Delete app.py

app.py

@@ -1,1474 +0,0 @@
-import os, sys
-import datetime, subprocess
-from mega import Mega
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import logging
-import shutil
-import threading
-import traceback
-import warnings
-from random import shuffle
-from subprocess import Popen
-from time import sleep
-import json
-import pathlib
-
-import fairseq
-import faiss
-import gradio as gr
-import numpy as np
-import torch
-from dotenv import load_dotenv
-from sklearn.cluster import MiniBatchKMeans
-
-from configs.config import Config
-from i18n.i18n import I18nAuto
-from infer.lib.train.process_ckpt import (
-    change_info,
-    extract_small_model,
-    merge,
-    show_info,
-)
-from infer.modules.uvr5.modules import uvr
-from infer.modules.vc.modules import VC
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-logger = logging.getLogger(__name__)
-
-tmp = os.path.join(now_dir, "TEMP")
-shutil.rmtree(tmp, ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
-os.makedirs(tmp, exist_ok=True)
-os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
-os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True)
-os.makedirs(os.path.join(now_dir, "audios"), exist_ok=True)
-os.environ["TEMP"] = tmp
-warnings.filterwarnings("ignore")
-torch.manual_seed(114514)
-
-
-load_dotenv()
-config = Config()
-vc = VC(config)
-
-if config.dml == True:
-
-    def forward_dml(ctx, x, scale):
-        ctx.scale = scale
-        res = x.clone().detach()
-        return res
-
-    fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
-i18n = I18nAuto()
-logger.info(i18n)
-# 判断是否有能用来训练和加速推理的N卡
-ngpu = torch.cuda.device_count()
-gpu_infos = []
-mem = []
-if_gpu_ok = False
-
-if torch.cuda.is_available() or ngpu != 0:
-    for i in range(ngpu):
-        gpu_name = torch.cuda.get_device_name(i)
-        if any(
-            value in gpu_name.upper()
-            for value in [
-                "10",
-                "16",
-                "20",
-                "30",
-                "40",
-                "A2",
-                "A3",
-                "A4",
-                "P4",
-                "A50",
-                "500",
-                "A60",
-                "70",
-                "80",
-                "90",
-                "M4",
-                "T4",
-                "TITAN",
-            ]
-        ):
-            # A10#A100#V100#A40#P40#M40#K80#A4500
-            if_gpu_ok = True  # 至少有一张能用的N卡
-            gpu_infos.append("%s\t%s" % (i, gpu_name))
-            mem.append(
-                int(
-                    torch.cuda.get_device_properties(i).total_memory
-                    / 1024
-                    / 1024
-                    / 1024
-                    + 0.4
-                )
-            )
-if if_gpu_ok and len(gpu_infos) > 0:
-    gpu_info = "\n".join(gpu_infos)
-    default_batch_size = min(mem) // 2
-else:
-    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
-    default_batch_size = 1
-gpus = "-".join([i[0] for i in gpu_infos])
-
-
-class ToolButton(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-weight_root = os.getenv("weight_root")
-weight_uvr5_root = os.getenv("weight_uvr5_root")
-index_root = os.getenv("index_root")
-
-names = []
-for name in os.listdir(weight_root):
-    if name.endswith(".pth"):
-        names.append(name)
-index_paths = []
-for root, dirs, files in os.walk(index_root, topdown=False):
-    for name in files:
-        if name.endswith(".index") and "trained" not in name:
-            index_paths.append("%s/%s" % (root, name))
-uvr5_names = []
-for name in os.listdir(weight_uvr5_root):
-    if name.endswith(".pth") or "onnx" in name:
-        uvr5_names.append(name.replace(".pth", ""))
-
-
-def 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))
-    audio_files=[]
-    for filename in os.listdir("./audios"):
-        if filename.endswith(('.wav','.mp3','.ogg')):
-            audio_files.append('./audios/'+filename)
-    return {"choices": sorted(names), "__type__": "update"}, {
-        "choices": sorted(index_paths),
-        "__type__": "update",
-    }, {"choices": sorted(audio_files), "__type__": "update"}
-
-def clean():
-    return {"value": "", "__type__": "update"}
-
-
-def export_onnx():
-    from infer.modules.onnx.export import export_onnx as eo
-
-    eo()
-
-
-sr_dict = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-def if_done(done, p):
-    while 1:
-        if p.poll() is 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() is 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()
-    per = 3.0 if config.is_half else 3.7
-    cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
-        config.python_cmd,
-        trainset_dir,
-        sr,
-        n_p,
-        now_dir,
-        exp_dir,
-        config.noparallel,
-        per,
-    )
-    logger.info(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]:
-            break
-    with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
-        log = f.read()
-    logger.info(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, gpus_rmvpe):
-    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:
-        if f0method != "rmvpe_gpu":
-            cmd = (
-                '"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
-                % (
-                    config.python_cmd,
-                    now_dir,
-                    exp_dir,
-                    n_p,
