Upload GUI.py

GUI.py

@@ -0,0 +1,2205 @@
+import zipfile, glob, subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
+from mega import Mega
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+import threading
+from time import sleep
+from subprocess import Popen
+import faiss
+from random import shuffle
+import json, datetime, requests
+from gtts import gTTS
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
+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, "weights"), exist_ok=True)
+os.environ["TEMP"] = tmp
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
+from i18n import I18nAuto
+import ffmpeg
+#from MDXNet import MDXNetDereverb
+
+i18n = I18nAuto()
+#i18n.print()
+# 判断是否有能用来训练和加速推理的N卡
+ngpu = torch.cuda.device_count()
+gpu_infos = []
+mem = []
+if (not torch.cuda.is_available()) or ngpu == 0:
+    if_gpu_ok = False
+else:
+    if_gpu_ok = False
+    for i in range(ngpu):
+        gpu_name = torch.cuda.get_device_name(i)
+        if (
+            "10" in gpu_name
+            or "16" in gpu_name
+            or "20" in gpu_name
+            or "30" in gpu_name
+            or "40" in gpu_name
+            or "A2" in gpu_name.upper()
+            or "A3" in gpu_name.upper()
+            or "A4" in gpu_name.upper()
+            or "P4" in gpu_name.upper()
+            or "A50" in gpu_name.upper()
+            or "A60" in gpu_name.upper()
+            or "70" in gpu_name
+            or "80" in gpu_name
+            or "90" in gpu_name
+            or "M4" in gpu_name.upper()
+            or "T4" in gpu_name.upper()
+            or "TITAN" in gpu_name.upper()
+        ):  # A10#A100#V100#A40#P40#M40#K80#A4500
+            if_gpu_ok = True  # 至少有一张能用的N卡
+            gpu_infos.append("%s\t%s" % (i, gpu_name))
+            mem.append(
+                int(
+                    torch.cuda.get_device_properties(i).total_memory
+                    / 1024
+                    / 1024
+                    / 1024
+                    + 0.4
+                )
+            )
+if if_gpu_ok == True and len(gpu_infos) > 0:
+    gpu_info = "\n".join(gpu_infos)
+    default_batch_size = min(mem) // 2
+else:
+    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
+    default_batch_size = 1
+gpus = "-".join([i[0] for i in gpu_infos])
+
+from infer_pack.models import (SynthesizerTrnMs256NSFsid,SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid,SynthesizerTrnMs768NSFsid_nono)
+
+import soundfile as sf
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import Config
+from infer_uvr5 import _audio_pre_, _audio_pre_new
+from my_utils import load_audio
+from train.process_ckpt import show_info, change_info, merge, extract_small_model
+
+config = Config()
+# from trainset_preprocess_pipeline import PreProcess
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+hubert_model = None
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+
+weight_root = "weights"
+weight_uvr5_root = "uvr5_weights"
+index_root = "logs"
+names = []
+for name in os.listdir(weight_root):
+    if name.endswith(".pth"):
+        names.append(name)
+index_paths = []
+for root, dirs, files in os.walk(index_root, topdown=False):
+    for name in files:
+        if name.endswith(".index") and "trained" not in name:
+            index_paths.append("%s/%s" % (root, name))
+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 vc_single(
+    sid,
+    input_audio_path,
+    f0_up_key,
+    f0_file,
+    f0_method,
+    file_index,
+    #file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    crepe_hop_length,
+    root_location='./audios'
+):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+    global tgt_sr, net_g, vc, hubert_model, version
+    if input_audio_path is None:
+        gr.Warning("You need to provide the path to an audio file")
+        return "You need to provide the path to an audio file", None
+    full_audio_path = root_location + '/' + input_audio_path
+    if not os.path.exists(full_audio_path):
+        gr.Warning(f"Could not find that file in audios/{input_audio_path}")
+        return f"Could not find that file in audios/{input_audio_path}", None
+    f0_up_key = int(f0_up_key)
+    try:
+        audio = load_audio(full_audio_path, 16000)
+        audio_max = np.abs(audio).max() / 0.95
+        if audio_max > 1:
+            audio /= audio_max
+        times = [0, 0, 0]
+        if hubert_model == None:
+            load_hubert()
+        if_f0 = cpt.get("f0", 1)
+        file_index = (
+            (
+                file_index.strip(" ")
+                .strip('"')
+                .strip("\n")
+                .strip('"')
+                .strip(" ")
+                .replace("trained", "added")
+            )
+        )  # 防止小白写错，自动帮他替换掉
+        # file_big_npy = (
+        #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        # )
+        audio_opt = vc.pipeline(
+            hubert_model,
+            net_g,
+            sid,
+            audio,
+            input_audio_path,
+            times,
+            f0_up_key,
+            f0_method,
+            file_index,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            crepe_hop_length,
+            f0_file=f0_file,
+        )
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
+            tgt_sr = resample_sr
+        index_info = (
+            "Using index:%s." % file_index
+            if os.path.exists(file_index)
+            else "Index not used."
+        )
+        gr.Info('Success.')
+        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
+            index_info,
+            times[0],
+            times[1],
+            times[2],
+        ), (tgt_sr, audio_opt)
+    except:
+        info = traceback.format_exc()
+        print(info)
+        return info, (None, None)
+
+
+def vc_multi(
+    sid,
+    dir_path,
+    opt_root,
+    paths,
+    f0_up_key,
+    f0_method,
+    file_index,
+    file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    format1,
+    crepe_hop_length,
+):
+    try:
+        dir_path = (
+            dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # 防止小白拷路径头尾带了空格和"和回车
+        opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        os.makedirs(opt_root, exist_ok=True)
+        try:
+            if dir_path != "":
+                paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
+            else:
+                paths = [path.name for path in paths]
+        except:
+            traceback.print_exc()
+            paths = [path.name for path in paths]
+        infos = []
+        for path in paths:
+            info, opt = vc_single(
+                sid,
+                path,
+                f0_up_key,
+                None,
+                f0_method,
+                file_index,
+                file_index2,
+                # file_big_npy,
+                index_rate,
+                filter_radius,
+                resample_sr,
+                rms_mix_rate,
+                protect,
+                crepe_hop_length
+            )
+            if "Success" in info:
+                try:
+                    tgt_sr, audio_opt = opt
+                    if format1 in ["wav", "flac"]:
+                        sf.write(
+                            "%s/%s.%s" % (opt_root, os.path.basename(path), format1),
+                            audio_opt,
+                            tgt_sr,
+                        )
+                    else:
+                        path = "%s/%s.wav" % (opt_root, os.path.basename(path))
+                        sf.write(
+                            path,
+                            audio_opt,
+                            tgt_sr,
+                        )
+                        if os.path.exists(path):
+                            os.system(
+                                "ffmpeg -i %s -vn %s -q:a 2 -y"
+                                % (path, path[:-4] + ".%s" % format1)
+                            )
+                except:
+                    info += traceback.format_exc()
+            infos.append("%s->%s" % (os.path.basename(path), info))
+            yield "\n".join(infos)
+        yield "\n".join(infos)
+    except:
+        yield traceback.format_exc()
+
+
+def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
+    infos = []
+    try:
+        inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        save_root_vocal = (
+            save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )
+        save_root_ins = (
+            save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )
+        if model_name == "onnx_dereverb_By_FoxJoy":
+            pre_fun = MDXNetDereverb(15)
+        else:
+            func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new
+            pre_fun = func(
+                agg=int(agg),
+                model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
+                device=config.device,
+                is_half=config.is_half,
+            )
+        if inp_root != "":
+            paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
+        else:
+            paths = [path.name for path in paths]
+        for path in paths:
+            inp_path = os.path.join(inp_root, path)
+            need_reformat = 1
+            done = 0
+            try:
+                info = ffmpeg.probe(inp_path, cmd="ffprobe")
+                if (
+                    info["streams"][0]["channels"] == 2
+                    and info["streams"][0]["sample_rate"] == "44100"
+                ):
+                    need_reformat = 0
+                    pre_fun._path_audio_(
+                        inp_path, save_root_ins, save_root_vocal, format0
+                    )
+                    done = 1
+            except:
+                need_reformat = 1
+                traceback.print_exc()
