diff --git "a/main/app/app.py" "b/main/app/app.py"
deleted file mode 100644--- "a/main/app/app.py"
+++ /dev/null
@@ -1,3070 +0,0 @@
-import os
-import re
-import ssl
-import sys
-import json
-import torch
-import codecs
-import shutil
-import asyncio
-import librosa
-import logging
-import datetime
-import platform
-import requests
-import warnings
-import threading
-import subprocess
-import logging.handlers
-
-import numpy as np
-import gradio as gr
-import pandas as pd
-import soundfile as sf
-
-from time import sleep
-from multiprocessing import cpu_count
-
-sys.path.append(os.getcwd())
-
-from main.tools import huggingface
-from main.configs.config import Config
-
-ssl._create_default_https_context = ssl._create_unverified_context
-logger = logging.getLogger(__name__)
-logger.propagate = False
-
-if logger.hasHandlers(): logger.handlers.clear()
-else:
-    console_handler = logging.StreamHandler()
-    console_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    console_handler.setFormatter(console_formatter)
-    console_handler.setLevel(logging.INFO)
-    file_handler = logging.handlers.RotatingFileHandler(os.path.join("assets", "logs", "app.log"), maxBytes=5*1024*1024, backupCount=3, encoding='utf-8')
-    file_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    file_handler.setFormatter(file_formatter)
-    file_handler.setLevel(logging.DEBUG)
-    logger.addHandler(console_handler)
-    logger.addHandler(file_handler)
-    logger.setLevel(logging.DEBUG)
-
-warnings.filterwarnings("ignore")
-for l in ["httpx", "gradio", "uvicorn", "httpcore", "urllib3"]:
-    logging.getLogger(l).setLevel(logging.ERROR)
-
-config = Config()
-python = sys.executable
-translations = config.translations 
-configs_json = os.path.join("main", "configs", "config.json")
-configs = json.load(open(configs_json, "r"))
-
-os.environ["TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD"] = "1"
-os.environ["TORCH_FORCE_WEIGHTS_ONLY_LOAD"] = "0"
-
-if config.device in ["cpu", "mps"]  and configs.get("fp16", False):
-    logger.warning(translations["fp16_not_support"])
-    configs["fp16"] = config.is_half = False
-    with open(configs_json, "w") as f:
-        json.dump(configs, f, indent=4)
-
-models, model_options = {}, {}
-method_f0 = ["mangio-crepe-full", "crepe-full", "fcpe", "rmvpe", "harvest", "pyin"]
-method_f0_full = ["pm", "dio", "mangio-crepe-tiny", "mangio-crepe-small", "mangio-crepe-medium", "mangio-crepe-large", "mangio-crepe-full", "crepe-tiny", "crepe-small", "crepe-medium", "crepe-large", "crepe-full", "fcpe", "fcpe-legacy", "rmvpe", "rmvpe-legacy", "harvest", "yin", "pyin", "swipe"]
-embedders_model = ["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "portuguese_hubert_base", "custom"]
-
-paths_for_files = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk("audios") for f in files if os.path.splitext(f)[1].lower() in (".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3")])
-model_name, index_path, delete_index = sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith((".pth", ".onnx")) and not model.startswith("G_") and not model.startswith("D_"))), sorted([os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index")]), sorted([os.path.join("assets", "logs", f) for f in os.listdir(os.path.join("assets", "logs")) if "mute" not in f and os.path.isdir(os.path.join("assets", "logs", f))])
-pretrainedD, pretrainedG, Allpretrained = ([model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "D" in model], [model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "G" in model], [os.path.join("assets", "models", path, model) for path in ["pretrained_v1", "pretrained_v2", "pretrained_custom"] for model in os.listdir(os.path.join("assets", "models", path)) if model.endswith(".pth") and ("D" in model or "G" in model)])
-
-separate_model = sorted([os.path.join("assets", "models", "uvr5", models) for models in os.listdir(os.path.join("assets", "models", "uvr5")) if models.endswith((".th", ".yaml", ".onnx"))])
-presets_file = sorted(list(f for f in os.listdir(os.path.join("assets", "presets")) if f.endswith(".json")))
-f0_file = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk(os.path.join("assets", "f0")) for f in files if f.endswith(".txt")])
-
-language, theme, edgetts, google_tts_voice, mdx_model, uvr_model, font = configs.get("language", "vi-VN"), configs.get("theme", "NoCrypt/miku"), configs.get("edge_tts", ["vi-VN-HoaiMyNeural", "vi-VN-NamMinhNeural"]), configs.get("google_tts_voice", ["vi", "en"]), configs.get("mdx_model", "MDXNET_Main"), (configs.get("demucs_model", "HD_MMI") + configs.get("mdx_model", "MDXNET_Main")), configs.get("font", "https://fonts.googleapis.com/css2?family=Courgette&display=swap")
-
-csv_path = os.path.join("assets", "spreadsheet.csv")
-logger.info(config.device)
-
-if "--allow_all_disk" in sys.argv:
-    import win32api
-
-    allow_disk = win32api.GetLogicalDriveStrings().split('\x00')[:-1]
-else: allow_disk = []
-
-if language == "vi-VN": 
-    import gradio.strings
-    gradio.strings.en = {"RUNNING_LOCALLY": "* Chạy trên liên kết nội bộ:  {}://{}:{}", "RUNNING_LOCALLY_SSR": "* Chạy trên liên kết nội bộ:  {}://{}:{}, với SSR ⚡ (thử nghiệm, để tắt hãy dùng `ssr=False` trong `launch()`)", "SHARE_LINK_DISPLAY": "* Chạy trên liên kết công khai: {}", "COULD_NOT_GET_SHARE_LINK": "\nKhông thể tạo liên kết công khai. Vui lòng kiểm tra kết nối mạng của bạn hoặc trang trạng thái của chúng tôi: https://status.gradio.app.", "COULD_NOT_GET_SHARE_LINK_MISSING_FILE": "\nKhông thể tạo liên kết công khai. Thiếu tập tin: {}. \n\nVui lòng kiểm tra kết nối internet của bạn. Điều này có thể xảy ra nếu phần mềm chống vi-rút của bạn chặn việc tải xuống tệp này. Bạn có thể cài đặt thủ công bằng cách làm theo các bước sau: \n\n1. Tải xuống tệp này: {}\n2. Đổi tên tệp đã tải xuống thành: {}\n3. Di chuyển tệp đến vị trí này: {}", "COLAB_NO_LOCAL": "Không thể hiển thị giao diện nội bộ trên google colab, liên kết công khai đã được tạo.", "PUBLIC_SHARE_TRUE": "\nĐể tạo một liên kết công khai, hãy đặt `share=True` trong `launch()`.", "MODEL_PUBLICLY_AVAILABLE_URL": "Mô hình được cung cấp công khai tại: {} (có thể mất tới một phút để sử dụng được liên kết)", "GENERATING_PUBLIC_LINK": "Đang tạo liên kết công khai (có thể mất vài giây...):", "BETA_INVITE": "\nCảm ơn bạn đã là người dùng Gradio! Nếu bạn có thắc mắc hoặc phản hồi, vui lòng tham gia máy chủ Discord của chúng tôi và trò chuyện với chúng tôi: https://discord.gg/feTf9x3ZSB", "COLAB_DEBUG_TRUE": "Đã phát hiện thấy sổ tay Colab. Ô này sẽ chạy vô thời hạn để bạn có thể xem lỗi và nhật ký. " "Để tắt, hãy đặt debug=False trong launch().", "COLAB_DEBUG_FALSE": "Đã phát hiện thấy sổ tay Colab. Để hiển thị lỗi trong sổ ghi chép colab, hãy đặt debug=True trong launch()", "COLAB_WARNING": "Lưu ý: việc mở Chrome Inspector có thể làm hỏng bản demo trong sổ tay Colab.", "SHARE_LINK_MESSAGE": "\nLiên kết công khai sẽ hết hạn sau 72 giờ. Để nâng cấp GPU và lưu trữ vĩnh viễn miễn phí, hãy chạy `gradio deploy` từ terminal trong thư mục làm việc để triển khai lên huggingface (https://huggingface.co/spaces)", "INLINE_DISPLAY_BELOW": "Đang tải giao diện bên dưới...", "COULD_NOT_GET_SHARE_LINK_CHECKSUM": "\nKhông thể tạo liên kết công khai. Tổng kiểm tra không khớp cho tập tin: {}."}
-
-if os.path.exists(csv_path): cached_data = pd.read_csv(csv_path) 
-else:
-    cached_data = pd.read_csv(codecs.decode("uggcf://qbpf.tbbtyr.pbz/fcernqfurrgf/q/1gNHnDeRULtEfz1Yieaw14USUQjWJy0Oq9k0DrCrjApb/rkcbeg?sbezng=pfi&tvq=1977693859", "rot13"))
-    cached_data.to_csv(csv_path, index=False)
-
-for _, row in cached_data.iterrows():
-    filename = row['Filename']
-    url = None
-
-    for value in row.values:
-        if isinstance(value, str) and "huggingface" in value:
-            url = value
-            break
-
-    if url: models[filename] = url
-
-
-
-def gr_info(message):
-    gr.Info(message, duration=2)
-    logger.info(message)
-
-def gr_warning(message):
-    gr.Warning(message, duration=2)
-    logger.warning(message)
-
-def gr_error(message):
-    gr.Error(message=message, duration=6)
-    logger.error(message)
-
-def get_gpu_info():
-    ngpu = torch.cuda.device_count()
-    gpu_infos = [f"{i}: {torch.cuda.get_device_name(i)} ({int(torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4)} GB)" for i in range(ngpu) if torch.cuda.is_available() or ngpu != 0]
-    return "\n".join(gpu_infos) if len(gpu_infos) > 0 else translations["no_support_gpu"]
-
-def change_f0_choices(): 
-    f0_file = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk(os.path.join("assets", "f0")) for f in files if f.endswith(".txt")])
-    return {"value": f0_file[0] if len(f0_file) >= 1 else "", "choices": f0_file, "__type__": "update"}
-
-def change_audios_choices(input_audio): 
-    audios = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk("audios") for f in files if os.path.splitext(f)[1].lower() in (".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3")])
-    return {"value": input_audio if input_audio != "" else (audios[0] if len(audios) >= 1 else ""), "choices": audios, "__type__": "update"}
-
-def change_separate_choices():
-    return [{"choices": sorted([os.path.join("assets", "models", "uvr5", models) for models in os.listdir(os.path.join("assets", "models", "uvr5")) if model.endswith((".th", ".yaml", ".onnx"))]), "__type__": "update"}]
-
-def change_models_choices():
-    model, index = sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith((".pth", ".onnx")) and not model.startswith("G_") and not model.startswith("D_"))), sorted([os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index")])
-    return [{"value": model[0] if len(model) >= 1 else "", "choices": model, "__type__": "update"}, {"value": index[0] if len(index) >= 1 else "", "choices": index, "__type__": "update"}]
-
-def change_allpretrained_choices():
-    return [{"choices": sorted([os.path.join("assets", "models", path, model) for path in ["pretrained_v1", "pretrained_v2", "pretrained_custom"] for model in os.listdir(os.path.join("assets", "models", path)) if model.endswith(".pth") and ("D" in model or "G" in model)]), "__type__": "update"}]
-
-def change_pretrained_choices():
-    return [{"choices": sorted([model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "D" in model]), "__type__": "update"}, {"choices": sorted([model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "G" in model]), "__type__": "update"}]
-
-def change_choices_del():
-    return [{"choices": sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith(".pth") and not model.startswith("G_") and not model.startswith("D_"))), "__type__": "update"}, {"choices": sorted([os.path.join("assets", "logs", f) for f in os.listdir(os.path.join("assets", "logs")) if "mute" not in f and os.path.isdir(os.path.join("assets", "logs", f))]), "__type__": "update"}]
-
-def change_preset_choices():
-    return {"value": "", "choices": sorted(list(f for f in os.listdir(os.path.join("assets", "presets")) if f.endswith(".json"))), "__type__": "update"}
-
-def change_tts_voice_choices(google):
-    return {"choices": google_tts_voice if google else edgetts, "value": google_tts_voice[0] if google else edgetts[0], "__type__": "update"}
-
-def change_backing_choices(backing, merge):
-    if backing or merge: return {"value": False, "interactive": False, "__type__": "update"}
-    elif not backing or not merge: return  {"interactive": True, "__type__": "update"}
-    else: gr_warning(translations["option_not_valid"])
-
-def change_download_choices(select):
-    selects = [False]*10
-
-    if select == translations["download_url"]: selects[0] = selects[1] = selects[2] = True
-    elif select == translations["download_from_csv"]:  selects[3] = selects[4] = True
-    elif select == translations["search_models"]: selects[5] = selects[6] = True
-    elif select == translations["upload"]: selects[9] = True
-    else: gr_warning(translations["option_not_valid"])
-
-    return [{"visible": selects[i], "__type__": "update"} for i in range(len(selects))]
-
-def change_download_pretrained_choices(select):
-    selects = [False]*8
-
-    if select == translations["download_url"]: selects[0] = selects[1] = selects[2] = True
-    elif select == translations["list_model"]: selects[3] = selects[4] = selects[5] = True
-    elif select == translations["upload"]: selects[6] = selects[7] = True
-    else: gr_warning(translations["option_not_valid"])
-
-    return [{"visible": selects[i], "__type__": "update"} for i in range(len(selects))]
-
-def get_index(model):
-    model = os.path.basename(model).split("_")[0]
-    return {"value": next((f for f in [os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index") and "trained" not in name] if model.split(".")[0] in f), ""), "__type__": "update"} if model else None
-
-def index_strength_show(index):
-    return {"visible": index != "" and os.path.exists(index), "value": 0.5, "__type__": "update"}
-
-def hoplength_show(method, hybrid_method=None):
-    show_hop_length_method = ["mangio-crepe-tiny", "mangio-crepe-small", "mangio-crepe-medium", "mangio-crepe-large", "mangio-crepe-full", "fcpe", "fcpe-legacy", "yin", "pyin"]
-
-    if method in show_hop_length_method: visible = True
-    elif method == "hybrid":
-        methods_str = re.search("hybrid\[(.+)\]", hybrid_method)
-        if methods_str: methods = [method.strip() for method in methods_str.group(1).split("+")]
-
-        for i in methods:
-            visible = i in show_hop_length_method
-            if visible: break
-    else: visible = False
-    
-    return {"visible": visible, "__type__": "update"}
-
-def visible(value):
-    return {"visible": value, "__type__": "update"}
-
-def valueFalse_interactive(inp): 
-    return {"value": False, "interactive": inp, "__type__": "update"}
-
-def valueEmpty_visible1(inp1): 
-    return {"value": "", "visible": inp1, "__type__": "update"}
-
-def process_input(file_path):
-    file_contents = ""
-
-    if not file_path.endswith(".srt"):
-        with open(file_path, "r", encoding="utf-8") as file:
-            file_contents = file.read()
-
-    gr_info(translations["upload_success"].format(name=translations["text"]))
-    return file_contents
-
-def fetch_pretrained_data():
-    response = requests.get(codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/wfba/phfgbz_cergenvarq.wfba", "rot13"))
-    response.raise_for_status()
-
-    return response.json()
-
-def update_sample_rate_dropdown(model):
-    data = fetch_pretrained_data()
-    if model != translations["success"]: return {"choices": list(data[model].keys()), "value": list(data[model].keys())[0], "__type__": "update"}
-
-def if_done(done, p):
-    while 1:
-        if p.poll() is None: sleep(0.5)
-        else: break
-
-    done[0] = True
-
-def restart_app():
-    global app
-
-    gr_info(translations["15s"])
-    os.system("cls" if platform.system() == "Windows" else "clear")
-    
-    app.close()
-    subprocess.run([python, os.path.join("main", "app", "app.py")] + sys.argv[1:])
-
-def change_language(lang):
-    configs = json.load(open(configs_json, "r"))
-    configs["language"] = lang
-
-    with open(configs_json, "w") as f:
-        json.dump(configs, f, indent=4)
-
-    restart_app()
-
-def change_theme(theme):
-    with open(configs_json, "r") as f:
-        configs = json.load(f)
-
-    configs["theme"] = theme
-    with open(configs_json, "w") as f:
-        json.dump(configs, f, indent=4)
-
-    restart_app()
-
-def change_font(font):
-    with open(configs_json, "r") as f:
-        configs = json.load(f)
-
-    configs["font"] = font
-    with open(configs_json, "w") as f:
-        json.dump(configs, f, indent=4)
-
-    restart_app()
-
-def zip_file(name, pth, index):
-    pth_path = os.path.join("assets", "weights", pth)
-    if not pth or not os.path.exists(pth_path) or not pth.endswith((".pth", ".onnx")): return gr_warning(translations["provide_file"].format(filename=translations["model"]))
-
-    zip_file_path = os.path.join("assets", "logs", name, name + ".zip")
-    gr_info(translations["start"].format(start=translations["zip"]))
-
-    import zipfile
-    with zipfile.ZipFile(zip_file_path, 'w') as zipf:
-        zipf.write(pth_path, os.path.basename(pth_path))
-        if index: zipf.write(index, os.path.basename(index))
-
-    gr_info(translations["success"])
-    return {"visible": True, "value": zip_file_path, "__type__": "update"}
-
-def fetch_models_data(search):
-    all_table_data = [] 
-    page = 1 
-
-    while 1:
-        try:
-            response = requests.post(url=codecs.decode("uggcf://ibvpr-zbqryf.pbz/srgpu_qngn.cuc", "rot13"), data={"page": page, "search": search})
-
-            if response.status_code == 200:
-                table_data = response.json().get("table", "")
-                if not table_data.strip(): break  
-                all_table_data.append(table_data)
-                page += 1
-            else:
-                logger.debug(f"{translations['code_error']} {response.status_code}")
-                break  
-        except json.JSONDecodeError:
-            logger.debug(translations["json_error"])
-            break
-        except requests.RequestException as e:
-            logger.debug(translations["requests_error"].format(e=e))
-            break
-    return all_table_data
-
-def search_models(name):
-    gr_info(translations["start"].format(start=translations["search"]))
-    tables = fetch_models_data(name)
-
-    if len(tables) == 0:
-        gr_info(translations["not_found"].format(name=name))
-        return [None]*2
-    else:
-        model_options.clear()
-        
-        from bs4 import BeautifulSoup
-
-        for table in tables:
-            for row in BeautifulSoup(table, "html.parser").select("tr"):
-                name_tag, url_tag = row.find("a", {"class": "fs-5"}), row.find("a", {"class": "btn btn-sm fw-bold btn-light ms-0 p-1 ps-2 pe-2"})
-                url = url_tag["href"].replace("https://easyaivoice.com/run?url=", "")
-                if "huggingface" in url:
-                    if name_tag and url_tag: model_options[name_tag.text.replace(".onnx", "").replace(".pth", "").replace(".index", "").replace(".zip", "").replace(" ", "_").replace("(", "").replace(")", "").replace("[", "").replace("]", "").replace(",", "").replace('"', "").replace("'", "").replace("|", "").strip()] = url
-
-        gr_info(translations["found"].format(results=len(model_options)))
-        return [{"value": "", "choices": model_options, "interactive": True, "visible": True, "__type__": "update"}, {"value": translations["downloads"], "visible": True, "__type__": "update"}]
-
-def move_files_from_directory(src_dir, dest_weights, dest_logs, model_name):
-    for root, _, files in os.walk(src_dir):
-        for file in files:
-            file_path = os.path.join(root, file)
-            if file.endswith(".index"):
-                model_log_dir = os.path.join(dest_logs, model_name)
-                os.makedirs(model_log_dir, exist_ok=True)
-
-                filepath = os.path.join(model_log_dir, file.replace(' ', '_').replace('(', '').replace(')', '').replace('[', '').replace(']', '').replace(",", "").replace('"', "").replace("'", "").replace("|", "").strip())
-                if os.path.exists(filepath): os.remove(filepath)
-
-                shutil.move(file_path, filepath)
-            elif file.endswith(".pth") and not file.startswith("D_") and not file.startswith("G_"):
-                pth_path = os.path.join(dest_weights, model_name + ".pth")
-                if os.path.exists(pth_path): os.remove(pth_path)
-
-                shutil.move(file_path, pth_path)
-            elif file.endswith(".onnx") and not file.startswith("D_") and not file.startswith("G_"):
-                pth_path = os.path.join(dest_weights, model_name + ".onnx")
-                if os.path.exists(pth_path): os.remove(pth_path)
-
-                shutil.move(file_path, pth_path)
-
-def download_url(url):
-    import yt_dlp
-
-    if not url: return gr_warning(translations["provide_url"])
-    if not os.path.exists("audios"): os.makedirs("audios", exist_ok=True)
-
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore")
-        ydl_opts = {"format": "bestaudio/best", "postprocessors": [{"key": "FFmpegExtractAudio", "preferredcodec": "wav", "preferredquality": "192"}], "quiet": True, "no_warnings": True, "noplaylist": True, "verbose": False}
-
-        gr_info(translations["start"].format(start=translations["download_music"]))
-
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            audio_output = os.path.join("audios", re.sub(r'\s+', '-', re.sub(r'[^\w\s\u4e00-\u9fff\uac00-\ud7af\u0400-\u04FF\u1100-\u11FF]', '', ydl.extract_info(url, download=False).get('title', 'video')).strip()))
-            if os.path.exists(audio_output): shutil.rmtree(audio_output, ignore_errors=True)
-
-            ydl_opts['outtmpl'] = audio_output
-            
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl: 
-            audio_output = audio_output + ".wav"
-            if os.path.exists(audio_output): os.remove(audio_output)
-            
-            ydl.download([url])
-
-        gr_info(translations["success"])
-        return [audio_output, audio_output, translations["success"]]
-
-def download_model(url=None, model=None):
-    if not url: return gr_warning(translations["provide_url"])
-    if not model: return gr_warning(translations["provide_name_is_save"])
-
-    model = model.replace(".onnx", "").replace(".pth", "").replace(".index", "").replace(".zip", "").replace(" ", "_").replace("(", "").replace(")", "").replace("[", "").replace("]", "").replace(",", "").replace('"', "").replace("'", "").replace("|", "").strip()
-    url = url.replace("/blob/", "/resolve/").replace("?download=true", "").strip()
-
-    download_dir = os.path.join("download_model")
-    weights_dir = os.path.join("assets", "weights")
-    logs_dir = os.path.join("assets", "logs")
-
-    if not os.path.exists(download_dir): os.makedirs(download_dir, exist_ok=True)
-    if not os.path.exists(weights_dir): os.makedirs(weights_dir, exist_ok=True)
-    if not os.path.exists(logs_dir): os.makedirs(logs_dir, exist_ok=True)
-    
-    try:
-        gr_info(translations["start"].format(start=translations["download"]))
-
-        if url.endswith(".pth"): huggingface.HF_download_file(url, os.path.join(weights_dir, f"{model}.pth"))
-        elif url.endswith(".onnx"): huggingface.HF_download_file(url, os.path.join(weights_dir, f"{model}.onnx"))
-        elif url.endswith(".index"):
-            model_log_dir = os.path.join(logs_dir, model)
-            os.makedirs(model_log_dir, exist_ok=True)
-
-            huggingface.HF_download_file(url, os.path.join(model_log_dir, f"{model}.index"))
-        elif url.endswith(".zip"):
-            output_path = huggingface.HF_download_file(url, os.path.join(download_dir, model + ".zip"))
-            shutil.unpack_archive(output_path, download_dir)
-
-            move_files_from_directory(download_dir, weights_dir, logs_dir, model)
-        else:
-            if "drive.google.com" in url or "drive.usercontent.google.com" in url:
-                file_id = None
-
-                from main.tools import gdown
-
-                if "/file/d/" in url: file_id = url.split("/d/")[1].split("/")[0]
-                elif "open?id=" in url: file_id = url.split("open?id=")[1].split("/")[0]
-                elif "/download?id=" in url: file_id = url.split("/download?id=")[1].split("&")[0]
-                
-                if file_id:
-                    file = gdown.gdown_download(id=file_id, output=download_dir)
-                    if file.endswith(".zip"): shutil.unpack_archive(file, download_dir)
-
-                    move_files_from_directory(download_dir, weights_dir, logs_dir, model)
-            elif "mega.nz" in url:
-                from main.tools import meganz
-                
-                meganz.mega_download_url(url, download_dir)
-
-                file_download = next((f for f in os.listdir(download_dir)), None)
-                if file_download.endswith(".zip"): shutil.unpack_archive(os.path.join(download_dir, file_download), download_dir)
-
-                move_files_from_directory(download_dir, weights_dir, logs_dir, model)