-                    f0method,
-                )
-            )
-            logger.info(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()
-        else:
-            if gpus_rmvpe != "-":
-                gpus_rmvpe = gpus_rmvpe.split("-")
-                leng = len(gpus_rmvpe)
-                ps = []
-                for idx, n_g in enumerate(gpus_rmvpe):
-                    cmd = (
-                        '"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
-                        % (
-                            config.python_cmd,
-                            leng,
-                            idx,
-                            n_g,
-                            now_dir,
-                            exp_dir,
-                            config.is_half,
-                        )
-                    )
-                    logger.info(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()
-            else:
-                cmd = (
-                    config.python_cmd
-                    + ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
-                    % (
-                        now_dir,
-                        exp_dir,
-                    )
-                )
-                logger.info(cmd)
-                p = Popen(
-                    cmd, shell=True, cwd=now_dir
-                )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
-                p.wait()
-                done = [True]
-        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]:
-                break
-        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
-            log = f.read()
-        logger.info(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 = (
-            '"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s True'
-            % (
-                config.python_cmd,
-                config.device,
-                leng,
-                idx,
-                n_g,
-                now_dir,
-                exp_dir,
-                version19,
-            )
-        )
-        logger.info(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]:
-            break
-    with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
-        log = f.read()
-    logger.info(log)
-    yield log
-
-
-def get_pretrained_models(path_str, f0_str, sr2):
-    if_pretrained_generator_exist = os.access(
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if_pretrained_discriminator_exist = os.access(
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if not if_pretrained_generator_exist:
-        logger.warn(
-            "assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
-            path_str,
-            f0_str,
-            sr2,
-        )
-    if not if_pretrained_discriminator_exist:
-        logger.warn(
-            "assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
-            path_str,
-            f0_str,
-            sr2,
-        )
-    return (
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
-        if if_pretrained_generator_exist
-        else "",
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
-        if if_pretrained_discriminator_exist
-        else "",
-    )
-
-
-def change_sr2(sr2, if_f0_3, version19):
-    path_str = "" if version19 == "v1" else "_v2"
-    f0_str = "f0" if if_f0_3 else ""
-    return get_pretrained_models(path_str, f0_str, sr2)
-
-
-def change_version19(sr2, if_f0_3, version19):
-    path_str = "" if version19 == "v1" else "_v2"
-    if sr2 == "32k" and version19 == "v1":
-        sr2 = "40k"
-    to_return_sr2 = (
-        {"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
-        if version19 == "v1"
-        else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
-    )
-    f0_str = "f0" if if_f0_3 else ""
-    return (
-        *get_pretrained_models(path_str, f0_str, sr2),
-        to_return_sr2,
-    )
-
-
-def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
-    path_str = "" if version19 == "v1" else "_v2"
-    return (
-        {"visible": if_f0_3, "__type__": "update"},
-        *get_pretrained_models(path_str, "f0", sr2),
-    )
-
-
-# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
-def click_train(
-    exp_dir1,
-    sr2,
-    if_f0_3,
-    spk_id5,
-    save_epoch10,
-    total_epoch11,
-    batch_size12,
-    if_save_latest13,
-    pretrained_G14,
-    pretrained_D15,
-    gpus16,
-    if_cache_gpu17,
-    if_save_every_weights18,
-    version19,
-):
-    # 生成filelist
-    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if if_f0_3:
-        f0_dir = "%s/2a_f0" % (exp_dir)
-        f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
-        names = (
-            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
-            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
-        )
-    else:
-        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
-            [name.split(".")[0] for name in os.listdir(feature_dir)]
-        )
-    opt = []
-    for name in names:
-        if if_f0_3:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    f0_dir.replace("\\", "\\\\"),
-                    name,
-                    f0nsf_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-        else:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-    fea_dim = 256 if version19 == "v1" else 768
-    if if_f0_3:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
-            )
