Upload easy_infer.py

easy_infer.py

@@ -0,0 +1,1468 @@
+import subprocess
+import os
+import sys
+import errno
+import shutil
+import yt_dlp
+from mega import Mega
+import datetime
+import unicodedata
+import torch
+import glob
+import gradio as gr
+import gdown
+import zipfile
+import traceback
+import json
+import requests
+import wget
+import ffmpeg
+import hashlib
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from unidecode import unidecode
+import re
+import time
+from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
+from vc_infer_pipeline import VC
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from MDXNet import MDXNetDereverb
+from config import Config
+from infer_uvr5 import _audio_pre_, _audio_pre_new
+from huggingface_hub import HfApi, list_models
+from huggingface_hub import login
+from i18n import I18nAuto
+i18n = I18nAuto()
+from bs4 import BeautifulSoup
+from sklearn.cluster import MiniBatchKMeans
+
+config = Config()
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, ignore_errors=True)
+os.environ["TEMP"] = tmp
+weight_root = "weights"
+weight_uvr5_root = "uvr5_weights"
+index_root = "./logs/"
+audio_root = "audios"
+names = []
+for name in os.listdir(weight_root):
+    if name.endswith(".pth"):
+        names.append(name)
+index_paths = []
+
+global indexes_list
+indexes_list = []
+
+audio_paths = []
+for root, dirs, files in os.walk(index_root, topdown=False):
+    for name in files:
+        if name.endswith(".index") and "trained" not in name:
+            index_paths.append("%s\\%s" % (root, name))
+
+for root, dirs, files in os.walk(audio_root, topdown=False):
+    for name in files:
+        audio_paths.append("%s/%s" % (root, name))
+
+uvr5_names = []
+for name in os.listdir(weight_uvr5_root):
+    if name.endswith(".pth") or "onnx" in name:
+        uvr5_names.append(name.replace(".pth", ""))
+
+def calculate_md5(file_path):
+    hash_md5 = hashlib.md5()
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+def silentremove(filename):
+    try:
+        os.remove(filename)
+    except OSError as e: # this would be "except OSError, e:" before Python 2.6
+        if e.errno != errno.ENOENT: # errno.ENOENT = no such file or directory
+            raise # re-raise exception if a different error occurred
+def get_md5(temp_folder):
+  for root, subfolders, files in os.walk(temp_folder):
+    for file in files:
+      if not file.startswith("G_") and not file.startswith("D_") and file.endswith(".pth") and not "_G_" in file and not "_D_" in file:
+        md5_hash = calculate_md5(os.path.join(root, file))
+        return md5_hash
+
+  return None
+
+def find_parent(search_dir, file_name):
+    for dirpath, dirnames, filenames in os.walk(search_dir):
+        if file_name in filenames:
+            return os.path.abspath(dirpath)
+    return None
+
+def find_folder_parent(search_dir, folder_name):
+    for dirpath, dirnames, filenames in os.walk(search_dir):
+        if folder_name in dirnames:
+            return os.path.abspath(dirpath)
+    return None
+
+def get_drive_folder_id(url):
+    if "drive.google.com" in url:
+        if "file/d/" in url:
+            file_id = url.split("file/d/")[1].split("/")[0]
+        elif "id=" in url:
+            file_id = url.split("id=")[1].split("&")[0]
+        else:
+            return None
+
+def download_from_url(url):
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    zips_path = os.path.join(parent_path, 'zips')
+    
+    if url != '':
+        print(i18n("下载文件：") + f"{url}")
+        if "drive.google.com" in url:
+            if "file/d/" in url:
+                file_id = url.split("file/d/")[1].split("/")[0]
+            elif "id=" in url:
+                file_id = url.split("id=")[1].split("&")[0]
+            else:
+                return None
+            
+            if file_id:
+                os.chdir('./zips')
+                result = subprocess.run(["gdown", f"https://drive.google.com/uc?id={file_id}", "--fuzzy"], capture_output=True, text=True, encoding='utf-8')
+                if "Too many users have viewed or downloaded this file recently" in str(result.stderr):
+                    return "demasiado uso"
+                if "Cannot retrieve the public link of the file." in str(result.stderr):
+                    return "link privado"
+                print(result.stderr)
+                
+        elif "/blob/" in url:
+            os.chdir('./zips')
+            url = url.replace("blob", "resolve")
+          # print("Resolved URL:", url)  # Print the resolved URL
+            wget.download(url)
+        elif "mega.nz" in url:
+            if "#!" in url:
+                file_id = url.split("#!")[1].split("!")[0]
+            elif "file/" in url:
+                file_id = url.split("file/")[1].split("/")[0]
+            else:
+                return None
+            if file_id:
+                m = Mega()
+                m.download_url(url, zips_path)
+        elif "/tree/main" in url:
+           response = requests.get(url)
+           soup = BeautifulSoup(response.content, 'html.parser')
+           temp_url = ''
+           for link in soup.find_all('a', href=True):
+               if link['href'].endswith('.zip'):
+                  temp_url = link['href']
+                  break
+           if temp_url:
+              url = temp_url
+          # print("Updated URL:", url)  # Print the updated URL
+              url = url.replace("blob", "resolve")
+          # print("Resolved URL:", url)  # Print the resolved URL
+
+              if "huggingface.co" not in url:
+                 url = "https://huggingface.co" + url
+
+                 wget.download(url)
+           else:
+                 print("No .zip file found on the page.")