+            if need_reformat == 1:
+                tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path))
+                os.system(
+                    "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
+                    % (inp_path, tmp_path)
+                )
+                inp_path = tmp_path
+            try:
+                if done == 0:
+                    pre_fun._path_audio_(
+                        inp_path, save_root_ins, save_root_vocal, format0
+                    )
+                infos.append("%s->Success" % (os.path.basename(inp_path)))
+                yield "\n".join(infos)
+            except:
+                infos.append(
+                    "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
+                )
+                yield "\n".join(infos)
+    except:
+        infos.append(traceback.format_exc())
+        yield "\n".join(infos)
+    finally:
+        try:
+            if model_name == "onnx_dereverb_By_FoxJoy":
+                del pre_fun.pred.model
+                del pre_fun.pred.model_
+            else:
+                del pre_fun.model
+                del pre_fun
+        except:
+            traceback.print_exc()
+        print("clean_empty_cache")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    yield "\n".join(infos)
+
+
+# 一个选项卡全局只能有一个音色
+def get_vc(sid):
+    global n_spk, tgt_sr, net_g, vc, cpt, version
+    if sid == "" or sid == []:
+        global hubert_model
+        if hubert_model != None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+            print("clean_empty_cache")
+            del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
+            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            ###楼下不这么折腾清理不干净
+            if_f0 = cpt.get("f0", 1)
+            version = cpt.get("version", "v1")
+            if version == "v1":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs256NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            elif version == "v2":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+            del net_g, cpt
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            cpt = None
+        return {"visible": False, "__type__": "update"}
+    person = "%s/%s" % (weight_root, sid)
+    print("loading %s" % person)
+    cpt = torch.load(person, map_location="cpu")
+    tgt_sr = cpt["config"][-1]
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    if_f0 = cpt.get("f0", 1)
+    version = cpt.get("version", "v1")
+    if version == "v1":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif version == "v2":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+    del net_g.enc_q
+    print(net_g.load_state_dict(cpt["weight"], strict=False))
+    net_g.eval().to(config.device)
+    if config.is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, config)
+    n_spk = cpt["config"][-3]
+    return {"visible": False, "maximum": n_spk, "__type__": "update"}
+
+
+def change_choices():
+    names = []
+    for name in os.listdir(weight_root):
+        if name.endswith(".pth"):
+            names.append(name)
+    index_paths = []
+    for root, dirs, files in os.walk(index_root, topdown=False):
+        for name in files:
+            if name.endswith(".index") and "trained" not in name:
+                index_paths.append("%s/%s" % (root, name))
+    return {"choices": sorted(names), "__type__": "update"}, {
+        "choices": sorted(index_paths),
+        "__type__": "update",
+    }
+
+
+def clean():
+    return {"value": "", "__type__": "update"}
+
+
+sr_dict = {
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+
+def if_done(done, p):
+    while 1:
+        if p.poll() == None:
+            sleep(0.5)
+        else:
+            break
+    done[0] = True
+
+
+def if_done_multi(done, ps):
+    while 1:
+        # poll==None代表进程未结束
+        # 只要有一个进程未结束都不停
+        flag = 1
+        for p in ps:
+            if p.poll() == None:
+                flag = 0
+                sleep(0.5)
+                break
+        if flag == 1:
+            break
+    done[0] = True
+
+
+def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
+    sr = sr_dict[sr]
+    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
+    f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
+    f.close()
+    cmd = (
+        config.python_cmd
+        + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
+        % (trainset_dir, sr, n_p, now_dir, exp_dir)
+        + str(config.noparallel)
+    )
+    print(cmd)
+    p = Popen(cmd, shell=True)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
+    ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+    done = [False]
+    threading.Thread(
+        target=if_done,
+        args=(
+            done,
+            p,
+        ),
+    ).start()
+    while 1:
+        with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+            yield (f.read())
+        sleep(1)
+        if done[0] == True:
+            break
+    with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+        log = f.read()
+    print(log)
+    gr.Info("End Preprocess means you're done with this step. Go to step 2.")
+    yield log
+
+
+# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
+def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl):
+    gpus = gpus.split("-")
+    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
+    f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
+    f.close()
+    if if_f0:
+        cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % (
+            now_dir,
+            exp_dir,
+            n_p,
+            f0method,
+            echl,
+        )
+        print(cmd)
+        p = Popen(cmd, shell=True, cwd=now_dir)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE
+        ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+        done = [False]
+        threading.Thread(
+            target=if_done,
+            args=(
+                done,
+                p,
+            ),
+        ).start()
+        while 1:
+            with open(
+                "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
+            ) as f:
+                yield (f.read())
+            sleep(1)
+            if done[0] == True:
+                break
+        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+            log = f.read()
+        print(log)
+        gr.Info('Wait to see "all feature done" in the status box to know it finished.')
+        yield log
+    ####对不同part分别开多进程
+    """
+    n_part=int(sys.argv[1])
+    i_part=int(sys.argv[2])
+    i_gpu=sys.argv[3]
+    exp_dir=sys.argv[4]
+    os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
+    """
+    leng = len(gpus)
+    ps = []
+    for idx, n_g in enumerate(gpus):
+        cmd = (
+            config.python_cmd
+            + " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
+            % (
+                config.device,
+                leng,
+                idx,
+                n_g,
+                now_dir,
+                exp_dir,
+                version19,
+            )
+        )
+        print(cmd)
+        p = Popen(
+            cmd, shell=True, cwd=now_dir
+        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+        ps.append(p)
+    ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+    done = [False]
+    threading.Thread(
+        target=if_done_multi,
+        args=(
+            done,
+            ps,
+        ),
+    ).start()
+    while 1:
+        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+            yield (f.read())
+        sleep(1)
+        if done[0] == True:
+            break
+    with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+        log = f.read()
+    print(log)
+    yield log
+
+
+def change_sr2(sr2, if_f0_3, version19):
+    path_str = "" if version19 == "v1" else "_v2"
+    f0_str = "f0" if if_f0_3 else ""
+    if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    return (
+        ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+        {"visible": True, "__type__": "update"}
+    )
+
+def change_version19(sr2, if_f0_3, version19):
+    path_str = "" if version19 == "v1" else "_v2"
+    f0_str = "f0" if if_f0_3 else ""
+    if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    return (
+        ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+    )
+
+
+def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
+    path_str = "" if version19 == "v1" else "_v2"
+    if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+    if if_f0_3:
+        return (
+            {"visible": True, "__type__": "update"},
+            "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
+            "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
+        )
+    return (
+        {"visible": False, "__type__": "update"},
+        ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
+    )
+
+
+# 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))
+    print("write filelist done")
+    # 生成config#无需生成config
+    # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
+    print("use gpus:", gpus16)
+    if pretrained_G14 == "":
+        print("no pretrained Generator")
+    if pretrained_D15 == "":
+        print("no pretrained Discriminator")
+    if gpus16:
+        cmd = (
+            config.python_cmd
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+            % (
+                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 = (
+            config.python_cmd
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b",
+                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,
+            )
+        )
+    print(cmd)
+    p = Popen(cmd, shell=True, cwd=now_dir)
+    p.wait()
+    gr.Warning('Done! Check your console in Colab to see if it trained successfully.')