-    else:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
-            )
-    shuffle(opt)
-    with open("%s/filelist.txt" % exp_dir, "w") as f:
-        f.write("\n".join(opt))
-    logger.debug("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"
-    logger.info("Use gpus: %s", str(gpus16))
-    if pretrained_G14 == "":
-        logger.info("No pretrained Generator")
-    if pretrained_D15 == "":
-        logger.info("No pretrained Discriminator")
-    if version19 == "v1" or sr2 == "40k":
-        config_path = "v1/%s.json" % sr2
-    else:
-        config_path = "v2/%s.json" % sr2
-    config_save_path = os.path.join(exp_dir, "config.json")
-    if not pathlib.Path(config_save_path).exists():
-        with open(config_save_path, "w", encoding="utf-8") as f:
-            json.dump(
-                config.json_config[config_path],
-                f,
-                ensure_ascii=False,
-                indent=4,
-                sort_keys=True,
-            )
-            f.write("\n")
-    if gpus16:
-        cmd = (
-            '"%s" infer/modules/train/train.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'
-            % (
-                config.python_cmd,
-                exp_dir1,
-                sr2,
-                1 if if_f0_3 else 0,
-                batch_size12,
-                gpus16,
-                total_epoch11,
-                save_epoch10,
-                "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
-                "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
-                1 if if_save_latest13 == i18n("是") else 0,
-                1 if if_cache_gpu17 == i18n("是") else 0,
-                1 if if_save_every_weights18 == i18n("是") else 0,
-                version19,
-            )
-        )
-    else:
-        cmd = (
-            '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
-            % (
-                config.python_cmd,
-                exp_dir1,
-                sr2,
-                1 if if_f0_3 else 0,
-                batch_size12,
-                total_epoch11,
-                save_epoch10,
-                "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
-                "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
-                1 if if_save_latest13 == i18n("是") else 0,
-                1 if if_cache_gpu17 == i18n("是") else 0,
-                1 if if_save_every_weights18 == i18n("是") else 0,
-                version19,
-            )
-        )
-    logger.info(cmd)
-    p = Popen(cmd, shell=True, cwd=now_dir)
-    p.wait()
-    return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"
-
-
-# but4.click(train_index, [exp_dir1], info3)
-def train_index(exp_dir1, version19):
-    # exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    exp_dir = "logs/%s" % (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 not os.path.exists(feature_dir):
-        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！"
-    infos = []
-    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]
-    if big_npy.shape[0] > 2e5:
-        infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
-        yield "\n".join(infos)
-        try:
-            big_npy = (
-                MiniBatchKMeans(
-                    n_clusters=10000,
-                    verbose=True,
-                    batch_size=256 * config.n_cpu,
-                    compute_labels=False,
-                    init="random",
-                )
-                .fit(big_npy)
-                .cluster_centers_
-            )
-        except:
-            info = traceback.format_exc()
-            logger.info(info)
-            infos.append(info)
-            yield "\n".join(infos)
-
-    np.save("%s/total_fea.npy" % exp_dir, big_npy)
-    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-    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),
-    )
-
-    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,
-    gpus_rmvpe,
-):
-    infos = []
-
-    def get_info_str(strr):
-        infos.append(strr)
-        return "\n".join(infos)
-
-    ####### step1:处理数据
-    yield get_info_str(i18n("step1:正在处理数据"))
-    [get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)]
-
-    ####### step2a:提取音高
-    yield get_info_str(i18n("step2:正在提取音高&正在提取特征"))
-    [
-        get_info_str(_)
-        for _ in extract_f0_feature(
-            gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe
-        )
-    ]
-
-    ####### step3a:训练模型
-    yield get_info_str(i18n("step3a:正在训练模型"))
-    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,
-    )
-    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
-    [get_info_str(_) for _ in train_index(exp_dir1, version19)]
-    yield get_info_str(i18n("全流程结束！"))
-
-
-#                    ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
-def change_info_(ckpt_path):
-    if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
-        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"}
-
-
-F0GPUVisible = config.dml == False
-
-
-def change_f0_method(f0method8):
-    if f0method8 == "rmvpe_gpu":
-        visible = F0GPUVisible
-    else:
-        visible = False
-    return {"visible": visible, "__type__": "update"}
-
-def find_model():
-    if len(names) > 0:
-        vc.get_vc(sorted(names)[0],None,None)
-        return sorted(names)[0]
-    else:
-        try:
-            gr.Info("Do not forget to choose a model.")