+            # Handle the case when no .zip file is found
+        else:
+            os.chdir('./zips')
+            wget.download(url)
+            
+        os.chdir(parent_path)
+        print(i18n("完整下载"))
+        return "downloaded"
+    else:
+        return None
+                
+class error_message(Exception):
+    def __init__(self, mensaje):
+        self.mensaje = mensaje
+        super().__init__(mensaje)
+
+# 一个选项卡全局只能有一个音色
+def get_vc(sid, to_return_protect0, to_return_protect1):
+    global n_spk, tgt_sr, net_g, vc, cpt, version
+    if sid == "" or sid == []:
+        global hubert_model
+        if hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+            print("clean_empty_cache")
+            del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
+            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            ###楼下不这么折腾清理不干净
+            if_f0 = cpt.get("f0", 1)
+            version = cpt.get("version", "v1")
+            if version == "v1":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs256NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            elif version == "v2":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+            del net_g, cpt
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            cpt = None
+        return (
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+        )
+    person = "%s/%s" % (weight_root, sid)
+    print("loading %s" % person)
+    cpt = torch.load(person, map_location="cpu")
+    tgt_sr = cpt["config"][-1]
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    if_f0 = cpt.get("f0", 1)
+    if if_f0 == 0:
+        to_return_protect0 = to_return_protect1 = {
+            "visible": False,
+            "value": 0.5,
+            "__type__": "update",
+        }
+    else:
+        to_return_protect0 = {
+            "visible": True,
+            "value": to_return_protect0,
+            "__type__": "update",
+        }
+        to_return_protect1 = {
+            "visible": True,
+            "value": to_return_protect1,
+            "__type__": "update",
+        }
+    version = cpt.get("version", "v1")
+    if version == "v1":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif version == "v2":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+    del net_g.enc_q
+    print(net_g.load_state_dict(cpt["weight"], strict=False))
+    net_g.eval().to(config.device)
+    if config.is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, config)
+    n_spk = cpt["config"][-3]
+    return (
+        {"visible": True, "maximum": n_spk, "__type__": "update"},
+        to_return_protect0,
+        to_return_protect1,
+    )
+        
+def load_downloaded_model(url):
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    try:
+        infos = []
+        logs_folders = ['0_gt_wavs','1_16k_wavs','2a_f0','2b-f0nsf','3_feature256','3_feature768']
+        zips_path = os.path.join(parent_path, 'zips')
+        unzips_path = os.path.join(parent_path, 'unzips')
+        weights_path = os.path.join(parent_path, 'weights')
+        logs_dir = ""
+        
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+        if os.path.exists(unzips_path):
+            shutil.rmtree(unzips_path)
+
+        os.mkdir(zips_path)
+        os.mkdir(unzips_path)
+        
+        download_file = download_from_url(url)
+        if not download_file:
+            print(i18n("无法下载模型。"))
+            infos.append(i18n("无法下载模型。"))
+            yield "\n".join(infos)
+        elif download_file == "downloaded":
+            print(i18n("模型下载成功。"))
+            infos.append(i18n("模型下载成功。"))
+            yield "\n".join(infos)
+        elif download_file == "demasiado uso":
+            raise Exception(i18n("最近查看或下载此文件的用户过多"))
+        elif download_file == "link privado":
+            raise Exception(i18n("无法从该私人链接获取文件"))
+        
+        # Descomprimir archivos descargados
+        for filename in os.listdir(zips_path):
+            if filename.endswith(".zip"):
+                zipfile_path = os.path.join(zips_path,filename)
+                print(i18n("继续提取..."))
+                infos.append(i18n("继续提取..."))
+                shutil.unpack_archive(zipfile_path, unzips_path, 'zip')
+                model_name = os.path.basename(zipfile_path)
+                logs_dir = os.path.join(parent_path,'logs', os.path.normpath(str(model_name).replace(".zip","")))
+                yield "\n".join(infos)
+            else:
+                print(i18n("解压缩出错。"))
+                infos.append(i18n("解压缩出错。"))
+                yield "\n".join(infos)
+        
+        index_file = False
+        model_file = False
+        D_file = False
+        G_file = False
+        
+        # Copiar archivo pth
+        for path, subdirs, files in os.walk(unzips_path):
+            for item in files:
+                item_path = os.path.join(path, item)
+                if not 'G_' in item and not 'D_' in item and item.endswith('.pth'):
+                    model_file = True
+                    model_name = item.replace(".pth","")
+                    logs_dir = os.path.join(parent_path,'logs', model_name)
+                    if os.path.exists(logs_dir):
+                        shutil.rmtree(logs_dir)
+                    os.mkdir(logs_dir)
+                    if not os.path.exists(weights_path):
+                        os.mkdir(weights_path)
+                    if os.path.exists(os.path.join(weights_path, item)):
+                        os.remove(os.path.join(weights_path, item))
+                    if os.path.exists(item_path):
+                        shutil.move(item_path, weights_path)
+        
+        if not model_file and not os.path.exists(logs_dir):
+            os.mkdir(logs_dir)
+        # Copiar index
+        for path, subdirs, files in os.walk(unzips_path):
+            for item in files:
+                item_path = os.path.join(path, item)
+                if item.startswith('added_') and item.endswith('.index'):
+                    index_file = True
+                    if os.path.exists(item_path):
+                        if os.path.exists(os.path.join(logs_dir, item)):
+                            os.remove(os.path.join(logs_dir, item))
+                        shutil.move(item_path, logs_dir)
+                if item.startswith('total_fea.npy') or item.startswith('events.'):
+                    if os.path.exists(item_path):
+                        if os.path.exists(os.path.join(logs_dir, item)):
+                            os.remove(os.path.join(logs_dir, item))
+                        shutil.move(item_path, logs_dir)
+        
+                
+        result = ""
+        if model_file:
+            if index_file:
+                print(i18n("该模型可用于推理，并有 .index 文件。"))
+                infos.append("\n" + i18n("该模型可用于推理，并有 .index 文件。"))
+                yield "\n".join(infos)
+            else:
+                print(i18n("该模型可用于推理，但没有 .index 文件。"))
+                infos.append("\n" + i18n("该模型可用于推理，但没有 .index 文件。"))
+                yield "\n".join(infos)
+        
+        if not index_file and not model_file:
+            print(i18n("未找到可上传的相关文件"))
+            infos.append(i18n("未找到可上传的相关文件"))
+            yield "\n".join(infos)
+        
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+        if os.path.exists(unzips_path):
+            shutil.rmtree(unzips_path)
+        os.chdir(parent_path)    
+        return result
+    except Exception as e:
+        os.chdir(parent_path)
+        if "demasiado uso" in str(e):
+            print(i18n("最近查看或下载此文件的用户过多"))
+            yield i18n("最近查看或下载此文件的用户过多")
+        elif "link privado" in str(e):
+            print(i18n("无法从该私人链接获取文件"))
+            yield i18n("无法从该私人链接获取文件")
+        else:
+            print(e)
+            yield i18n("下载模型时发生错误。")
+    finally:
+        os.chdir(parent_path)
+      
+def load_dowloaded_dataset(url):
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    infos = []
+    try:
+        zips_path = os.path.join(parent_path, 'zips')
+        unzips_path = os.path.join(parent_path, 'unzips')
+        datasets_path = os.path.join(parent_path, 'datasets')
+        audio_extenions =["flac","wav"]
+        
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+        if os.path.exists(unzips_path):
+            shutil.rmtree(unzips_path)
+            
+        if not os.path.exists(datasets_path):
+            os.mkdir(datasets_path)
+            
+        os.mkdir(zips_path)
+        os.mkdir(unzips_path)
+        
+        download_file = download_from_url(url)
+        
+        if not download_file:
+            print(i18n("下载模型时发生错误。"))
+            infos.append(i18n("下载模型时发生错误。"))
+            yield "\n".join(infos)
+            raise Exception(i18n("下载模型时发生错误。"))
+        elif download_file == "downloaded":
+            print(i18n("模型下载成功。"))
+            infos.append(i18n("模型下载成功。"))
+            yield "\n".join(infos)
+        elif download_file == "demasiado uso":
+            raise Exception(i18n("最近查看或下载此文件的用户过多"))
+        elif download_file == "link privado":
+            raise Exception(i18n("无法从该私人链接获取文件"))
+  
+        zip_path = os.listdir(zips_path)
+        foldername = ""
+        for file in zip_path:
+            if file.endswith('.zip'):
+                file_path = os.path.join(zips_path, file)
+                print("....")
