Upload infer-web.py with huggingface_hub

infer-web.py

@@ -0,0 +1,2471 @@
+import os
+import shutil
+import sys
+
+import json # Mangio fork using json for preset saving
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import traceback, pdb
+import warnings
+
+import numpy as np
+import torch
+
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+import logging
+import threading
+from random import shuffle
+from subprocess import Popen
+from time import sleep
+
+import faiss
+import ffmpeg
+import gradio as gr
+import soundfile as sf
+from config import Config
+from fairseq import checkpoint_utils
+from i18n import I18nAuto
+from infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
+from infer_uvr5 import _audio_pre_, _audio_pre_new
+from MDXNet import MDXNetDereverb
+from my_utils import load_audio
+from train.process_ckpt import change_info, extract_small_model, merge, show_info
+from vc_infer_pipeline import VC
+from sklearn.cluster import MiniBatchKMeans
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+
+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)
+
+
+config = Config()
+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_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"
+
+
+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,
+):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+    global tgt_sr, net_g, vc, hubert_model, version
+    if input_audio_path is None:
+        return "You need to upload an audio", None
+    f0_up_key = int(f0_up_key)
+    try:
+        audio = load_audio(input_audio_path, 16000)
+        audio_max = np.abs(audio).max() / 0.95
+        if audio_max > 1:
+            audio /= audio_max
+        times = [0, 0, 0]
+        if not hubert_model:
+            load_hubert()
+        if_f0 = cpt.get("f0", 1)
+        file_index = (
+            (
+                file_index.strip(" ")
+                .strip('"')
+                .strip("\n")
+                .strip('"')
+                .strip(" ")
+                .replace("trained", "added")
+            )
+            if file_index != ""
+            else file_index2
+        )  # 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 tgt_sr != resample_sr >= 16000:
+            tgt_sr = resample_sr
+        index_info = (
+            "Using index:%s." % file_index
+            if os.path.exists(file_index)
+            else "Index not used."
+        )
+        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
+            index_info,
+            times[0],
+            times[1],
+            times[2],
+        ), (tgt_sr, audio_opt)
+    except:
+        info = traceback.format_exc()
+        print(info)
+        return info, (None, None)
+
+
+def vc_multi(
+    sid,
+    dir_path,
+    opt_root,
+    paths,
+    f0_up_key,
+    f0_method,
+    file_index,
+    file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    format1,
+    crepe_hop_length,
+):
+    try:
+        dir_path = (
+            dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # 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, to_return_protect0, to_return_protect1):
+    global n_spk, tgt_sr, net_g, vc, cpt, version
+    if sid == "" or sid == []:
+        global hubert_model
+        if hubert_model is not 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)
+    if if_f0 == 0:
+        to_return_protect0 = to_return_protect1 = {
+            "visible": False,
+            "value": 0.5,
+            "__type__": "update",
+        }
+    else:
+        to_return_protect0 = {
+            "visible": True,
+            "value": to_return_protect0,
+            "__type__": "update",
+        }
+        to_return_protect1 = {
+            "visible": True,
+            "value": to_return_protect1,
+            "__type__": "update",
+        }
+    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": True, "maximum": n_spk, "__type__": "update"},
+        to_return_protect0,
+        to_return_protect1,
+    )
+
+
+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() 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()
+    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]:
+            break
+    with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+        log = f.read()
+    print(log)
+    yield log
+
+
+# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
+def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl):
+    gpus = gpus.split("-")
+    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
+    f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
+    f.close()
+    if if_f0:
+        cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % (
+            now_dir,
+            exp_dir,
+            n_p,
+            f0method,
+            echl,
+        )
+        print(cmd)
+        p = Popen(cmd, shell=True, cwd=now_dir)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE
+        ###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]:
+                break
+        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+            log = f.read()
+        print(log)
+        yield log
+    ####对不同part分别开多进程
+    """
+    n_part=int(sys.argv[1])
+    i_part=int(sys.argv[2])
+    i_gpu=sys.argv[3]
+    exp_dir=sys.argv[4]
+    os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
+    """
+    leng = len(gpus)
+    ps = []
+    for idx, n_g in enumerate(gpus):
+        cmd = (
+            config.python_cmd
+            + " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
+            % (
+                config.device,
+                leng,
+                idx,
+                n_g,
+                now_dir,
+                exp_dir,
+                version19,
+            )
+        )
+        print(cmd)
+        p = Popen(
+            cmd, shell=True, cwd=now_dir
+        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+        ps.append(p)
+    ###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()
+    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 not if_pretrained_generator_exist:
+        print(
+            "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
+            "not exist, will not use pretrained model",
+        )
+    if not if_pretrained_discriminator_exist:
+        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_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 ""
+    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 not if_pretrained_generator_exist:
+        print(
+            "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
+            "not exist, will not use pretrained model",
+        )
+    if not if_pretrained_discriminator_exist:
+        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 "",
+        to_return_sr2,
+    )
+
+
+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 not if_pretrained_generator_exist:
+        print(
+            "pretrained%s/f0G%s.pth" % (path_str, sr2),
+            "not exist, will not use pretrained model",
+        )
+    if not if_pretrained_discriminator_exist:
+        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()
+    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)
+    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:
+        # if(1):
+        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()
+            print(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),
+    )
+    # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+    infos.append("adding")
+    yield "\n".join(infos)
+    batch_size_add = 8192
+    for i in range(0, big_npy.shape[0], batch_size_add):
+        index.add(big_npy[i : i + batch_size_add])
+    faiss.write_index(
+        index,
+        "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+    )
+    infos.append(
+        "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+    )
+    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
+    yield "\n".join(infos)
+
+
+# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
+def train1key(
+    exp_dir1,
+    sr2,
+    if_f0_3,
+    trainset_dir4,
+    spk_id5,
+    np7,
+    f0method8,
+    save_epoch10,
+    total_epoch11,
+    batch_size12,
+    if_save_latest13,
+    pretrained_G14,
+    pretrained_D15,
+    gpus16,
+    if_cache_gpu17,
+    if_save_every_weights18,
+    version19,
+    echl
+):
+    infos = []
+
+    def get_info_str(strr):
+        infos.append(strr)
+        return "\n".join(infos)
+
+    model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+    preprocess_log_path = "%s/preprocess.log" % model_log_dir
+    extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
+    gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
+    feature_dir = (
+        "%s/3_feature256" % model_log_dir
+        if version19 == "v1"
+        else "%s/3_feature768" % model_log_dir
