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app.py

@@ -0,0 +1,1441 @@
+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.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'
+            % (
+                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 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'./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.'
+
+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���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", "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=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,
+                        )
+                    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():
+                        pretrained_G14 = gr.Textbox(
+                            label=i18n("加载预训练底模G路径"),
+                            value="assets/pretrained_v2/f0G40k.pth",
+                            interactive=True,
+                            visible=False
+                        )
+                        pretrained_D15 = gr.Textbox(
+                            label=i18n("加载预训练底模D路径"),
+                            value="assets/pretrained_v2/f0D40k.pth",
+                            interactive=True,
+                            visible=False
+                        )
+                        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(concurrency_count=511, max_size=1022).launch(share=True)
+    else:
+        app.queue(concurrency_count=511, max_size=1022).launch(
+            server_name="0.0.0.0",
+            inbrowser=not config.noautoopen,
+            server_port=config.listen_port,
+            quiet=True,
+        )