+                foldername = file.replace(".zip","").replace(" ","").replace("-","_")
+                dataset_path = os.path.join(datasets_path, foldername)
+                print(i18n("继续提取..."))
+                infos.append(i18n("继续提取..."))
+                yield "\n".join(infos)
+                shutil.unpack_archive(file_path, unzips_path, 'zip')
+                if os.path.exists(dataset_path):
+                    shutil.rmtree(dataset_path)
+                    
+                os.mkdir(dataset_path)
+                
+                for root, subfolders, songs in os.walk(unzips_path):
+                    for song in songs:
+                        song_path = os.path.join(root, song)
+                        if song.endswith(tuple(audio_extenions)):
+                            shutil.move(song_path, dataset_path)
+            else:
+                print(i18n("解压缩出错。"))
+                infos.append(i18n("解压缩出错。"))
+                yield "\n".join(infos)
+                
+                
+
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+        if os.path.exists(unzips_path):
+            shutil.rmtree(unzips_path)
+            
+        print(i18n("数据集加载成功。"))
+        infos.append(i18n("数据集加载成功。"))
+        yield "\n".join(infos)
+    except Exception as e:
+        os.chdir(parent_path)
+        if "demasiado uso" in str(e):
+            print(i18n("最近查看或下载此文件的用户过多"))
+            yield i18n("最近查看或下载此文件的用户过多")   
+        elif "link privado" in str(e):
+            print(i18n("无法从该私人链接获取文件"))
+            yield i18n("无法从该私人链接获取文件")
+        else:
+            print(e)
+            yield i18n("下载模型时发生错误。")
+    finally:
+        os.chdir(parent_path)
+
+def save_model(modelname, save_action):
+       
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    zips_path = os.path.join(parent_path, 'zips')
+    dst = os.path.join(zips_path,modelname)
+    logs_path = os.path.join(parent_path, 'logs', modelname)
+    weights_path = os.path.join(parent_path, 'weights', f"{modelname}.pth")
+    save_folder = parent_path
+    infos = []    
+    
+    try:
+        if not os.path.exists(logs_path):
+            raise Exception("No model found.")
+        
+        if not 'content' in parent_path:
+            save_folder = os.path.join(parent_path, 'RVC_Backup')
+        else:
+            save_folder = '/content/drive/MyDrive/RVC_Backup'
+        
+        infos.append(i18n("保存模型..."))
+        yield "\n".join(infos)
+        
+        # Si no existe el folder RVC para guardar los modelos
+        if not os.path.exists(save_folder):
+            os.mkdir(save_folder)
+        if not os.path.exists(os.path.join(save_folder, 'ManualTrainingBackup')):
+            os.mkdir(os.path.join(save_folder, 'ManualTrainingBackup'))
+        if not os.path.exists(os.path.join(save_folder, 'Finished')):
+            os.mkdir(os.path.join(save_folder, 'Finished'))
+
+        # Si ya existe el folders zips borro su contenido por si acaso
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+            
+        os.mkdir(zips_path)
+        added_file = glob.glob(os.path.join(logs_path, "added_*.index"))
+        d_file = glob.glob(os.path.join(logs_path, "D_*.pth"))
+        g_file = glob.glob(os.path.join(logs_path, "G_*.pth"))
+        
+        if save_action == i18n("选择模型保存方法"):
+            raise Exception("No method choosen.")
+        
+        if save_action == i18n("保存所有"):
+            print(i18n("保存所有"))
+            save_folder = os.path.join(save_folder, 'ManualTrainingBackup')
+            shutil.copytree(logs_path, dst)
+        else:
+            # Si no existe el folder donde se va a comprimir el modelo
+            if not os.path.exists(dst):
+                os.mkdir(dst)
+            
+        if save_action == i18n("保存 D 和 G"):
+            print(i18n("保存 D 和 G"))
+            save_folder = os.path.join(save_folder, 'ManualTrainingBackup')
+            if len(d_file) > 0:
+                shutil.copy(d_file[0], dst)
+            if len(g_file) > 0:
+                shutil.copy(g_file[0], dst)    
+                
+            if len(added_file) > 0:
+                shutil.copy(added_file[0], dst)
+            else:
+                infos.append(i18n("保存时未编制索引..."))
+                
+        if save_action == i18n("保存声音"):
+            print(i18n("保存声音"))
+            save_folder = os.path.join(save_folder, 'Finished')
+            if len(added_file) > 0:
+                shutil.copy(added_file[0], dst)
+            else:
+                infos.append(i18n("保存时未编制索引..."))
+                #raise gr.Error("¡No ha generado el archivo added_*.index!")
+        
+        yield "\n".join(infos)
+        # Si no existe el archivo del modelo no copiarlo
+        if not os.path.exists(weights_path):
+            infos.append(i18n("无模型保存（PTH）"))
+            #raise gr.Error("¡No ha generado el modelo pequeño!")
+        else:
+            shutil.copy(weights_path, dst)
+        
+        yield "\n".join(infos)
+        infos.append("\n" + i18n("这可能需要几分钟时间，请稍候..."))