-        except:
-            pass
-        return ''
-    
-def find_audios(index=False):     
-    audio_files=[]
-    if not os.path.exists('./audios'): os.mkdir("./audios")
-    for filename in os.listdir("./audios"):
-        if filename.endswith(('.wav','.mp3','.ogg')):
-            audio_files.append("./audios/"+filename)
-    if index:
-        if len(audio_files) > 0: return sorted(audio_files)[0]
-        else: return ""
-    elif len(audio_files) > 0: return sorted(audio_files)
-    else: return []
-
-def get_index():
-    if find_model() != '':
-        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 save_wav(file):
-    try:
-        file_path=file.name
-        shutil.move(file_path,'./audios')
-        return './audios/'+os.path.basename(file_path)
-    except AttributeError:
-        try:
-            new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
-            new_path='./audios/'+new_name
-            shutil.move(file,new_path)
-            return new_path
-        except TypeError:
-            return None
-
-def download_from_url(url, model):
-    if model =='':
-        try:
-            model = url.split('/')[-1].split('?')[0]
-        except:
-            return "You need to name your model. For example: My-Model"
-    url=url.replace('/blob/main/','/resolve/main/')
-    model=model.replace('.pth','').replace('.index','').replace('.zip','')
-    if url == '':
-        return "URL cannot be left empty."
-    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 url.endswith('.pth'):
-            subprocess.run(["wget", url, "-O", f'./assets/weights/{model}.pth'])
-            return f"Sucessfully downloaded as {model}.pth"
-        if url.endswith('.index'):
-            if not os.path.exists(f'./logs/{model}'): os.makedirs(f'./logs/{model}')
-            subprocess.run(["wget", url, "-O", f'./logs/{model}/added_{model}.index'])
-            return f"Successfully downloaded as added_{model}.index"
-        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'./assets/weights/{model}.pth')
-        shutil.rmtree("zips")
-        shutil.rmtree("unzips")
-        return "Success."
-    except:
-        return "There's been an error."
-
-def upload_to_dataset(files, dir):
-    if dir == '':
-        dir = './dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-    if not os.path.exists(dir):
-        os.makedirs(dir)
-    for file in files:
-        path=file.name
-        shutil.copy2(path,dir)
-    try:
-        gr.Info(i18n("处理数据"))
-    except:
-        pass
-    return i18n("处理数据"), {"value":dir,"__type__":"update"}
-
-def download_model_files(model):
-    model_found = False
-    index_found = False
-    if os.path.exists(f'./assets/weights/{model}.pth'): model_found = True
-    if os.path.exists(f'./logs/{model}'):
-        for file in os.listdir(f'./logs/{model}'):
-            if file.endswith('.index') and 'added' in file:
-                log_file = file
-                index_found = True
-    if model_found and index_found:
-        return [f'./assets/weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done"
-    elif model_found and not index_found:
-        return f'./assets/weights/{model}.pth', "Could not find Index file."