-            elif "mediafire.com" in url:
-                from main.tools import mediafire
-
-                file = mediafire.Mediafire_Download(url, download_dir)
-                if file.endswith(".zip"): shutil.unpack_archive(file, download_dir)
-
-                move_files_from_directory(download_dir, weights_dir, logs_dir, model)
-            elif "pixeldrain.com" in url:
-                from main.tools import pixeldrain
-
-                file = pixeldrain.pixeldrain(url, download_dir)
-                if file.endswith(".zip"): shutil.unpack_archive(file, download_dir)
-
-                move_files_from_directory(download_dir, weights_dir, logs_dir, model)
-            else:
-                gr_warning(translations["not_support_url"])
-                return translations["not_support_url"]
-        
-        gr_info(translations["success"])
-        return translations["success"]
-    except Exception as e:
-        gr_error(message=translations["error_occurred"].format(e=e))
-        logger.debug(e)
-        return translations["error_occurred"].format(e=e)
-    finally:
-        shutil.rmtree(download_dir, ignore_errors=True)
-
-def save_drop_model(dropbox):
-    weight_folder = os.path.join("assets", "weights")
-    logs_folder = os.path.join("assets", "logs")
-    save_model_temp = os.path.join("save_model_temp")
-
-    if not os.path.exists(weight_folder): os.makedirs(weight_folder, exist_ok=True)
-    if not os.path.exists(logs_folder): os.makedirs(logs_folder, exist_ok=True)
-    if not os.path.exists(save_model_temp): os.makedirs(save_model_temp, exist_ok=True)
-
-    shutil.move(dropbox, save_model_temp)
-
-    try:
-        file_name = os.path.basename(dropbox)
-
-        if file_name.endswith(".pth") and file_name.endswith(".onnx") and file_name.endswith(".index"): gr_warning(translations["not_model"])
-        else:    
-            if file_name.endswith(".zip"):
-                shutil.unpack_archive(os.path.join(save_model_temp, file_name), save_model_temp)
-                move_files_from_directory(save_model_temp, weight_folder, logs_folder, file_name.replace(".zip", ""))
-            elif file_name.endswith((".pth", ".onnx")): 
-                output_file = os.path.join(weight_folder, file_name)
-                if os.path.exists(output_file): os.remove(output_file)
-                
-                shutil.move(os.path.join(save_model_temp, file_name), output_file)
-            elif file_name.endswith(".index"):
-                def extract_name_model(filename):
-                    match = re.search(r"([A-Za-z]+)(?=_v|\.|$)", filename)
-                    return match.group(1) if match else None
-                
-                model_logs = os.path.join(logs_folder, extract_name_model(file_name))
-                if not os.path.exists(model_logs): os.makedirs(model_logs, exist_ok=True)
-                shutil.move(os.path.join(save_model_temp, file_name), model_logs)
-            else: 
-                gr_warning(translations["unable_analyze_model"])
-                return None
-        
-        gr_info(translations["upload_success"].format(name=translations["model"]))
-        return None
-    except Exception as e:
-        gr_error(message=translations["error_occurred"].format(e=e))
-        logger.debug(e)
-        return None
-    finally:
-        shutil.rmtree(save_model_temp, ignore_errors=True)
-
-def download_pretrained_model(choices, model, sample_rate):
-    pretraineds_custom_path = os.path.join("assets", "models", "pretrained_custom")
-    if choices == translations["list_model"]:
-        paths = fetch_pretrained_data()[model][sample_rate]
-
-        if not os.path.exists(pretraineds_custom_path): os.makedirs(pretraineds_custom_path, exist_ok=True)
-        url = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cergenvarq_phfgbz/", "rot13") + paths
-
-        gr_info(translations["download_pretrain"])
-        file = huggingface.HF_download_file(url.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), os.path.join(pretraineds_custom_path, paths))
-
-        if file.endswith(".zip"): 
-            shutil.unpack_archive(file, pretraineds_custom_path)
-            os.remove(file)
-
-        gr_info(translations["success"])
-        return translations["success"]
-    elif choices == translations["download_url"]:
-        if not model: return gr_warning(translations["provide_pretrain"].format(dg="D"))
-        if not sample_rate: return gr_warning(translations["provide_pretrain"].format(dg="G"))
-
-        gr_info(translations["download_pretrain"])
-
-        huggingface.HF_download_file(model.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), pretraineds_custom_path)
-        huggingface.HF_download_file(sample_rate.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), pretraineds_custom_path)
-
-        gr_info(translations["success"])
-        return translations["success"]
-
-def fushion_model_pth(name, pth_1, pth_2, ratio):
-    if not name.endswith(".pth"): name = name + ".pth"
-
-    if not pth_1 or not os.path.exists(pth_1) or not pth_1.endswith(".pth"):
-        gr_warning(translations["provide_file"].format(filename=translations["model"] + " 1"))
-        return [translations["provide_file"].format(filename=translations["model"] + " 1"), None]
-    
-    if not pth_2 or not os.path.exists(pth_2) or not pth_2.endswith(".pth"):
-        gr_warning(translations["provide_file"].format(filename=translations["model"] + " 2"))
-        return [translations["provide_file"].format(filename=translations["model"] + " 2"), None]
-    
-    from collections import OrderedDict
-
-    def extract(ckpt):
-        a = ckpt["model"]
-        opt = OrderedDict()
-        opt["weight"] = {}
-
-        for key in a.keys():
-            if "enc_q" in key: continue
-
-            opt["weight"][key] = a[key]
-
-        return opt
-    
-    try:
-        ckpt1 = torch.load(pth_1, map_location="cpu")
-        ckpt2 = torch.load(pth_2, map_location="cpu")
-
-        if ckpt1["sr"] != ckpt2["sr"]: 
-            gr_warning(translations["sr_not_same"])
-            return [translations["sr_not_same"], None]
-
-        cfg = ckpt1["config"]
-        cfg_f0 = ckpt1["f0"]
-        cfg_version = ckpt1["version"]
-        cfg_sr = ckpt1["sr"]
-
-        vocoder = ckpt1.get("vocoder", "Default")
-
-        ckpt1 = extract(ckpt1) if "model" in ckpt1 else ckpt1["weight"]
-        ckpt2 = extract(ckpt2) if "model" in ckpt2 else ckpt2["weight"]
-
-        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): 
-            gr_warning(translations["architectures_not_same"])
-            return [translations["architectures_not_same"], None]
-         
-        gr_info(translations["start"].format(start=translations["fushion_model"]))
-
-        opt = OrderedDict()
-        opt["weight"] = {}
-
-        for key in ckpt1.keys():
-            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
-                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
-                opt["weight"][key] = (ratio * (ckpt1[key][:min_shape0].float()) + (1 - ratio) * (ckpt2[key][:min_shape0].float())).half()
-            else: opt["weight"][key] = (ratio * (ckpt1[key].float()) + (1 - ratio) * (ckpt2[key].float())).half()
-
-        opt["config"] = cfg
-        opt["sr"] = cfg_sr
-        opt["f0"] = cfg_f0
-        opt["version"] = cfg_version
-        opt["infos"] = translations["model_fushion_info"].format(name=name, pth_1=pth_1, pth_2=pth_2, ratio=ratio)
-        opt["vocoder"] = vocoder
-
-        output_model = os.path.join("assets", "weights")
-        if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True)
-
-        torch.save(opt, os.path.join(output_model, name))
-
-        gr_info(translations["success"])
-        return [translations["success"], os.path.join(output_model, name)]
-    except Exception as e:
-        gr_error(message=translations["error_occurred"].format(e=e))
-        logger.debug(e)
-        return [e, None]
-
-def fushion_model(name, path_1, path_2, ratio):
-    if not name:
-        gr_warning(translations["provide_name_is_save"]) 
-        return [translations["provide_name_is_save"], None]
-
-    if path_1.endswith(".pth") and path_2.endswith(".pth"): return fushion_model_pth(name.replace(".onnx", ".pth"), path_1, path_2, ratio)
-    else:
-        gr_warning(translations["format_not_valid"])
-        return [None, None]
-    
-def onnx_export(model_path):
-    from main.library.algorithm.onnx_export import onnx_exporter
-    
-    if not model_path.endswith(".pth"): model_path + ".pth"
-    if not model_path or not os.path.exists(model_path) or not model_path.endswith(".pth"):
-        gr_warning(translations["provide_file"].format(filename=translations["model"]))
-        return [None, translations["provide_file"].format(filename=translations["model"])]
-    
-    try:
-        gr_info(translations["start_onnx_export"])
-        output = onnx_exporter(model_path, model_path.replace(".pth", ".onnx"), is_half=config.is_half, device=config.device)
-
-        gr_info(translations["success"])
-        return [output, translations["success"]]
-    except Exception as e:
-        return [None, e]
-    
-def model_info(path):
-    if not path or not os.path.exists(path) or os.path.isdir(path) or not path.endswith((".pth", ".onnx")): return gr_warning(translations["provide_file"].format(filename=translations["model"]))
-    
-    def prettify_date(date_str):
-        if date_str == translations["not_found_create_time"]: return None
-
-        try:
-            return datetime.datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S.%f").strftime("%Y-%m-%d %H:%M:%S")
-        except ValueError as e:
-            logger.debug(e)
-            return translations["format_not_valid"]
-    
-    if path.endswith(".pth"): model_data = torch.load(path, map_location=torch.device("cpu"))
-    else:
-        import onnx
-        
-        model = onnx.load(path)
-        model_data = None
-
-        for prop in model.metadata_props:
-            if prop.key == "model_info":
-                model_data = json.loads(prop.value)
-                break
-
-    gr_info(translations["read_info"])
-
-    epochs = model_data.get("epoch", None)
-    if epochs is None: 
-        epochs = model_data.get("info", None)
-        try:
-            epoch = epochs.replace("epoch", "").replace("e", "").isdigit()
-            if epoch and epochs is None: epochs = translations["not_found"].format(name=translations["epoch"])
-        except: 
-            pass
-
-    steps = model_data.get("step", translations["not_found"].format(name=translations["step"]))
-    sr = model_data.get("sr", translations["not_found"].format(name=translations["sr"]))
-    f0 = model_data.get("f0", translations["not_found"].format(name=translations["f0"]))
-    version = model_data.get("version", translations["not_found"].format(name=translations["version"]))
-    creation_date = model_data.get("creation_date", translations["not_found_create_time"])
-    model_hash = model_data.get("model_hash", translations["not_found"].format(name="model_hash"))
-    pitch_guidance = translations["trained_f0"] if f0 else translations["not_f0"]
-    creation_date_str = prettify_date(creation_date) if creation_date else translations["not_found_create_time"]
-    model_name = model_data.get("model_name", translations["unregistered"])
-    model_author = model_data.get("author", translations["not_author"])
-    vocoder = model_data.get("vocoder", "Default")
-
-    gr_info(translations["success"])
-    return translations["model_info"].format(model_name=model_name, model_author=model_author, epochs=epochs, steps=steps, version=version, sr=sr, pitch_guidance=pitch_guidance, model_hash=model_hash, creation_date_str=creation_date_str, vocoder=vocoder)
-
-def audio_effects(input_path, output_path, resample, resample_sr, chorus_depth, chorus_rate, chorus_mix, chorus_delay, chorus_feedback, distortion_drive, reverb_room_size, reverb_damping, reverb_wet_level, reverb_dry_level, reverb_width, reverb_freeze_mode, pitch_shift, delay_seconds, delay_feedback, delay_mix, compressor_threshold, compressor_ratio, compressor_attack_ms, compressor_release_ms, limiter_threshold, limiter_release, gain_db, bitcrush_bit_depth, clipping_threshold, phaser_rate_hz, phaser_depth, phaser_centre_frequency_hz, phaser_feedback, phaser_mix, bass_boost_db, bass_boost_frequency, treble_boost_db, treble_boost_frequency, fade_in_duration, fade_out_duration, export_format, chorus, distortion, reverb, delay, compressor, limiter, gain, bitcrush, clipping, phaser, treble_bass_boost, fade_in_out, audio_combination, audio_combination_input):
-    if not input_path or not os.path.exists(input_path) or os.path.isdir(input_path): 
-        gr_warning(translations["input_not_valid"])
-        return None
-        
-    if not output_path:
-        gr_warning(translations["output_not_valid"])
-        return None
-    
-    if os.path.isdir(output_path): output_path = os.path.join(output_path, f"audio_effects.{export_format}")
-    output_dir = os.path.dirname(output_path) or output_path
-
-    if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
-    if os.path.exists(output_path): os.remove(output_path)
-    
-    gr_info(translations["start"].format(start=translations["apply_effect"]))
-    subprocess.run([python, "main/inference/audio_effects.py", "--input_path", input_path, "--output_path", output_path, "--resample", str(resample), "--resample_sr", str(resample_sr), "--chorus_depth", str(chorus_depth), "--chorus_rate", str(chorus_rate), "--chorus_mix", str(chorus_mix), "--chorus_delay", str(chorus_delay), "--chorus_feedback", str(chorus_feedback), "--drive_db", str(distortion_drive), "--reverb_room_size", str(reverb_room_size), "--reverb_damping", str(reverb_damping), "--reverb_wet_level", str(reverb_wet_level), "--reverb_dry_level", str(reverb_dry_level), "--reverb_width", str(reverb_width), "--reverb_freeze_mode", str(reverb_freeze_mode), "--pitch_shift", str(pitch_shift), "--delay_seconds", str(delay_seconds), "--delay_feedback", str(delay_feedback), "--delay_mix", str(delay_mix), "--compressor_threshold", str(compressor_threshold), "--compressor_ratio", str(compressor_ratio), "--compressor_attack_ms", str(compressor_attack_ms), "--compressor_release_ms", str(compressor_release_ms), "--limiter_threshold", str(limiter_threshold), "--limiter_release", str(limiter_release), "--gain_db", str(gain_db), "--bitcrush_bit_depth", str(bitcrush_bit_depth), "--clipping_threshold", str(clipping_threshold), "--phaser_rate_hz", str(phaser_rate_hz), "--phaser_depth", str(phaser_depth), "--phaser_centre_frequency_hz", str(phaser_centre_frequency_hz), "--phaser_feedback", str(phaser_feedback), "--phaser_mix", str(phaser_mix), "--bass_boost_db", str(bass_boost_db), "--bass_boost_frequency", str(bass_boost_frequency), "--treble_boost_db", str(treble_boost_db), "--treble_boost_frequency", str(treble_boost_frequency), "--fade_in_duration", str(fade_in_duration), "--fade_out_duration", str(fade_out_duration), "--export_format", export_format, "--chorus", str(chorus), "--distortion", str(distortion), "--reverb", str(reverb), "--pitchshift", str(pitch_shift != 0), "--delay", str(delay), "--compressor", str(compressor), "--limiter", str(limiter), "--gain", str(gain), "--bitcrush", str(bitcrush), "--clipping", str(clipping), "--phaser", str(phaser), "--treble_bass_boost", str(treble_bass_boost), "--fade_in_out", str(fade_in_out), "--audio_combination", str(audio_combination), "--audio_combination_input", audio_combination_input])
-
-    gr_info(translations["success"])
-    return output_path.replace("wav", export_format)
-
-def synthesize_tts(prompt, voice, speed, output, pitch, google):
-    if not google: 
-        from edge_tts import Communicate
-
-        asyncio.run(Communicate(text=prompt, voice=voice, rate=f"+{speed}%" if speed >= 0 else f"{speed}%", pitch=f"+{pitch}Hz" if pitch >= 0 else f"{pitch}Hz").save(output))
-    else: 
-        response = requests.get(codecs.decode("uggcf://genafyngr.tbbtyr.pbz/genafyngr_ggf", "rot13"), params={"ie": "UTF-8", "q": prompt, "tl": voice, "ttsspeed": speed, "client": "tw-ob"}, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36"})
-
-        if response.status_code == 200:
-            with open(output, "wb") as f:
-                f.write(response.content)
-
-            if pitch != 0 or speed != 0:
-                y, sr = librosa.load(output, sr=None)
-
-                if pitch != 0: y = librosa.effects.pitch_shift(y, sr=sr, n_steps=pitch)
-                if speed != 0: y = librosa.effects.time_stretch(y, rate=speed)
-
-                sf.write(file=output, data=y, samplerate=sr, format=os.path.splitext(os.path.basename(output))[-1].lower().replace('.', ''))
-        else: gr_error(f"{response.status_code}, {response.text}")
-
-def time_stretch(y, sr, target_duration):
-    rate = (len(y) / sr) / target_duration
-    if rate != 1.0: y = librosa.effects.time_stretch(y=y.astype(np.float32), rate=rate)
-
-    n_target = int(round(target_duration * sr))
-    return np.pad(y, (0, n_target - len(y))) if len(y) < n_target else y[:n_target]
-
-def pysrttime_to_seconds(t):
-    return (t.hours * 60 + t.minutes) * 60 + t.seconds + t.milliseconds / 1000
-
-def srt_tts(srt_file, out_file, voice, rate = 0, sr = 24000, google = False):
-    import pysrt
-    import tempfile
-
-    subs = pysrt.open(srt_file)
-    if not subs: raise ValueError(translations["srt"])
-
-    final_audio = np.zeros(int(round(pysrttime_to_seconds(subs[-1].end) * sr)), dtype=np.float32)
-
-    with tempfile.TemporaryDirectory() as tempdir:
-        for idx, seg in enumerate(subs):
-            wav_path = os.path.join(tempdir, f"seg_{idx}.wav")
-            synthesize_tts(" ".join(seg.text.splitlines()), voice, 0, wav_path, rate, google)
-
-            audio, file_sr = sf.read(wav_path, dtype=np.float32)
-            if file_sr != sr: audio = np.interp(np.linspace(0, len(audio) - 1, int(len(audio) * sr / file_sr)), np.arange(len(audio)), audio)
-            adjusted = time_stretch(audio, sr, pysrttime_to_seconds(seg.duration))
-
-            start_sample = int(round(pysrttime_to_seconds(seg.start) * sr))
-            end_sample = start_sample + adjusted.shape[0]
-
-            if end_sample > final_audio.shape[0]:
-                adjusted = adjusted[: final_audio.shape[0] - start_sample]
-                end_sample = final_audio.shape[0]
-
-            final_audio[start_sample:end_sample] += adjusted
-
-    sf.write(out_file, final_audio, sr)
-
-def TTS(prompt, voice, speed, output, pitch, google, srt_input):
-    if not srt_input: srt_input = ""
-
-    if not prompt and not srt_input.endswith(".srt"):
-        gr_warning(translations["enter_the_text"])
-        return None
-    
-    if not voice:
-        gr_warning(translations["choose_voice"])
-        return None
-    
-    if not output: 
-        gr_warning(translations["output_not_valid"])
-        return None
-    
-    if os.path.isdir(output): output = os.path.join(output, f"tts.wav")
-    gr_info(translations["convert"].format(name=translations["text"]))
-
-    output_dir = os.path.dirname(output) or output
-    if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
-
-    if srt_input.endswith(".srt"): srt_tts(srt_input, output, voice, 0, 24000, google)
-    else: synthesize_tts(prompt, voice, speed, output, pitch, google)
-
-    gr_info(translations["success"])
-    return output
-
-def separator_music(input, output_audio, format, shifts, segments_size, overlap, clean_audio, clean_strength, denoise, separator_model, kara_model, backing, reverb, backing_reverb, hop_length, batch_size, sample_rate):
-    output = os.path.dirname(output_audio) or output_audio
-
-    if not input or not os.path.exists(input) or os.path.isdir(input): 
-        gr_warning(translations["input_not_valid"])
-        return [None]*4
-    
-    if not os.path.exists(output): 
-        gr_warning(translations["output_not_valid"])
-        return [None]*4
-
-    if not os.path.exists(output): os.makedirs(output)
-    gr_info(translations["start"].format(start=translations["separator_music"]))
-
-    subprocess.run([python, "main/inference/separator_music.py", "--input_path", input, "--output_path", output, "--format", format, "--shifts", str(shifts), "--segments_size", str(segments_size), "--overlap", str(overlap), "--mdx_hop_length", str(hop_length), "--mdx_batch_size", str(batch_size), "--clean_audio", str(clean_audio), "--clean_strength", str(clean_strength), "--kara_model", kara_model, "--backing", str(backing), "--mdx_denoise", str(denoise), "--reverb", str(reverb), "--backing_reverb", str(backing_reverb), "--model_name", separator_model, "--sample_rate", str(sample_rate)])
-    gr_info(translations["success"])
-
-    filename, _ = os.path.splitext(os.path.basename(input))
-    output = os.path.join(output, filename)
-
-    return [os.path.join(output, f"Original_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Original_Vocals.{format}"), os.path.join(output, f"Instruments.{format}"), (os.path.join(output, f"Main_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Main_Vocals.{format}") if backing else None), (os.path.join(output, f"Backing_Vocals_No_Reverb.{format}") if backing_reverb else os.path.join(output, f"Backing_Vocals.{format}") if backing else None)] if os.path.isfile(input) else [None]*4
-
-def convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0_method, input_path, output_path, pth_path, index_path, f0_autotune, clean_audio, clean_strength, export_format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, embedders_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file):    
-    subprocess.run([python, "main/inference/convert.py", "--pitch", str(pitch), "--filter_radius", str(filter_radius), "--index_rate", str(index_rate), "--volume_envelope", str(volume_envelope), "--protect", str(protect), "--hop_length", str(hop_length), "--f0_method", f0_method, "--input_path", input_path, "--output_path", output_path, "--pth_path", pth_path, "--index_path", index_path if index_path else "", "--f0_autotune", str(f0_autotune), "--clean_audio", str(clean_audio), "--clean_strength", str(clean_strength), "--export_format", export_format, "--embedder_model", embedder_model, "--resample_sr", str(resample_sr), "--split_audio", str(split_audio), "--f0_autotune_strength", str(f0_autotune_strength), "--checkpointing", str(checkpointing), "--f0_onnx", str(onnx_f0_mode), "--embedders_mode", embedders_mode, "--formant_shifting", str(formant_shifting), "--formant_qfrency", str(formant_qfrency), "--formant_timbre", str(formant_timbre), "--f0_file", f0_file])
-
-def convert_audio(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, input_audio_name, checkpointing, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_mode):
-    model_path = os.path.join("assets", "weights", model)
-
-    return_none = [None]*6
-    return_none[5] = {"visible": True, "__type__": "update"}
-
-    if not use_audio:
-        if merge_instrument or not_merge_backing or convert_backing or use_original:
-            gr_warning(translations["turn_on_use_audio"])
-            return return_none
-
-    if use_original:
-        if convert_backing:
-            gr_warning(translations["turn_off_convert_backup"])
-            return return_none
-        elif not_merge_backing:
-            gr_warning(translations["turn_off_merge_backup"])
-            return return_none
-
-    if not model or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith((".pth", ".onnx")):
-        gr_warning(translations["provide_file"].format(filename=translations["model"]))
-        return return_none
-
-    f0method, embedder_model = (method if method != "hybrid" else hybrid_method), (embedders if embedders != "custom" else custom_embedders)
-
-    if use_audio:
-        output_audio = os.path.join("audios", input_audio_name)
-
-        from main.library.utils import pydub_convert, pydub_load
-        
-        def get_audio_file(label):
-            matching_files = [f for f in os.listdir(output_audio) if label in f]
-