+        yield "\n".join(infos)
+        
+        shutil.make_archive(os.path.join(zips_path,f"{modelname}"), 'zip', zips_path)
+        shutil.move(os.path.join(zips_path,f"{modelname}.zip"), os.path.join(save_folder, f'{modelname}.zip'))
+        
+        shutil.rmtree(zips_path)
+        #shutil.rmtree(zips_path)
+        
+        infos.append("\n" + i18n("正确存储模型"))
+        yield "\n".join(infos)
+        
+    except Exception as e:
+        print(e)
+        if "No model found." in str(e):
+            infos.append(i18n("您要保存的模型不存在，请确保输入的名称正确。"))
+        else:
+            infos.append(i18n("保存模型时发生错误"))
+            
+        yield "\n".join(infos)
+    
+def load_downloaded_backup(url):
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    try:
+        infos = []
+        logs_folders = ['0_gt_wavs','1_16k_wavs','2a_f0','2b-f0nsf','3_feature256','3_feature768']
+        zips_path = os.path.join(parent_path, 'zips')
+        unzips_path = os.path.join(parent_path, 'unzips')
+        weights_path = os.path.join(parent_path, 'weights')
+        logs_dir = os.path.join(parent_path, 'logs')
+        
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+        if os.path.exists(unzips_path):
+            shutil.rmtree(unzips_path)
+
+        os.mkdir(zips_path)
+        os.mkdir(unzips_path)
+        
+        download_file = download_from_url(url)
+        if not download_file:
+            print(i18n("无法下载模型。"))
+            infos.append(i18n("无法下载模型。"))
+            yield "\n".join(infos)
+        elif download_file == "downloaded":
+            print(i18n("模型下载成功。"))
+            infos.append(i18n("模型下载成功。"))
+            yield "\n".join(infos)
+        elif download_file == "demasiado uso":
+            raise Exception(i18n("最近查看或下载此文件的用户过多"))
+        elif download_file == "link privado":
+            raise Exception(i18n("无法从该私人链接获取文件"))
+        
+        # Descomprimir archivos descargados
+        for filename in os.listdir(zips_path):
+            if filename.endswith(".zip"):
+                zipfile_path = os.path.join(zips_path,filename)
+                zip_dir_name = os.path.splitext(filename)[0]
+                unzip_dir = unzips_path
+                print(i18n("继续提取..."))
+                infos.append(i18n("继续提取..."))
+                shutil.unpack_archive(zipfile_path, unzip_dir, 'zip')
+                
+                if os.path.exists(os.path.join(unzip_dir, zip_dir_name)):
+                    # Move the inner directory with the same name
+                    shutil.move(os.path.join(unzip_dir, zip_dir_name), logs_dir)
+                else:
+                    # Create a folder with the same name and move files
+                    new_folder_path = os.path.join(logs_dir, zip_dir_name)
+                    os.mkdir(new_folder_path)
+                    for item_name in os.listdir(unzip_dir):
+                        item_path = os.path.join(unzip_dir, item_name)
+                        if os.path.isfile(item_path):
+                            shutil.move(item_path, new_folder_path)
+                        elif os.path.isdir(item_path):
+                            shutil.move(item_path, new_folder_path)
+                    
+                yield "\n".join(infos)
+            else:
+                print(i18n("解压缩出错。"))
+                infos.append(i18n("解压缩出错。"))
+                yield "\n".join(infos)
+                
+        result = ""
+        
+        for filename in os.listdir(unzips_path):
+            if filename.endswith(".zip"):
+                silentremove(filename)
+        
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+        if os.path.exists(os.path.join(parent_path, 'unzips')):
+            shutil.rmtree(os.path.join(parent_path, 'unzips'))
+        print(i18n("备份已成功上传。"))
+        infos.append("\n" + i18n("备份已成功上传。"))
+        yield "\n".join(infos)
+        os.chdir(parent_path)    
+        return result
+    except Exception as e:
+        os.chdir(parent_path)
+        if "demasiado uso" in str(e):
+            print(i18n("最近查看或下载此文件的用户过多"))
+            yield i18n("最近查看或下载此文件的用户过多")
+        elif "link privado" in str(e):
+            print(i18n("无法从该私人链接获取文件"))
+            yield i18n("无法从该私人链接获取文件") 
+        else:
+            print(e)
+            yield i18n("下载模型时发生错误。")
+    finally:
+        os.chdir(parent_path)
+
+def save_to_wav(record_button):
+    if record_button is None:
+        pass
+    else:
+        path_to_file=record_button
+        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
+        new_path='./audios/'+new_name
+        shutil.move(path_to_file,new_path)
+        return new_name
+
+def save_to_wav2(dropbox):
+    file_path = dropbox.name
+    target_path = os.path.join('./audios', os.path.basename(file_path))
+
+    if os.path.exists(target_path):
+        os.remove(target_path)
+      # print('Replacing old dropdown file...')
+
+    shutil.move(file_path, target_path)
+    return target_path
+
+def change_choices2():
+    audio_paths=[]
+    for filename in os.listdir("./audios"):
+        if filename.endswith(('wav', 'mp3', 'flac', 'ogg', 'opus',
+                'm4a', 'mp4', 'aac', 'alac', 'wma',
+                'aiff', 'webm', 'ac3')):
+            audio_paths.append(os.path.join('./audios',filename).replace('\\', '/'))
+    return {"choices": sorted(audio_paths), "__type__": "update"}, {"__type__": "update"}
+
+def get_models_by_name(modelname):
+    url = "https://script.google.com/macros/s/AKfycbzyrdLZzUww9qbjxnbnI08budD4yxbmRPHkWbp3UEJ9h3Id5cnNNVg0UtfFAnqqX5Rr/exec"
+    
+    response = requests.post(url, json={
+        'type': 'search_by_filename',
+        'filename': unidecode(modelname.strip().lower())
+    })
+
+    response_json = response.json()
+    models = response_json['ocurrences']
+    
+    result = []
+    message = "Busqueda realizada"
+    if len(models) == 0:
+        message = "No se han encontrado resultados."
