app.py

import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
from mega import Mega

os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
import threading
from time import sleep
from subprocess import Popen
import faiss
from random import shuffle
import json, datetime, requests
from gtts import gTTS

now_dir = os.getcwd()
sys.path.append(now_dir)
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
from i18n import I18nAuto

import signal

import math

from utils import load_audio, CSVutil

global DoFormant, Quefrency, Timbre

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

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


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

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

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

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


download_models()

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


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

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


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

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


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

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

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


    else:

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

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


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

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


def update_fshift_presets(preset, qfrency, tmbre):
    qfrency, tmbre = preset_apply(preset, qfrency, tmbre)

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

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


i18n = I18nAuto()
# i18n.print()

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("Unfortunately you don't have a working graphics card to support your training")
    default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])
from 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")
            )
        )  # Prevent the novice from making mistakes and automatically replace them for him
        # file_big_npy = (
        #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
        # )
        audio_opt = vc.pipeline(
            hubert_model,
            net_g,
            sid,
            audio,
            input_audio_path,
            times,
            f0_up_key,
            f0_method,
            file_index,
            # file_big_npy,
            index_rate,
            if_f0,
            filter_radius,
            tgt_sr,
            resample_sr,
            rms_mix_rate,
            version,
            protect,
            crepe_hop_length,
            f0_file=f0_file,
        )
        if resample_sr >= 16000 and tgt_sr != resample_sr:
            tgt_sr = resample_sr
        index_info = (
            "Using index:%s." % file_index
            if os.path.exists(file_index)
            else "Index not used."
        )
        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
            index_info,
            times[0],
            times[1],
            times[2],
        ), (tgt_sr, audio_opt)
    except:
        info = traceback.format_exc()
        print(info)
        return info, (None, None)


def vc_multi(
        sid,
        dir_path,
        opt_root,
        paths,
        f0_up_key,
        f0_method,
        file_index,
        file_index2,
        # file_big_npy,
        index_rate,
        filter_radius,
        resample_sr,
        rms_mix_rate,
        protect,
        format1,
        crepe_hop_length,
):
    try:
        dir_path = (
            dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
        )  # Prevent the copy path from containing spaces, " and carriage returns" at the beginning and end
        opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
        os.makedirs(opt_root, exist_ok=True)
        try:
            if dir_path != "":
                paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
            else:
                paths = [path.name for path in paths]
        except:
            traceback.print_exc()
            paths = [path.name for path in paths]
        infos = []
        for path in paths:
            info, opt = vc_single(
                sid,
                path,
                f0_up_key,
                None,
                f0_method,
                file_index,
                # file_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()


# A tab can only have one sound globally.
def get_vc(sid):
    global n_spk, tgt_sr, net_g, vc, cpt, version
    if sid == "" or sid == []:
        global hubert_model
        if hubert_model != None:  # Considering polling, we need to add a check to see if the sid is switched from a model to a model-free state.
            print("clean_empty_cache")
            del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            ###The downstairs won't be clean without this much trouble
            if_f0 = cpt.get("f0", 1)
            version = cpt.get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            del net_g, cpt
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            cpt = None
        return {"visible": False, "__type__": "update"}
    person = "%s/%s" % (weight_root, sid)
    print("loading %s" % person)
    cpt = torch.load(person, map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
    if_f0 = cpt.get("f0", 1)
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt["weight"], strict=False))
    net_g.eval().to(config.device)
    if config.is_half:
        net_g = net_g.half()
    else:
        net_g = net_g.float()
    vc = VC(tgt_sr, config)
    n_spk = cpt["config"][-3]
    return {"visible": False, "maximum": n_spk, "__type__": "update"}


def change_choices():
    names = []
    for name in os.listdir(weight_root):
        if name.endswith(".pth"):
            names.append(name)
    index_paths = []
    for root, dirs, files in os.walk(index_root, topdown=False):
        for name in files:
            if name.endswith(".index") and "trained" not in name:
                index_paths.append("%s/%s" % (root, name))
    return {"choices": sorted(names), "__type__": "update"}, {
        "choices": sorted(index_paths),
        "__type__": "update",
    }