-            if not matching_files: return translations["notfound"]   
-            return os.path.join(output_audio, matching_files[0])
-
-        output_path = os.path.join(output_audio, f"Convert_Vocals.{format}")
-        output_backing = os.path.join(output_audio, f"Convert_Backing.{format}")
-        output_merge_backup = os.path.join(output_audio, f"Vocals+Backing.{format}")
-        output_merge_instrument = os.path.join(output_audio, f"Vocals+Instruments.{format}")
-
-        if os.path.exists(output_audio): os.makedirs(output_audio, exist_ok=True)
-        if os.path.exists(output_path): os.remove(output_path)
-
-        if use_original:
-            original_vocal = get_audio_file('Original_Vocals_No_Reverb.')
-
-            if original_vocal == translations["notfound"]: original_vocal = get_audio_file('Original_Vocals.')
-
-            if original_vocal == translations["notfound"]: 
-                gr_warning(translations["not_found_original_vocal"])
-                return return_none
-            
-            input_path = original_vocal
-        else:
-            main_vocal = get_audio_file('Main_Vocals_No_Reverb.')
-            backing_vocal = get_audio_file('Backing_Vocals_No_Reverb.')
-
-            if main_vocal == translations["notfound"]: main_vocal = get_audio_file('Main_Vocals.')
-            if not not_merge_backing and backing_vocal == translations["notfound"]: backing_vocal = get_audio_file('Backing_Vocals.')
-
-            if main_vocal == translations["notfound"]: 
-                gr_warning(translations["not_found_main_vocal"])
-                return return_none
-            
-            if not not_merge_backing and backing_vocal == translations["notfound"]: 
-                gr_warning(translations["not_found_backing_vocal"])
-                return return_none
-            
-            input_path = main_vocal
-            backing_path = backing_vocal
-
-        gr_info(translations["convert_vocal"])
-
-        convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input_path, output_path, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, embedders_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
-        gr_info(translations["convert_success"])
-
-        if convert_backing:
-            if os.path.exists(output_backing): os.remove(output_backing)
-
-            gr_info(translations["convert_backup"])
-
-            convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, backing_path, output_backing, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, embedders_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
-            gr_info(translations["convert_backup_success"])
-
-        try:
-            if not not_merge_backing and not use_original:
-                backing_source = output_backing if convert_backing else backing_vocal
-
-                if os.path.exists(output_merge_backup): os.remove(output_merge_backup)
-
-                gr_info(translations["merge_backup"])
-
-                pydub_convert(pydub_load(output_path)).overlay(pydub_convert(pydub_load(backing_source))).export(output_merge_backup, format=format)
-
-                gr_info(translations["merge_success"])
-
-            if merge_instrument:    
-                vocals = output_merge_backup if not not_merge_backing and not use_original else output_path
-
-                if os.path.exists(output_merge_instrument): os.remove(output_merge_instrument)
-
-                gr_info(translations["merge_instruments_process"])
-
-                instruments = get_audio_file('Instruments.')
-                
-                if instruments == translations["notfound"]: 
-                    gr_warning(translations["not_found_instruments"])
-                    output_merge_instrument = None
-                else: pydub_convert(pydub_load(instruments)).overlay(pydub_convert(pydub_load(vocals))).export(output_merge_instrument, format=format)
-                
-                gr_info(translations["merge_success"])
-        except:
-            return return_none
-
-        return [(None if use_original else output_path), output_backing, (None if not_merge_backing and use_original else output_merge_backup), (output_path if use_original else None), (output_merge_instrument if merge_instrument else None), {"visible": True, "__type__": "update"}]
-    else:
-        if not input or not os.path.exists(input) or os.path.isdir(input): 
-            gr_warning(translations["input_not_valid"])
-            return return_none
-        
-        if not output:
-            gr_warning(translations["output_not_valid"])
-            return return_none
-        
-        output = output.replace("wav", format)
-
-        if os.path.isdir(input):
-            gr_info(translations["is_folder"])
-
-            if not [f for f in os.listdir(input) if f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))]:
-                gr_warning(translations["not_found_in_folder"])
-                return return_none
-            
-            gr_info(translations["batch_convert"])
-
-            output_dir = os.path.dirname(output) or output
-            convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output_dir, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, embedders_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
-            gr_info(translations["batch_convert_success"])
-
-            return return_none
-        else:
-            output_dir = os.path.dirname(output) or output
-
-            if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
-            if os.path.exists(output): os.remove(output)
-
-            gr_info(translations["convert_vocal"])
-
-            convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, embedders_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
-            gr_info(translations["convert_success"])
-
-            return_none[0] = output
-            return return_none
-
-def convert_selection(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_mode):
-    if use_audio:
-        gr_info(translations["search_separate"])
-
-        choice = [f for f in os.listdir("audios") if os.path.isdir(os.path.join("audios", f))]
-
-        gr_info(translations["found_choice"].format(choice=len(choice)))
-
-        if len(choice) == 0: 
-            gr_warning(translations["separator==0"])
-
-            return [{"choices": [], "value": "", "interactive": False, "visible": False, "__type__": "update"}, None, None, None, None, None, {"visible": True, "__type__": "update"}]
-        elif len(choice) == 1:
-            convert_output = convert_audio(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, None, None, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, choice[0], checkpointing, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_mode)
-
-            return [{"choices": [], "value": "", "interactive": False, "visible": False, "__type__": "update"}, convert_output[0], convert_output[1], convert_output[2], convert_output[3], convert_output[4], {"visible": True, "__type__": "update"}]
-        else: return [{"choices": choice, "value": "", "interactive": True, "visible": True, "__type__": "update"}, None, None, None, None, None, {"visible": False, "__type__": "update"}]
-    else:
-        main_convert = convert_audio(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, None, checkpointing, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_mode)
-
-        return [{"choices": [], "value": "", "interactive": False, "visible": False, "__type__": "update"}, main_convert[0], None, None, None, None, {"visible": True, "__type__": "update"}]
-    
-def convert_with_whisper(num_spk, model_size, cleaner, clean_strength, autotune, f0_autotune_strength, checkpointing, model_1, model_2, model_index_1, model_index_2, pitch_1, pitch_2, index_strength_1, index_strength_2, export_format, input_audio, output_audio, onnx_f0_mode, method, hybrid_method, hop_length, embed_mode, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, formant_shifting, formant_qfrency_1, formant_timbre_1, formant_qfrency_2, formant_timbre_2):
-    from pydub import AudioSegment
-    from sklearn.cluster import AgglomerativeClustering
-    
-    from main.library.speaker_diarization.audio import Audio
-    from main.library.speaker_diarization.segment import Segment
-    from main.library.speaker_diarization.whisper import load_model
-    from main.library.utils import check_spk_diarization, pydub_convert, pydub_load
-    from main.library.speaker_diarization.embedding import SpeechBrainPretrainedSpeakerEmbedding
-    
-    check_spk_diarization(model_size)
-    model_pth_1, model_pth_2 = os.path.join("assets", "weights", model_1), os.path.join("assets", "weights", model_2)
-
-    if (not model_1 or not os.path.exists(model_pth_1) or os.path.isdir(model_pth_1) or not model_pth_1.endswith((".pth", ".onnx"))) and (not model_2 or not os.path.exists(model_pth_2) or os.path.isdir(model_pth_2) or not model_pth_2.endswith((".pth", ".onnx"))):
-        gr_warning(translations["provide_file"].format(filename=translations["model"]))
-        return None
-    
-    if not model_1: model_pth_1 = model_pth_2
-    if not model_2: model_pth_2 = model_pth_1
-
-    if not input_audio or not os.path.exists(input_audio) or os.path.isdir(input_audio): 
-        gr_warning(translations["input_not_valid"])
-        return None
-        
-    if not output_audio:
-        gr_warning(translations["output_not_valid"])
-        return None
-    
-    if os.path.exists(output_audio): os.remove(output_audio)
-    gr_info(translations["start_whisper"])
-    
-    try:
-        audio = Audio()
-
-        embedding_model = SpeechBrainPretrainedSpeakerEmbedding(device=config.device)
-        segments = load_model(model_size, device=config.device).transcribe(input_audio, fp16=configs.get("fp16", False), word_timestamps=True)["segments"]
-
-        y, sr = librosa.load(input_audio, sr=None)  
-        duration = len(y) / sr
-            
-        def segment_embedding(segment):
-            waveform, _ = audio.crop(input_audio, Segment(segment["start"], min(duration, segment["end"])))
-            return embedding_model(waveform.mean(dim=0, keepdim=True)[None] if waveform.shape[0] == 2 else waveform[None])  
-        
-        def time(secs):
-            return datetime.timedelta(seconds=round(secs))
-        
-        def merge_audio(files_list, time_stamps, original_file_path, output_path, format):
-            def extract_number(filename):
-                match = re.search(r'_(\d+)', filename)
-                return int(match.group(1)) if match else 0
-
-            total_duration = len(pydub_load(original_file_path))
-            combined = AudioSegment.empty() 
-            current_position = 0 
-
-            for file, (start_i, end_i) in zip(sorted(files_list, key=extract_number), time_stamps):
-                if start_i > current_position: combined += AudioSegment.silent(duration=start_i - current_position)  
-                
-                combined += pydub_load(file)  
-                current_position = end_i
-
-            if current_position < total_duration: combined += AudioSegment.silent(duration=total_duration - current_position)
-            combined.export(output_path, format=format)
-
-            return output_path
-
-        embeddings = np.zeros(shape=(len(segments), 192))
-        for i, segment in enumerate(segments):
-            embeddings[i] = segment_embedding(segment)
-
-        labels = AgglomerativeClustering(num_spk).fit(np.nan_to_num(embeddings)).labels_
-        for i in range(len(segments)):
-            segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
-
-        merged_segments, current_text = [], []
-        current_speaker, current_start = None, None
-
-        for i, segment in enumerate(segments):
-            speaker = segment["speaker"]
-            start_time = segment["start"]
-            text = segment["text"][1:]  
-
-            if speaker == current_speaker:
-                current_text.append(text)
-                end_time = segment["end"]
-            else:
-                if current_speaker is not None: merged_segments.append({"speaker": current_speaker, "start": current_start, "end": end_time, "text": " ".join(current_text)})
-                
-                current_speaker = speaker
-                current_start = start_time
-                current_text = [text]
-                end_time = segment["end"]
-
-        if current_speaker is not None: merged_segments.append({"speaker": current_speaker, "start": current_start, "end": end_time, "text": " ".join(current_text)})
-
-        gr_info(translations["whisper_done"])
-
-        x = ""
-        for segment in merged_segments:
-            x += f"\n{segment['speaker']} {str(time(segment['start']))} - {str(time(segment['end']))}\n"
-            x += segment["text"] + "\n"
-
-        logger.info(x)
-
-        gr_info(translations["process_audio"])
-
-        audio = pydub_convert(pydub_load(input_audio))
-        output_folder = "audios_temp"
-
-        if os.path.exists(output_folder): shutil.rmtree(output_folder, ignore_errors=True)
-        for f in [output_folder, os.path.join(output_folder, "1"), os.path.join(output_folder, "2")]:
-            os.makedirs(f, exist_ok=True)
-
-        time_stamps, processed_segments = [], []
-        for i, segment in enumerate(merged_segments):
-            start_ms = int(segment["start"] * 1000) 
-            end_ms = int(segment["end"] * 1000)
-
-            index = i + 1
-
-            segment_filename = os.path.join(output_folder, "1" if i % 2 == 1 else "2", f"segment_{index}.wav")
-            audio[start_ms:end_ms].export(segment_filename, format="wav")
-
-            processed_segments.append(os.path.join(output_folder, "1" if i % 2 == 1 else "2", f"segment_{index}_output.wav"))
-            time_stamps.append((start_ms, end_ms))
-
-        f0method, embedder_model = (method if method != "hybrid" else hybrid_method), (embedders if embedders != "custom" else custom_embedders)
-
-        gr_info(translations["process_done_start_convert"])
-
-        convert(pitch_1, filter_radius, index_strength_1, volume_envelope, protect, hop_length, f0method, os.path.join(output_folder, "1"), output_folder, model_pth_1, model_index_1, autotune, cleaner, clean_strength, "wav", embedder_model, resample_sr, False, f0_autotune_strength, checkpointing, onnx_f0_mode, embed_mode, formant_shifting, formant_qfrency_1, formant_timbre_1, "")
-        convert(pitch_2, filter_radius, index_strength_2, volume_envelope, protect, hop_length, f0method, os.path.join(output_folder, "2"), output_folder, model_pth_2, model_index_2, autotune, cleaner, clean_strength, "wav", embedder_model, resample_sr, False, f0_autotune_strength, checkpointing, onnx_f0_mode, embed_mode, formant_shifting, formant_qfrency_2, formant_timbre_2, "")
-
-        gr_info(translations["convert_success"])
-        return merge_audio(processed_segments, time_stamps, input_audio, output_audio.replace("wav", export_format), export_format)
-    except Exception as e:
-        gr_error(translations["error_occurred"].format(e=e))
-        import traceback
-        logger.debug(traceback.format_exc())
-        return None
-    finally:
-        if os.path.exists("audios_temp"): shutil.rmtree("audios_temp", ignore_errors=True)
-
-def convert_tts(clean, autotune, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_mode):
-    model_path = os.path.join("assets", "weights", model)
-
-    if not model_path or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith((".pth", ".onnx")):
-        gr_warning(translations["provide_file"].format(filename=translations["model"]))
-        return None
-
-    if not input or not os.path.exists(input): 
-        gr_warning(translations["input_not_valid"])
-        return None
-    
-    if os.path.isdir(input): 
-        input_audio = [f for f in os.listdir(input) if "tts" in f and f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))]
-        
-        if not input_audio:
-            gr_warning(translations["not_found_in_folder"])
-            return None
-        
-        input = os.path.join(input, input_audio[0])
-    
-    if not output:
-        gr_warning(translations["output_not_valid"])
-        return None
-    
-    output = output.replace("wav", format)
-    if os.path.isdir(output): output = os.path.join(output, f"tts.{format}")
-
-    output_dir = os.path.dirname(output)
-    if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
-    
-    if os.path.exists(output): os.remove(output)
-
-    f0method = method if method != "hybrid" else hybrid_method
-    embedder_model = embedders if embedders != "custom" else custom_embedders
-
-    gr_info(translations["convert_vocal"])
-
-    convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing, onnx_f0_mode, embedders_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
-    gr_info(translations["convert_success"])
-    return output
-
-def log_read(log_file, done):
-    f = open(log_file, "w", encoding="utf-8")
-    f.close()
-
-    while 1:
-        with open(log_file, "r", encoding="utf-8") as f:
-            yield "".join(line for line in f.readlines() if "DEBUG" not in line and line.strip() != "")
-
-        sleep(1)
-        if done[0]: break
-
-    with open(log_file, "r", encoding="utf-8") as f:
-        log = "".join(line for line in f.readlines() if "DEBUG" not in line and line.strip() != "")
-
-    yield log
-
-def create_dataset(input_audio, output_dataset, clean_dataset, clean_strength, separator_reverb, kim_vocals_version, overlap, segments_size, denoise_mdx, skip, skip_start, skip_end, hop_length, batch_size, sample_rate):
-    version = 1 if kim_vocals_version == "Version-1" else 2
-
-    gr_info(translations["start"].format(start=translations["create"]))
-
-    p = subprocess.Popen(f'{python} main/inference/create_dataset.py --input_audio "{input_audio}" --output_dataset "{output_dataset}" --clean_dataset {clean_dataset} --clean_strength {clean_strength} --separator_reverb {separator_reverb} --kim_vocal_version {version} --overlap {overlap} --segments_size {segments_size} --mdx_hop_length {hop_length} --mdx_batch_size {batch_size} --denoise_mdx {denoise_mdx} --skip {skip} --skip_start_audios "{skip_start}" --skip_end_audios "{skip_end}" --sample_rate {sample_rate}', shell=True)
-    done = [False]
-
-    threading.Thread(target=if_done, args=(done, p)).start()
-
-    for log in log_read(os.path.join("assets", "logs", "create_dataset.log"), done):
-        yield log
-
-def preprocess(model_name, sample_rate, cpu_core, cut_preprocess, process_effects, path, clean_dataset, clean_strength):
-    dataset = os.path.join(path)
-    sr = int(float(sample_rate.rstrip("k")) * 1000)
-
-    if not model_name: return gr_warning(translations["provide_name"])
-    if not any(f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3")) for f in os.listdir(dataset) if os.path.isfile(os.path.join(dataset, f))): return gr_warning(translations["not_found_data"])
-    
-    model_dir = os.path.join("assets", "logs", model_name)
-    if os.path.exists(model_dir): shutil.rmtree(model_dir, ignore_errors=True)
-
-    p = subprocess.Popen(f'{python} main/inference/preprocess.py --model_name "{model_name}" --dataset_path "{dataset}" --sample_rate {sr} --cpu_cores {cpu_core} --cut_preprocess {cut_preprocess} --process_effects {process_effects} --clean_dataset {clean_dataset} --clean_strength {clean_strength}', shell=True)
-    done = [False]
-
-    threading.Thread(target=if_done, args=(done, p)).start()
-    os.makedirs(model_dir, exist_ok=True)
-
-    for log in log_read(os.path.join(model_dir, "preprocess.log"), done):
-        yield log
-
-def extract(model_name, version, method, pitch_guidance, hop_length, cpu_cores, gpu, sample_rate, embedders, custom_embedders, onnx_f0_mode, embedders_mode):
-    embedder_model = embedders if embedders != "custom" else custom_embedders
-    sr = int(float(sample_rate.rstrip("k")) * 1000)
-
-    if not model_name: return gr_warning(translations["provide_name"])
-
-    model_dir = os.path.join("assets", "logs", model_name)
-    if not any(os.path.isfile(os.path.join(model_dir, "sliced_audios", f)) for f in os.listdir(os.path.join(model_dir, "sliced_audios"))) or not any(os.path.isfile(os.path.join(model_dir, "sliced_audios_16k", f)) for f in os.listdir(os.path.join(model_dir, "sliced_audios_16k"))): return gr_warning(translations["not_found_data_preprocess"])
-
-    p = subprocess.Popen(f'{python} main/inference/extract.py --model_name "{model_name}" --rvc_version {version} --f0_method {method} --pitch_guidance {pitch_guidance} --hop_length {hop_length} --cpu_cores {cpu_cores} --gpu {gpu} --sample_rate {sr} --embedder_model {embedder_model} --f0_onnx {onnx_f0_mode} --embedders_mode {embedders_mode}', shell=True)
-    done = [False]
-
-    threading.Thread(target=if_done, args=(done, p)).start()
-    os.makedirs(model_dir, exist_ok=True)
-
-    for log in log_read(os.path.join(model_dir, "extract.log"), done):
-        yield log
-
-def create_index(model_name, rvc_version, index_algorithm):
-    if not model_name: return gr_warning(translations["provide_name"])
-    model_dir = os.path.join("assets", "logs", model_name)
-
-    if not any(os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f)) for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted"))): return gr_warning(translations["not_found_data_extract"])
-
-    p = subprocess.Popen(f'{python} main/inference/create_index.py --model_name "{model_name}" --rvc_version {rvc_version} --index_algorithm {index_algorithm}', shell=True)
-    done = [False]
-
-    threading.Thread(target=if_done, args=(done, p)).start()
-    os.makedirs(model_dir, exist_ok=True)
-
-    for log in log_read(os.path.join(model_dir, "create_index.log"), done):
-        yield log
-
-def training(model_name, rvc_version, save_every_epoch, save_only_latest, save_every_weights, total_epoch, sample_rate, batch_size, gpu, pitch_guidance, not_pretrain, custom_pretrained, pretrain_g, pretrain_d, detector, threshold, clean_up, cache, model_author, vocoder, checkpointing, deterministic, benchmark):
-    sr = int(float(sample_rate.rstrip("k")) * 1000)
-    if not model_name: return gr_warning(translations["provide_name"])
-
-    model_dir = os.path.join("assets", "logs", model_name)
-    if os.path.exists(os.path.join(model_dir, "train_pid.txt")): os.remove(os.path.join(model_dir, "train_pid.txt"))
-
-    if not any(os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f)) for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted"))): return gr_warning(translations["not_found_data_extract"])
-
-    if not not_pretrain:
-        if not custom_pretrained: 
-            pretrained_selector = {True: {32000: ("f0G32k.pth", "f0D32k.pth"), 40000: ("f0G40k.pth", "f0D40k.pth"), 48000: ("f0G48k.pth", "f0D48k.pth")}, False: {32000: ("G32k.pth", "D32k.pth"), 40000: ("G40k.pth", "D40k.pth"), 48000: ("G48k.pth", "D48k.pth")}}
-
-            pg, pd = pretrained_selector[pitch_guidance][sr]
-        else:
-            if not pretrain_g: return gr_warning(translations["provide_pretrained"].format(dg="G"))
-            if not pretrain_d: return gr_warning(translations["provide_pretrained"].format(dg="D"))
-            
-            pg, pd = pretrain_g, pretrain_d
-
-        pretrained_G, pretrained_D = (os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder}_{pg}" if vocoder != 'Default' else pg), os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder}_{pd}" if vocoder != 'Default' else pd)) if not custom_pretrained else (os.path.join("assets", "models", f"pretrained_custom", pg), os.path.join("assets", "models", f"pretrained_custom", pd))
-        download_version = codecs.decode(f"uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cergenvarq_i{'2' if rvc_version == 'v2' else '1'}/", "rot13")
-        
-        if not custom_pretrained:
-            try:
-                if not os.path.exists(pretrained_G):
-                    gr_info(translations["download_pretrained"].format(dg="G", rvc_version=rvc_version))