+    else:
+        message = f"Se han encontrado {len(models)} resultados para {modelname}"
+        
+    for i in range(20):
+        if i  < len(models):
+            urls = models[i].get('url')
+            url = eval(urls)[0]
+            name = str(models[i].get('name'))
+            filename = str(models[i].get('filename')) if not name or name.strip() == "" else name
+            # Nombre
+            result.append(
+                {
+                    "visible": True,
+                    "value": str("### ") + filename,
+                    "__type__": "update",
+                })
+            # Url
+            result.append(
+                {
+                    "visible": False,
+                    "value": url,
+                    "__type__": "update",
+                })
+            # Boton
+            result.append({
+                    "visible": True,
+                    "__type__": "update",
+                })
+            
+            # Linea separadora
+            if i == len(models) - 1:
+                result.append({
+                        "visible": False,
+                        "__type__": "update",
+                })
+            else:
+                result.append({
+                    "visible": True,
+                    "__type__": "update",
+                })
+                
+            # Row
+            result.append(
+                {
+                    "visible": True,
+                    "__type__": "update",
+                })
+        else:
+            # Nombre
+            result.append(
+                {
+                    "visible": False,
+                    "__type__": "update",
+                })
+            # Url
+            result.append(
+                {
+                    "visible": False,
+                    "value": False,
+                    "__type__": "update",
+                })
+            # Boton
+            result.append({
+                    "visible": False,
+                    "__type__": "update",
+                })
+            # Linea
+            result.append({
+                    "visible": False,
+                    "__type__": "update",
+                })
+            # Row
+            result.append(
+                {
+                    "visible": False,
+                    "__type__": "update",
+                })
+     # Result
+    result.append(
+        {
+            "value": message,
+            "__type__": "update",
+        }
+    )
+    
+    return result
+
+def search_model():
+    gr.Markdown(value="# Buscar un modelo")
+    with gr.Row():
+        model_name = gr.inputs.Textbox(lines=1, label="Término de búsqueda")
+        search_model_button=gr.Button("Buscar modelo")
+        
+    models = []
+    results = gr.Textbox(label="Resultado", value="", max_lines=20)
+    with gr.Row(visible=False) as row1:
+        l1 = gr.Markdown(value="", visible=False)
+        l1_url = gr.Textbox("Label 1", visible=False)
+        b1 = gr.Button("Cargar modelo", visible=False)
+    
+    mk1 = gr.Markdown(value="---", visible=False)
+    b1.click(fn=load_downloaded_model, inputs=l1_url, outputs=results)
+    
+    with gr.Row(visible=False) as row2:
+        l2 = gr.Markdown(value="", visible=False)
+        l2_url = gr.Textbox("Label 1", visible=False)
+        b2 = gr.Button("Cargar modelo", visible=False)
+    
+    mk2 = gr.Markdown(value="---", visible=False)
+    b2.click(fn=load_downloaded_model, inputs=l2_url, outputs=results)
+    
+    with gr.Row(visible=False) as row3:
+        l3 = gr.Markdown(value="", visible=False)
+        l3_url = gr.Textbox("Label 1", visible=False)
+        b3 = gr.Button("Cargar modelo", visible=False)
+    
+    mk3 = gr.Markdown(value="---", visible=False)
+    b3.click(fn=load_downloaded_model, inputs=l3_url, outputs=results)
+        
+    with gr.Row(visible=False) as row4:
+        l4 = gr.Markdown(value="", visible=False)
+        l4_url = gr.Textbox("Label 1", visible=False)
+        b4 = gr.Button("Cargar modelo", visible=False)
+    mk4 = gr.Markdown(value="---", visible=False)    
+    b4.click(fn=load_downloaded_model, inputs=l4_url, outputs=results)
+    
+    with gr.Row(visible=False) as row5:
+        l5 = gr.Markdown(value="", visible=False)
+        l5_url = gr.Textbox("Label 1", visible=False)
+        b5 = gr.Button("Cargar modelo", visible=False)
+    
+    mk5 = gr.Markdown(value="---", visible=False) 
+    b5.click(fn=load_downloaded_model, inputs=l5_url, outputs=results)
+        
+    with gr.Row(visible=False) as row6:
+        l6 = gr.Markdown(value="", visible=False)
+        l6_url = gr.Textbox("Label 1", visible=False)
+        b6 = gr.Button("Cargar modelo", visible=False)
+
+    mk6 = gr.Markdown(value="---", visible=False)        
+    b6.click(fn=load_downloaded_model, inputs=l6_url, outputs=results)
+        
+    with gr.Row(visible=False) as row7:
+        l7 = gr.Markdown(value="", visible=False)
+        l7_url = gr.Textbox("Label 1", visible=False)
+        b7 = gr.Button("Cargar modelo", visible=False)
+    
+    mk7 = gr.Markdown(value="---", visible=False)
+    b7.click(fn=load_downloaded_model, inputs=l7_url, outputs=results)
+        
+    with gr.Row(visible=False) as row8:
+        l8 = gr.Markdown(value="", visible=False)
+        l8_url = gr.Textbox("Label 1", visible=False)
+        b8 = gr.Button("Cargar modelo", visible=False)
+    
+    mk8 = gr.Markdown(value="---", visible=False)
+    b8.click(fn=load_downloaded_model, inputs=l8_url, outputs=results)
+        
+    with gr.Row(visible=False) as row9:
+        l9 = gr.Markdown(value="", visible=False)
+        l9_url = gr.Textbox("Label 1", visible=False)
+        b9 = gr.Button("Cargar modelo", visible=False)
+        
+    mk9 = gr.Markdown(value="---", visible=False)
+    b9.click(fn=load_downloaded_model, inputs=l9_url, outputs=results)
+        
+    with gr.Row(visible=False) as row10:
+        l10 = gr.Markdown(value="", visible=False)
+        l10_url = gr.Textbox("Label 1", visible=False)
+        b10 = gr.Button("Cargar modelo", visible=False)
+    
+    mk10 = gr.Markdown(value="---", visible=False) 
+    b10.click(fn=load_downloaded_model, inputs=l10_url, outputs=results)
+        
+    with gr.Row(visible=False) as row11:
+        l11 = gr.Markdown(value="", visible=False)
+        l11_url = gr.Textbox("Label 1", visible=False)
+        b11 = gr.Button("Cargar modelo", visible=False)
+        
+    mk11 = gr.Markdown(value="---", visible=False)
+    b11.click(fn=load_downloaded_model, inputs=l11_url, outputs=results)
+        
+    with gr.Row(visible=False) as row12:
+        l12 = gr.Markdown(value="", visible=False)
+        l12_url = gr.Textbox("Label 1", visible=False)
+        b12 = gr.Button("Cargar modelo", visible=False)
+
+    mk12 = gr.Markdown(value="---", visible=False)        
+    b12.click(fn=load_downloaded_model, inputs=l12_url, outputs=results)
+        
+    with gr.Row(visible=False) as row13:
+        l13 = gr.Markdown(value="", visible=False)
+        l13_url = gr.Textbox("Label 1", visible=False)
+        b13 = gr.Button("Cargar modelo", visible=False)