+    return 'Done! Check your console in Colab to see if it trained successfully.'
+
+
+# but4.click(train_index, [exp_dir1], info3)
+def train_index(exp_dir1, version19):
+    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+    os.makedirs(exp_dir, exist_ok=True)
+    feature_dir = (
+        "%s/3_feature256" % (exp_dir)
+        if version19 == "v1"
+        else "%s/3_feature768" % (exp_dir)
+    )
+    if os.path.exists(feature_dir) == False:
+        return "请先进行特征提取!"
+    listdir_res = list(os.listdir(feature_dir))
+    if len(listdir_res) == 0:
+        return "请先进行特征提取！"
+    npys = []
+    for name in sorted(listdir_res):
+        phone = np.load("%s/%s" % (feature_dir, name))
+        npys.append(phone)
+    big_npy = np.concatenate(npys, 0)
+    big_npy_idx = np.arange(big_npy.shape[0])
+    np.random.shuffle(big_npy_idx)
+    big_npy = big_npy[big_npy_idx]
+    np.save("%s/total_fea.npy" % exp_dir, big_npy)
+    # n_ivf =  big_npy.shape[0] // 39
+    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+    infos = []
+    infos.append("%s,%s" % (big_npy.shape, n_ivf))
+    yield "\n".join(infos)
+    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
+    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
+    infos.append("training")
+    yield "\n".join(infos)
+    index_ivf = faiss.extract_index_ivf(index)  #
+    index_ivf.nprobe = 1
+    index.train(big_npy)
+    faiss.write_index(
+        index,
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+    )
+    # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+    infos.append("adding")
+    yield "\n".join(infos)
+    batch_size_add = 8192
+    for i in range(0, big_npy.shape[0], batch_size_add):
+        index.add(big_npy[i : i + batch_size_add])
+    faiss.write_index(
+        index,
+        "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+    )
+    infos.append(
+        "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+    )
+    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
+    gr.Info('Successfully trained the index file!')
+    yield "\n".join(infos)
+
+
+# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
+def train1key(
+    exp_dir1,
+    sr2,
+    if_f0_3,
+    trainset_dir4,
+    spk_id5,
+    np7,
+    f0method8,
+    save_epoch10,
+    total_epoch11,
+    batch_size12,
+    if_save_latest13,
+    pretrained_G14,
+    pretrained_D15,
+    gpus16,
+    if_cache_gpu17,
+    if_save_every_weights18,
+    version19,
+    echl
+):
+    infos = []
+
+    def get_info_str(strr):
+        infos.append(strr)
+        return "\n".join(infos)
+
+    model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+    preprocess_log_path = "%s/preprocess.log" % model_log_dir
+    extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
+    gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
+    feature_dir = (
+        "%s/3_feature256" % model_log_dir
+        if version19 == "v1"
+        else "%s/3_feature768" % model_log_dir
+    )
+
+    os.makedirs(model_log_dir, exist_ok=True)
+    #########step1:处理数据
+    open(preprocess_log_path, "w").close()
+    cmd = (
+        config.python_cmd
+        + " trainset_preprocess_pipeline_print.py %s %s %s %s "
+        % (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
+        + str(config.noparallel)
+    )
+    yield get_info_str(i18n("step1:正在处理数据"))
+    yield get_info_str(cmd)
+    p = Popen(cmd, shell=True)
+    p.wait()
+    with open(preprocess_log_path, "r") as f:
+        print(f.read())
+    #########step2a:提取音高
+    open(extract_f0_feature_log_path, "w")
+    if if_f0_3:
+        yield get_info_str("step2a:正在提取音高")
+        cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % (
+            model_log_dir,
+            np7,
+            f0method8,
+            echl
+        )
+        yield get_info_str(cmd)
+        p = Popen(cmd, shell=True, cwd=now_dir)
+        p.wait()
+        with open(extract_f0_feature_log_path, "r") as f:
+            print(f.read())
+    else:
+        yield get_info_str(i18n("step2a:无需提取音高"))
+    #######step2b:提取特征
+    yield get_info_str(i18n("step2b:正在提取特征"))
+    gpus = gpus16.split("-")
+    leng = len(gpus)
+    ps = []
+    for idx, n_g in enumerate(gpus):
+        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
+            config.device,
+            leng,
+            idx,
+            n_g,
+            model_log_dir,
+            version19,
+        )
+        yield get_info_str(cmd)
+        p = Popen(
+            cmd, shell=True, cwd=now_dir
+        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+        ps.append(p)
+    for p in ps:
+        p.wait()
+    with open(extract_f0_feature_log_path, "r") as f:
+        print(f.read())
+    #######step3a:训练模型
+    yield get_info_str(i18n("step3a:正在训练模型"))
+    # 生成filelist
+    if if_f0_3:
+        f0_dir = "%s/2a_f0" % model_log_dir
+        f0nsf_dir = "%s/2b-f0nsf" % model_log_dir
+        names = (
+            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
+            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
+            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
+            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
+        )
+    else:
+        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
+            [name.split(".")[0] for name in os.listdir(feature_dir)]
+        )
+    opt = []
+    for name in names:
+        if if_f0_3:
+            opt.append(
+                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
+                % (
+                    gt_wavs_dir.replace("\\", "\\\\"),
+                    name,
+                    feature_dir.replace("\\", "\\\\"),
+                    name,
+                    f0_dir.replace("\\", "\\\\"),
+                    name,
+                    f0nsf_dir.replace("\\", "\\\\"),
+                    name,
+                    spk_id5,
+                )
+            )
+        else:
+            opt.append(
+                "%s/%s.wav|%s/%s.npy|%s"
+                % (
+                    gt_wavs_dir.replace("\\", "\\\\"),
+                    name,
+                    feature_dir.replace("\\", "\\\\"),
+                    name,
+                    spk_id5,
+                )
+            )
+    fea_dim = 256 if version19 == "v1" else 768
+    if if_f0_3:
+        for _ in range(2):
+            opt.append(
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
+            )
+    else:
+        for _ in range(2):
+            opt.append(
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
+            )
+    shuffle(opt)
+    with open("%s/filelist.txt" % model_log_dir, "w") as f:
+        f.write("\n".join(opt))
+    yield get_info_str("write filelist done")
+    if gpus16:
+        cmd = (
+            config.python_cmd
+            +" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                gpus16,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+                1 if if_save_latest13 == 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 = (
+            config.python_cmd
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+                1 if if_save_latest13 == i18n("是") else 0,
+                1 if if_cache_gpu17 == i18n("是") else 0,
+                1 if if_save_every_weights18 == i18n("是") else 0,
+                version19,
+            )
+        )
+    yield get_info_str(cmd)
+    p = Popen(cmd, shell=True, cwd=now_dir)
+    p.wait()
+    yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"))
+    #######step3b:训练索引
+    npys = []
+    listdir_res = list(os.listdir(feature_dir))
+    for name in sorted(listdir_res):
+        phone = np.load("%s/%s" % (feature_dir, name))
