gr.Textbox(label=i18n("打标工具进程输出信息"))
            if_label.change(change_label, [if_label,path_list], [label_info])
            if_uvr5.change(change_uvr5, [if_uvr5], [uvr5_info])
            open_asr_button.click(open_asr, [asr_inp_dir], [asr_info,open_asr_button,close_asr_button])
            close_asr_button.click(close_asr, [], [asr_info,open_asr_button,close_asr_button])
            open_slicer_button.click(open_slice, [slice_inp_path,slice_opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_process], [slicer_info,open_slicer_button,close_slicer_button])
            close_slicer_button.click(close_slice, [], [slicer_info,open_slicer_button,close_slicer_button])
        with gr.TabItem(i18n("1-GPT-SoVITS-TTS")):
            with gr.Row():
                exp_name = gr.Textbox(label=i18n("*实验/模型名"), value="xxx", interactive=True)
                gpu_info = gr.Textbox(label=i18n("显卡信息"), value=gpu_info, visible=True, interactive=False)
                pretrained_s2G = gr.Textbox(label=i18n("预训练的SoVITS-G模型路径"), value="GPT_SoVITS/pretrained_models/s2G488k.pth", interactive=True)
                pretrained_s2D = gr.Textbox(label=i18n("预训练的SoVITS-D模型路径"), value="GPT_SoVITS/pretrained_models/s2D488k.pth", interactive=True)
                pretrained_s1 = gr.Textbox(label=i18n("预训练的GPT模型路径"), value="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", interactive=True)
            with gr.TabItem(i18n("1A-训练集格式化工具")):
                gr.Markdown(value=i18n("输出logs/实验名目录下应有23456开头的文件和文件夹"))
                with gr.Row():
                    inp_text = gr.Textbox(label=i18n("*文本标注文件"),value=r"D:\RVC1006\GPT-SoVITS\raw\xxx.list",interactive=True)
                    inp_wav_dir = gr.Textbox(
                        label=i18n("*训练集音频文件目录"),
                        # value=r"D:\RVC1006\GPT-SoVITS\raw\xxx",
                        interactive=True,
                        placeholder=i18n("训练集音频文件目录-拼接-list文件里波形对应的文件名（不是全路径）。")
                    )
                gr.Markdown(value=i18n("1Aa-文本内容"))
                with gr.Row():
                    gpu_numbers1a = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"),value="%s-%s"%(gpus,gpus),interactive=True)
                    bert_pretrained_dir = gr.Textbox(label=i18n("预训练的中文BERT模型路径"),value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",interactive=False)
                    button1a_open = gr.Button(i18n("开启文本获取"), variant="primary",visible=True)
                    button1a_close = gr.Button(i18n("终止文本获取进程"), variant="primary",visible=False)
                    info1a=gr.Textbox(label=i18n("文本进程输出信息"))
                gr.Markdown(value=i18n("1Ab-SSL自监督特征提取"))
                with gr.Row():
                    gpu_numbers1Ba = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"),value="%s-%s"%(gpus,gpus),interactive=True)
                    cnhubert_base_dir = gr.Textbox(label=i18n("预训练的SSL模型路径"),value="GPT_SoVITS/pretrained_models/chinese-hubert-base",interactive=False)
                    button1b_open = gr.Button(i18n("开启SSL提取"), variant="primary",visible=True)
                    button1b_close = gr.Button(i18n("终止SSL提取进程"), variant="primary",visible=False)
                    info1b=gr.Textbox(label=i18n("SSL进程输出信息"))
                gr.Markdown(value=i18n("1Ac-语义token提取"))
                with gr.Row():
                    gpu_numbers1c = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"),value="%s-%s"%(gpus,gpus),interactive=True)
                    button1c_open = gr.Button(i18n("开启语义token提取"), variant="primary",visible=True)
                    button1c_close = gr.Button(i18n("终止语义token提取进程"), variant="primary",visible=False)
                    info1c=gr.Textbox(label=i18n("语义token提取进程输出信息"))
                gr.Markdown(value=i18n("1Aabc-训练集格式化一键三连"))
                with gr.Row():
                    button1abc_open = gr.Button(i18n("开启一键三连"), variant="primary",visible=True)
                    button1abc_close = gr.Button(i18n("终止一键三连"), variant="primary",visible=False)
                    info1abc=gr.Textbox(label=i18n("一键三连进程输出信息"))
            button1a_open.click(open1a, [inp_text,inp_wav_dir,exp_name,gpu_numbers1a,bert_pretrained_dir], [info1a,button1a_open,button1a_close])
            button1a_close.click(close1a, [], [info1a,button1a_open,button1a_close])
            button1b_open.click(open1b, [inp_text,inp_wav_dir,exp_name,gpu_numbers1Ba,cnhubert_base_dir], [info1b,button1b_open,button1b_close])
            button1b_close.click(close1b, [], [info1b,button1b_open,button1b_close])
            button1c_open.click(open1c, [inp_text,exp_name,gpu_numbers1c,pretrained_s2G], [info1c,button1c_open,button1c_close])
            button1c_close.click(close1c, [], [info1c,button1c_open,button1c_close])