+    
+    mk13 = gr.Markdown(value="---", visible=False)
+    b13.click(fn=load_downloaded_model, inputs=l13_url, outputs=results)
+    
+    with gr.Row(visible=False) as row14:
+        l14 = gr.Markdown(value="", visible=False)
+        l14_url = gr.Textbox("Label 1", visible=False)
+        b14 = gr.Button("Cargar modelo", visible=False)
+    
+    mk14 = gr.Markdown(value="---", visible=False)
+    b14.click(fn=load_downloaded_model, inputs=l14_url, outputs=results)
+    
+    with gr.Row(visible=False) as row15:
+        l15 = gr.Markdown(value="", visible=False)
+        l15_url = gr.Textbox("Label 1", visible=False)
+        b15 = gr.Button("Cargar modelo", visible=False)
+
+    mk15 = gr.Markdown(value="---", visible=False)        
+    b15.click(fn=load_downloaded_model, inputs=l15_url, outputs=results)
+        
+    with gr.Row(visible=False) as row16:
+        l16 = gr.Markdown(value="", visible=False)
+        l16_url = gr.Textbox("Label 1", visible=False)
+        b16 = gr.Button("Cargar modelo", visible=False)
+        
+    mk16 = gr.Markdown(value="---", visible=False)
+    b16.click(fn=load_downloaded_model, inputs=l16_url, outputs=results)
+        
+    with gr.Row(visible=False) as row17:
+        l17 = gr.Markdown(value="", visible=False)
+        l17_url = gr.Textbox("Label 1", visible=False)
+        b17 = gr.Button("Cargar modelo", visible=False)
+    
+    mk17 = gr.Markdown(value="---", visible=False)    
+    b17.click(fn=load_downloaded_model, inputs=l17_url, outputs=results)
+        
+    with gr.Row(visible=False) as row18:
+        l18 = gr.Markdown(value="", visible=False)
+        l18_url = gr.Textbox("Label 1", visible=False)
+        b18 = gr.Button("Cargar modelo", visible=False)
+        
+    mk18 = gr.Markdown(value="---", visible=False)
+    b18.click(fn=load_downloaded_model, inputs=l18_url, outputs=results)
+        
+    with gr.Row(visible=False) as row19:
+        l19 = gr.Markdown(value="", visible=False)
+        l19_url = gr.Textbox("Label 1", visible=False)
+        b19 = gr.Button("Cargar modelo", visible=False)
+        
+    mk19 = gr.Markdown(value="---", visible=False)
+    b19.click(fn=load_downloaded_model, inputs=l19_url, outputs=results)
+        
+    with gr.Row(visible=False) as row20:
+        l20 = gr.Markdown(value="", visible=False)
+        l20_url = gr.Textbox("Label 1", visible=False)
+        b20 = gr.Button("Cargar modelo", visible=False)
+    
+    mk20 = gr.Markdown(value="---", visible=False)
+    b20.click(fn=load_downloaded_model, inputs=l20_url, outputs=results)
+    
+    #   to_return_protect1 = 
+
+    search_model_button.click(fn=get_models_by_name, inputs=model_name, outputs=[l1,l1_url, b1, mk1, row1,
+                                                                                 l2,l2_url, b2, mk2, row2,
+                                                                                 l3,l3_url, b3, mk3, row3,
+                                                                                 l4,l4_url, b4, mk4, row4,
+                                                                                 l5,l5_url, b5, mk5, row5,
+                                                                                 l6,l6_url, b6, mk6, row6,
+                                                                                 l7,l7_url, b7, mk7, row7,
+                                                                                 l8,l8_url, b8, mk8, row8,
+                                                                                 l9,l9_url, b9, mk9, row9,
+                                                                                 l10,l10_url, b10, mk10, row10,
+                                                                                 l11,l11_url, b11, mk11, row11,
+                                                                                 l12,l12_url, b12, mk12, row12,
+                                                                                 l13,l13_url, b13, mk13, row13,
+                                                                                 l14,l14_url, b14, mk14, row14,
+                                                                                 l15,l15_url, b15, mk15, row15,
+                                                                                 l16,l16_url, b16, mk16, row16,
+                                                                                 l17,l17_url, b17, mk17, row17,
+                                                                                 l18,l18_url, b18, mk18, row18,
+                                                                                 l19,l19_url, b19, mk19, row19,
+                                                                                 l20,l20_url, b20, mk20, row20,
+                                                                                 results
+                                                                                 ])
+    
+
+def descargar_desde_drive(url, name, output_file):
+
+    print(f"Descargando {name} de drive")
+    
+    try:
+        downloaded_file = gdown.download(url, output=output_file, fuzzy=True)
+        return downloaded_file
+    except:
+        print("El intento de descargar con drive no funcionó")
+        return None
+
+def descargar_desde_mega(url, name):
+  response = False
+  try:
+    file_id = None
+
+    if "#!" in url:
+      file_id = url.split("#!")[1].split("!")[0]
+    elif "file/" in url:
+      file_id = url.split("file/")[1].split("/")[0]
+    else:
+      file_id = None
+
+    if file_id:
+      mega = Mega()
+      m = mega.login()
+
+      print(f"Descargando {name} de mega")
+      downloaded_file = m.download_url(url)
+
+      return downloaded_file
+    else:
+      return None
+
+  except Exception as e:
+    print("Error**")
+    print(e)
+    return None
+
+def descargar_desde_url_basica(url, name, output_file):
+  try:
+    print(f"Descargando {name} de URL BASICA")
+    filename = wget.download(url=url, out=output_file)
+    return filename
+  except Exception as e:
+     print(f"Error al descargar el archivo: {str(e)}")
+
+def is_valid_model(name):
+  parent_path = find_folder_parent(".", "pretrained_v2")
+  unzips_path = os.path.join(parent_path, 'unzips')
+  
+  response = []
+  file_path = os.path.join(unzips_path, name)
+
+  has_model = False
+  has_index = False
+
+  for root, subfolders, files in os.walk(file_path):
+    for file in files:
+      current_file_path = os.path.join(root, file)
+      if not file.startswith("G_") and not file.startswith("D_") and file.endswith(".pth") and not "_G_" in file and not "_D_" in file:
+        has_model = True
+      if file.startswith('added_') and file.endswith('.index'):
+        has_index = True
+
+  #if has_model and has_index:
+  if has_index:
+    response.append(".index")
+
+  if has_model:
+    response.append(".pth")
+
+  return response
+
+
+def create_zip(new_name):
+    
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    temp_folder_path = os.path.join(parent_path, 'temp_models')
+    unzips_path = os.path.join(parent_path, 'unzips')