-                    huggingface.HF_download_file("".join([download_version, vocoder, "_", pg]) if vocoder != 'Default' else (download_version + pg), os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder}_{pg}" if vocoder != 'Default' else pg))
-                        
-                if not os.path.exists(pretrained_D):
-                    gr_info(translations["download_pretrained"].format(dg="D", rvc_version=rvc_version))
-                    huggingface.HF_download_file("".join([download_version, vocoder, "_", pd]) if vocoder != 'Default' else (download_version + pd), os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder}_{pd}" if vocoder != 'Default' else pd))
-            except:
-                gr_warning(translations["not_use_pretrain_error_download"])
-                pretrained_G, pretrained_D = None, None
-        else:
-            if not os.path.exists(pretrained_G): return gr_warning(translations["not_found_pretrain"].format(dg="G"))
-            if not os.path.exists(pretrained_D): return gr_warning(translations["not_found_pretrain"].format(dg="D"))
-    else: gr_warning(translations["not_use_pretrain"])
-
-    gr_info(translations["start"].format(start=translations["training"]))
-
-    p = subprocess.Popen(f'{python} main/inference/train.py --model_name "{model_name}" --rvc_version {rvc_version} --save_every_epoch {save_every_epoch} --save_only_latest {save_only_latest} --save_every_weights {save_every_weights} --total_epoch {total_epoch} --sample_rate {sr} --batch_size {batch_size} --gpu {gpu} --pitch_guidance {pitch_guidance} --overtraining_detector {detector} --overtraining_threshold {threshold} --cleanup {clean_up} --cache_data_in_gpu {cache} --g_pretrained_path "{pretrained_G}" --d_pretrained_path "{pretrained_D}" --model_author "{model_author}" --vocoder "{vocoder}" --checkpointing {checkpointing} --deterministic {deterministic} --benchmark {benchmark}', shell=True)
-    done = [False]
-
-    with open(os.path.join(model_dir, "train_pid.txt"), "w") as pid_file:
-        pid_file.write(str(p.pid))
-
-    threading.Thread(target=if_done, args=(done, p)).start()
-
-    for log in log_read(os.path.join(model_dir, "train.log"), done):
-        if len(log.split("\n")) > 100: log = log[-100:]
-        yield log
-
-def stop_pid(pid_file, model_name=None, train=False):
-    try:
-        pid_file_path = os.path.join("assets", f"{pid_file}.txt") if model_name is None else os.path.join("assets", "logs", model_name, f"{pid_file}.txt")
-
-        if not os.path.exists(pid_file_path): return gr_warning(translations["not_found_pid"])
-        else:
-            with open(pid_file_path, "r") as pid_file:
-                pids = [int(pid) for pid in pid_file.readlines()]
-
-            for pid in pids:
-                os.kill(pid, 9)
-
-            if os.path.exists(pid_file_path): os.remove(pid_file_path)
-
-        pid_file_path = os.path.join("assets", "logs", model_name, "config.json")
-
-        if train and os.path.exists(pid_file_path):
-            with open(pid_file_path, "r") as pid_file:
-                pid_data = json.load(pid_file)
-                pids = pid_data.get("process_pids", [])
-
-            with open(pid_file_path, "w") as pid_file:
-                pid_data.pop("process_pids", None)
-
-                json.dump(pid_data, pid_file, indent=4)
-
-            for pid in pids:
-                os.kill(pid, 9)
-
-            gr_info(translations["end_pid"])
-    except:
-        pass
-
-def load_presets(presets, cleaner, autotune, pitch, clean_strength, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, formant_shifting, formant_qfrency, formant_timbre):
-    if not presets: return gr_warning(translations["provide_file_settings"])
-
-    with open(os.path.join("assets", "presets", presets)) as f:
-        file = json.load(f)
-
-    gr_info(translations["load_presets"].format(presets=presets))
-    return file.get("cleaner", cleaner), file.get("autotune", autotune), file.get("pitch", pitch), file.get("clean_strength", clean_strength), file.get("index_strength", index_strength), file.get("resample_sr", resample_sr), file.get("filter_radius", filter_radius), file.get("volume_envelope", volume_envelope), file.get("protect", protect), file.get("split_audio", split_audio), file.get("f0_autotune_strength", f0_autotune_strength), file.get("formant_shifting", formant_shifting), file.get("formant_qfrency", formant_qfrency), file.get("formant_timbre", formant_timbre)
-
-def save_presets(name, cleaner, autotune, pitch, clean_strength, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, cleaner_chbox, autotune_chbox, pitch_chbox, index_strength_chbox, resample_sr_chbox, filter_radius_chbox, volume_envelope_chbox, protect_chbox, split_audio_chbox, formant_shifting_chbox, formant_shifting, formant_qfrency, formant_timbre):  
-    if not name: return gr_warning(translations["provide_filename_settings"])
-    if not any([cleaner_chbox, autotune_chbox, pitch_chbox, index_strength_chbox, resample_sr_chbox, filter_radius_chbox, volume_envelope_chbox, protect_chbox, split_audio_chbox, formant_shifting_chbox]): return gr_warning(translations["choose1"])
-
-    settings = {}
-
-    for checkbox, data in [(cleaner_chbox, {"cleaner": cleaner, "clean_strength": clean_strength}), (autotune_chbox, {"autotune": autotune, "f0_autotune_strength": f0_autotune_strength}), (pitch_chbox, {"pitch": pitch}), (index_strength_chbox, {"index_strength": index_strength}), (resample_sr_chbox, {"resample_sr": resample_sr}), (filter_radius_chbox, {"filter_radius": filter_radius}), (volume_envelope_chbox, {"volume_envelope": volume_envelope}), (protect_chbox, {"protect": protect}), (split_audio_chbox, {"split_audio": split_audio}), (formant_shifting_chbox, {"formant_shifting": formant_shifting, "formant_qfrency": formant_qfrency, "formant_timbre": formant_timbre})]:
-        if checkbox: settings.update(data)
-
-    with open(os.path.join("assets", "presets", name + ".json"), "w") as f:
-        json.dump(settings, f, indent=4)
-
-    gr_info(translations["export_settings"])
-    return change_preset_choices()
-
-def report_bug(error_info, provide):
-    report_path = os.path.join("assets", "logs", "report_bugs.log")
-    if os.path.exists(report_path): os.remove(report_path)
-
-    report_url = codecs.decode(requests.get(codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/jroubbx.gkg", "rot13")).text, "rot13")
-    if not error_info: error_info = "Không Có"
-
-    gr_info(translations["thank"])
-
-    if provide:
-        try:
-            for log in [os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".log")]:
-                with open(log, "r", encoding="utf-8") as r:
-                    with open(report_path, "a", encoding="utf-8") as w:
-                        w.write(str(r.read()))
-                        w.write("\n")
-        except Exception as e:
-            gr_error(translations["error_read_log"])
-            logger.debug(e)
-
-        try:
-            with open(report_path, "r", encoding="utf-8") as f:
-                content = f.read()
-
-            requests.post(report_url, json={"embeds": [{"title": "Báo Cáo Lỗi", "description": f"Mô tả lỗi: {error_info}", "color": 15158332, "author": {"name": "Vietnamese_RVC", "icon_url": codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/vpb.cat", "rot13"), "url": codecs.decode("uggcf://tvguho.pbz/CunzUhlauNau16/Ivrganzrfr-EIP/gerr/znva","rot13")}, "thumbnail": {"url": codecs.decode("uggcf://p.grabe.pbz/7dADJbv-36fNNNNq/grabe.tvs", "rot13")}, "fields": [{"name": "Số Lượng Gỡ Lỗi", "value": content.count("DEBUG")}, {"name": "Số Lượng Thông Tin", "value": content.count("INFO")}, {"name": "Số Lượng Cảnh Báo", "value": content.count("WARNING")}, {"name": "Số Lượng Lỗi", "value": content.count("ERROR")}], "footer": {"text": f"Tên Máy: {platform.uname().node} - Hệ Điều Hành: {platform.system()}-{platform.version()}\nThời Gian Báo Cáo Lỗi: {datetime.datetime.now()}."}}]})
-
-            with open(report_path, "rb") as f:
-                requests.post(report_url, files={"file": f})
-        except Exception as e:
-            gr_error(translations["error_send"])
-            logger.debug(e)
-        finally:
-            if os.path.exists(report_path): os.remove(report_path)
-    else: requests.post(report_url, json={"embeds": [{"title": "Báo Cáo Lỗi", "description": error_info}]})
-
-def f0_extract(audio, f0_method, f0_onnx):
-    if not audio or not os.path.exists(audio) or os.path.isdir(audio): 
-        gr_warning(translations["input_not_valid"])
-        return [None]*2
-
-    from matplotlib import pyplot as plt
-    from main.library.utils import check_predictors
-    from main.inference.extract import FeatureInput
-
-    check_predictors(f0_method, f0_onnx)
-
-    f0_path = os.path.join("assets", "f0", os.path.splitext(os.path.basename(audio))[0])
-    image_path = os.path.join(f0_path, "f0.png")
-    txt_path = os.path.join(f0_path, "f0.txt")
-
-    gr_info(translations["start_extract"])
-
-    if not os.path.exists(f0_path): os.makedirs(f0_path, exist_ok=True)
-
-    y, sr = librosa.load(audio, sr=None)
-
-    feats = FeatureInput(sample_rate=sr, is_half=config.is_half, device=config.device)
-    feats.f0_max = 1600.0
-
-    F_temp = np.array(feats.compute_f0(y.flatten(), f0_method, 160, f0_onnx), dtype=np.float32)
-    F_temp[F_temp == 0] = np.nan
-
-    f0 = 1200 * np.log2(F_temp / librosa.midi_to_hz(0))
-
-    plt.figure(figsize=(10, 4))
-    plt.plot(f0)
-    plt.title(f0_method)
-    plt.xlabel(translations["time_frames"])
-    plt.ylabel(translations["Frequency"])
-    plt.savefig(image_path)
-    plt.close()
-
-    with open(txt_path, "w") as f:
-        for i, f0_value in enumerate(f0):
-            f.write(f"{i * sr / 160},{f0_value}\n")
-
-    gr_info(translations["extract_done"])
-
-    return [txt_path, image_path]
-
-def pitch_guidance_lock(vocoders):
-    return {"value": True, "interactive": vocoders == "Default", "__type__": "update"}
-
-def vocoders_lock(pitch, vocoders):
-    return {"value": vocoders if pitch else "Default", "interactive": pitch, "__type__": "update"}
-
-def run_audioldm2(input_path, output_path, export_format, sample_rate, audioldm_model, source_prompt, target_prompt, steps, cfg_scale_src, cfg_scale_tar, t_start, save_compute):
-    if not input_path or not os.path.exists(input_path) or os.path.isdir(input_path): 
-        gr_warning(translations["input_not_valid"])
-        return None
-        
-    if not output_path:
-        gr_warning(translations["output_not_valid"])
-        return None
-    
-    output_path = output_path.replace("wav", export_format)
-
-    if os.path.exists(output_path): os.remove(output_path)
-
-    gr_info(translations["start_edit"].format(input_path=input_path))
-    subprocess.run([python, "main/inference/audioldm2.py", "--input_path", input_path, "--output_path", output_path, "--export_format", str(export_format), "--sample_rate", str(sample_rate), "--audioldm_model", audioldm_model, "--source_prompt", source_prompt, "--target_prompt", target_prompt, "--steps", str(steps), "--cfg_scale_src", str(cfg_scale_src), "--cfg_scale_tar", str(cfg_scale_tar), "--t_start", str(t_start), "--save_compute", str(save_compute)])
-    
-    gr_info(translations["success"])
-    return output_path
-
-def change_fp(fp):
-    fp16 = fp == "fp16"
-
-    if fp16 and config.device == "cpu": 
-        gr_warning(translations["fp16_not_support"])
-        return "fp32"
-    else:
-        gr_info(translations["start_update_precision"])
-
-        configs = json.load(open(configs_json, "r"))
-        configs["fp16"] = config.is_half = fp16
-
-        with open(configs_json, "w") as f:
-            json.dump(configs, f, indent=4)
-
-        gr_info(translations["success"])
-        return "fp16" if fp16 else "fp32"
-
-def unlock_f0(value):
-    return {"choices": method_f0_full if value else method_f0, "__type__": "update"} 
-
-def unlock_vocoder(value, vocoder):
-    return {"value": vocoder if value == "v2" else "Default", "interactive": value == "v2", "__type__": "update"} 
-
-def unlock_ver(value, vocoder):
-    return {"value": "v2" if vocoder == "Default" else value, "interactive": vocoder == "Default", "__type__": "update"}
-
-
-
-with gr.Blocks(title="📱 Vietnamese-RVC GUI BY ANH", theme=theme, css="<style> @import url('{fonts}'); * {{font-family: 'Courgette', cursive !important;}} body, html {{font-family: 'Courgette', cursive !important;}} h1, h2, h3, h4, h5, h6, p, button, input, textarea, label, span, div, select {{font-family: 'Courgette', cursive !important;}} </style>".format(fonts=font or "https://fonts.googleapis.com/css2?family=Courgette&display=swap")) as app:
-    gr.HTML("<h1 style='text-align: center;'>🎵VIETNAMESE RVC BY ANH🎵</h1>")
-    gr.HTML(f"<h3 style='text-align: center;'>{translations['title']}</h3>")
-
-    with gr.Tabs():      
-        with gr.TabItem(translations["separator_tab"], visible=configs.get("separator_tab", True)):
-            gr.Markdown(f"## {translations['separator_tab']}")
-            with gr.Row(): 
-                gr.Markdown(translations["4_part"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():       
-                            cleaner = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True, min_width=140)       
-                            backing = gr.Checkbox(label=translations["separator_backing"], value=False, interactive=True, min_width=140)
-                            reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True, min_width=140)
-                            backing_reverb = gr.Checkbox(label=translations["dereveb_backing"], value=False, interactive=False, min_width=140)               
-                            denoise = gr.Checkbox(label=translations["denoise_mdx"], value=False, interactive=False, min_width=140)     
-                        with gr.Row():
-                            separator_model = gr.Dropdown(label=translations["separator_model"], value=uvr_model[0], choices=uvr_model, interactive=True)
-                            separator_backing_model = gr.Dropdown(label=translations["separator_backing_model"], value="Version-1", choices=["Version-1", "Version-2"], interactive=True, visible=backing.value)
-            with gr.Row():
-                with gr.Column():
-                    separator_button = gr.Button(translations["separator_tab"], variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            shifts = gr.Slider(label=translations["shift"], info=translations["shift_info"], minimum=1, maximum=20, value=2, step=1, interactive=True)
-                            segment_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True)
-                        with gr.Row():
-                            mdx_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True, visible=backing.value or reverb.value or separator_model.value in mdx_model)
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True)
-                        with gr.Row():
-                            mdx_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True, visible=backing.value or reverb.value or separator_model.value in mdx_model)
-            with gr.Row():
-                with gr.Column():
-                    input = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])    
-                    with gr.Accordion(translations["use_url"], open=False):
-                        url = gr.Textbox(label=translations["url_audio"], value="", placeholder="https://www.youtube.com/...", scale=6)
-                        download_button = gr.Button(translations["downloads"])
-                with gr.Column():
-                    with gr.Row():
-                        clean_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner.value)
-                        sample_rate1 = gr.Slider(minimum=8000, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True)
-                    with gr.Accordion(translations["input_output"], open=False):
-                        format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-                        input_audio = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True)
-                        refesh_separator = gr.Button(translations["refesh"])
-                        output_separator = gr.Textbox(label=translations["output_folder"], value="audios", placeholder="audios", info=translations["output_folder_info"], interactive=True)
-                    audio_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-            with gr.Row():
-                gr.Markdown(translations["output_separator"])
-            with gr.Row():
-                instruments_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["instruments"])
-                original_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["original_vocal"])
-                main_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["main_vocal"], visible=backing.value)
-                backing_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["backing_vocal"], visible=backing.value)
-            with gr.Row():
-                separator_model.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), visible(c not in mdx_model)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, shifts])
-                backing.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), visible(a), visible(a), visible(a), valueFalse_interactive(a and b)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, separator_backing_model, main_vocals, backing_vocals, backing_reverb])
-                reverb.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), valueFalse_interactive(a and b)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, backing_reverb])
-            with gr.Row():
-                input_audio.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio], outputs=[audio_input])
-                cleaner.change(fn=visible, inputs=[cleaner], outputs=[clean_strength])
-            with gr.Row():
-                input.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input], outputs=[input_audio])
-                refesh_separator.click(fn=change_audios_choices, inputs=[input_audio], outputs=[input_audio])
-            with gr.Row():
-                download_button.click(
-                    fn=download_url, 
-                    inputs=[url], 
-                    outputs=[input_audio, audio_input, url],
-                    api_name='download_url'
-                )
-                separator_button.click(
-                    fn=separator_music, 
-                    inputs=[
-                        input_audio, 
-                        output_separator,
-                        format, 
-                        shifts, 
-                        segment_size, 
-                        overlap, 
-                        cleaner, 
-                        clean_strength, 
-                        denoise, 
-                        separator_model, 
-                        separator_backing_model, 
-                        backing,
-                        reverb, 
-                        backing_reverb,
-                        mdx_hop_length,
-                        mdx_batch_size,
-                        sample_rate1
-                    ],
-                    outputs=[original_vocals, instruments_audio, main_vocals, backing_vocals],
-                    api_name='separator_music'
-                )
-
-        with gr.TabItem(translations["convert_audio"], visible=configs.get("convert_tab", True)):
-            gr.Markdown(f"## {translations['convert_audio']}")
-            with gr.Row():
-                gr.Markdown(translations["convert_info"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            cleaner0 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
-                            autotune = gr.Checkbox(label=translations["autotune"], value=False, interactive=True)
-                            use_audio = gr.Checkbox(label=translations["use_audio"], value=False, interactive=True)
-                            checkpointing = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
-                        with gr.Row():
-                            use_original = gr.Checkbox(label=translations["convert_original"], value=False, interactive=True, visible=use_audio.value) 
-                            convert_backing = gr.Checkbox(label=translations["convert_backing"], value=False, interactive=True, visible=use_audio.value)   
-                            not_merge_backing = gr.Checkbox(label=translations["not_merge_backing"], value=False, interactive=True, visible=use_audio.value)
-                            merge_instrument = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True, visible=use_audio.value) 
-                    with gr.Row():
-                        pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
-                        clean_strength0 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner0.value)
-                    with gr.Row(): 
-                        with gr.Column():
-                            audio_select = gr.Dropdown(label=translations["select_separate"], choices=[], value="", interactive=True, allow_custom_value=True, visible=False)
-                            convert_button_2 = gr.Button(translations["convert_audio"], visible=False)
-            with gr.Row():
-                with gr.Column():
-                    convert_button = gr.Button(translations["convert_audio"], variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    input0 = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])  
-                    play_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                with gr.Column():
-                    with gr.Accordion(translations["model_accordion"], open=True):
-                        with gr.Row():
-                            model_pth = gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
-                            model_index = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
-                        with gr.Row():
-                            refesh = gr.Button(translations["refesh"])
-                        with gr.Row():
-                            index_strength = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index.value != "")
-                    with gr.Accordion(translations["input_output"], open=False):
-                        with gr.Column():
-                            export_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-                            input_audio0 = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True)
-                            output_audio = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True)
-                        with gr.Column():
-                            refesh0 = gr.Button(translations["refesh"])
-                    with gr.Accordion(translations["setting"], open=False):
-                        with gr.Accordion(translations["f0_method"], open=False):
-                            with gr.Group():
-                                with gr.Row():
-                                    onnx_f0_mode = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                                    unlock_full_method = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
-                                method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], value="rmvpe", interactive=True)
-                                hybrid_method = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method.value == "hybrid")
-                            hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False)
-                        with gr.Accordion(translations["f0_file"], open=False):
-                            upload_f0_file = gr.File(label=translations["upload_f0"], file_types=[".txt"])  
-                            f0_file_dropdown = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
-                            refesh_f0_file = gr.Button(translations["refesh"])
-                        with gr.Accordion(translations["hubert_model"], open=False):
-                            embed_mode = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=["fairseq", "onnx", "transformers"], interactive=True, visible=True)
-                            embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