-    elif index_found and not model_found:
-        return f'./logs/{model}/{log_file}', f'Make sure the Voice Name is correct. I could not find {model}.pth'
-    else:
-        return None, f'Could not find {model}.pth or corresponding Index file.'
-
-def update_visibility(visible):
-    if visible:
-        return {"visible":True,"__type__":"update"},{"visible":True,"__type__":"update"}
-    else:
-        return {"visible":False,"__type__":"update"},{"visible":False,"__type__":"update"}
-
-def get_pretrains(string):
-    pretrains = []
-    for file in os.listdir('assets/pretrained_v2'):
-        if string in file:
-            pretrains.append(os.path.join('assets/pretrained_v2',file))
-    return pretrains
-
-with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app:
-    with gr.Row():
-        gr.HTML("<img  src='file/a.png' alt='image'>")
-    with gr.Tabs():
-        with gr.TabItem(i18n("模型推理")):
-            with gr.Row():
-                sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value=find_model())
-                refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary")
-                #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], api_name="infer_clean"
-                #)
-                vc_transform0 = gr.Number(
-                    label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
-                )
-                but0 = gr.Button(i18n("转换"), 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=i18n("输入待处理音频文件路径(默认是正确格式示例)"),
-                            value=find_audios(True),
-                            choices=find_audios()
-                        )
-                        record_button.change(fn=save_wav, inputs=[record_button], outputs=[input_audio0])
-                        dropbox.upload(fn=save_wav, inputs=[dropbox], outputs=[input_audio0])
-                with gr.Column():
-                    with gr.Accordion(label=i18n("自动检测index路径,下拉式选择(dropdown)"), open=False):
-                        file_index2 = gr.Dropdown(
-                            label=i18n("自动检测index路径,下拉式选择(dropdown)"),
-                            choices=get_indexes(),
-                            interactive=True,
-                            value=get_index()
-                        )
-                        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=i18n("输出音频(右下角三个点,点了可以下载)"))
-                    with gr.Accordion(label=i18n("常规设置"), open=False):
-                        f0method0 = 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,rmvpe效果最好且微吃GPU"
-                            ),
-                            choices=["pm", "harvest", "crepe", "rmvpe"]
-                            if config.dml == False
-                            else ["pm", "harvest", "rmvpe"],
-                            value="rmvpe",
-                            interactive=True,
-                        )
-                        filter_radius0 = gr.Slider(
-                            minimum=0,
-                            maximum=7,
-                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
-                            value=3,
-                            step=1,
-                            interactive=True,
-                        )
-                        resample_sr0 = gr.Slider(
-                            minimum=0,
-                            maximum=48000,
-                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
-                            value=0,
-                            step=1,
-                            interactive=True,
-                            visible=False
-                        )
-                        rms_mix_rate0 = gr.Slider(
-                            minimum=0,
-                            maximum=1,
-                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
-                            value=0.21,
-                            interactive=True,
-                        )
-                        protect0 = gr.Slider(
-                            minimum=0,
-                            maximum=0.5,
-                            label=i18n(
-                                "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
-                            ),
-                            value=0.33,
-                            step=0.01,
-                            interactive=True,
-                        )
-                    file_index1 = 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,
-                        visible=False
-                    )
-                    refresh_button.click(
-                        fn=change_choices,
-                        inputs=[],
-                        outputs=[sid0, file_index2, input_audio0],
-                        api_name="infer_refresh",
-                    )
-                    # 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,
-                    # )
-            with gr.Row():
-                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)
-            with gr.Row():
-                vc_output1 = gr.Textbox(label=i18n("输出信息"))
-                but0.click(
-                    vc.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,
-                    ],
-                    [vc_output1, vc_output2],
-                    api_name="infer_convert",
-                )
-            with gr.Row():
-                with gr.Accordion(open=False, label=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ")):                
-                    with gr.Row():
-                        opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
-                        vc_transform1 = gr.Number(
-                            label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
-                        )
-                        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,rmvpe效果最好且微吃GPU"
-                            ),
-                            choices=["pm", "harvest", "crepe", "rmvpe"]
-                            if config.dml == False
-                            else ["pm", "harvest", "rmvpe"],
-                            value="pm",
-                            interactive=True,
-                        )
