Update app.py

app.py

@@ -1,136 +1,32 @@
+import logging
 import os
-import sys
-from dotenv import load_dotenv
 
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-load_dotenv()
-from infer.modules.vc.modules import VC
-from infer.modules.uvr5.modules import uvr
-from infer.lib.train.process_ckpt import (
-    change_info,
-    extract_small_model,
-    merge,
-    show_info,
-)
-from i18n.i18n import I18nAuto
-from configs.config import Config
-from sklearn.cluster import MiniBatchKMeans
-import torch
-import numpy as np
+# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
 import gradio as gr
-import faiss
-import fairseq
-import pathlib
-import json
-from time import sleep
-from subprocess import Popen
-from random import shuffle
-import warnings
-import traceback
-import threading
-import shutil
-import logging
+from dotenv import load_dotenv
 
+from configs.config import Config
+from i18n.i18n import I18nAuto
+from infer.modules.vc.modules import VC
 
 logging.getLogger("numba").setLevel(logging.WARNING)
-
+logging.getLogger("markdown_it").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("matplotlib").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)
-
-
-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"
 
+load_dotenv()
+config = Config()
+vc = VC(config)
 
 weight_root = os.getenv("weight_root")
 weight_uvr5_root = os.getenv("weight_uvr5_root")
 index_root = os.getenv("index_root")
-
 names = []
+hubert_model = None
 for name in os.listdir(weight_root):
     if name.endswith(".pth"):
         names.append(name)
@@ -139,650 +35,19 @@ 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))
-    return {"choices": sorted(names), "__type__": "update"}, {
-        "choices": sorted(index_paths),
-        "__type__": "update",
-    }
-
-
-def clean():
-    return {"value": "", "__type__": "update"}
-
-
-def export_onnx(ModelPath, ExportedPath):
-    from infer.modules.onnx.export import export_onnx as eo
-
-    eo(ModelPath, ExportedPath)
-
-
-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)
-    # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
-    p = Popen(cmd, shell=True)
-    # 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.warning(
-            "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.warning(
-            "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"},
-        {"visible": if_f0_3, "__type__": "update"},
-        *get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", 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"}
-
-
-with gr.Blocks(title="RVC WebUI") as app:
-    gr.Markdown("## RVC WebUI")
-    gr.Markdown(
-        value=i18n(
-            "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>."
-        )
-    )
+app = gr.Blocks()
+with app:
     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))
-                with gr.Column():
-                    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")
+        with gr.TabItem("在线demo"):
+            gr.Markdown(
+                value="""
+                RVC 在线demo
+                """
+            )
+            sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
+            with gr.Column():
                 spk_item = gr.Slider(
                     minimum=0,
                     maximum=2333,
@@ -792,729 +57,91 @@ with gr.Blocks(title="RVC WebUI") as app:
                     visible=False,
                     interactive=True,
                 )
-                clean_button.click(
-                    fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
-                )
-            with gr.TabItem(i18n("单次推理")):
-                with gr.Group():
-                    with gr.Row():
-                        with gr.Column():
-                            vc_transform0 = gr.Number(
-                                label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
-                            )
-                            input_audio0 = gr.Textbox(
-                                label=i18n("输入待处理音频文件路径(默认是正确格式示例)"),
-                                placeholder="C:\\Users\\Desktop\\audio_example.wav",
-                            )
-                            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"),
-                                placeholder="C:\\Users\\Desktop\\model_example.index",
-                                interactive=True,
-                            )
-                            file_index2 = gr.Dropdown(
-                                label=i18n("自动检测index路径,下拉式选择(dropdown)"),
-                                choices=sorted(index_paths),
-                                interactive=True,
-                            )
-                            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,
-                            )
-
-                        with gr.Column():
-                            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,
-                            )
-                            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=0.25,
-                                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,
-                            )
-                            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,
-                            )
-                            index_rate1 = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label=i18n("检索特征占比"),
-                                value=0.75,
-                                interactive=True,
-                            )
-                            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,
-                            )
-
-                            refresh_button.click(
-                                fn=change_choices,
-                                inputs=[],
-                                outputs=[sid0, file_index2],
-                                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.Group():
-                    with gr.Column():
-                        but0 = gr.Button(i18n("转换"), variant="primary")
-                        with gr.Row():
-                            vc_output1 = gr.Textbox(label=i18n("输出信息"))
-                            vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
-
-                        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.TabItem(i18n("批量推理")):