-                            custom_embedders = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders.value == "custom")
-                        with gr.Accordion(translations["use_presets"], open=False):
-                            with gr.Row():
-                                presets_name = gr.Dropdown(label=translations["file_preset"], choices=presets_file, value=presets_file[0] if len(presets_file) > 0 else '', interactive=True, allow_custom_value=True)
-                            with gr.Row():
-                                load_click = gr.Button(translations["load_file"], variant="primary")
-                                refesh_click = gr.Button(translations["refesh"])
-                            with gr.Accordion(translations["export_file"], open=False):
-                                with gr.Row():
-                                    with gr.Column():
-                                        with gr.Group():
-                                            with gr.Row():
-                                                cleaner_chbox = gr.Checkbox(label=translations["save_clean"], value=True, interactive=True)
-                                                autotune_chbox = gr.Checkbox(label=translations["save_autotune"], value=True, interactive=True)
-                                                pitch_chbox = gr.Checkbox(label=translations["save_pitch"], value=True, interactive=True)
-                                                index_strength_chbox = gr.Checkbox(label=translations["save_index_2"], value=True, interactive=True)
-                                                resample_sr_chbox = gr.Checkbox(label=translations["save_resample"], value=True, interactive=True)
-                                                filter_radius_chbox = gr.Checkbox(label=translations["save_filter"], value=True, interactive=True)
-                                                volume_envelope_chbox = gr.Checkbox(label=translations["save_envelope"], value=True, interactive=True)
-                                                protect_chbox = gr.Checkbox(label=translations["save_protect"], value=True, interactive=True)
-                                                split_audio_chbox = gr.Checkbox(label=translations["save_split"], value=True, interactive=True)
-                                                formant_shifting_chbox = gr.Checkbox(label=translations["formantshift"], value=True, interactive=True)
-                                with gr.Row():
-                                    with gr.Column():
-                                        name_to_save_file = gr.Textbox(label=translations["filename_to_save"])
-                                        save_file_button = gr.Button(translations["export_file"])
-                            with gr.Row():
-                                upload_presets = gr.File(label=translations["upload_presets"], file_types=[".json"])  
-                        with gr.Column():
-                            with gr.Row():
-                                split_audio = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
-                                formant_shifting = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
-                            f0_autotune_strength = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune.value)
-                            resample_sr = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True)
-                            filter_radius = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True)
-                            volume_envelope = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True)
-                            protect = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.5, step=0.01, interactive=True)
-                        with gr.Row():
-                            formant_qfrency = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-                            formant_timbre = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-            with gr.Row():
-                gr.Markdown(translations["output_convert"])
-            with gr.Row():
-                main_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["main_convert"])
-                backing_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_backing"], visible=convert_backing.value)
-                main_backing = gr.Audio(show_download_button=True, interactive=False, label=translations["main_or_backing"], visible=convert_backing.value)  
-            with gr.Row():
-                original_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_original"], visible=use_original.value)
-                vocal_instrument = gr.Audio(show_download_button=True, interactive=False, label=translations["voice_or_instruments"], visible=merge_instrument.value)  
-            with gr.Row():
-                upload_f0_file.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file], outputs=[f0_file_dropdown])
-                refesh_f0_file.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown])
-                unlock_full_method.change(fn=unlock_f0, inputs=[unlock_full_method], outputs=[method])
-            with gr.Row():
-                load_click.click(
-                    fn=load_presets, 
-                    inputs=[
-                        presets_name, 
-                        cleaner0, 
-                        autotune, 
-                        pitch, 
-                        clean_strength0, 
-                        index_strength, 
-                        resample_sr, 
-                        filter_radius, 
-                        volume_envelope, 
-                        protect, 
-                        split_audio, 
-                        f0_autotune_strength, 
-                        formant_qfrency, 
-                        formant_timbre
-                    ], 
-                    outputs=[
-                        cleaner0, 
-                        autotune, 
-                        pitch, 
-                        clean_strength0, 
-                        index_strength, 
-                        resample_sr, 
-                        filter_radius, 
-                        volume_envelope, 
-                        protect, 
-                        split_audio, 
-                        f0_autotune_strength, 
-                        formant_shifting, 
-                        formant_qfrency, 
-                        formant_timbre
-                    ]
-                )
-                refesh_click.click(fn=change_preset_choices, inputs=[], outputs=[presets_name])
-                save_file_button.click(
-                    fn=save_presets, 
-                    inputs=[
-                        name_to_save_file, 
-                        cleaner0, 
-                        autotune, 
-                        pitch, 
-                        clean_strength0, 
-                        index_strength, 
-                        resample_sr, 
-                        filter_radius, 
-                        volume_envelope, 
-                        protect, 
-                        split_audio, 
-                        f0_autotune_strength, 
-                        cleaner_chbox, 
-                        autotune_chbox, 
-                        pitch_chbox, 
-                        index_strength_chbox, 
-                        resample_sr_chbox, 
-                        filter_radius_chbox, 
-                        volume_envelope_chbox, 
-                        protect_chbox, 
-                        split_audio_chbox, 
-                        formant_shifting_chbox, 
-                        formant_shifting, 
-                        formant_qfrency, 
-                        formant_timbre
-                    ], 
-                    outputs=[presets_name]
-                )
-            with gr.Row():
-                upload_presets.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("assets", "presets")), inputs=[upload_presets], outputs=[presets_name])
-                autotune.change(fn=visible, inputs=[autotune], outputs=[f0_autotune_strength])
-                use_audio.change(fn=lambda a: [visible(a), visible(a), visible(a), visible(a), visible(a), valueFalse_interactive(a), valueFalse_interactive(a), valueFalse_interactive(a), valueFalse_interactive(a), visible(not a), visible(not a), visible(not a), visible(not a)], inputs=[use_audio], outputs=[main_backing, use_original, convert_backing, not_merge_backing, merge_instrument, use_original, convert_backing, not_merge_backing, merge_instrument, input_audio0, output_audio, input0, play_audio])
-            with gr.Row():
-                convert_backing.change(fn=lambda a,b: [change_backing_choices(a, b), visible(a)], inputs=[convert_backing, not_merge_backing], outputs=[use_original, backing_convert])
-                use_original.change(fn=lambda audio, original: [visible(original), visible(not original), visible(audio and not original), valueFalse_interactive(not original), valueFalse_interactive(not original)], inputs=[use_audio, use_original], outputs=[original_convert, main_convert, main_backing, convert_backing, not_merge_backing])
-                cleaner0.change(fn=visible, inputs=[cleaner0], outputs=[clean_strength0])
-            with gr.Row():
-                merge_instrument.change(fn=visible, inputs=[merge_instrument], outputs=[vocal_instrument])
-                not_merge_backing.change(fn=lambda audio, merge, cvb: [visible(audio and not merge), change_backing_choices(cvb, merge)], inputs=[use_audio, not_merge_backing, convert_backing], outputs=[main_backing, use_original])
-                method.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method, hybrid_method], outputs=[hybrid_method, hop_length])
-            with gr.Row():
-                hybrid_method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length])
-                refesh.click(fn=change_models_choices, inputs=[], outputs=[model_pth, model_index])
-                model_pth.change(fn=get_index, inputs=[model_pth], outputs=[model_index])
-            with gr.Row():
-                input0.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input0], outputs=[input_audio0])
-                input_audio0.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio0], outputs=[play_audio])
-                formant_shifting.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting], outputs=[formant_qfrency, formant_timbre])
-            with gr.Row():
-                embedders.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders], outputs=[custom_embedders])
-                refesh0.click(fn=change_audios_choices, inputs=[input_audio0], outputs=[input_audio0])
-                model_index.change(fn=index_strength_show, inputs=[model_index], outputs=[index_strength])
-            with gr.Row():
-                audio_select.change(fn=lambda: visible(True), inputs=[], outputs=[convert_button_2])
-                convert_button.click(fn=lambda: visible(False), inputs=[], outputs=[convert_button])
-                convert_button_2.click(fn=lambda: [visible(False), visible(False)], inputs=[], outputs=[audio_select, convert_button_2])
-            with gr.Row():
-                convert_button.click(
-                    fn=convert_selection,
-                    inputs=[
-                        cleaner0,
-                        autotune,
-                        use_audio,
-                        use_original,
-                        convert_backing,
-                        not_merge_backing,
-                        merge_instrument,
-                        pitch,
-                        clean_strength0,
-                        model_pth,
-                        model_index,
-                        index_strength,
-                        input_audio0,
-                        output_audio,
-                        export_format,
-                        method,
-                        hybrid_method,
-                        hop_length,
-                        embedders,
-                        custom_embedders,
-                        resample_sr,
-                        filter_radius,
-                        volume_envelope,
-                        protect,
-                        split_audio,
-                        f0_autotune_strength,
-                        checkpointing,
-                        onnx_f0_mode,
-                        formant_shifting, 
-                        formant_qfrency, 
-                        formant_timbre,
-                        f0_file_dropdown,
-                        embed_mode
-                    ],
-                    outputs=[audio_select, main_convert, backing_convert, main_backing, original_convert, vocal_instrument, convert_button],
-                    api_name="convert_selection"
-                )
-                convert_button_2.click(
-                    fn=convert_audio,
-                    inputs=[
-                        cleaner0,
-                        autotune,
-                        use_audio,
-                        use_original,
-                        convert_backing,
-                        not_merge_backing,
-                        merge_instrument,
-                        pitch,
-                        clean_strength0,
-                        model_pth,
-                        model_index,
-                        index_strength,
-                        input_audio0,
-                        output_audio,
-                        export_format,
-                        method,
-                        hybrid_method,
-                        hop_length,
-                        embedders,
-                        custom_embedders,
-                        resample_sr,
-                        filter_radius,
-                        volume_envelope,
-                        protect,
-                        split_audio,
-                        f0_autotune_strength,
-                        audio_select,
-                        checkpointing,
-                        onnx_f0_mode,
-                        formant_shifting, 
-                        formant_qfrency, 
-                        formant_timbre,
-                        f0_file_dropdown,
-                        embed_mode
-                    ],
-                    outputs=[main_convert, backing_convert, main_backing, original_convert, vocal_instrument, convert_button],
-                    api_name="convert_audio"
-                )
-
-        with gr.TabItem(translations["convert_with_whisper"], visible=configs.get("convert_with_whisper", True)):
-            gr.Markdown(f"## {translations['convert_with_whisper']}")
-            with gr.Row():
-                gr.Markdown(translations["convert_with_whisper_info"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            cleaner2 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
-                            autotune2 = gr.Checkbox(label=translations["autotune"], value=False, interactive=True)
-                            checkpointing2 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
-                            formant_shifting2 = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
-                        with gr.Row():
-                            num_spk = gr.Slider(minimum=2, maximum=8, step=1, info=translations["num_spk_info"], label=translations["num_spk"], value=2, interactive=True)
-            with gr.Row():
-                with gr.Column():
-                    convert_button3 = gr.Button(translations["convert_audio"], variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    with gr.Accordion(translations["model_accordion"] + " 1", open=True):
-                        with gr.Row():
-                            model_pth2 = gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
-                            model_index2 = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
-                        with gr.Row():
-                            refesh2 = gr.Button(translations["refesh"])
-                        with gr.Row():
-                            pitch3 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
-                            index_strength2 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index2.value != "")
-                    with gr.Accordion(translations["input_output"], open=False):
-                        with gr.Column():
-                            export_format2 = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-                            input_audio1 = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True)
-                            output_audio2 = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True)
-                        with gr.Column():
-                            refesh4 = gr.Button(translations["refesh"])
-                        with gr.Row():
-                            input2 = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
-                with gr.Column():
-                    with gr.Accordion(translations["model_accordion"] + " 2", open=True):
-                        with gr.Row():
-                            model_pth3 = gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
-                            model_index3 = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
-                        with gr.Row():
-                            refesh3 = gr.Button(translations["refesh"])
-                        with gr.Row():
-                            pitch4 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
-                            index_strength3 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index3.value != "")
-                    with gr.Accordion(translations["setting"], open=False):
-                        with gr.Row():
-                            model_size = gr.Radio(label=translations["model_size"], info=translations["model_size_info"], choices=["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v1", "large-v2", "large-v3", "large-v3-turbo"], value="medium", interactive=True)
-                        with gr.Accordion(translations["f0_method"], open=False):
-                            with gr.Group():
-                                with gr.Row():
-                                    onnx_f0_mode4 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                                    unlock_full_method2 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
-                                method3 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], value="rmvpe", interactive=True)
-                                hybrid_method3 = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method3.value == "hybrid")
-                            hop_length3 = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False)
-                        with gr.Accordion(translations["hubert_model"], open=False):
-                            embed_mode3 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=["fairseq", "onnx", "transformers"], interactive=True, visible=True)
-                            embedders3 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
-                            custom_embedders3 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders3.value == "custom")
-                        with gr.Column():      
-                            clean_strength3 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner2.value)
-                            f0_autotune_strength3 = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune.value)
-                            resample_sr3 = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True)
-                            filter_radius3 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True)
-                            volume_envelope3 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True)
-                            protect3 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.5, step=0.01, interactive=True)
-                        with gr.Row():
-                            formant_qfrency3 = gr.Slider(value=1.0, label=translations["formant_qfrency"] + " 1", info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-                            formant_timbre3 = gr.Slider(value=1.0, label=translations["formant_timbre"] + " 1", info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-                        with gr.Row():
-                            formant_qfrency4 = gr.Slider(value=1.0, label=translations["formant_qfrency"] + " 2", info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-                            formant_timbre4 = gr.Slider(value=1.0, label=translations["formant_timbre"] + " 2", info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-            with gr.Row():
-                gr.Markdown(translations["input_output"])
-            with gr.Row():
-                play_audio2 = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                play_audio3 = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"])
-            with gr.Row():
-                autotune2.change(fn=visible, inputs=[autotune2], outputs=[f0_autotune_strength3])
-                cleaner2.change(fn=visible, inputs=[cleaner2], outputs=[clean_strength3])
-                method3.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method3, hybrid_method3], outputs=[hybrid_method3, hop_length3])
-            with gr.Row():
-                hybrid_method3.change(fn=hoplength_show, inputs=[method3, hybrid_method3], outputs=[hop_length3])
-                refesh2.click(fn=change_models_choices, inputs=[], outputs=[model_pth2, model_index2])
-                model_pth2.change(fn=get_index, inputs=[model_pth2], outputs=[model_index2])
-            with gr.Row():
-                refesh3.click(fn=change_models_choices, inputs=[], outputs=[model_pth3, model_index3])
-                model_pth3.change(fn=get_index, inputs=[model_pth3], outputs=[model_index3])
-                input2.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input2], outputs=[input_audio1])
-            with gr.Row():
-                input_audio1.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio1], outputs=[play_audio2])
-                formant_shifting2.change(fn=lambda a: [visible(a)]*4, inputs=[formant_shifting2], outputs=[formant_qfrency3, formant_timbre3, formant_qfrency4, formant_timbre4])
-                embedders3.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders3], outputs=[custom_embedders3])
-            with gr.Row():
-                refesh4.click(fn=change_audios_choices, inputs=[input_audio1], outputs=[input_audio1])
-                model_index2.change(fn=index_strength_show, inputs=[model_index2], outputs=[index_strength2])
-                model_index3.change(fn=index_strength_show, inputs=[model_index3], outputs=[index_strength3])
-            with gr.Row():
-                unlock_full_method2.change(fn=unlock_f0, inputs=[unlock_full_method2], outputs=[method3])
-                convert_button3.click(
-                    fn=convert_with_whisper,
-                    inputs=[
-                        num_spk,
-                        model_size,
-                        cleaner2,
-                        clean_strength3,
-                        autotune2,
-                        f0_autotune_strength3,
-                        checkpointing2,
-                        model_pth2,
-                        model_pth3,
-                        model_index2,
-                        model_index3,
-                        pitch3,
-                        pitch4,
-                        index_strength2,
-                        index_strength3,
-                        export_format2,
-                        input_audio1,
-                        output_audio2,
-                        onnx_f0_mode4,
-                        method3,
-                        hybrid_method3,
-                        hop_length3,
-                        embed_mode3,
-                        embedders3,
-                        custom_embedders3,
-                        resample_sr3,
-                        filter_radius3,