def clean():
    return {"value": "", "__type__": "update"}


sr_dict = {
    "32k": 32000,
    "40k": 40000,
    "48k": 48000,
}


def if_done(done, p):
    while 1:
        if p.poll() == None:
            sleep(0.5)
        else:
            break
    done[0] = True


def if_done_multi(done, ps):
    while 1:
        # poll==None means the process has not ended
        # Stop as long as one process is not finished
        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
    ###Both gr and popen read have to be run through and then read in one go. Without gr, you can read one sentence and output one sentence normally; you can only create an additional text stream for regular reading.
    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
        ###Both gr and popen read have to be run through and then read in one go. Without gr, you can read one sentence and output one sentence normally; you can only create an additional text stream for regular reading.
        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)
    ###Both gr and popen read have to be run through and then read in one go. Without gr, you can read one sentence and output one sentence normally; you can only create an additional text stream for regular reading.
    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 (
    "After the training is completed, you can view the console training log or train.log under the experiment folder",
    {"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:processing data"))
    yield get_info_str(cmd)
    p = Popen(cmd, shell=True)
    p.wait()
    with open(preprocess_log_path, "r") as f:
        print(f.read())
    #########step2a:提取音高
    open(extract_f0_feature_log_path, "w")
    if if_f0_3:
        yield get_info_str("step2a:No need to extract pitch")
        cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % (
            model_log_dir,
            np7,
            f0method8,
            echl
        )
        yield get_info_str(cmd)
        p = Popen(cmd, shell=True, cwd=now_dir)
        p.wait()
        with open(extract_f0_feature_log_path, "r") as f:
            print(f.read())
    else:
        yield get_info_str(i18n("step2a:No need to extract pitch"))

    yield get_info_str(i18n("step2b:extracting features"))
    gpus = gpus16.split("-")
    leng = len(gpus)
    ps = []
    for idx, n_g in enumerate(gpus):
        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
            config.device,
            leng,
            idx,
            n_g,
            model_log_dir,
            version19,
        )
        yield get_info_str(cmd)
        p = Popen(
            cmd, shell=True, cwd=now_dir
        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
        ps.append(p)
    for p in ps:
        p.wait()
    with open(extract_f0_feature_log_path, "r") as f:
        print(f.read())
    yield get_info_str(i18n("step3a:training model"))

    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(
        "After the training is completed, you can view the console training log or train.log under the experiment folder"))
    #######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("The whole process is completed!"))


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='Voice DeepFake 💻') as app:
    with gr.Tabs():
        with gr.TabItem("Interfeys"):
            gr.HTML("<center><h1>  Voice DeepFake   </h1></span>")
            # gr.HTML("<center><h3>  Если вы хотите использовать это пространство в частном порядке, я рекомендую продублировать его.  </h3></span>")
            # with gr.Row():
            #     gr.Markdown(
            #     """
            #     Если не работает, то пробуйте тут https://huggingface.co/spaces/Clebersla/RVC_V2_Huggingface_Version
            #     """
            #     )