-                gr.Markdown(
-                    value=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():
-                    with gr.Column():
-                        vc_transform1 = gr.Number(
-                            label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
-                        )
-                        opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
-                        file_index3 = gr.Textbox(
-                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
-                            value="",
-                            interactive=True,
-                        )
-                        file_index4 = gr.Dropdown(
-                            label=i18n("自动检测index路径,下拉式选择(dropdown)"),
-                            choices=sorted(index_paths),
-                            interactive=True,
-                        )
-                        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="rmvpe",
-                            interactive=True,
-                        )
-                        format1 = gr.Radio(
-                            label=i18n("导出文件格式"),
-                            choices=["wav", "flac", "mp3", "m4a"],
-                            value="wav",
-                            interactive=True,
-                        )
-
-                        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,
-                        # )
-
-                    with gr.Column():
-                        resample_sr1 = gr.Slider(
-                            minimum=0,
-                            maximum=48000,
-                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
-                            value=0,
-                            step=1,
-                            interactive=True,
-                        )
-                        rms_mix_rate1 = gr.Slider(
-                            minimum=0,
-                            maximum=1,
-                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
-                            value=1,
-                            interactive=True,
-                        )
-                        protect1 = gr.Slider(
-                            minimum=0,
-                            maximum=0.5,
-                            label=i18n(
-                                "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
-                            ),
-                            value=0.33,
-                            step=0.01,
-                            interactive=True,
-                        )
-                        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,
-                        )
-                        index_rate2 = gr.Slider(
-                            minimum=0,
-                            maximum=1,
-                            label=i18n("检索特征占比"),
-                            value=1,
-                            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)"),
-                        placeholder="C:\\Users\\Desktop\\input_vocal_dir",
-                    )
-                    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():
-                    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_index3,
-                            file_index4,
-                            # file_big_npy2,
-                            index_rate2,
-                            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],
-                    api_name="infer_change_voice",
-                )
-        with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "人声伴奏分离批量处理， 使用UVR5模型。 <br>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br>  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br>&emsp;(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
-                    )
-                )
-                with gr.Row():
-                    with gr.Column():
-                        dir_wav_input = gr.Textbox(
-                            label=i18n("输入待处理音频文件夹路径"),
-                            placeholder="C:\\Users\\Desktop\\todo-songs",
-                        )
-                        wav_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.Column():
-                        model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
-                        agg = gr.Slider(
-                            minimum=0,
-                            maximum=20,
-                            step=1,
-                            label="人声提取激进程度",
-                            value=10,
-                            interactive=True,
-                            visible=False,  # 先不开放调整
-                        )
-                        opt_vocal_root = gr.Textbox(
-                            label=i18n("指定输出主人声文件夹"), value="opt"
-                        )
-                        opt_ins_root = gr.Textbox(
-                            label=i18n("指定输出非主人声文件夹"), value="opt"
-                        )
-                        format0 = gr.Radio(
-                            label=i18n("导出文件格式"),
-                            choices=["wav", "flac", "mp3", "m4a"],
-                            value="flac",
-                            interactive=True,
-                        )
-                    but2 = gr.Button(i18n("转换"), variant="primary")
-                    vc_output4 = gr.Textbox(label=i18n("输出信息"))
-                    but2.click(
-                        uvr,
-                        [
-                            model_choose,
-                            dir_wav_input,
-                            opt_vocal_root,
-                            wav_inputs,
-                            opt_ins_root,
-                            agg,
-                            format0,
-                        ],
-                        [vc_output4],
-                        api_name="uvr_convert",
-                    )
-        with gr.TabItem(i18n("训练")):
+            sid.change(fn=vc.get_vc, inputs=[sid], outputs=[spk_item])
             gr.Markdown(
-                value=i18n(
-                    "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. "
-                )
+                value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
             )
-            with gr.Row():
-                exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