+    )
+
+    os.makedirs(model_log_dir, exist_ok=True)
+    #########step1:处理数据
+    open(preprocess_log_path, "w").close()
+    cmd = (
+        config.python_cmd
+        + " trainset_preprocess_pipeline_print.py %s %s %s %s "
+        % (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
+        + str(config.noparallel)
+    )
+    yield get_info_str(i18n("step1: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]
+
+    if big_npy.shape[0] > 2e5:
+        # if(1):
+        info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
+        print(info)
+        yield get_info_str(info)
+        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()
+            print(info)
+            yield get_info_str(info)
+
+    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 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"}
+
+
+def export_onnx(ModelPath, ExportedPath):
+    cpt = torch.load(ModelPath, map_location="cpu")
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
+    vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
+
+    test_phone = torch.rand(1, 200, vec_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=13,
+        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
+
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.HTML("<h1> The Mangio-RVC-Fork 💻 </h1>")
+    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>使用需遵守的协议-LICENSE.txt</b>."
+        )
+    )
+    with gr.Tabs():
+        with gr.TabItem(i18n("模型推理")):
+            # 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=i18n("推理音色"), choices=sorted(names))
+                refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary")
+                clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
+                spk_item = gr.Slider(
+                    minimum=0,
+                    maximum=2333,
+                    step=1,
+                    label=i18n("请选择说话人id"),
+                    value=0,
+                    visible=False,
+                    interactive=True,
+                )
+                clean_button.click(fn=clean, inputs=[], outputs=[sid0])
+            with gr.Group():
+                gr.Markdown(
+                    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():
+                    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)"),
+                            value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
+                        )
+                        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", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny
+                            value="pm",
+                            interactive=True,
+                        )
+                        crepe_hop_length = gr.Slider(
+                            minimum=1,
+                            maximum=512,
+                            step=1,
+                            label=i18n("crepe_hop_length"),
+                            value=160,
+                            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=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,
+                        )
+                        refresh_button.click(
+                            fn=change_choices, inputs=[], outputs=[sid0, file_index2]
+                        )
+                        # 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=0.88,
+                            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=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")
+                    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_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.Group():
+                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")
+                        f0method1 = gr.Radio(
+                            label=i18n(
+                                "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
+                            ),
+                            choices=["pm", "harvest", "crepe"],
+                            value="pm",
+                            interactive=True,
+                        )
+                        filter_radius1 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        file_index3 = gr.Textbox(
+                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
+                            value="",
+                            interactive=True,
+                        )
+                        file_index4 = gr.Dropdown(
+                            label=i18n("自动检测index路径,下拉式选择(dropdown)"),
+                            choices=sorted(index_paths),
+                            interactive=True,
+                        )
+                        refresh_button.click(
+                            fn=lambda: change_choices()[1],
+                            inputs=[],
+                            outputs=file_index4,
+                        )
+                        # file_big_npy2 = gr.Textbox(
+                        #     label=i18n("特征文件路径"),
+                        #     value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
+                        #     interactive=True,
+                        # )
+                        index_rate2 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("检索特征占比"),
+                            value=1,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        resample_sr1 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                        )
+                        rms_mix_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=1,
+                            interactive=True,
+                        )
+                        protect1 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n(
+                                "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
+                            ),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        dir_input = gr.Textbox(
+                            label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
+                            value="E:\codes\py39\\test-20230416b\\todo-songs",
+                        )
+                        inputs = gr.File(
+                            file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
+                        )
+                    with gr.Row():
+                        format1 = gr.Radio(
+                            label=i18n("导出文件格式"),
+                            choices=["wav", "flac", "mp3", "m4a"],
+                            value="flac",
+                            interactive=True,
+                        )
+                        but1 = gr.Button(i18n("转换"), variant="primary")
+                        vc_output3 = gr.Textbox(label=i18n("输出信息"))
+                    but1.click(
+                        vc_multi,
+                        [
+                            spk_item,
+                            dir_input,
+                            opt_input,
+                            inputs,
+                            vc_transform1,
+                            f0method1,
+                            file_index3,
+                            file_index4,
+                            # file_big_npy2,
+                            index_rate2,
+                            filter_radius1,
+                            resample_sr1,
+                            rms_mix_rate1,
+                            protect1,
+                            format1,
+                            crepe_hop_length,
+                        ],
+                        [vc_output3],
+                    )
+            sid0.change(
+                fn=get_vc,
+                inputs=[sid0, protect0, protect1],
+                outputs=[spk_item, protect0, protect1],
+            )
+        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"),
+                            value="E:\\codes\\py39\\test-20230416b\\todo-songs\\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],
+                    )
+        with gr.TabItem(i18n("训练")):
+            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. "
+                )
+            )
+            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="v1",
+                    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="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]
+                    )
+            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,
+                        )
+                        gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
+                    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"
+                            ),
+                            choices=["pm", "harvest", "dio", "crepe", "mangio-crepe"], # Fork feature: Crepe on f0 extraction for training.