            # 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. Modelni tanlang.", choices=sorted(names), value=check_for_name())
                refresh_button = gr.Button("Yangilash", variant="primary")
                if check_for_name() != '':
                    get_vc(sorted(names)[0])
                vc_transform0 = gr.Number(
                    label="Kengaytirilgan: Bu yerda siz ovoz balandligini o'zgartirishingiz yoki uni 0 da qoldirishingiz mumkin.",
                    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("O'zgartiring", variant="primary")
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        dropbox = gr.File(
                            label='Ovoz yozishingizni shu yerga yuboring va "Qayta yuklash" tugmasini bosing.')
                    with gr.Row():
                        record_button = gr.Audio(source="microphone", label="Ovozni mikrofondan yozib oling.",
                                                 type="filepath")
                    with gr.Row():
                        input_audio0 = gr.Dropdown(
                            label="2.Audio yozuvni tanlang.",
                            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("Yangilash", variant="primary", size='sm')
                        record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
                        record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
                    # with gr.Row():
                    #     with gr.Accordion('Текст в речь', open=False):
                    #         with gr.Column():
                    #             lang = gr.Radio(label='Выберите язык для озвучки.',choices=['en','es','ru','uk','pl','fr','de','tr'], value='en')
                    #             elevenid=gr.Dropdown(label="Голос:", choices=eleven_voices)
                    #         with gr.Column():
                    #             tfs = gr.Textbox(label="Введите свой текст", interactive=True, value="This is a test.")
                    #             tts_button = gr.Button(value="Генерировать")
                    #             tts_button.click(fn=elevenTTS, inputs=[tfs, elevenid, lang], outputs=[record_button, input_audio0])
                    with gr.Row():
                        with gr.Accordion('Wav2Lip', open=False, visible=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("Indeks sozlamalari", open=False):
                        file_index1 = gr.Dropdown(
                            label="3. added.index fayliga yo'l (agar u avtomatik ravishda topilmasa).",
                            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("Qidiruv funksiyasining nisbati (men uni 0 ga o'rnatishni tavsiya qilaman):"),
                            value=0.66,
                            interactive=True,
                        )
                    vc_output2 = gr.Audio(
                        label="Audio ma'lumotlarni chiqarish (yuklab olish uchun o'ng burchakdagi uchta nuqta ustiga bosing)",
                        type='filepath',
                        interactive=False,
                    )
                    animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview])
                    with gr.Accordion("Qo'shimcha sozlamalar", open=False):
                        f0method0 = gr.Radio(
                            label='Majburiy emas: Ovozni ajratib olish algoritmini o\'zgartiring.',
                            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. Kattaroq raqamlar tovush balandligining keskin o'zgarishi ehtimolini kamaytiradi, lekin pastroq raqamlar aniqlikni oshiradi. 64-192 - tajriba uchun yaxshi diapazon.",
                            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(
                                "Agar >=3 boʻlsa: toʻplangan ovoz balandligi natijalariga median filtrlashni qoʻllang. Qiymat filtrlash radiusini ifodalaydi va nafas olishni qisqartirishi mumkin"),
                            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(
                                "Chiqish ovozi konvertini almashtirish yoki aralashtirish uchun kirish ovozi konvertidan foydalaning. Bu nisbat 1 ga qanchalik yaqin bo'lsa, chiqish signali konvertidan shunchalik ko'p foydalaniladi:"),
                            value=0.21,
                            interactive=True,
                        )
                        protect0 = gr.Slider(
                            minimum=0,
                            maximum=0.5,
                            label=i18n(
                                "Elektron musiqadagi tanaffuslar kabi artefaktlarning oldini olish uchun ovozsiz undosh tovushlarni va nafas tovushlarini himoya qiladi. O'chirish uchun qiymatni 0,5 ga o'rnating. Xavfsizlikni oshirish uchun qiymatni kamaytiring, lekin bu indekslash aniqligini kamaytirishi mumkin:"),
                            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=False,
                        )

                        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, visible=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("Modelni yuklash"):
            # with gr.Row():
            #     gr.Markdown(
            #     """
            #     Если не работает, то пробуйте тут https://huggingface.co/spaces/Clebersla/RVC_V2_Huggingface_Version
            #     """
            #     )
            with gr.Row():
                url = gr.Textbox(label="Model URL manzilini kiriting:",
                                 placeholder=".pth va .index fayllarni o'z ichiga olgan zipga havolani kiriting")
            with gr.Row():
                model = gr.Textbox(label="Model nomi:")
                download_button = gr.Button("Yuklash")
            with gr.Row():
                status_bar = gr.Textbox(label="")
                download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])


        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,
                    )

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