-                    with gr.Row():
-                        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,
-                            visible=False
-                        )
-                    with gr.Row():
-                        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,
-                            visible=False
-                        )
-                        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,
-                            visible=False
-                        )
-                        refresh_button.click(
-                            fn=lambda: change_choices()[1],
-                            inputs=[],
-                            outputs=file_index4,
-                            api_name="infer_refresh_batch",
-                        )
-                        # 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,
-                            visible=False
-                        )
-                    with gr.Row():
-                        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,
-                            visible=False
-                        )
-                        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=0.21,
-                            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.Row():
-                        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="./audios",
-                        )
-                        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="wav",
-                            interactive=True,
-                        )
-                        but1 = gr.Button(i18n("转换"), variant="primary")
-                        vc_output3 = gr.Textbox(label=i18n("输出信息"))
-                        but1.click(
-                            vc.vc_multi,
-                            [
-                                spk_item,
-                                dir_input,
-                                opt_input,
-                                inputs,
-                                vc_transform1,
-                                f0method1,
-                                file_index1,
-                                file_index2,
-                                # file_big_npy2,
-                                index_rate1,
-                                filter_radius1,
-                                resample_sr1,
-                                rms_mix_rate1,
-                                protect1,
-                                format1,
-                            ],
-                            [vc_output3],
-                            api_name="infer_convert_batch",
-                        )
-            sid0.change(
-                fn=vc.get_vc,
-                inputs=[sid0, protect0, protect1],
-                outputs=[spk_item, protect0, protect1, file_index2, file_index4],
-            )
-        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(
-                """
-                ❤️ If you use this and like it, help me keep it.❤️ 
-                https://paypal.me/lesantillan
-                """
-                )
-        with gr.TabItem(i18n("训练")):
-            with gr.Row():
-                with gr.Column():
-                    exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="My-Voice")
-                    np7 = gr.Slider(
-                        minimum=0,
-                        maximum=config.n_cpu,
-                        step=1,
-                        label=i18n("提取音高和处理数据使用的CPU进程数"),
-                        value=int(np.ceil(config.n_cpu / 1.5)),
-                        interactive=True,
-                    )
-                    sr2 = gr.Radio(
-                        label=i18n("目标采样率"),
-                        choices=["40k", "32k"],
-                        value="40k",
-                        interactive=True,
-                        visible=True
-                    )
-                    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=i18n("版本"),
-                        choices=["v1", "v2"],
-                        value="v2",
-                        interactive=True,
-                        visible=False,
-                    )
-                    trainset_dir4 = gr.Textbox(
-                        label=i18n("输入训练文件夹路径"), value='./dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-                    )
-                    easy_uploader = gr.Files(label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),file_types=['audio'])
-                    but1 = gr.Button(i18n("处理数据"), variant="primary")
-                    info1 = gr.Textbox(label=i18n("输出信息"), value="")
-                    easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1, trainset_dir4])
-                    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=F0GPUVisible,
-                    )
-                    gpu_info9 = gr.Textbox(
-                        label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible
-                    )
-                    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
-                    )
-                    but1.click(
-                        preprocess_dataset,
-                        [trainset_dir4, exp_dir1, sr2, np7],
-                        [info1],
-                        api_name="train_preprocess",
-                    ) 
-                with gr.Column():
-                    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,rmvpe效果最好且微吃CPU/GPU"
-                        ),
-                        choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
-                        value="rmvpe_gpu",
-                        interactive=True,
-                    )
-                    gpus_rmvpe = gr.Textbox(
-                        label=i18n(
-                            "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"
-                        ),
-                        value="%s-%s" % (gpus, gpus),
-                        interactive=True,
-                        visible=F0GPUVisible,
-                    )
-                    but2 = gr.Button(i18n("特征提取"), variant="primary")