            button1abc_open.click(open1abc, [inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G], [info1abc,button1abc_open,button1abc_close])
            button1abc_close.click(close1abc, [], [info1abc,button1abc_open,button1abc_close])
            with gr.TabItem(i18n("1B-微调训练")):
                gr.Markdown(value=i18n("1Ba-SoVITS训练。用于分享的模型文件输出在SoVITS_weights下。"))
                with gr.Row():
                    batch_size = gr.Slider(minimum=1,maximum=40,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True)
                    total_epoch = gr.Slider(minimum=1,maximum=25,step=1,label=i18n("总训练轮数total_epoch，不建议太高"),value=8,interactive=True)
                    text_low_lr_rate = gr.Slider(minimum=0.2,maximum=0.6,step=0.05,label=i18n("文本模块学习率权重"),value=0.4,interactive=True)
                    save_every_epoch = gr.Slider(minimum=1,maximum=25,step=1,label=i18n("保存频率save_every_epoch"),value=4,interactive=True)
                    if_save_latest = gr.Checkbox(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), value=True, interactive=True, show_label=True)
                    if_save_every_weights = gr.Checkbox(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"), value=True, interactive=True, show_label=True)
                    gpu_numbers1Ba = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"), value="%s" % (gpus), interactive=True)
                with gr.Row():
                    button1Ba_open = gr.Button(i18n("开启SoVITS训练"), variant="primary",visible=True)
                    button1Ba_close = gr.Button(i18n("终止SoVITS训练"), variant="primary",visible=False)
                    info1Ba=gr.Textbox(label=i18n("SoVITS训练进程输出信息"))
                gr.Markdown(value=i18n("1Bb-GPT训练。用于分享的模型文件输出在GPT_weights下。"))
                with gr.Row():
                    batch_size1Bb = gr.Slider(minimum=1,maximum=40,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True)
                    total_epoch1Bb = gr.Slider(minimum=2,maximum=50,step=1,label=i18n("总训练轮数total_epoch"),value=15,interactive=True)
                    if_save_latest1Bb = gr.Checkbox(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), value=True, interactive=True, show_label=True)
                    if_save_every_weights1Bb = gr.Checkbox(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"), value=True, interactive=True, show_label=True)
                    save_every_epoch1Bb = gr.Slider(minimum=1,maximum=50,step=1,label=i18n("保存频率save_every_epoch"),value=5,interactive=True)
                    gpu_numbers1Bb = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"), value="%s" % (gpus), interactive=True)
                with gr.Row():
                    button1Bb_open = gr.Button(i18n("开启GPT训练"), variant="primary",visible=True)
                    button1Bb_close = gr.Button(i18n("终止GPT训练"), variant="primary",visible=False)
                    info1Bb=gr.Textbox(label=i18n("GPT训练进程输出信息"))
            button1Ba_open.click(open1Ba, [batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D], [info1Ba,button1Ba_open,button1Ba_close])
            button1Ba_close.click(close1Ba, [], [info1Ba,button1Ba_open,button1Ba_close])
            button1Bb_open.click(open1Bb, [batch_size1Bb,total_epoch1Bb,exp_name,if_save_latest1Bb,if_save_every_weights1Bb,save_every_epoch1Bb,gpu_numbers1Bb,pretrained_s1],   [info1Bb,button1Bb_open,button1Bb_close])
            button1Bb_close.click(close1Bb, [], [info1Bb,button1Bb_open,button1Bb_close])
            with gr.TabItem(i18n("1C-推理")):
                gr.Markdown(value=i18n("选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的一个是底模，体验5秒Zero Shot TTS用。"))
                with gr.Row():
                    GPT_dropdown = gr.Dropdown(label=i18n("*GPT模型列表"), choices=sorted(GPT_names,key=custom_sort_key),value=pretrained_gpt_name,interactive=True)
                    SoVITS_dropdown = gr.Dropdown(label=i18n("*SoVITS模型列表"), choices=sorted(SoVITS_names,key=custom_sort_key),value=pretrained_sovits_name,interactive=True)
                    gpu_number_1C=gr.Textbox(label=i18n("GPU卡号,只能填1个整数"), value=gpus, interactive=True)
                    refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
                    refresh_button.click(fn=change_choices,inputs=[],outputs=[SoVITS_dropdown,GPT_dropdown])
                with gr.Row():
                    if_tts = gr.Checkbox(label=i18n("是否开启TTS推理WebUI"), show_label=True)
                    tts_info = gr.Textbox(label=i18n("TTS推理WebUI进程输出信息"))
                    if_tts.change(change_tts_inference, [if_tts,bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown], [tts_info])
        with gr.TabItem(i18n("2-GPT-SoVITS-变声")):gr.Markdown(value=i18n("施工中，请静候佳音"))
    app.queue(concurrency_count=511, max_size=1022).launch(
        server_name="0.0.0.0",
        inbrowser=True,
        share=is_share,
        server_port=webui_port_main,
        quiet=True,
    )