+        npys.append(phone)
+    big_npy = np.concatenate(npys, 0)
+
+    big_npy_idx = np.arange(big_npy.shape[0])
+    np.random.shuffle(big_npy_idx)
+    big_npy = big_npy[big_npy_idx]
+    np.save("%s/total_fea.npy" % model_log_dir, big_npy)
+
+    # n_ivf =  big_npy.shape[0] // 39
+    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+    yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
+    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
+    yield get_info_str("training index")
+    index_ivf = faiss.extract_index_ivf(index)  #
+    index_ivf.nprobe = 1
+    index.train(big_npy)
+    faiss.write_index(
+        index,
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+    )
+    yield get_info_str("adding index")
+    batch_size_add = 8192
+    for i in range(0, big_npy.shape[0], batch_size_add):
+        index.add(big_npy[i : i + batch_size_add])
+    faiss.write_index(
+        index,
+        "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+    )
+    yield get_info_str(
+        "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+    )
+    yield get_info_str(i18n("全流程结束！"))
+
+
+#                    ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
+def change_info_(ckpt_path):
+    if (
+        os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
+        == False
+    ):
+        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
+    try:
+        with open(
+            ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
+        ) as f:
+            info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
+            sr, f0 = info["sample_rate"], info["if_f0"]
+            version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
+            return sr, str(f0), version
+    except:
+        traceback.print_exc()
+        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
+
+
+from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
+
+
+def export_onnx(ModelPath, ExportedPath, MoeVS=True):
+    cpt = torch.load(ModelPath, map_location="cpu")
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2]  # hidden_channels，为768Vec做准备
+
+    test_phone = torch.rand(1, 200, hidden_channels)  # hidden unit
+    test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
+    test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # 基频（单位赫兹）
+    test_pitchf = torch.rand(1, 200)  # nsf基频
+    test_ds = torch.LongTensor([0])  # 说话人ID
+    test_rnd = torch.rand(1, 192, 200)  # 噪声（加入随机因子）
+
+    device = "cpu"  # 导出时设备（不影响使用模型）
+
+
+    net_g = SynthesizerTrnMsNSFsidM(
+        *cpt["config"], is_half=False,version=cpt.get("version","v1")
+    )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
+    net_g.load_state_dict(cpt["weight"], strict=False)
+    input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
+    output_names = [
+        "audio",
+    ]
+    # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
+    torch.onnx.export(
+        net_g,
+        (
+            test_phone.to(device),
+            test_phone_lengths.to(device),
+            test_pitch.to(device),
+            test_pitchf.to(device),
+            test_ds.to(device),
+            test_rnd.to(device),
+        ),
+        ExportedPath,
+        dynamic_axes={
+            "phone": [1],
+            "pitch": [1],
+            "pitchf": [1],
+            "rnd": [2],
+        },
+        do_constant_folding=False,
+        opset_version=16,
+        verbose=False,
+        input_names=input_names,
+        output_names=output_names,
+    )
+    return "Finished"
+
+
+#region Mangio-RVC-Fork CLI App
+import re as regex
+import scipy.io.wavfile as wavfile
+
+cli_current_page = "HOME"
+
+def cli_split_command(com):
+    exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)'
+    split_array = regex.findall(exp, com)
+    split_array = [group[0] if group[0] else group[1] for group in split_array]
+    return split_array
+
+def execute_generator_function(genObject):
+    for _ in genObject: pass
+
+def cli_infer(com):
+    # get VC first
+    com = cli_split_command(com)
+    model_name = com[0]
+    source_audio_path = com[1]
+    output_file_name = com[2]
+    feature_index_path = com[3]
+    f0_file = None # Not Implemented Yet
+
+    # Get parameters for inference
+    speaker_id = int(com[4])
+    transposition = float(com[5])
+    f0_method = com[6]
+    crepe_hop_length = int(com[7])
+    harvest_median_filter = int(com[8])
+    resample = int(com[9])
+    mix = float(com[10])
+    feature_ratio = float(com[11])
+    protection_amnt = float(com[12])
+
+    print("Mangio-RVC-Fork Infer-CLI: Starting the inference...")
+    vc_data = get_vc(model_name)
+    print(vc_data)
+    print("Mangio-RVC-Fork Infer-CLI: Performing inference...")
+    conversion_data = vc_single(
+        speaker_id,
+        source_audio_path,
+        transposition,
+        f0_file,
+        f0_method,
+        feature_index_path,
+        #feature_index_path,
+        feature_ratio,
+        harvest_median_filter,
+        resample,
+        mix,
+        protection_amnt,
+        crepe_hop_length,        
+    )
+    if "Success." in conversion_data[0]:
+        print("Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..." % ('audio-outputs', output_file_name))
+        wavfile.write('%s/%s' % ('audio-outputs', output_file_name), conversion_data[1][0], conversion_data[1][1])
+        print("Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s" % ('audio-outputs', output_file_name))
+    else:
+        print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ")
+        print(conversion_data[0])
+
+def cli_pre_process(com):
+    com = cli_split_command(com)
+    model_name = com[0]
+    trainset_directory = com[1]
+    sample_rate = com[2]
+    num_processes = int(com[3])
+
+    print("Mangio-RVC-Fork Pre-process: Starting...")
+    generator = preprocess_dataset(
+        trainset_directory, 
+        model_name, 
+        sample_rate, 
+        num_processes
+    )
+    execute_generator_function(generator)
+    print("Mangio-RVC-Fork Pre-process: Finished")
+
+def cli_extract_feature(com):
+    com = cli_split_command(com)
+    model_name = com[0]
+    gpus = com[1]
+    num_processes = int(com[2])
+    has_pitch_guidance = True if (int(com[3]) == 1) else False
+    f0_method = com[4]
+    crepe_hop_length = int(com[5])
+    version = com[6] # v1 or v2
+    
+    print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance))
+    print("Mangio-RVC-CLI: Extract Feature Version: " + str(version))
+    print("Mangio-RVC-Fork Feature Extraction: Starting...")
+    generator = extract_f0_feature(
+        gpus, 
+        num_processes, 
+        f0_method, 
+        has_pitch_guidance, 
+        model_name, 
+        version, 
+        crepe_hop_length
+    )
+    execute_generator_function(generator)
+    print("Mangio-RVC-Fork Feature Extraction: Finished")
+
+def cli_train(com):
+    com = cli_split_command(com)
+    model_name = com[0]
+    sample_rate = com[1]
+    has_pitch_guidance = True if (int(com[2]) == 1) else False
+    speaker_id = int(com[3])
+    save_epoch_iteration = int(com[4])
+    total_epoch = int(com[5]) # 10000
+    batch_size = int(com[6])
+    gpu_card_slot_numbers = com[7]
+    if_save_latest = i18n("是") if (int(com[8]) == 1) else i18n("否")
+    if_cache_gpu = i18n("是") if (int(com[9]) == 1) else i18n("否")
+    if_save_every_weight = i18n("是") if (int(com[10]) == 1) else i18n("否")
+    version = com[11]
+
+    pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/" 
+    
+    g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate)
+    d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate)
+
+    print("Mangio-RVC-Fork Train-CLI: Training...")