-                        volume_envelope3,
-                        protect3,
-                        formant_shifting2,
-                        formant_qfrency3,
-                        formant_timbre3,
-                        formant_qfrency4,
-                        formant_timbre4,
-                    ],
-                    outputs=[play_audio3],
-                    api_name="convert_with_whisper"
-                )
-
-        with gr.TabItem(translations["convert_text"], visible=configs.get("tts_tab", True)):
-            gr.Markdown(translations["convert_text_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["convert_text_markdown_2"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            use_txt = gr.Checkbox(label=translations["input_txt"], value=False, interactive=True)
-                            google_tts_check_box = gr.Checkbox(label=translations["googletts"], value=False, interactive=True)
-                        prompt = gr.Textbox(label=translations["text_to_speech"], value="", placeholder="Hello Words", lines=3)
-                with gr.Column():
-                    speed = gr.Slider(label=translations["voice_speed"], info=translations["voice_speed_info"], minimum=-100, maximum=100, value=0, step=1)
-                    pitch0 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
-            with gr.Row():
-                tts_button = gr.Button(translations["tts_1"], variant="primary", scale=2)
-                convert_button0 = gr.Button(translations["tts_2"], variant="secondary", scale=2)
-            with gr.Row():
-                with gr.Column():
-                    txt_input = gr.File(label=translations["drop_text"], file_types=[".txt", ".srt"], visible=use_txt.value)  
-                    tts_voice = gr.Dropdown(label=translations["voice"], choices=edgetts, interactive=True, value="vi-VN-NamMinhNeural")
-                    tts_pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info_2"], label=translations["pitch"], value=0, interactive=True)
-                with gr.Column():
-                    with gr.Accordion(translations["model_accordion"], open=True):
-                        with gr.Row():
-                            model_pth0 = gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
-                            model_index0 = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
-                        with gr.Row():
-                            refesh1 = gr.Button(translations["refesh"])
-                        with gr.Row():
-                            index_strength0 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index0.value != "")
-                    with gr.Accordion(translations["output_path"], open=False):
-                        export_format0 = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-                        output_audio0 = gr.Textbox(label=translations["output_tts"], value="audios/tts.wav", placeholder="audios/tts.wav", info=translations["tts_output"], interactive=True)
-                        output_audio1 = gr.Textbox(label=translations["output_tts_convert"], value="audios/tts-convert.wav", placeholder="audios/tts-convert.wav", info=translations["tts_output"], interactive=True)
-                    with gr.Accordion(translations["setting"], open=False):
-                        with gr.Accordion(translations["f0_method"], open=False):
-                            with gr.Group():
-                                with gr.Row():
-                                    onnx_f0_mode1 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                                    unlock_full_method3 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
-                                method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], value="rmvpe", interactive=True)
-                                hybrid_method0 = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method0.value == "hybrid")
-                            hop_length0 = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False)
-                        with gr.Accordion(translations["f0_file"], open=False):
-                            upload_f0_file0 = gr.File(label=translations["upload_f0"], file_types=[".txt"])  
-                            f0_file_dropdown0 = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
-                            refesh_f0_file0 = gr.Button(translations["refesh"])
-                        with gr.Accordion(translations["hubert_model"], open=False):
-                            embed_mode1 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=["fairseq", "onnx", "transformers"], interactive=True, visible=True)
-                            embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
-                            custom_embedders0 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders0.value == "custom")
-                        with gr.Group():
-                            with gr.Row():
-                                formant_shifting1 = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)  
-                                split_audio0 = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)   
-                                cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)     
-                                autotune3 = gr.Checkbox(label=translations["autotune"], value=False, interactive=True) 
-                                checkpointing0 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)         
-                        with gr.Column():
-                            f0_autotune_strength0 = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune3.value)
-                            clean_strength1 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner1.value)
-                            resample_sr0 = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True)
-                            filter_radius0 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True)
-                            volume_envelope0 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True)
-                            protect0 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.5, step=0.01, interactive=True)
-                        with gr.Row():
-                            formant_qfrency1 = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-                            formant_timbre1 = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
-            with gr.Row():
-                gr.Markdown(translations["output_tts_markdown"])
-            with gr.Row():
-                tts_voice_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["output_text_to_speech"])
-                tts_voice_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"])
-            with gr.Row():
-                unlock_full_method3.change(fn=unlock_f0, inputs=[unlock_full_method3], outputs=[method0])
-                upload_f0_file0.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file0], outputs=[f0_file_dropdown0])
-                refesh_f0_file0.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown0])
-            with gr.Row():
-                autotune3.change(fn=visible, inputs=[autotune3], outputs=[f0_autotune_strength0])
-                model_pth0.change(fn=get_index, inputs=[model_pth0], outputs=[model_index0])
-            with gr.Row():
-                cleaner1.change(fn=visible, inputs=[cleaner1], outputs=[clean_strength1])
-                method0.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method0, hybrid_method0], outputs=[hybrid_method0, hop_length0])
-                hybrid_method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0])
-            with gr.Row():
-                refesh1.click(fn=change_models_choices, inputs=[], outputs=[model_pth0, model_index0])
-                embedders0.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders0], outputs=[custom_embedders0])
-                formant_shifting1.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting1], outputs=[formant_qfrency1, formant_timbre1])
-            with gr.Row():
-                model_index0.change(fn=index_strength_show, inputs=[model_index0], outputs=[index_strength0])
-                txt_input.upload(fn=process_input, inputs=[txt_input], outputs=[prompt])
-                use_txt.change(fn=visible, inputs=[use_txt], outputs=[txt_input])
-            with gr.Row():
-                google_tts_check_box.change(fn=change_tts_voice_choices, inputs=[google_tts_check_box], outputs=[tts_voice])
-                tts_button.click(
-                    fn=TTS, 
-                    inputs=[
-                        prompt, 
-                        tts_voice, 
-                        speed, 
-                        output_audio0,
-                        tts_pitch,
-                        google_tts_check_box,
-                        txt_input
-                    ], 
-                    outputs=[tts_voice_audio],
-                    api_name="text-to-speech"
-                )
-                convert_button0.click(
-                    fn=convert_tts,
-                    inputs=[
-                        cleaner1, 
-                        autotune3, 
-                        pitch0, 
-                        clean_strength1, 
-                        model_pth0, 
-                        model_index0, 
-                        index_strength0, 
-                        output_audio0, 
-                        output_audio1,
-                        export_format0,
-                        method0, 
-                        hybrid_method0, 
-                        hop_length0, 
-                        embedders0, 
-                        custom_embedders0, 
-                        resample_sr0, 
-                        filter_radius0, 
-                        volume_envelope0, 
-                        protect0,
-                        split_audio0,
-                        f0_autotune_strength0,
-                        checkpointing0,
-                        onnx_f0_mode1,
-                        formant_shifting1, 
-                        formant_qfrency1, 
-                        formant_timbre1,
-                        f0_file_dropdown0,
-                        embed_mode1
-                    ],
-                    outputs=[tts_voice_convert],
-                    api_name="convert_tts"
-                )
-
-        with gr.TabItem(translations["audio_editing"], visible=configs.get("audioldm2", True)):
-            gr.Markdown(translations["audio_editing_info"])
-            with gr.Row():
-                gr.Markdown(translations["audio_editing_markdown"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            save_compute = gr.Checkbox(label=translations["save_compute"], value=True, interactive=True)
-                        tar_prompt = gr.Textbox(label=translations["target_prompt"], info=translations["target_prompt_info"], placeholder="Piano and violin cover", lines=5, interactive=True)
-                with gr.Column():
-                    cfg_scale_src = gr.Slider(value=3, minimum=0.5, maximum=25, label=translations["cfg_scale_src"], info=translations["cfg_scale_src_info"], interactive=True)
-                    cfg_scale_tar = gr.Slider(value=12, minimum=0.5, maximum=25, label=translations["cfg_scale_tar"], info=translations["cfg_scale_tar_info"], interactive=True)
-            with gr.Row():
-                edit_button = gr.Button(translations["editing"], variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    drop_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])  
-                    display_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                with gr.Column():
-                    with gr.Accordion(translations["input_output"], open=False):
-                        with gr.Column():
-                            export_audio_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-                            input_audiopath = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True)
-                            output_audiopath = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True)
-                        with gr.Column():
-                            refesh_audio = gr.Button(translations["refesh"])
-                    with gr.Accordion(translations["setting"], open=False):
-                        audioldm2_model = gr.Radio(label=translations["audioldm2_model"], info=translations["audioldm2_model_info"], choices=["audioldm2", "audioldm2-large", "audioldm2-music"], value="audioldm2-music", interactive=True)
-                        with gr.Row():
-                            src_prompt = gr.Textbox(label=translations["source_prompt"], lines=2, interactive=True, info=translations["source_prompt_info"], placeholder="A recording of a happy upbeat classical music piece")
-                        with gr.Row():
-                            with gr.Column(): 
-                                audioldm2_sample_rate = gr.Slider(minimum=8000, maximum=96000, label=translations["sr"], info=translations["sr_info"], value=44100, step=1, interactive=True)
-                                t_start = gr.Slider(minimum=15, maximum=85, value=45, step=1, label=translations["t_start"], interactive=True, info=translations["t_start_info"])
-                                steps = gr.Slider(value=50, step=1, minimum=10, maximum=300, label=translations["steps_label"], info=translations["steps_info"], interactive=True)
-            with gr.Row():
-                gr.Markdown(translations["output_audio"])
-            with gr.Row():
-                output_audioldm2 = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"])
-            with gr.Row():
-                refesh_audio.click(fn=change_audios_choices, inputs=[input_audiopath], outputs=[input_audiopath])
-                drop_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[drop_audio_file], outputs=[input_audiopath])
-                input_audiopath.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audiopath], outputs=[display_audio])
-            with gr.Row():
-                edit_button.click(
-                    fn=run_audioldm2,
-                    inputs=[
-                        input_audiopath, 
-                        output_audiopath, 
-                        export_audio_format, 
-                        audioldm2_sample_rate, 
-                        audioldm2_model, 
-                        src_prompt, 
-                        tar_prompt, 
-                        steps, 
-                        cfg_scale_src, 
-                        cfg_scale_tar, 
-                        t_start, 
-                        save_compute
-                    ],
-                    outputs=[output_audioldm2],
-                    api_name="audioldm2"
-                )
-
-        with gr.TabItem(translations["audio_effects"], visible=configs.get("effects_tab", True)):
-            gr.Markdown(translations["apply_audio_effects"])
-            with gr.Row():
-                gr.Markdown(translations["audio_effects_edit"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        reverb_check_box = gr.Checkbox(label=translations["reverb"], value=False, interactive=True)
-                        chorus_check_box = gr.Checkbox(label=translations["chorus"], value=False, interactive=True)
-                        delay_check_box = gr.Checkbox(label=translations["delay"], value=False, interactive=True)
-                        phaser_check_box = gr.Checkbox(label=translations["phaser"], value=False, interactive=True)
-                        compressor_check_box = gr.Checkbox(label=translations["compressor"], value=False, interactive=True)
-                        more_options = gr.Checkbox(label=translations["more_option"], value=False, interactive=True)    
-            with gr.Row():
-                with gr.Accordion(translations["input_output"], open=False):
-                    with gr.Row():
-                        upload_audio = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
-                    with gr.Row():
-                        audio_in_path = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True)
-                        audio_out_path = gr.Textbox(label=translations["output_audio"], value="audios/audio_effects.wav", placeholder="audios/audio_effects.wav", info=translations["provide_output"], interactive=True)
-                    with gr.Row():
-                        with gr.Column():
-                            audio_combination = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True)
-                            audio_combination_input = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True, visible=audio_combination.value)
-                    with gr.Row():
-                        audio_effects_refesh = gr.Button(translations["refesh"])
-                    with gr.Row():
-                        audio_output_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-            with gr.Row():
-                apply_effects_button = gr.Button(translations["apply"], variant="primary", scale=2)
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Accordion(translations["reverb"], open=False, visible=reverb_check_box.value) as reverb_accordion:
-                            reverb_freeze_mode = gr.Checkbox(label=translations["reverb_freeze"], info=translations["reverb_freeze_info"], value=False, interactive=True)
-                            reverb_room_size = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.15, label=translations["room_size"], info=translations["room_size_info"], interactive=True)
-                            reverb_damping = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label=translations["damping"], info=translations["damping_info"], interactive=True)
-                            reverb_wet_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.2, label=translations["wet_level"], info=translations["wet_level_info"], interactive=True)
-                            reverb_dry_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.8, label=translations["dry_level"], info=translations["dry_level_info"], interactive=True)
-                            reverb_width = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label=translations["width"], info=translations["width_info"], interactive=True)
-                    with gr.Row():
-                        with gr.Accordion(translations["chorus"], open=False, visible=chorus_check_box.value) as chorus_accordion:
-                            chorus_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_depth"], info=translations["chorus_depth_info"], interactive=True)
-                            chorus_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.5, label=translations["chorus_rate_hz"], info=translations["chorus_rate_hz_info"], interactive=True)
-                            chorus_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_mix"], info=translations["chorus_mix_info"], interactive=True)
-                            chorus_centre_delay_ms = gr.Slider(minimum=0, maximum=50, step=1, value=10, label=translations["chorus_centre_delay_ms"], info=translations["chorus_centre_delay_ms_info"], interactive=True)
-                            chorus_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["chorus_feedback"], info=translations["chorus_feedback_info"], interactive=True)
-                    with gr.Row():
-                        with gr.Accordion(translations["delay"], open=False, visible=delay_check_box.value) as delay_accordion:
-                            delay_second = gr.Slider(minimum=0, maximum=5, step=0.01, value=0.5, label=translations["delay_seconds"], info=translations["delay_seconds_info"], interactive=True)
-                            delay_feedback = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_feedback"], info=translations["delay_feedback_info"], interactive=True)
-                            delay_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_mix"], info=translations["delay_mix_info"], interactive=True)
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Accordion(translations["more_option"], open=False, visible=more_options.value) as more_accordion:
-                            with gr.Row():
-                                fade = gr.Checkbox(label=translations["fade"], value=False, interactive=True)
-                                bass_or_treble = gr.Checkbox(label=translations["bass_or_treble"], value=False, interactive=True)
-                                limiter = gr.Checkbox(label=translations["limiter"], value=False, interactive=True)
-                                resample_checkbox = gr.Checkbox(label=translations["resample"], value=False, interactive=True)
-                            with gr.Row():
-                                distortion_checkbox = gr.Checkbox(label=translations["distortion"], value=False, interactive=True)
-                                gain_checkbox = gr.Checkbox(label=translations["gain"], value=False, interactive=True)
-                                bitcrush_checkbox = gr.Checkbox(label=translations["bitcrush"], value=False, interactive=True)
-                                clipping_checkbox = gr.Checkbox(label=translations["clipping"], value=False, interactive=True)
-                            with gr.Accordion(translations["fade"], open=True, visible=fade.value) as fade_accordion:
-                                with gr.Row():
-                                    fade_in = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_in"], info=translations["fade_in_info"], interactive=True)
-                                    fade_out = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_out"], info=translations["fade_out_info"], interactive=True)
-                            with gr.Accordion(translations["bass_or_treble"], open=True, visible=bass_or_treble.value) as bass_treble_accordion:
-                                with gr.Row():
-                                    bass_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["bass_boost"], info=translations["bass_boost_info"], interactive=True)
-                                    bass_frequency = gr.Slider(minimum=20, maximum=200, step=10, value=100, label=translations["bass_frequency"], info=translations["bass_frequency_info"], interactive=True)
-                                with gr.Row():
-                                    treble_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["treble_boost"], info=translations["treble_boost_info"], interactive=True)
-                                    treble_frequency = gr.Slider(minimum=1000, maximum=10000, step=500, value=3000, label=translations["treble_frequency"], info=translations["treble_frequency_info"], interactive=True)
-                            with gr.Accordion(translations["limiter"], open=True, visible=limiter.value) as limiter_accordion:
-                                with gr.Row():
-                                    limiter_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["limiter_threashold_db"], info=translations["limiter_threashold_db_info"], interactive=True)
-                                    limiter_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["limiter_release_ms"], info=translations["limiter_release_ms_info"], interactive=True)
-                            with gr.Column():
-                                pitch_shift_semitones = gr.Slider(minimum=-20, maximum=20, step=1, value=0, label=translations["pitch"], info=translations["pitch_info"], interactive=True)
-                                audio_effect_resample_sr = gr.Slider(minimum=0, maximum=96000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=resample_checkbox.value)
-                                distortion_drive_db = gr.Slider(minimum=0, maximum=50, step=1, value=20, label=translations["distortion"], info=translations["distortion_info"], interactive=True, visible=distortion_checkbox.value)
-                                gain_db = gr.Slider(minimum=-60, maximum=60, step=1, value=0, label=translations["gain"], info=translations["gain_info"], interactive=True, visible=gain_checkbox.value)