+    zips_path = os.path.join(parent_path, 'zips')
+  
+    file_path = os.path.join(unzips_path, new_name)
+    file_name = os.path.join(temp_folder_path, new_name)
+
+    if not os.path.exists(zips_path):
+        os.mkdir(zips_path)
+
+    if os.path.exists(file_name):
+        shutil.rmtree(file_name)
+
+    os.mkdir(file_name)
+
+    while not os.path.exists(file_name):
+        time.sleep(1)
+
+    for root, subfolders, files in os.walk(file_path):
+        for file in files:
+            current_file_path = os.path.join(root, file)
+            if not file.startswith("G_") and not file.startswith("D_") and file.endswith(".pth") and not "_G_" in file and not "_D_" in file:
+                print(f'Copiando {current_file_path} a {os.path.join(temp_folder_path, new_name)}')
+                shutil.copy(current_file_path, file_name)
+            if file.startswith('added_') and file.endswith('.index'):
+                print(f'Copiando {current_file_path} a {os.path.join(temp_folder_path, new_name)}')
+                shutil.copy(current_file_path, file_name)
+
+    print("Comprimiendo modelo")
+    zip_path =  os.path.join(zips_path, new_name)
+    
+    print(f"Comprimiendo {file_name} en {zip_path}")
+    shutil.make_archive(zip_path, 'zip', file_name)
+  
+def upload_to_huggingface(file_path, new_filename):
+    api = HfApi()
+    login(token="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")
+    api.upload_file(
+        path_or_fileobj=file_path,
+        path_in_repo=new_filename,
+        repo_id="juuxn/RVCModels",
+        repo_type="model",
+    )
+    return f"https://huggingface.co/juuxn/RVCModels/resolve/main/{new_filename}"
+
+
+def publish_model_clicked(model_name, model_url, model_version, model_creator):
+    
+    web_service_url = "https://script.google.com/macros/s/AKfycbzyrdLZzUww9qbjxnbnI08budD4yxbmRPHkWbp3UEJ9h3Id5cnNNVg0UtfFAnqqX5Rr/exec"
+    name = unidecode(model_name)
+    new_name = unidecode(name.strip().replace(" ","_").replace("'",""))
+    
+    downloaded_path = ""
+    url = model_url
+    version = model_version
+    creator = model_creator
+    parent_path = find_folder_parent(".", "pretrained_v2")
+    output_folder = os.path.join(parent_path, 'archivos_descargados')
+    output_file = os.path.join(output_folder, f'{new_name}.zip')
+    unzips_path = os.path.join(parent_path, 'unzips')
+    zips_path = os.path.join(parent_path, 'zips')
+    temp_folder_path = os.path.join(parent_path, 'temp_models')
+    
+    if os.path.exists(output_folder):
+        shutil.rmtree(output_folder)
+    os.mkdir(output_folder)
+    
+    if os.path.exists(temp_folder_path):
+        shutil.rmtree(temp_folder_path)
+    os.mkdir(temp_folder_path)
+    
+    
+    if url and 'drive.google.com' in url:
+    # Descargar el elemento si la URL es de Google Drive
+        downloaded_path = descargar_desde_drive(url, new_name, output_file)
+    elif url and 'mega.nz' in url:
+        downloaded_path = descargar_desde_mega(url, new_name, output_file)
+    elif url and 'pixeldrain' in url:
+        print("No se puede descargar de pixeldrain")
+    else:
+        downloaded_path = descargar_desde_url_basica(url, new_name, output_file)
+        
+    if not downloaded_path:
+        print(f"No se pudo descargar: {name}")
+    else:
+        filename = name.strip().replace(" ","_")
+        dst =f'{filename}.zip'
+        shutil.unpack_archive(downloaded_path, os.path.join(unzips_path, filename))
+        md5_hash = get_md5(os.path.join(unzips_path, filename))
+
+        if not md5_hash:
+            print("No tiene modelo pequeño")
+            return
+
+        md5_response_raw = requests.post(web_service_url, json={
+            'type': 'check_md5',
+            'md5_hash': md5_hash
+        })
+        
+        md5_response = md5_response_raw.json()
+        ok = md5_response["ok"]
+        exists = md5_response["exists"]
+        message = md5_response["message"]
+
+        is_valid = is_valid_model(filename)
+        
+        if md5_hash and exists:
+            print(f"El archivo ya se ha publicado en spreadsheet con md5: {md5_hash}")
+            return f"El archivo ya se ha publicado con md5: {md5_hash}"
+        
+        if ".pth" in is_valid and not exists:
+
+            create_zip(filename)
+            huggingface_url = upload_to_huggingface(os.path.join(zips_path,dst), dst)
+                
+            response = requests.post(web_service_url, json={
+        'type': 'save_model',
+        'elements': [{
+                'name': name,
+                'filename': filename,
+                'url': [huggingface_url],
+                'version': version,
+                'creator': creator,
+                'md5_hash': md5_hash,
+                'content': is_valid
+            }]})
+            
+            response_data = response.json()
+            ok = response_data["ok"]
+            message = response_data["message"]
+
+            print({
+                'name': name,
+                'filename': filename,
+                'url': [huggingface_url],
+                'version': version,
+                'creator': creator,
+                'md5_hash': md5_hash,
+                'content': is_valid
+            })   
+            
+            if ok:
+                return f"El archivo se ha publicado con md5: {md5_hash}"
+            else:
+                print(message)
+                return message         
+            
+        # Eliminar folder donde se decarga el modelo zip
+        if os.path.exists(output_folder):
+            shutil.rmtree(output_folder)
+            
+        # Eliminar folder de zips, donde se descomprimio el modelo descargado
+        if os.path.exists(unzips_path):
+            shutil.rmtree(unzips_path)
+            
+        # Eliminar folder donde se copiaron los archivos indispensables del modelo
+        if os.path.exists(temp_folder_path):
+            shutil.rmtree(temp_folder_path)
+        
+        # Eliminar folder donde se comprimio el modelo para enviarse a huggingface
+        if os.path.exists(zips_path):
+            shutil.rmtree(zips_path)
+
+def uvr(input_url, output_path, model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
+    carpeta_a_eliminar = "yt_downloads"
+    if os.path.exists(carpeta_a_eliminar) and os.path.isdir(carpeta_a_eliminar):
+        # Eliminar todos los archivos en la carpeta
+        for archivo in os.listdir(carpeta_a_eliminar):
+            ruta_archivo = os.path.join(carpeta_a_eliminar, archivo)
+            if os.path.isfile(ruta_archivo):
+                os.remove(ruta_archivo)
+            elif os.path.isdir(ruta_archivo):
+                shutil.rmtree(ruta_archivo)  # Eliminar subcarpetas recursivamente
+      
+    def format_title(title):
+     # Eliminar caracteres no alfanuméricos excepto espacios y guiones bajos
+     formatted_title = re.sub(r'[^\w\s-]', '', title)
+     # Reemplazar espacios por guiones bajos
+     formatted_title = formatted_title.replace(" ", "_")
+     return formatted_title
+
+    ydl_opts = {
+     'no-windows-filenames': True,
+     'restrict-filenames': True,
+     'extract_audio': True,