+    click_train(
+        model_name,
+        sample_rate,
+        has_pitch_guidance,
+        speaker_id,
+        save_epoch_iteration,
+        total_epoch,
+        batch_size,
+        if_save_latest,
+        g_pretrained_path,
+        d_pretrained_path,
+        gpu_card_slot_numbers,
+        if_cache_gpu,
+        if_save_every_weight,
+        version
+    )
+
+def cli_train_feature(com):
+    com = cli_split_command(com)
+    model_name = com[0]
+    version = com[1]
+    print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait")
+    generator = train_index(
+        model_name,
+        version
+    )
+    execute_generator_function(generator)
+    print("Mangio-RVC-Fork Train Feature Index-CLI: Done!")
+
+def cli_extract_model(com):
+    com = cli_split_command(com)
+    model_path = com[0]
+    save_name = com[1]
+    sample_rate = com[2]
+    has_pitch_guidance = com[3]
+    info = com[4]
+    version = com[5]
+    extract_small_model_process = extract_small_model(
+        model_path,
+        save_name,
+        sample_rate,
+        has_pitch_guidance,
+        info,
+        version
+    )
+    if extract_small_model_process == "Success.":
+        print("Mangio-RVC-Fork Extract Small Model: Success!")
+    else:
+        print(str(extract_small_model_process))        
+        print("Mangio-RVC-Fork Extract Small Model: Failed!")
+
+def print_page_details():
+    if cli_current_page == "HOME":
+        print("    go home            : Takes you back to home with a navigation list.")
+        print("    go infer           : Takes you to inference command execution.\n")
+        print("    go pre-process     : Takes you to training step.1) pre-process command execution.")
+        print("    go extract-feature : Takes you to training step.2) extract-feature command execution.")
+        print("    go train           : Takes you to training step.3) being or continue training command execution.")
+        print("    go train-feature   : Takes you to the train feature index command execution.\n")
+        print("    go extract-model   : Takes you to the extract small model command execution.")
+    elif cli_current_page == "INFER":
+        print("    arg 1) model name with .pth in ./weights: mi-test.pth")
+        print("    arg 2) source audio path: myFolder\\MySource.wav")
+        print("    arg 3) output file name to be placed in './audio-outputs': MyTest.wav")
+        print("    arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index")
+        print("    arg 5) speaker id: 0")
+        print("    arg 6) transposition: 0")
+        print("    arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny)")
+        print("    arg 8) crepe hop length: 160")
+        print("    arg 9) harvest median filter radius: 3 (0-7)")
+        print("    arg 10) post resample rate: 0")
+        print("    arg 11) mix volume envelope: 1")
+        print("    arg 12) feature index ratio: 0.78 (0-1)")
+        print("    arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.) \n")
+        print("Example: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33")
+    elif cli_current_page == "PRE-PROCESS":
+        print("    arg 1) Model folder name in ./logs: mi-test")
+        print("    arg 2) Trainset directory: mydataset (or) E:\\my-data-set")
+        print("    arg 3) Sample rate: 40k (32k, 40k, 48k)")
+        print("    arg 4) Number of CPU threads to use: 8 \n")
+        print("Example: mi-test mydataset 40k 24")
+    elif cli_current_page == "EXTRACT-FEATURE":
+        print("    arg 1) Model folder name in ./logs: mi-test")
+        print("    arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)")
+        print("    arg 3) Number of CPU threads to use: 8")
+        print("    arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)")
+        print("    arg 5) f0 Method: harvest (pm, harvest, dio, crepe)")
+        print("    arg 6) Crepe hop length: 128")
+        print("    arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n")
+        print("Example: mi-test 0 24 1 harvest 128 v2")
+    elif cli_current_page == "TRAIN":
+        print("    arg 1) Model folder name in ./logs: mi-test")
+        print("    arg 2) Sample rate: 40k (32k, 40k, 48k)")
+        print("    arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)")
+        print("    arg 4) speaker id: 0")
+        print("    arg 5) Save epoch iteration: 50")
+        print("    arg 6) Total epochs: 10000")
+        print("    arg 7) Batch size: 8")
+        print("    arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)")
+        print("    arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)")
+        print("    arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)")
+        print("    arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)")
+        print("    arg 12) Model architecture version: v2 (use either v1 or v2)\n")
+        print("Example: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2")
+    elif cli_current_page == "TRAIN-FEATURE":
+        print("    arg 1) Model folder name in ./logs: mi-test")
+        print("    arg 2) Model architecture version: v2 (use either v1 or v2)\n")
+        print("Example: mi-test v2")
+    elif cli_current_page == "EXTRACT-MODEL":
+        print("    arg 1) Model Path: logs/mi-test/G_168000.pth")
+        print("    arg 2) Model save name: MyModel")
+        print("    arg 3) Sample rate: 40k (32k, 40k, 48k)")
+        print("    arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)")
+        print('    arg 5) Model information: "My Model"')
+        print("    arg 6) Model architecture version: v2 (use either v1 or v2)\n")
+        print('Example: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2')
+    print("")
+
+def change_page(page):
+    global cli_current_page
+    cli_current_page = page
+    return 0
+
+def execute_command(com):
+    if com == "go home":
+        return change_page("HOME")
+    elif com == "go infer":
+        return change_page("INFER")
+    elif com == "go pre-process":
+        return change_page("PRE-PROCESS")
+    elif com == "go extract-feature":
+        return change_page("EXTRACT-FEATURE")
+    elif com == "go train":
+        return change_page("TRAIN")
+    elif com == "go train-feature":
+        return change_page("TRAIN-FEATURE")
+    elif com == "go extract-model":
+        return change_page("EXTRACT-MODEL")
+    else:
+        if com[:3] == "go ":
+            print("page '%s' does not exist!" % com[3:])
+            return 0
+    
+    if cli_current_page == "INFER":
+        cli_infer(com)
+    elif cli_current_page == "PRE-PROCESS":
+        cli_pre_process(com)
+    elif cli_current_page == "EXTRACT-FEATURE":
+        cli_extract_feature(com)
+    elif cli_current_page == "TRAIN":
+        cli_train(com)
+    elif cli_current_page == "TRAIN-FEATURE":
+        cli_train_feature(com)
+    elif cli_current_page == "EXTRACT-MODEL":
+        cli_extract_model(com)
+
+def cli_navigation_loop():
+    while True:
+        print("You are currently in '%s':" % cli_current_page)
+        print_page_details()
+        command = input("%s: " % cli_current_page)
+        try:
+            execute_command(command)
+        except:
+            print(traceback.format_exc())
+
+if(config.is_cli):
+    print("\n\nMangio-RVC-Fork v2 CLI App!\n")
+    print("Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n")
+    cli_navigation_loop()
+
+#endregion
+
+#region RVC WebUI App
+
+def get_presets():
+    data = None
+    with open('../inference-presets.json', 'r') as file:
+        data = json.load(file)
+    preset_names = []
+    for preset in data['presets']:
+        preset_names.append(preset['name'])
+    
+    return preset_names
+
+def change_choices2():
+    audio_files=[]
+    for filename in os.listdir("./audios"):
+        if filename.endswith(('.wav','.mp3','.ogg')):
+            audio_files.append(filename)
+    return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}
+    
+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(filename)
+        
+def get_index():
+    if check_for_name() != '':
+        chosen_model=sorted(names)[0].split(".")[0]
+        logs_path="./logs/"+chosen_model
+        if os.path.exists(logs_path):
+            for file in os.listdir(logs_path):
+                if file.endswith(".index"):
+                    return os.path.join(logs_path, file)
+            return ''
+        else:
+            return ''
+        
+def get_indexes():
+    indexes_list=[]
+    for dirpath, dirnames, filenames in os.walk("./logs/"):
+        for filename in filenames:
+            if filename.endswith(".index"):
+                indexes_list.append(os.path.join(dirpath,filename))
+    if len(indexes_list) > 0:
+        return indexes_list
+    else:
+        return ''
+        
+def get_name():
+    if len(audio_files) > 0:
+        return sorted(audio_files)[0]
+    else:
+        return ''
+        
+def save_to_wav(record_button):
+    if record_button is None:
+        pass
+    else:
+        path_to_file=record_button
+        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
+        new_path='./audios/'+new_name
+        shutil.move(path_to_file,new_path)
+        return os.path.basename(new_path)
+    
+def save_to_wav2(dropbox):
+    file_path=dropbox.name
+    shutil.move(file_path,'./audios')
+    return os.path.basename(file_path)
+    
+def match_index(sid0):
+    folder=sid0.split(".")[0]
+    parent_dir="./logs/"+folder
+    if os.path.exists(parent_dir):
+        for filename in os.listdir(parent_dir):
+            if filename.endswith(".index"):
+                index_path=os.path.join(parent_dir,filename)
+                return index_path
+    else:
+        return ''
+                
+def check_for_name():
+    if len(names) > 0:
+        return sorted(names)[0]
+    else:
+        return ''
+            
+def download_from_url(url, model):
+    if url == '':
+        return "URL cannot be left empty."