-                                clipping_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["clipping_threashold_db"], info=translations["clipping_threashold_db_info"], interactive=True, visible=clipping_checkbox.value)
-                                bitcrush_bit_depth = gr.Slider(minimum=1, maximum=24, step=1, value=16, label=translations["bitcrush_bit_depth"], info=translations["bitcrush_bit_depth_info"], interactive=True, visible=bitcrush_checkbox.value)
-                    with gr.Row():
-                        with gr.Accordion(translations["phaser"], open=False, visible=phaser_check_box.value) as phaser_accordion:
-                            phaser_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_depth"], info=translations["phaser_depth_info"], interactive=True)
-                            phaser_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1, label=translations["phaser_rate_hz"], info=translations["phaser_rate_hz_info"], interactive=True)
-                            phaser_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_mix"], info=translations["phaser_mix_info"], interactive=True)
-                            phaser_centre_frequency_hz = gr.Slider(minimum=50, maximum=5000, step=10, value=1000, label=translations["phaser_centre_frequency_hz"], info=translations["phaser_centre_frequency_hz_info"], interactive=True)
-                            phaser_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["phaser_feedback"], info=translations["phaser_feedback_info"], interactive=True)
-                    with gr.Row():
-                        with gr.Accordion(translations["compressor"], open=False, visible=compressor_check_box.value) as compressor_accordion:
-                            compressor_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-20, label=translations["compressor_threashold_db"], info=translations["compressor_threashold_db_info"], interactive=True)
-                            compressor_ratio = gr.Slider(minimum=1, maximum=20, step=0.1, value=1, label=translations["compressor_ratio"], info=translations["compressor_ratio_info"], interactive=True)
-                            compressor_attack_ms = gr.Slider(minimum=0.1, maximum=100, step=0.1, value=10, label=translations["compressor_attack_ms"], info=translations["compressor_attack_ms_info"], interactive=True)
-                            compressor_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["compressor_release_ms"], info=translations["compressor_release_ms_info"], interactive=True)   
-            with gr.Row():
-                gr.Markdown(translations["output_audio"])
-            with gr.Row():
-                audio_play_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                audio_play_output = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"])
-            with gr.Row():
-                reverb_check_box.change(fn=visible, inputs=[reverb_check_box], outputs=[reverb_accordion])
-                chorus_check_box.change(fn=visible, inputs=[chorus_check_box], outputs=[chorus_accordion])
-                delay_check_box.change(fn=visible, inputs=[delay_check_box], outputs=[delay_accordion])
-            with gr.Row():
-                compressor_check_box.change(fn=visible, inputs=[compressor_check_box], outputs=[compressor_accordion])
-                phaser_check_box.change(fn=visible, inputs=[phaser_check_box], outputs=[phaser_accordion])
-                more_options.change(fn=visible, inputs=[more_options], outputs=[more_accordion])
-            with gr.Row():
-                fade.change(fn=visible, inputs=[fade], outputs=[fade_accordion])
-                bass_or_treble.change(fn=visible, inputs=[bass_or_treble], outputs=[bass_treble_accordion])
-                limiter.change(fn=visible, inputs=[limiter], outputs=[limiter_accordion])
-                resample_checkbox.change(fn=visible, inputs=[resample_checkbox], outputs=[audio_effect_resample_sr])
-            with gr.Row():
-                distortion_checkbox.change(fn=visible, inputs=[distortion_checkbox], outputs=[distortion_drive_db])
-                gain_checkbox.change(fn=visible, inputs=[gain_checkbox], outputs=[gain_db])
-                clipping_checkbox.change(fn=visible, inputs=[clipping_checkbox], outputs=[clipping_threashold_db])
-                bitcrush_checkbox.change(fn=visible, inputs=[bitcrush_checkbox], outputs=[bitcrush_bit_depth])
-            with gr.Row():
-                upload_audio.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio], outputs=[audio_in_path])
-                audio_in_path.change(fn=lambda audio: audio if audio else None, inputs=[audio_in_path], outputs=[audio_play_input])
-                audio_effects_refesh.click(fn=lambda a, b: [change_audios_choices(a), change_audios_choices(b)], inputs=[audio_in_path, audio_combination_input], outputs=[audio_in_path, audio_combination_input])
-            with gr.Row():
-                more_options.change(fn=lambda: [False]*8, inputs=[], outputs=[fade, bass_or_treble, limiter, resample_checkbox, distortion_checkbox, gain_checkbox, clipping_checkbox, bitcrush_checkbox])
-                audio_combination.change(fn=visible, inputs=[audio_combination], outputs=[audio_combination_input])
-            with gr.Row():
-                apply_effects_button.click(
-                    fn=audio_effects,
-                    inputs=[
-                        audio_in_path, 
-                        audio_out_path, 
-                        resample_checkbox, 
-                        audio_effect_resample_sr, 
-                        chorus_depth, 
-                        chorus_rate_hz, 
-                        chorus_mix, 
-                        chorus_centre_delay_ms, 
-                        chorus_feedback, 
-                        distortion_drive_db, 
-                        reverb_room_size, 
-                        reverb_damping, 
-                        reverb_wet_level, 
-                        reverb_dry_level, 
-                        reverb_width, 
-                        reverb_freeze_mode, 
-                        pitch_shift_semitones, 
-                        delay_second, 
-                        delay_feedback, 
-                        delay_mix, 
-                        compressor_threashold_db, 
-                        compressor_ratio, 
-                        compressor_attack_ms, 
-                        compressor_release_ms, 
-                        limiter_threashold_db, 
-                        limiter_release_ms, 
-                        gain_db, 
-                        bitcrush_bit_depth, 
-                        clipping_threashold_db, 
-                        phaser_rate_hz, 
-                        phaser_depth, 
-                        phaser_centre_frequency_hz, 
-                        phaser_feedback, 
-                        phaser_mix, 
-                        bass_boost, 
-                        bass_frequency, 
-                        treble_boost, 
-                        treble_frequency, 
-                        fade_in, 
-                        fade_out, 
-                        audio_output_format, 
-                        chorus_check_box, 
-                        distortion_checkbox, 
-                        reverb_check_box, 
-                        delay_check_box, 
-                        compressor_check_box, 
-                        limiter, 
-                        gain_checkbox, 
-                        bitcrush_checkbox, 
-                        clipping_checkbox, 
-                        phaser_check_box, 
-                        bass_or_treble, 
-                        fade,
-                        audio_combination,
-                        audio_combination_input
-                    ],
-                    outputs=[audio_play_output],
-                    api_name="audio_effects"
-                )
-
-        with gr.TabItem(translations["createdataset"], visible=configs.get("create_dataset_tab", True)):
-            gr.Markdown(translations["create_dataset_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["create_dataset_markdown_2"])
-            with gr.Row():
-                dataset_url = gr.Textbox(label=translations["url_audio"], info=translations["create_dataset_url"], value="", placeholder="https://www.youtube.com/...", interactive=True)
-                output_dataset = gr.Textbox(label=translations["output_data"], info=translations["output_data_info"], value="dataset", placeholder="dataset", interactive=True)
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            separator_reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True)
-                            denoise_mdx = gr.Checkbox(label=translations["denoise"], value=False, interactive=True)
-                        with gr.Row():
-                            kim_vocal_version = gr.Radio(label=translations["model_ver"], info=translations["model_ver_info"], choices=["Version-1", "Version-2"], value="Version-2", interactive=True)
-                            kim_vocal_overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True)
-                        with gr.Row():    
-                            kim_vocal_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True)
-                            kim_vocal_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True) 
-                        with gr.Row():
-                            kim_vocal_segments_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True)
-                        with gr.Row():
-                            sample_rate0 = gr.Slider(minimum=8000, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True)
-                with gr.Column():
-                    create_button = gr.Button(translations["createdataset"], variant="primary", scale=2, min_width=4000)
-                    with gr.Group():
-                        with gr.Row():
-                            clean_audio = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
-                            skip = gr.Checkbox(label=translations["skip"], value=False, interactive=True)
-                        with gr.Row():   
-                            dataset_clean_strength = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.5, label=translations["clean_strength"], info=translations["clean_strength_info"], interactive=True, visible=clean_audio.value)
-                        with gr.Row():
-                            skip_start = gr.Textbox(label=translations["skip_start"], info=translations["skip_start_info"], value="", placeholder="0,...", interactive=True, visible=skip.value)
-                            skip_end = gr.Textbox(label=translations["skip_end"], info=translations["skip_end_info"], value="", placeholder="0,...", interactive=True, visible=skip.value)
-                    create_dataset_info = gr.Textbox(label=translations["create_dataset_info"], value="", interactive=False)
-            with gr.Row():
-                clean_audio.change(fn=visible, inputs=[clean_audio], outputs=[dataset_clean_strength])
-                skip.change(fn=lambda a: [valueEmpty_visible1(a)]*2, inputs=[skip], outputs=[skip_start, skip_end])
-            with gr.Row():
-                create_button.click(
-                    fn=create_dataset,
-                    inputs=[
-                        dataset_url, 
-                        output_dataset, 
-                        clean_audio, 
-                        dataset_clean_strength, 
-                        separator_reverb, 
-                        kim_vocal_version, 
-                        kim_vocal_overlap, 
-                        kim_vocal_segments_size, 
-                        denoise_mdx, 
-                        skip, 
-                        skip_start, 
-                        skip_end,
-                        kim_vocal_hop_length,
-                        kim_vocal_batch_size,
-                        sample_rate0
-                    ],
-                    outputs=[create_dataset_info],
-                    api_name="create_dataset"
-                )
-
-        with gr.TabItem(translations["training_model"], visible=configs.get("training_tab", True)):
-            gr.Markdown(f"## {translations['training_model']}")
-            with gr.Row():
-                gr.Markdown(translations["training_markdown"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            training_name = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True)
-                            training_sr = gr.Radio(label=translations["sample_rate"], info=translations["sample_rate_info"], choices=["32k", "40k", "48k"], value="48k", interactive=True) 
-                            training_ver = gr.Radio(label=translations["training_version"], info=translations["training_version_info"], choices=["v1", "v2"], value="v2", interactive=True) 
-                            with gr.Row():
-                                clean_dataset = gr.Checkbox(label=translations["clear_dataset"], value=False, interactive=True)
-                                preprocess_cut = gr.Checkbox(label=translations["split_audio"], value=True, interactive=True)
-                                process_effects = gr.Checkbox(label=translations["preprocess_effect"], value=False, interactive=True)
-                                checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
-                                training_f0 = gr.Checkbox(label=translations["training_pitch"], value=True, interactive=True)
-                                upload = gr.Checkbox(label=translations["upload_dataset"], value=False, interactive=True)
-                            with gr.Row():
-                                clean_dataset_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.7, step=0.1, interactive=True, visible=clean_dataset.value)
-                        with gr.Column():
-                            preprocess_button = gr.Button(translations["preprocess_button"], scale=2)
-                            upload_dataset = gr.Files(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"], visible=upload.value)
-                            preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False)
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Accordion(label=translations["f0_method"], open=False):
-                                with gr.Group():
-                                    with gr.Row():
-                                        onnx_f0_mode2 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                                        unlock_full_method4 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
-                                    extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
-                                extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False)
-                            with gr.Accordion(label=translations["hubert_model"], open=False):
-                                with gr.Group():
-                                    embed_mode2 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=["fairseq", "onnx", "transformers"], interactive=True, visible=True)
-                                    extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
-                                with gr.Row():
-                                    extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=extract_embedders.value == "custom")
-                        with gr.Column():
-                            extract_button = gr.Button(translations["extract_button"], scale=2)
-                            extract_info = gr.Textbox(label=translations["extract_info"], value="", interactive=False)
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            total_epochs = gr.Slider(label=translations["total_epoch"], info=translations["total_epoch_info"], minimum=1, maximum=10000, value=300, step=1, interactive=True)
-                            save_epochs = gr.Slider(label=translations["save_epoch"], info=translations["save_epoch_info"], minimum=1, maximum=10000, value=50, step=1, interactive=True)
-                        with gr.Column():
-                            index_button = gr.Button(f"3. {translations['create_index']}", variant="primary", scale=2)
-                            training_button = gr.Button(f"4. {translations['training_model']}", variant="primary", scale=2)
-                    with gr.Row():
-                        with gr.Accordion(label=translations["setting"], open=False):
-                            with gr.Row():
-                                index_algorithm = gr.Radio(label=translations["index_algorithm"], info=translations["index_algorithm_info"], choices=["Auto", "Faiss", "KMeans"], value="Auto", interactive=True)
-                            with gr.Row():
-                                custom_dataset = gr.Checkbox(label=translations["custom_dataset"], info=translations["custom_dataset_info"], value=False, interactive=True)
-                                overtraining_detector = gr.Checkbox(label=translations["overtraining_detector"], info=translations["overtraining_detector_info"], value=False, interactive=True)
-                                clean_up = gr.Checkbox(label=translations["cleanup_training"], info=translations["cleanup_training_info"], value=False, interactive=True)
-                                cache_in_gpu = gr.Checkbox(label=translations["cache_in_gpu"], info=translations["cache_in_gpu_info"], value=False, interactive=True)
-                            with gr.Column():
-                                dataset_path = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True, visible=custom_dataset.value)
-                            with gr.Column():
-                                threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=overtraining_detector.value)
-                                with gr.Accordion(translations["setting_cpu_gpu"], open=False):
-                                    with gr.Column():
-                                        gpu_number = gr.Textbox(label=translations["gpu_number"], value=str("-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-"), info=translations["gpu_number_info"], interactive=True)
-                                        gpu_info = gr.Textbox(label=translations["gpu_info"], value=get_gpu_info(), info=translations["gpu_info_2"], interactive=False)
-                                        cpu_core = gr.Slider(label=translations["cpu_core"], info=translations["cpu_core_info"], minimum=0, maximum=cpu_count(), value=cpu_count(), step=1, interactive=True)          
-                                        train_batch_size = gr.Slider(label=translations["batch_size"], info=translations["batch_size_info"], minimum=1, maximum=64, value=8, step=1, interactive=True)
-                            with gr.Row():
-                                save_only_latest = gr.Checkbox(label=translations["save_only_latest"], info=translations["save_only_latest_info"], value=True, interactive=True)
-                                save_every_weights = gr.Checkbox(label=translations["save_every_weights"], info=translations["save_every_weights_info"], value=True, interactive=True)
-                                not_use_pretrain = gr.Checkbox(label=translations["not_use_pretrain_2"], info=translations["not_use_pretrain_info"], value=False, interactive=True)
-                                custom_pretrain = gr.Checkbox(label=translations["custom_pretrain"], info=translations["custom_pretrain_info"], value=False, interactive=True)
-                            with gr.Row():
-                                vocoders = gr.Radio(label=translations["vocoder"], info=translations["vocoder_info"], choices=["Default", "MRF-HiFi-GAN", "RefineGAN"], value="Default", interactive=True) 
-                            with gr.Row():
-                                deterministic = gr.Checkbox(label=translations["deterministic"], info=translations["deterministic_info"], value=False, interactive=True)
-                                benchmark = gr.Checkbox(label=translations["benchmark"], info=translations["benchmark_info"], value=False, interactive=True)
-                            with gr.Row():
-                                model_author = gr.Textbox(label=translations["training_author"], info=translations["training_author_info"], value="", placeholder=translations["training_author"], interactive=True)
-                            with gr.Row():
-                                with gr.Column():
-                                    with gr.Accordion(translations["custom_pretrain_info"], open=False, visible=custom_pretrain.value and not not_use_pretrain.value) as pretrain_setting:
-                                        pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=pretrainedD, value=pretrainedD[0] if len(pretrainedD) > 0 else '', interactive=True, allow_custom_value=True)
-                                        pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=pretrainedG, value=pretrainedG[0] if len(pretrainedG) > 0 else '', interactive=True, allow_custom_value=True)
-                                        refesh_pretrain = gr.Button(translations["refesh"], scale=2)
-                    with gr.Row():
-                        training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False)
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Accordion(translations["export_model"], open=False):
-                                with gr.Row():
-                                    model_file= gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
-                                    index_file = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
-                                with gr.Row():
-                                    refesh_file = gr.Button(f"1. {translations['refesh']}", scale=2)
-                                    zip_model = gr.Button(translations["zip_model"], variant="primary", scale=2)
-                                with gr.Row():
-                                    zip_output = gr.File(label=translations["output_zip"], file_types=[".zip"], interactive=False, visible=False)
-            with gr.Row():
-                vocoders.change(fn=pitch_guidance_lock, inputs=[vocoders], outputs=[training_f0])
-                training_f0.change(fn=vocoders_lock, inputs=[training_f0, vocoders], outputs=[vocoders])
-                unlock_full_method4.change(fn=unlock_f0, inputs=[unlock_full_method4], outputs=[extract_method])
-            with gr.Row():
-                refesh_file.click(fn=change_models_choices, inputs=[], outputs=[model_file, index_file]) 
-                zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output])                
-                dataset_path.change(fn=lambda folder: os.makedirs(folder, exist_ok=True), inputs=[dataset_path], outputs=[])
-            with gr.Row():
-                upload.change(fn=visible, inputs=[upload], outputs=[upload_dataset]) 
-                overtraining_detector.change(fn=visible, inputs=[overtraining_detector], outputs=[threshold]) 
-                clean_dataset.change(fn=visible, inputs=[clean_dataset], outputs=[clean_dataset_strength])
-            with gr.Row():
-                custom_dataset.change(fn=lambda custom_dataset: [visible(custom_dataset), "dataset"],inputs=[custom_dataset], outputs=[dataset_path, dataset_path])
-                training_ver.change(fn=unlock_vocoder, inputs=[training_ver, vocoders], outputs=[vocoders])
-                vocoders.change(fn=unlock_ver, inputs=[training_ver, vocoders], outputs=[training_ver])
-                upload_dataset.upload(
-                    fn=lambda files, folder: [shutil.move(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr_warning(translations["dataset_folder1"]),
-                    inputs=[upload_dataset, dataset_path], 
-                    outputs=[], 
-                    api_name="upload_dataset"
-                )           
-            with gr.Row():
-                not_use_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting])
-                custom_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting])