-                sr2 = gr.Radio(
-                    label=i18n("目标采样率"),
-                    choices=["40k", "48k"],
-                    value="40k",
-                    interactive=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,
-                )
-                version19 = gr.Radio(
-                    label=i18n("版本"),
-                    choices=["v1", "v2"],
-                    value="v2",
-                    interactive=True,
-                    visible=True,
-                )
-                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,
-                )
-            with gr.Group():  # 暂时单人的, 后面支持最多4人的#数据处理
-                gr.Markdown(
-                    value=i18n(
-                        "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. "
-                    )
-                )
-                with gr.Row():
-                    trainset_dir4 = gr.Textbox(
-                        label=i18n("输入训练文件夹路径"), value=i18n("E:\\语音音频+标注\\米津玄师\\src")
-                    )
-                    spk_id5 = gr.Slider(
-                        minimum=0,
-                        maximum=4,
-                        step=1,
-                        label=i18n("请指定说话人id"),
-                        value=0,
-                        interactive=True,
-                    )
-                    but1 = gr.Button(i18n("处理数据"), variant="primary")
-                    info1 = gr.Textbox(label=i18n("输出信息"), value="")
-                    but1.click(
-                        preprocess_dataset,
-                        [trainset_dir4, exp_dir1, sr2, np7],
-                        [info1],
-                        api_name="train_preprocess",
-                    )
-            with gr.Group():
-                gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"))
-                with gr.Row():
-                    with gr.Column():
-                        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
-                        )
-                    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.Group():
-                gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
-                with gr.Row():
-                    save_epoch10 = gr.Slider(
-                        minimum=1,
-                        maximum=50,
-                        step=1,
-                        label=i18n("保存频率save_every_epoch"),
-                        value=5,
-                        interactive=True,
-                    )
-                    total_epoch11 = gr.Slider(
-                        minimum=2,
-                        maximum=1000,
-                        step=1,
-                        label=i18n("总训练轮数total_epoch"),
-                        value=20,
-                        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("否"),
-                        interactive=True,
-                    )
-                    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("否"),
-                        interactive=True,
-                    )
-                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,
-                    )
-                    pretrained_D15 = gr.Textbox(
-                        label=i18n("加载预训练底模D路径"),
-                        value="assets/pretrained_v2/f0D40k.pth",
-                        interactive=True,
-                    )
-                    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, gpus_rmvpe, pretrained_G14, pretrained_D15],
-                    )
-                    gpus16 = gr.Textbox(
-                        label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
-                        value=gpus,
-                        interactive=True,
-                    )
-                    but3 = gr.Button(i18n("训练模型"), variant="primary")
-                    but4 = gr.Button(i18n("训练特征索引"), variant="primary")
-                    but5 = gr.Button(i18n("一键训练"), variant="primary")
-                    info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10)
-                    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",
-                    )
-
-        with gr.TabItem(i18n("ckpt处理")):
-            with gr.Group():
-                gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
-                with gr.Row():
-                    ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True)
-                    ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True)
-                    alpha_a = gr.Slider(
-                        minimum=0,
-                        maximum=1,
-                        label=i18n("A模型权重"),
-                        value=0.5,
-                        interactive=True,
-                    )
-                with gr.Row():
-                    sr_ = gr.Radio(
-                        label=i18n("目标采样率"),
-                        choices=["40k", "48k"],
-                        value="40k",
-                        interactive=True,
-                    )
-                    if_f0_ = gr.Radio(
-                        label=i18n("模型是否带音高指导"),
-                        choices=[i18n("是"), i18n("否")],
-                        value=i18n("是"),
-                        interactive=True,
-                    )
-                    info__ = gr.Textbox(
-                        label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
-                    )
-                    name_to_save0 = gr.Textbox(
-                        label=i18n("保存的模型名不带后缀"),
-                        value="",
-                        max_lines=1,
-                        interactive=True,
-                    )
-                    version_2 = gr.Radio(
-                        label=i18n("模型版本型号"),
-                        choices=["v1", "v2"],
-                        value="v1",
-                        interactive=True,
-                    )
-                with gr.Row():
-                    but6 = gr.Button(i18n("融合"), variant="primary")
-                    info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                but6.click(
-                    merge,
-                    [
-                        ckpt_a,
-                        ckpt_b,
-                        alpha_a,
-                        sr_,
-                        if_f0_,
-                        info__,
-                        name_to_save0,