+    if model =='':
+        return "You need to name your model. For example: My-Model"
+    url = url.strip()
+    zip_dirs = ["zips", "unzips"]
+    for directory in zip_dirs:
+        if os.path.exists(directory):
+            shutil.rmtree(directory)
+    os.makedirs("zips", exist_ok=True)
+    os.makedirs("unzips", exist_ok=True)
+    zipfile = model + '.zip'
+    zipfile_path = './zips/' + zipfile
+    try:
+        if "drive.google.com" in url:
+            subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
+        elif "mega.nz" in url:
+            m = Mega()
+            m.download_url(url, './zips')
+        else:
+            subprocess.run(["wget", url, "-O", zipfile_path])
+        for filename in os.listdir("./zips"):
+            if filename.endswith(".zip"):
+                zipfile_path = os.path.join("./zips/",filename)
+                shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
+            else:
+                return "No zipfile found."
+        for root, dirs, files in os.walk('./unzips'):
+            for file in files:
+                file_path = os.path.join(root, file)
+                if file.endswith(".index"):
+                    os.mkdir(f'./logs/{model}')
+                    shutil.copy2(file_path,f'./logs/{model}')
+                elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
+                    shutil.copy(file_path,f'./weights/{model}.pth')
+        shutil.rmtree("zips")
+        shutil.rmtree("unzips")
+        return "Success."
+    except:
+        return "There's been an error."
+def success_message(face):
+    return f'{face.name} has been uploaded.', 'None'
+def mouth(size, face, voice, faces):
+    if size == 'Half':
+        size = 2
+    else:
+        size = 1
+    if faces == 'None':
+        character = face.name
+    else:
+        if faces == 'Ben Shapiro':
+            character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4'
+        elif faces == 'Andrew Tate':
+            character = '/content/wav2lip-HD/inputs/tate-7.mp4'
+    command = "python inference.py " \
+            "--checkpoint_path checkpoints/wav2lip.pth " \
+            f"--face {character} " \
+            f"--audio {voice} " \
+            "--pads 0 20 0 0 " \
+            "--outfile /content/wav2lip-HD/outputs/result.mp4 " \
+            "--fps 24 " \
+            f"--resize_factor {size}"
+    process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master')
+    stdout, stderr = process.communicate()
+    return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.'
+eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli']
+eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O']
+chosen_voice = dict(zip(eleven_voices, eleven_voices_ids))
+def elevenTTS(xiapi, text, id, lang):
+    if xiapi!= '' and id !='': 
+        choice = chosen_voice[id]
+        CHUNK_SIZE = 1024
+        url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}"
+        headers = {
+        "Accept": "audio/mpeg",
+        "Content-Type": "application/json",
+        "xi-api-key": xiapi
+        }
+        if lang == 'en':
+            data = {
+            "text": text,
+            "model_id": "eleven_monolingual_v1",
+            "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.5
+            }
+            }
+        else:
+            data = {
+            "text": text,
+            "model_id": "eleven_multilingual_v1",
+            "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.5
+            }
+            }
+
+        response = requests.post(url, json=data, headers=headers)
+        with open('./temp_eleven.mp3', 'wb') as f:
+          for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
+              if chunk:
+                  f.write(chunk)
+        aud_path = save_to_wav('./temp_eleven.mp3')
+        return aud_path, aud_path
+    else:
+        tts = gTTS(text, lang=lang)
+        tts.save('./temp_gTTS.mp3')
+        aud_path = save_to_wav('./temp_gTTS.mp3')
+        return aud_path, aud_path
+
+def upload_to_dataset(files, dir):
+    gr.Warning('Wait until your data is uploaded...')
+    if dir == '':
+        dir = './dataset'
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+    count = 0
+    for file in files:
+        path=file.name
+        shutil.copy2(path,dir)
+        count += 1
+    gr.Info(f'Done! {count} files were uploaded. Now click "1.Process The Dataset."')
+    return f' {count} files uploaded to {dir}.'     
+    
+def zip_downloader(model):
+    if not os.path.exists(f'./weights/{model}.pth'):
+        return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth'
+    index_found = False
+    for file in os.listdir(f'./logs/{model}'):
+        if file.endswith('.index') and 'added' in file:
+            log_file = file
+            index_found = True
+    if index_found:
+        return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done"
+    else:
+        return f'./weights/{model}.pth', "Could not find Index file."
+    
+def fast(filepath, spk_item, vc_transform0,f0method0,file_index1,index_rate1,filter_radius0, resample_sr0,rms_mix_rate0, protect0, hop):
+    source_audio_path = filepath
+    output_file_name = os.path.basename(filepath)
+    conversion_data = vc_single(
+        spk_item,
+        source_audio_path,
+        vc_transform0,
+        f0_file,
+        f0method0,
+        file_index1,
+        index_rate1,
+        filter_radius0,
+        resample_sr0,
+        rms_mix_rate0,
+        protect0,
+        hop,
+        ""        
+    )
+    if "Success." in conversion_data[0]:
+        wavfile.write(f'audio-outputs/{output_file_name}', conversion_data[1][0], conversion_data[1][1])
+        return f"audio-outputs/{output_file_name}", None, conversion_data[0]
+    else:
+        return gr.update(visible=True), None, conversion_data[0]
+
+with gr.Blocks(theme=gr.themes.Base()) as app:
+    with gr.Tabs():
+        with gr.TabItem("Inference"):
+            gr.HTML("<h1> RVC V2 by https://www.youtube.com/@ba1yya 💻 </h1>")
+            # Inference Preset Row
+            # with gr.Row():
+            #     mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets()))
+            #     mangio_preset_name_save = gr.Textbox(
+            #         label="Your preset name"
+            #     )
+            #     mangio_preset_save_btn = gr.Button('Save Preset', variant="primary")
+
+            # Other RVC stuff
+            with gr.Row():
+                sid0 = gr.Dropdown(label="1.Выберете модель.", choices=sorted(names), value=check_for_name())
+                refresh_button = gr.Button("Обновить", variant="primary")
+                if check_for_name() != '':
+                    get_vc(sorted(names)[0])
+                vc_transform0 = gr.Number(label="Необязательно: здесь вы можете изменить высоту тона или оставить значение 0.", value=0)
+                #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
+                spk_item = gr.Slider(
+                    minimum=0,
+                    maximum=2333,
+                    step=1,
+                    label=i18n("请选择说话人id"),
+                    value=0,
+                    visible=False,
+                    interactive=True,
+                )
+                #clean_button.click(fn=clean, inputs=[], outputs=[sid0])
+                sid0.change(
+                    fn=get_vc,
+                    inputs=[sid0],
+                    outputs=[spk_item],
+                )
+                but0 = gr.Button("Обработка", variant="primary")
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        dropbox = gr.File(label="Перетащите сюда свой аудиофайл и нажмите кнопку «Обновить».")