-                refesh_pretrain.click(fn=change_pretrained_choices, inputs=[], outputs=[pretrained_D, pretrained_G])
-            with gr.Row():
-                preprocess_button.click(
-                    fn=preprocess,
-                    inputs=[
-                        training_name, 
-                        training_sr, 
-                        cpu_core,
-                        preprocess_cut, 
-                        process_effects,
-                        dataset_path,
-                        clean_dataset,
-                        clean_dataset_strength
-                    ],
-                    outputs=[preprocess_info],
-                    api_name="preprocess"
-                )
-            with gr.Row():
-                extract_method.change(fn=hoplength_show, inputs=[extract_method], outputs=[extract_hop_length])
-                extract_embedders.change(fn=lambda extract_embedders: visible(extract_embedders == "custom"), inputs=[extract_embedders], outputs=[extract_embedders_custom])
-            with gr.Row():
-                extract_button.click(
-                    fn=extract,
-                    inputs=[
-                        training_name, 
-                        training_ver, 
-                        extract_method, 
-                        training_f0, 
-                        extract_hop_length, 
-                        cpu_core,
-                        gpu_number,
-                        training_sr, 
-                        extract_embedders, 
-                        extract_embedders_custom,
-                        onnx_f0_mode2,
-                        embed_mode2
-                    ],
-                    outputs=[extract_info],
-                    api_name="extract"
-                )
-            with gr.Row():
-                index_button.click(
-                    fn=create_index,
-                    inputs=[
-                        training_name, 
-                        training_ver, 
-                        index_algorithm
-                    ],
-                    outputs=[training_info],
-                    api_name="create_index"
-                )
-            with gr.Row():
-                training_button.click(
-                    fn=training,
-                    inputs=[
-                        training_name, 
-                        training_ver, 
-                        save_epochs, 
-                        save_only_latest, 
-                        save_every_weights, 
-                        total_epochs, 
-                        training_sr,
-                        train_batch_size, 
-                        gpu_number,
-                        training_f0,
-                        not_use_pretrain,
-                        custom_pretrain,
-                        pretrained_G,
-                        pretrained_D,
-                        overtraining_detector,
-                        threshold,
-                        clean_up,
-                        cache_in_gpu,
-                        model_author,
-                        vocoders,
-                        checkpointing1,
-                        deterministic, 
-                        benchmark
-                    ],
-                    outputs=[training_info],
-                    api_name="training_model"
-                )
-
-        with gr.TabItem(translations["fushion"], visible=configs.get("fushion_tab", True)):
-            gr.Markdown(translations["fushion_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["fushion_markdown_2"])
-            with gr.Row():
-                name_to_save = gr.Textbox(label=translations["modelname"], placeholder="Model.pth", value="", max_lines=1, interactive=True)
-            with gr.Row():
-                fushion_button = gr.Button(translations["fushion"], variant="primary", scale=4)
-            with gr.Column():
-                with gr.Row():
-                    model_a = gr.File(label=f"{translations['model_name']} 1", file_types=[".pth", ".onnx"]) 
-                    model_b = gr.File(label=f"{translations['model_name']} 2", file_types=[".pth", ".onnx"])
-                with gr.Row():
-                    model_path_a = gr.Textbox(label=f"{translations['model_path']} 1", value="", placeholder="assets/weights/Model_1.pth")
-                    model_path_b = gr.Textbox(label=f"{translations['model_path']} 2", value="", placeholder="assets/weights/Model_2.pth")
-            with gr.Row():
-                ratio = gr.Slider(minimum=0, maximum=1, label=translations["model_ratio"], info=translations["model_ratio_info"], value=0.5, interactive=True)
-            with gr.Row():
-                output_model = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False)
-            with gr.Row():
-                model_a.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_a], outputs=[model_path_a])
-                model_b.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_b], outputs=[model_path_b])
-            with gr.Row():
-                fushion_button.click(
-                    fn=fushion_model,
-                    inputs=[
-                        name_to_save, 
-                        model_path_a, 
-                        model_path_b, 
-                        ratio
-                    ],
-                    outputs=[name_to_save, output_model],
-                    api_name="fushion_model"
-                )
-                fushion_button.click(fn=lambda: visible(True), inputs=[], outputs=[output_model])  
-
-        with gr.TabItem(translations["read_model"], visible=configs.get("read_tab", True)):
-            gr.Markdown(translations["read_model_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["read_model_markdown_2"])
-            with gr.Row():
-                model = gr.File(label=translations["drop_model"], file_types=[".pth", ".onnx"]) 
-            with gr.Row():
-                read_button = gr.Button(translations["readmodel"], variant="primary", scale=2)
-            with gr.Column():
-                model_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True)
-                output_info = gr.Textbox(label=translations["modelinfo"], value="", interactive=False, scale=6)
-            with gr.Row():
-                model.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model], outputs=[model_path])
-                read_button.click(
-                    fn=model_info,
-                    inputs=[model_path],
-                    outputs=[output_info],
-                    api_name="read_model"
-                )
-
-        with gr.TabItem(translations["convert_model"], visible=configs.get("onnx_tab", True)):
-            gr.Markdown(translations["pytorch2onnx"])
-            with gr.Row():
-                gr.Markdown(translations["pytorch2onnx_markdown"])
-            with gr.Row():
-                model_pth_upload = gr.File(label=translations["drop_model"], file_types=[".pth"]) 
-            with gr.Row():
-                convert_onnx = gr.Button(translations["convert_model"], variant="primary", scale=2)
-            with gr.Row():
-                model_pth_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True)
-            with gr.Row():
-                output_model2 = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False)
-            with gr.Row():
-                model_pth_upload.upload(fn=lambda model_pth_upload: shutil.move(model_pth_upload.name, os.path.join("assets", "weights")), inputs=[model_pth_upload], outputs=[model_pth_path])
-                convert_onnx.click(
-                    fn=onnx_export,
-                    inputs=[model_pth_path],
-                    outputs=[output_model2, output_info],
-                    api_name="model_onnx_export"
-                )
-                convert_onnx.click(fn=lambda: visible(True), inputs=[], outputs=[output_model2])  
-
-        with gr.TabItem(translations["downloads"], visible=configs.get("downloads_tab", True)):
-            gr.Markdown(translations["download_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["download_markdown_2"])
-            with gr.Row():
-                with gr.Accordion(translations["model_download"], open=True):
-                    with gr.Row():
-                        downloadmodel = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["download_from_csv"], translations["search_models"], translations["upload"]], interactive=True, value=translations["download_url"])
-                    with gr.Row():
-                        gr.Markdown("___")
-                    with gr.Column():
-                        with gr.Row():
-                            url_input = gr.Textbox(label=translations["model_url"], value="", placeholder="https://...", scale=6)
-                            download_model_name = gr.Textbox(label=translations["modelname"], value="", placeholder=translations["modelname"], scale=2)
-                        url_download = gr.Button(value=translations["downloads"], scale=2)
-                    with gr.Column():
-                        model_browser = gr.Dropdown(choices=models.keys(), label=translations["model_warehouse"], scale=8, allow_custom_value=True, visible=False)
-                        download_from_browser = gr.Button(value=translations["get_model"], scale=2, variant="primary", visible=False)
-                    with gr.Column():
-                        search_name = gr.Textbox(label=translations["name_to_search"], placeholder=translations["modelname"], interactive=True, scale=8, visible=False)
-                        search = gr.Button(translations["search_2"], scale=2, visible=False)
-                        search_dropdown = gr.Dropdown(label=translations["select_download_model"], value="", choices=[], allow_custom_value=True, interactive=False, visible=False)
-                        download = gr.Button(translations["downloads"], variant="primary", visible=False)
-                    with gr.Column():
-                        model_upload = gr.File(label=translations["drop_model"], file_types=[".pth", ".onnx", ".index", ".zip"], visible=False)
-            with gr.Row():
-                with gr.Accordion(translations["download_pretrained_2"], open=False):
-                    with gr.Row():
-                        pretrain_download_choices = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["list_model"], translations["upload"]], value=translations["download_url"], interactive=True)  
-                    with gr.Row():
-                        gr.Markdown("___")
-                    with gr.Column():
-                        with gr.Row():
-                            pretrainD = gr.Textbox(label=translations["pretrained_url"].format(dg="D"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4)
-                            pretrainG = gr.Textbox(label=translations["pretrained_url"].format(dg="G"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4)
-                        download_pretrain_button = gr.Button(translations["downloads"], scale=2)
-                    with gr.Column():
-                        with gr.Row():
-                            pretrain_choices = gr.Dropdown(label=translations["select_pretrain"], info=translations["select_pretrain_info"], choices=list(fetch_pretrained_data().keys()), value="Titan_Medium", allow_custom_value=True, interactive=True, scale=6, visible=False)
-                            sample_rate_pretrain = gr.Dropdown(label=translations["pretrain_sr"], info=translations["pretrain_sr"], choices=["48k", "40k", "32k"], value="48k", interactive=True, visible=False)
-                        download_pretrain_choices_button = gr.Button(translations["downloads"], scale=2, variant="primary", visible=False)
-                    with gr.Row():
-                        pretrain_upload_g = gr.File(label=translations["drop_pretrain"].format(dg="G"), file_types=[".pth"], visible=False)
-                        pretrain_upload_d = gr.File(label=translations["drop_pretrain"].format(dg="D"), file_types=[".pth"], visible=False)
-            with gr.Row():
-                url_download.click(
-                    fn=download_model, 
-                    inputs=[
-                        url_input, 
-                        download_model_name
-                    ], 
-                    outputs=[url_input],
-                    api_name="download_model"
-                )
-                download_from_browser.click(
-                    fn=lambda model: download_model(models[model], model), 
-                    inputs=[model_browser], 
-                    outputs=[model_browser],
-                    api_name="download_browser"
-                )
-            with gr.Row():
-                downloadmodel.change(fn=change_download_choices, inputs=[downloadmodel], outputs=[url_input, download_model_name, url_download, model_browser, download_from_browser, search_name, search, search_dropdown, download, model_upload])
-                search.click(fn=search_models, inputs=[search_name], outputs=[search_dropdown, download])
-                model_upload.upload(fn=save_drop_model, inputs=[model_upload], outputs=[model_upload])
-                download.click(
-                    fn=lambda model: download_model(model_options[model], model), 
-                    inputs=[search_dropdown], 
-                    outputs=[search_dropdown],
-                    api_name="search_models"
-                )
-            with gr.Row():
-                pretrain_download_choices.change(fn=change_download_pretrained_choices, inputs=[pretrain_download_choices], outputs=[pretrainD, pretrainG, download_pretrain_button, pretrain_choices, sample_rate_pretrain, download_pretrain_choices_button, pretrain_upload_d, pretrain_upload_g])
-                pretrain_choices.change(fn=update_sample_rate_dropdown, inputs=[pretrain_choices], outputs=[sample_rate_pretrain])
-            with gr.Row():
-                download_pretrain_button.click(
-                    fn=download_pretrained_model,
-                    inputs=[
-                        pretrain_download_choices, 
-                        pretrainD, 
-                        pretrainG
-                    ],
-                    outputs=[pretrainD],
-                    api_name="download_pretrain_link"
-                )
-                download_pretrain_choices_button.click(
-                    fn=download_pretrained_model,
-                    inputs=[
-                        pretrain_download_choices, 
-                        pretrain_choices, 
-                        sample_rate_pretrain
-                    ],
-                    outputs=[pretrain_choices],
-                    api_name="download_pretrain_choices"
-                )
-                pretrain_upload_g.upload(
-                    fn=lambda pretrain_upload_g: shutil.move(pretrain_upload_g.name, os.path.join("assets", "models", "pretrained_custom")), 
-                    inputs=[pretrain_upload_g], 
-                    outputs=[],
-                    api_name="upload_pretrain_g"
-                )
-                pretrain_upload_d.upload(
-                    fn=lambda pretrain_upload_d: shutil.move(pretrain_upload_d.name, os.path.join("assets", "models", "pretrained_custom")), 
-                    inputs=[pretrain_upload_d], 
-                    outputs=[],
-                    api_name="upload_pretrain_d"
-                )
-
-        with gr.TabItem(translations["f0_extractor_tab"], visible=configs.get("f0_extractor_tab", True)):
-            gr.Markdown(translations["f0_extractor_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["f0_extractor_markdown_2"])
-            with gr.Row():
-                extractor_button = gr.Button(translations["extract_button"].replace("2. ", ""), variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    upload_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
-                    audioplay = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                with gr.Column():
-                    with gr.Accordion(translations["f0_method"], open=False):
-                        with gr.Group():
-                            onnx_f0_mode3 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                            f0_method_extract = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
-                    with gr.Accordion(translations["audio_path"], open=True):
-                        input_audio_path = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True)
-                        refesh_audio_button = gr.Button(translations["refesh"])
-            with gr.Row():
-                gr.Markdown("___")
-            with gr.Row():
-                file_output = gr.File(label="", file_types=[".txt"], interactive=False)
-                image_output = gr.Image(label="", interactive=False, show_download_button=True)
-            with gr.Row():
-                upload_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio_file], outputs=[input_audio_path])
-                input_audio_path.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio_path], outputs=[audioplay])
-                refesh_audio_button.click(fn=change_audios_choices, inputs=[input_audio_path], outputs=[input_audio_path])
-            with gr.Row():
-                extractor_button.click(
-                    fn=f0_extract,
-                    inputs=[
-                        input_audio_path,
-                        f0_method_extract,
-                        onnx_f0_mode3
-                    ],
-                    outputs=[file_output, image_output],
-                    api_name="f0_extract"
-                )
-
-        with gr.TabItem(translations["settings"], visible=configs.get("settings_tab", True)):
-            gr.Markdown(translations["settings_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["settings_markdown_2"])
-            with gr.Row():
-                toggle_button = gr.Button(translations["change_light_dark"], variant="secondary", scale=2)
-            with gr.Row():
-                with gr.Column():
-                    language_dropdown = gr.Dropdown(label=translations["lang"], interactive=True, info=translations["lang_restart"], choices=configs.get("support_language", "vi-VN"), value=language)
-                    change_lang = gr.Button(translations["change_lang"], variant="primary", scale=2)
-                with gr.Column():
-                    theme_dropdown = gr.Dropdown(label=translations["theme"], interactive=True, info=translations["theme_restart"], choices=configs.get("themes", theme), value=theme, allow_custom_value=True)
-                    changetheme = gr.Button(translations["theme_button"], variant="primary", scale=2)
-            with gr.Row():
-                with gr.Column():
-                    fp_choice = gr.Radio(choices=["fp16","fp32"], value="fp16" if configs.get("fp16", False) else "fp32", label=translations["precision"], info=translations["precision_info"], interactive=True)
-                    fp_button = gr.Button(translations["update_precision"], variant="secondary", scale=2)
-                with gr.Column():
-                    font_choice = gr.Textbox(label=translations["font"], info=translations["font_info"], value=font, interactive=True)
-                    font_button = gr.Button(translations["change_font"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Accordion(translations["stop"], open=False):
-                        separate_stop = gr.Button(translations["stop_separate"])
-                        convert_stop = gr.Button(translations["stop_convert"])
-                        create_dataset_stop = gr.Button(translations["stop_create_dataset"])
-                        audioldm2_stop = gr.Button(translations["stop_audioldm2"])
-                        with gr.Accordion(translations["stop_training"], open=False):
-                            model_name_stop = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True)
-                            preprocess_stop = gr.Button(translations["stop_preprocess"])
-                            extract_stop = gr.Button(translations["stop_extract"])
-                            train_stop = gr.Button(translations["stop_training"])
-            with gr.Row():
-                toggle_button.click(fn=None, js="() => {document.body.classList.toggle('dark')}")
-                fp_button.click(fn=change_fp, inputs=[fp_choice], outputs=[fp_choice])
-            with gr.Row():
-                change_lang.click(fn=change_language, inputs=[language_dropdown], outputs=[])
-                changetheme.click(fn=change_theme, inputs=[theme_dropdown], outputs=[])
-                font_button.click(fn=change_font, inputs=[font_choice], outputs=[])
-            with gr.Row():
-                change_lang.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[])
-                changetheme.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[])
-                font_button.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[])
-            with gr.Row():
-                separate_stop.click(fn=lambda: stop_pid("separate_pid", None, False), inputs=[], outputs=[])
-                convert_stop.click(fn=lambda: stop_pid("convert_pid", None, False), inputs=[], outputs=[])
-                create_dataset_stop.click(fn=lambda: stop_pid("create_dataset_pid", None, False), inputs=[], outputs=[])
-            with gr.Row():
-                preprocess_stop.click(fn=lambda model_name_stop: stop_pid("preprocess_pid", model_name_stop, False), inputs=[model_name_stop], outputs=[])
-                extract_stop.click(fn=lambda model_name_stop: stop_pid("extract_pid", model_name_stop, False), inputs=[model_name_stop], outputs=[])
-                train_stop.click(fn=lambda model_name_stop: stop_pid("train_pid", model_name_stop, True), inputs=[model_name_stop], outputs=[])
-            with gr.Row():
-                audioldm2_stop.click(fn=lambda: stop_pid("audioldm2_pid", None, False), inputs=[], outputs=[])
-
-        with gr.TabItem(translations["report_bugs"], visible=configs.get("report_bug_tab", True)):
-            gr.Markdown(translations["report_bugs"])
-            with gr.Row():
-                gr.Markdown(translations["report_bug_info"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        agree_log = gr.Checkbox(label=translations["agree_log"], value=True, interactive=True) 
-                        report_text = gr.Textbox(label=translations["error_info"], info=translations["error_info_2"], interactive=True)
-                    report_button = gr.Button(translations["report_bugs"], variant="primary", scale=2)
-            with gr.Row():
-                gr.Markdown(translations["report_info"].format(github=codecs.decode("uggcf://tvguho.pbz/CunzUhlauNau16/Ivrganzrfr-EIP/vffhrf", "rot13")))
-            with gr.Row():
-                report_button.click(fn=report_bug, inputs=[report_text, agree_log], outputs=[])
-
-    with gr.Row(): 
-        gr.Markdown(translations["rick_roll"].format(rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13')))
-    with gr.Row(): 
-        gr.Markdown(translations["terms_of_use"])
-    with gr.Row():
-        gr.Markdown(translations["exemption"])
-
-    logger.info(translations["start_app"])
-    logger.info(translations["set_lang"].format(lang=language))
-
-    port = configs.get("app_port", 7860)
-
-    for i in range(configs.get("num_of_restart", 5)):
-        try:
-            app.queue().launch(
-                favicon_path=os.path.join("assets", "ico.png"), 
-                server_name=configs.get("server_name", "0.0.0.0"), 
-                server_port=port, 
-                show_error=configs.get("app_show_error", False), 
-                inbrowser="--open" in sys.argv, 
-                share="--share" in sys.argv, 
-                allowed_paths=allow_disk
-            )
-            break
-        except OSError:
-            logger.debug(translations["port"].format(port=port))
-            port -= 1
-        except Exception as e:
-            logger.error(translations["error_occurred"].format(e=e))
-            sys.exit(1)
\ No newline at end of file