-                        version_2,
-                    ],
-                    info4,
-                    api_name="ckpt_merge",
-                )  # def merge(path1,path2,alpha1,sr,f0,info):
-            with gr.Group():
-                gr.Markdown(value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)"))
-                with gr.Row():
-                    ckpt_path0 = gr.Textbox(
-                        label=i18n("模型路径"), value="", interactive=True
-                    )
-                    info_ = gr.Textbox(
-                        label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True
-                    )
-                    name_to_save1 = gr.Textbox(
-                        label=i18n("保存的文件名, 默认空为和源文件同名"),
-                        value="",
-                        max_lines=8,
-                        interactive=True,
-                    )
-                with gr.Row():
-                    but7 = gr.Button(i18n("修改"), variant="primary")
-                    info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                but7.click(
-                    change_info,
-                    [ckpt_path0, info_, name_to_save1],
-                    info5,
-                    api_name="ckpt_modify",
-                )
-            with gr.Group():
-                gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)"))
-                with gr.Row():
-                    ckpt_path1 = gr.Textbox(
-                        label=i18n("模型路径"), value="", interactive=True
-                    )
-                    but8 = gr.Button(i18n("查看"), variant="primary")
-                    info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况"
-                    )
-                )
-                with gr.Row():
-                    ckpt_path2 = gr.Textbox(
-                        label=i18n("模型路径"),
-                        value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
-                        interactive=True,
-                    )
-                    save_name = gr.Textbox(
-                        label=i18n("保存名"), value="", interactive=True
-                    )
-                    sr__ = gr.Radio(
-                        label=i18n("目标采样率"),
-                        choices=["32k", "40k", "48k"],
-                        value="40k",
-                        interactive=True,
-                    )
-                    if_f0__ = gr.Radio(
-                        label=i18n("模型是否带音高指导,1是0否"),
-                        choices=["1", "0"],
-                        value="1",
-                        interactive=True,
-                    )
-                    version_1 = gr.Radio(
-                        label=i18n("模型版本型号"),
-                        choices=["v1", "v2"],
-                        value="v2",
-                        interactive=True,
-                    )
-                    info___ = gr.Textbox(
-                        label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
-                    )
-                    but9 = gr.Button(i18n("提取"), variant="primary")
-                    info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                    ckpt_path2.change(
-                        change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
-                    )
-                but9.click(
-                    extract_small_model,
-                    [ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
-                    info7,
-                    api_name="ckpt_extract",
-                )
-
-        with gr.TabItem(i18n("Onnx导出")):
-            with gr.Row():
-                ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
-            with gr.Row():
-                onnx_dir = gr.Textbox(
-                    label=i18n("Onnx输出路径"), value="", interactive=True
+            vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
+            vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
+            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"),
+                choices=["pm", "harvest", "crepe", "rmvpe"],
+                value="pm",
+                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,
+            )
+            with gr.Column():
+                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=False,
+                    visible=False,
                 )
-            with gr.Row():
-                infoOnnx = gr.Label(label="info")
-            with gr.Row():
-                butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
-            butOnnx.click(
-                export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx"
+            file_index2 = gr.Dropdown(
+                label=i18n("自动检测index路径,下拉式选择(dropdown)"),
+                choices=sorted(index_paths),
+                interactive=True,
+            )
+            index_rate1 = gr.Slider(
+                minimum=0,
+                maximum=1,
+                label=i18n("检索特征占比"),
+                value=0.88,
+                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,
+            )
+            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=1,
+                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,
+            )
+            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"))
+            but0 = gr.Button(i18n("转换"), variant="primary")
+            vc_output1 = gr.Textbox(label=i18n("输出信息"))
+            vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
+            but0.click(
+                vc.vc_single,
+                [
+                    spk_item,
+                    vc_input3,
+                    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],
             )
 
-        tab_faq = i18n("常见问题解答")
-        with gr.TabItem(tab_faq):
-            try:
-                if tab_faq == "常见问题解答":
-                    with open("docs/cn/faq.md", "r", encoding="utf8") as f:
-                        info = f.read()
-                else:
-                    with open("docs/en/faq_en.md", "r", encoding="utf8") as f:
-                        info = f.read()
-                gr.Markdown(value=info)
-            except:
-                gr.Markdown(traceback.format_exc())
 
-    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,
-        )
+app.launch()