+                    with gr.Row():
+                        record_button=gr.Audio(source="microphone", label="ИЛИ Запись звука.", type="filepath")
+                    with gr.Row():
+                        input_audio0 = gr.Dropdown(
+                            label="2.Выберите аудио.",
+                            value="someguy.mp3",
+                            choices=audio_files
+                            )
+                        dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
+                        dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                        refresh_button2 = gr.Button("Обновить", variant="primary", size='sm')
+                        record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
+                        record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                    with gr.Row():
+                        with gr.Accordion('Текст в речь', open=False):
+                            with gr.Column():
+                                lang = gr.Radio(label='Китайский и японский языки в настоящее время не работают с ElevenLabs.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en')
+                                api_box = gr.Textbox(label="Введите свой ключ API для ElevenLabs или оставьте пустым, чтобы использовать GoogleTTS", value='')
+                                elevenid=gr.Dropdown(label="Голос:", choices=eleven_voices)
+                            with gr.Column():
+                                tfs = gr.Textbox(label="Введите свой текст", interactive=True, value="This is a test.")
+                                tts_button = gr.Button(value="Говорить")
+                                tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0])
+                    with gr.Row():
+                        with gr.Accordion('Wav2Lip', open=False):
+                            with gr.Row():
+                                size = gr.Radio(label='Разрешение:',choices=['Half','Full'])
+                                face = gr.UploadButton("Загрузите персонажа",type='file')
+                                faces = gr.Dropdown(label="ИЛИ Выберите один:", choices=['None','Ben Shapiro','Andrew Tate'])
+                            with gr.Row():
+                                preview = gr.Textbox(label="Статус:",interactive=False)
+                                face.upload(fn=success_message,inputs=[face], outputs=[preview, faces])
+                            with gr.Row():
+                                animation = gr.Video(type='filepath')
+                                refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation])
+                            with gr.Row():
+                                animate_button = gr.Button('Animate')
+
+                with gr.Column():
+                    with gr.Accordion("Настройки индекса", open=False):
+                        file_index1 = gr.Dropdown(
+                            label="3. Путь к файлу add.index (если он не был найден автоматически)",
+                            choices=get_indexes(),
+                            value=get_index(),
+                            interactive=True,
+                            )
+                        sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1])
+                        refresh_button.click(
+                            fn=change_choices, inputs=[], outputs=[sid0, file_index1]
+                            )
+                        # file_big_npy1 = gr.Textbox(
+                        #     label=i18n("特征文件路径"),
+                        #     value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
+                        #     interactive=True,
+                        # )
+                        index_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("检索特征占比"),
+                            value=0.66,
+                            interactive=True,
+                            )
+                    vc_output2 = gr.Audio(label="Вывод аудио (нажмите три точки в правом углу, чтобы загрузить)",type='filepath')
+                    animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview])
+                    with gr.Accordion("Расширенные настройки", open=False):
+                        f0method0 = gr.Radio(
+                            label="Необязательно: измените алгоритм извлечения высоты звука.",
+                            choices=["pm", "rmvpe", "dio", "mangio-crepe-tiny", "crepe-tiny", "crepe", "mangio-crepe", "harvest"], # Fork Feature. Add Crepe-Tiny
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        crepe_hop_length = gr.Slider(
+                            minimum=1,
+                            maximum=512,
+                            step=1,
+                            label="Mangio-Crepe Hop Length. Более высокие числа уменьшат вероятность резких изменений шага, но меньшие числа повысят точность.",
+                            value=120,
+                            interactive=True
+                            )
+                        filter_radius0 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                            )
+                        resample_sr0 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                            visible=False
+                            )
+                        rms_mix_rate0 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=0.21,
+                            interactive=True,
+                            )
+                        protect0 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                            )
+                    with gr.Accordion("Fast-Mode (TESTING)", open=False):
+                        fast_audio = gr.Audio(label="As soon as you stop recording, inference will start.",type="filepath", source="microphone", autoplay=False)
+                        fast_result = gr.Audio(label="Result",type="filepath", autoplay=True)
+                        
+            with gr.Row():
+                vc_output1 = gr.Textbox(label="Output Information:")
+                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)
+                fast_audio.stop_recording(
+                    fn=fast,
+                    inputs=[
+                        fast_audio,
+                        spk_item,
+                        vc_transform0,
+                        f0method0,
+                        file_index1,
+                        index_rate1,
+                        filter_radius0,
+                        resample_sr0,
+                        rms_mix_rate0,
+                        protect0,
+                        crepe_hop_length
+                        ],
+                    outputs=[
+                        fast_result,
+                        fast_audio, 
+                        vc_output1
+                        ]
+                    )
+                but0.click(
+                    vc_single,
+                    [
+                        spk_item,
+                        input_audio0,
+                        vc_transform0,
+                        f0_file,
+                        f0method0,
+                        file_index1,
+                        # file_index2,
+                        # file_big_npy1,
+                        index_rate1,
+                        filter_radius0,
+                        resample_sr0,
+                        rms_mix_rate0,
+                        protect0,
+                        crepe_hop_length
+                    ],
+                    [vc_output1, vc_output2],
+                )
+                        
+        with gr.TabItem("Загрузка готовой модели"):
+            with gr.Row():
+                url=gr.Textbox(label="Введите URL-адрес модели:")
+            with gr.Row():
+                model = gr.Textbox(label="Назовите свою модель:")
+                download_button=gr.Button("Загрузить")
+            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(
+                """
+                Original RVC:https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
+                ❤️ Если вам нравитс�� моя версия RVC, помогите мне сохранить ее.❤️
+                https://boosty.to/ba1yya
+                """
+                )
+                
+        with gr.TabItem("Train", visible=False):
+            with gr.Row():
+                with gr.Column():
+                    exp_dir1 = gr.Textbox(label="Голосовое имя:", value="Voice_1")
+                    sr2 = gr.Radio(
+                        label=i18n("目标采样率"),
+                        choices=["40k", "48k"],
+                        value="40k",
+                        interactive=True,
+                        visible=False
+                    )
+                    if_f0_3 = gr.Radio(
+                        label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
+                        choices=[True, False],
+                        value=True,
+                        interactive=True,
+                        visible=False
+                    )
+                    version19 = gr.Radio(
+                        label="RVC version",
+                        choices=["v1", "v2"],
+                        value="v2",
+                        interactive=True,
+                        visible=False,
+                    )
+                    np7 = gr.Slider(
+                        minimum=0,
+                        maximum=config.n_cpu,
+                        step=1,
+                        label="# of CPUs for data processing (Leave as it is)",
+                        value=config.n_cpu,
+                        interactive=True,
+                        visible=True
+                    )
+                    trainset_dir4 = gr.Textbox(label="Путь к вашему набору данных (аудиофайлы, а не zip):", value="./dataset")
+                    easy_uploader = gr.Files(label='ИЛИ Перетащите сюда свои аудиозаписи. Они будут загружены по указанному выше пути к набору данных.',file_types=['audio'])
+                    but1 = gr.Button("1.Обработать набор данных", variant="primary")
+                    info1 = gr.Textbox(label="Статус:", value="")
+                    easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1])
+                    but1.click(
+                        preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
+                    )
+                with gr.Column():
+                    spk_id5 = gr.Slider(
+                        minimum=0,
+                        maximum=4,
+                        step=1,
+                        label=i18n("请指定说话人id"),
+                        value=0,
+                        interactive=True,
+                        visible=False
+                    )
+                    with gr.Accordion('GPU Settings', open=False, visible=False):
+                        gpus6 = gr.Textbox(
+                            label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
+                            value=gpus,
+                            interactive=True,
+                            visible=False
+                        )
+                        gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
+                    f0method8 = gr.Radio(
+                        label=i18n(
+                            "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"
+                        ),
+                        choices=["harvest","crepe", "mangio-crepe"], # Fork feature: Crepe on f0 extraction for training.