-                    info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                    f0method8.change(
-                        fn=change_f0_method,
-                        inputs=[f0method8],
-                        outputs=[gpus_rmvpe],
-                    )
-                    but2.click(
-                        extract_f0_feature,
-                        [
-                            gpus6,
-                            np7,
-                            f0method8,
-                            if_f0_3,
-                            exp_dir1,
-                            version19,
-                            gpus_rmvpe,
-                        ],
-                        [info2],
-                        api_name="train_extract_f0_feature",
-                    )
-                with gr.Column():
-                    total_epoch11 = gr.Slider(
-                        minimum=2,
-                        maximum=1000,
-                        step=1,
-                        label=i18n("总训练轮数total_epoch"),
-                        value=150,
-                        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="0",
-                            interactive=True,
-                            visible=True
-                        )
-                    but3 = gr.Button(i18n("训练模型"), variant="primary")
-                    but4 = gr.Button(i18n("训练特征索引"), variant="primary")
-                    info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10)
-                    with gr.Accordion(label=i18n("常规设置"), open=False):
-                        save_epoch10 = gr.Slider(
-                            minimum=1,
-                            maximum=50,
-                            step=1,
-                            label=i18n("保存频率save_every_epoch"),
-                            value=25,
-                            interactive=True,
-                        )
-                        batch_size12 = gr.Slider(
-                            minimum=1,
-                            maximum=40,
-                            step=1,
-                            label=i18n("每张显卡的batch_size"),
-                            value=default_batch_size,
-                            interactive=True,
-                        )
-                        if_save_latest13 = gr.Radio(
-                            label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),
-                            choices=[i18n("是"), i18n("否")],
-                            value=i18n("是"),
-                            interactive=True,
-                            visible=False
-                        )
-                        if_cache_gpu17 = gr.Radio(
-                            label=i18n(
-                                "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"
-                            ),
-                            choices=[i18n("是"), i18n("否")],
-                            value=i18n("否"),
-                            interactive=True,
-                        )
-                        if_save_every_weights18 = gr.Radio(
-                            label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
-                            choices=[i18n("是"), i18n("否")],
-                            value=i18n("是"),
-                            interactive=True,
-                        )
-                        display_advanced_settings = gr.Checkbox(
-                            value=False,
-                            label="Advanced Settings"
-                        )
-                        pretrained_G14 = gr.Dropdown(
-                            label=i18n("加载预训练底模G路径"),
-                            value="assets/pretrained_v2/f0G40k.pth",
-                            choices=get_pretrains('G'),
-                            interactive=True,
-                            visible=False
-                        )
-                        pretrained_D15 = gr.Dropdown(
-                            label=i18n("加载预训练底模D路径"),
-                            value="assets/pretrained_v2/f0D40k.pth",
-                            interactive=True,
-                            choices=get_pretrains('D'),
-                            visible=False
-                        )
-                        display_advanced_settings.change(fn=update_visibility,inputs=[display_advanced_settings],outputs=[pretrained_G14,pretrained_D15])
-                    with gr.Row():
-                        download_model = gr.Button('5.Download Model')
-                    with gr.Row():
-                        model_files = gr.Files(label='Your Model and Index file can be downloaded here:')
-                        download_model.click(fn=download_model_files, inputs=[exp_dir1], outputs=[model_files, info3])
-                    with gr.Row():
-                        sr2.change(
-                            change_sr2,
-                            [sr2, if_f0_3, version19],
-                            [pretrained_G14, pretrained_D15],
-                        )
-                        version19.change(
-                            change_version19,
-                            [sr2, if_f0_3, version19],
-                            [pretrained_G14, pretrained_D15, sr2],
-                        )
-                        if_f0_3.change(
-                            change_f0,
-                            [if_f0_3, sr2, version19],
-                            [f0method8, pretrained_G14, pretrained_D15],
-                        )
-                    with gr.Row():
-                        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,
-                            api_name="train_start",
-                        )
-                        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,
-                                gpus_rmvpe,
-                            ],
-                            info3,
-                            api_name="train_start_all",
-                        )
-
-    if config.iscolab:
-        app.queue().launch(share=True,debug=True)
-    else:
-        app.queue().launch(
-            server_name="0.0.0.0",
-            inbrowser=not config.noautoopen,
-            server_port=config.listen_port,
-            quiet=True,
-        )