Delete app.py

app.py

@@ -1,2094 +0,0 @@
-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)
-
-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-------------------------------\n 🗣️ RVC Easy GUI (Colab Edition)\n-------------------------------\n")
-
-
-
-
-print("\n----------\n other repository : https://github.com/Tiger14n/RVC-GUI : https://github.com/HoshioPilio/Retrieval-based-Voice-Conversion : https://github.com/Mangio621/Mangio-RVC-Fork : https://github.com/IAHispano/Applio-RVC-Fork : https://github.com/777gt/-EVC-\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='RVC EASY GUI 🔊') as app:
-    with gr.Tabs():
-        with gr.TabItem("Inference"):
-            gr.HTML("<h1>  RVC  EASY GUI  </h1>")    
-            gr.HTML("<h10>   Colab version made by Clebersla   </h10>")   
-            gr.HTML("<h10>   Easy GUI coded by Rejekt's RVC GUI coded by Tiger14n   </h10>")
-            
-            # 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(
-                """
-                laynz28's huggingface:https://huggingface.co/LaynzID12
-                Original RVC:https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI Mangio’s RVC Fork:https://github.com/Mangio621/Mangio-RVC-Fork ❤️ RVC GUI:https://github.com/Tiger14n/RVC-GUI ❤️ 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=True)
-#endregion