+                        value="mangio-crepe",
+                        interactive=True,
+                    )
+                    extraction_crepe_hop_length = gr.Slider(
+                        minimum=1,
+                        maximum=512,
+                        step=1,
+                        label=i18n("crepe_hop_length"),
+                        value=128,
+                        interactive=True
+                    )
+                    but2 = gr.Button("2.Извлечение высоты тона", variant="primary")
+                    info2 = gr.Textbox(label="Статус:", value="", max_lines=8)
+                    but2.click(
+                            extract_f0_feature,
+                            [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length],
+                            [info2],
+                        )
+                with gr.Row():      
+                    with gr.Column():
+                        total_epoch11 = gr.Slider(
+                            minimum=0,
+                            maximum=10000,
+                            step=10,
+                            label="Общее количество эпох обучения (много не вводите, возможна перетренировка):",
+                            value=250,
+                            interactive=True,
+                        )
+                        but3 = gr.Button("3.Тренировать модель", variant="primary")
+                        but4 = gr.Button("4.Тренировать индекс", variant="primary")
+                        info3 = gr.Textbox(label="Статус:", value="", max_lines=10)
+                        with gr.Accordion("Настройки обучения (вы можете оставить их как есть)", open=False):
+                            #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
+                            with gr.Column():
+                                save_epoch10 = gr.Slider(
+                                    minimum=0,
+                                    maximum=100,
+                                    step=5,
+                                    label="Резервное копирование каждые # эпох:",
+                                    value=25,
+                                    interactive=True,
+                                )
+                                batch_size12 = gr.Slider(
+                                    minimum=1,
+                                    maximum=40,
+                                    step=1,
+                                    label="Размер пакета (Оставьте его, если вы не знаете, что это!):",
+                                    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,
+                                )
+                                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,
+                                )
+                        zip_model = gr.Button('5.Скачать модель')
+                        zipped_model = gr.Files(label='Файл вашей модели и индекса можно скачать здесь:')
+                        zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3])
+            with gr.Group():
+                with gr.Accordion("Расположение базовой модели:", open=False, visible=False):
+                    pretrained_G14 = gr.Textbox(
+                        label=i18n("加载预训练底模G路径"),
+                        value="pretrained_v2/f0G40k.pth",
+                        interactive=True,
+                    )
+                    pretrained_D15 = gr.Textbox(
+                        label=i18n("加载预训练底模D路径"),
+                        value="pretrained_v2/f0D40k.pth",
+                        interactive=True,
+                    )
+                    gpus16 = gr.Textbox(
+                        label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
+                        value=gpus,
+                        interactive=True,
+                    )
+                sr2.change(
+                    change_sr2,
+                    [sr2, if_f0_3, version19],
+                    [pretrained_G14, pretrained_D15, version19],
+                )
+                version19.change(
+                    change_version19,
+                    [sr2, if_f0_3, version19],
+                    [pretrained_G14, pretrained_D15],
+                )
+                if_f0_3.change(
+                    change_f0,
+                    [if_f0_3, sr2, version19],
+                    [f0method8, pretrained_G14, pretrained_D15],
+                )
+                but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False)
+                but3.click(
+                    click_train,
+                    [
+                        exp_dir1,
+                        sr2,
+                        if_f0_3,
+                        spk_id5,
+                        save_epoch10,
+                        total_epoch11,
+                        batch_size12,
+                        if_save_latest13,
+                        pretrained_G14,
+                        pretrained_D15,
+                        gpus16,
+                        if_cache_gpu17,
+                        if_save_every_weights18,
+                        version19,
+                    ],
+                    info3,
+                )
+                but4.click(train_index, [exp_dir1, version19], info3)
+                but5.click(
+                    train1key,
+                    [
+                        exp_dir1,
+                        sr2,
+                        if_f0_3,
+                        trainset_dir4,
+                        spk_id5,
+                        np7,
+                        f0method8,
+                        save_epoch10,
+                        total_epoch11,
+                        batch_size12,
+                        if_save_latest13,
+                        pretrained_G14,
+                        pretrained_D15,
+                        gpus16,
+                        if_cache_gpu17,
+                        if_save_every_weights18,
+                        version19,
+                        extraction_crepe_hop_length
+                    ],
+                    info3,
+                )
+
+
+            try:
+                if tab_faq == "常见问题解答":
+                    with open("docs/faq.md", "r", encoding="utf8") as f:
+                        info = f.read()
+                else:
+                    with open("docs/faq_en.md", "r", encoding="utf8") as f:
+                        info = f.read()
+                gr.Markdown(value=info)
+            except:
+                gr.Markdown("")
+
+
+    #region Mangio Preset Handler Region
+    def save_preset(preset_name,sid0,vc_transform,input_audio,f0method,crepe_hop_length,filter_radius,file_index1,file_index2,index_rate,resample_sr,rms_mix_rate,protect,f0_file):
+        data = None
+        with open('../inference-presets.json', 'r') as file:
+            data = json.load(file)
+        preset_json = {
+            'name': preset_name,
+            'model': sid0,
+            'transpose': vc_transform,
+            'audio_file': input_audio,
+            'f0_method': f0method,
+            'crepe_hop_length': crepe_hop_length,
+            'median_filtering': filter_radius,
+            'feature_path': file_index1,
+            'auto_feature_path': file_index2,
+            'search_feature_ratio': index_rate,
+            'resample': resample_sr,
+            'volume_envelope': rms_mix_rate,
+            'protect_voiceless': protect,
+            'f0_file_path': f0_file
+        }
+        data['presets'].append(preset_json)
+        with open('../inference-presets.json', 'w') as file:
+            json.dump(data, file)
+            file.flush()
+        print("Saved Preset %s into inference-presets.json!" % preset_name)
+
+                
+    if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE)
+        app.queue(concurrency_count=511, max_size=1022).launch(share=True, quiet=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,
+        )