+                            value="harvest",
+                            interactive=True,
+                        )
+                        extraction_crepe_hop_length = gr.Slider(
+                            minimum=1,
+                            maximum=512,
+                            step=1,
+                            label=i18n("crepe_hop_length"),
+                            value=64,
+                            interactive=True
+                        )
+                    but2 = gr.Button(i18n("特征提取"), variant="primary")
+                    info2 = gr.Textbox(label=i18n("输出信息"), 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.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=0,
+                        maximum=50,
+                        step=1,
+                        label=i18n("保存频率save_every_epoch"),
+                        value=5,
+                        interactive=True,
+                    )
+                    total_epoch11 = gr.Slider(
+                        minimum=0,
+                        maximum=10000,
+                        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="pretrained/f0G40k.pth",
+                        interactive=True,
+                    )
+                    pretrained_D15 = gr.Textbox(
+                        label=i18n("加载预训练底模D路径"),
+                        value="pretrained/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, 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,
+                    )
+                    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,
+                    )
+
+        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,
+                )  # 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)
+            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)
+            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,
+                )
+
+        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
+                )
+            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)
+
+        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/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(traceback.format_exc())
+
+
+    #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)
+
+
+    def on_preset_changed(preset_name):
+        print("Changed Preset to %s!" % preset_name)
+        data = None
+        with open('../inference-presets.json', 'r') as file:
+            data = json.load(file)
+
+        print("Searching for " + preset_name)
+        returning_preset = None
+        for preset in data['presets']:
+            if(preset['name'] == preset_name):
+                print("Found a preset")
+                returning_preset = preset
+        # return all new input values
+        return (
+            # returning_preset['model'],
+            # returning_preset['transpose'],
+            # returning_preset['audio_file'],
+            # returning_preset['f0_method'],
+            # returning_preset['crepe_hop_length'],
+            # returning_preset['median_filtering'],
+            # returning_preset['feature_path'],
+            # returning_preset['auto_feature_path'],
+            # returning_preset['search_feature_ratio'],
+            # returning_preset['resample'],
+            # returning_preset['volume_envelope'],
+            # returning_preset['protect_voiceless'],
+            # returning_preset['f0_file_path']
+        )
+
+    # Preset State Changes                
+    
+    # This click calls save_preset that saves the preset into inference-presets.json with the preset name
+    # mangio_preset_save_btn.click(
+    #     fn=save_preset, 
+    #     inputs=[
+    #         mangio_preset_name_save,
+    #         sid0,
+    #         vc_transform0,
+    #         input_audio0,
+    #         f0method0,
+    #         crepe_hop_length,
+    #         filter_radius0,
+    #         file_index1,
+    #         file_index2,
+    #         index_rate1,
+    #         resample_sr0,
+    #         rms_mix_rate0,
+    #         protect0,
+    #         f0_file
+    #     ], 
+    #     outputs=[]
+    # )
+
+    # mangio_preset.change(
+    #     on_preset_changed, 
+    #     inputs=[
+    #         # Pass inputs here
+    #         mangio_preset
+    #     ], 
+    #     outputs=[
+    #         # Pass Outputs here. These refer to the gradio elements that we want to directly change
+    #         # sid0,
+    #         # vc_transform0,
+    #         # input_audio0,
+    #         # f0method0,
+    #         # crepe_hop_length,
+    #         # filter_radius0,
+    #         # file_index1,
+    #         # file_index2,
+    #         # index_rate1,
+    #         # resample_sr0,
+    #         # rms_mix_rate0,
+    #         # protect0,
+    #         # f0_file
+    #     ]
+    # )
+    #endregion
+
+        # with gr.TabItem(i18n("招募音高曲线前端编辑器")):
+        #     gr.Markdown(value=i18n("加开发群联系我xxxxx"))
+        # with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
+        #     gr.Markdown(value=i18n("xxxxx"))
+
+    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)
+    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,
+        )
+
+#endregion