+     'format': 'bestaudio',
+     }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+     info_dict = ydl.extract_info(input_url, download=False)
+     formatted_title = format_title(info_dict.get('title', 'default_title'))
+     formatted_outtmpl = output_path + '/' + formatted_title + '.wav'
+     ydl_opts['outtmpl'] = formatted_outtmpl
+     ydl = yt_dlp.YoutubeDL(ydl_opts)
+     ydl.download([input_url])
+    
+    infos = []
+    pre_fun = None
+    try:
+        inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") if isinstance(x, str) else x for x in [inp_root, save_root_vocal, save_root_ins]]     
+        if model_name == "onnx_dereverb_By_FoxJoy":
+            pre_fun = MDXNetDereverb(15)
+        else:
+            func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new
+            pre_fun = func(
+                agg=10,
+                model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
+                device=config.device,
+                is_half=config.is_half,
+            )
+        if inp_root != "":
+            paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
+        else:
+            paths = [path.name for path in paths]
+        for path in paths:
+            inp_path = os.path.join(inp_root, path)
+            need_reformat = 1
+            done = 0
+            try:
+                info = ffmpeg.probe(inp_path, cmd="ffprobe")
+                if (
+                    info["streams"][0]["channels"] == 2
+                    and info["streams"][0]["sample_rate"] == "44100"
+                ):
+                    need_reformat = 0
+                    pre_fun._path_audio_(
+                        inp_path, save_root_ins, save_root_vocal, format0
+                    )
+                    done = 1
+            except:
+                need_reformat = 1
+                traceback.print_exc()
+            if need_reformat == 1:
+                tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path))
+                os.system(
+                    "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
+                    % (inp_path, tmp_path)
+                )
+                inp_path = tmp_path
+            try:
+                if done == 0:
+                    pre_fun._path_audio_(
+                        inp_path, save_root_ins, save_root_vocal, format0
+                    )
+                infos.append("%s->Success" % (os.path.basename(inp_path)))
+                yield "\n".join(infos)
+            except:
+                infos.append(
+                    "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
+                )
+                yield "\n".join(infos)
+    except:
+        infos.append(traceback.format_exc())
+        yield "\n".join(infos)
+    finally:
+        try:
+            if pre_fun is not None:  # Verificar si pre_fun existe antes de eliminarlo
+                if model_name == "onnx_dereverb_By_FoxJoy":
+                    del pre_fun.pred.model
+                    del pre_fun.pred.model_
+                else:
+                    del pre_fun.model
+                    del pre_fun
+        except:
+            traceback.print_exc()
+        print("clean_empty_cache")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    yield "\n".join(infos)
+            
+def publish_models():
+    with gr.Column():
+        gr.Markdown("# Publicar un modelo en la comunidad")
+        gr.Markdown("El modelo se va a verificar antes de publicarse. Importante que contenga el archivo **.pth** del modelo para que no sea rechazado.")
+        
+        model_name = gr.inputs.Textbox(lines=1, label="Nombre descriptivo del modelo Ej: (Ben 10 [Latino] - RVC V2 - 250 Epoch)")
+        url = gr.inputs.Textbox(lines=1, label="Enlace del modelo")
+        moder_version = gr.Radio(
+            label="Versión",
+            choices=["RVC v1", "RVC v2"],
+            value="RVC v1",
+            interactive=True,
+        )
+        model_creator = gr.inputs.Textbox(lines=1, label="ID de discord del creador del modelo Ej: <@123455656>")
+        publish_model_button=gr.Button("Publicar modelo")
+        results = gr.Textbox(label="Resultado", value="", max_lines=20)
+        
+        publish_model_button.click(fn=publish_model_clicked, inputs=[model_name, url, moder_version, model_creator], outputs=results)
+
+def download_model():
+    gr.Markdown(value="# " + i18n("下载模型"))
+    gr.Markdown(value=i18n("它用于下载您的推理模型。"))
+    with gr.Row():
+        model_url=gr.Textbox(label=i18n("网址"))
+    with gr.Row():
+        download_model_status_bar=gr.Textbox(label=i18n("地位"))
+    with gr.Row():
+        download_button=gr.Button(i18n("下载"))
+        download_button.click(fn=load_downloaded_model, inputs=[model_url], outputs=[download_model_status_bar])
+
+def download_backup():
+    gr.Markdown(value="# " + i18n("下载备份"))
+    gr.Markdown(value=i18n("它用于下载您的训练备份。"))
+    with gr.Row():
+        model_url=gr.Textbox(label=i18n("网址"))
+    with gr.Row():
+        download_model_status_bar=gr.Textbox(label=i18n("地位"))
+    with gr.Row():
+        download_button=gr.Button(i18n("下载"))
+        download_button.click(fn=load_downloaded_backup, inputs=[model_url], outputs=[download_model_status_bar])
+
+def update_dataset_list(name):
+    new_datasets = []
+    for foldername in os.listdir("./datasets"):
+        if "." not in foldername:
+            new_datasets.append(os.path.join(find_folder_parent(".","pretrained"),"datasets",foldername))
+    return gr.Dropdown.update(choices=new_datasets)
+
+def download_dataset(trainset_dir4):
+    gr.Markdown(value="# " + i18n("下载数据集"))
+    gr.Markdown(value=i18n("下载兼容格式（.wav/.flac）的音频数据集以训练模型。"))
+    with gr.Row():
+        dataset_url=gr.Textbox(label=i18n("网址"))
+    with gr.Row():
+        load_dataset_status_bar=gr.Textbox(label=i18n("地位"))
+    with gr.Row():
+        load_dataset_button=gr.Button(i18n("下载"))
+        load_dataset_button.click(fn=load_dowloaded_dataset, inputs=[dataset_url], outputs=[load_dataset_status_bar])
+        load_dataset_status_bar.change(update_dataset_list, dataset_url, trainset_dir4)
+    
+def youtube_separator():
+        gr.Markdown(value="# " + i18n("单独的 YouTube 曲目"))
+        gr.Markdown(value=i18n("下载 YouTube 视频的音频并自动分离声音和伴奏轨道"))
+        with gr.Row():
+            input_url = gr.inputs.Textbox(label=i18n("粘贴 YouTube 链接"))
+            output_path = gr.Textbox(
+                label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
+                value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/yt_downloads",
+                visible=False,
+                )
+            save_root_ins = gr.Textbox(
+                label=i18n("输入待处理音频文件夹路径"),
+                value=((os.getcwd()).replace('\\', '/') + "/yt_downloads"),
+                visible=False,
+                )
+            model_choose = gr.Textbox(
+                value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/uvr5_weights/HP5_only_main_vocal",
+                visible=False,
+                )
+            save_root_vocal = gr.Textbox(
+                label=i18n("指定输出主人声文件夹"), value="audios",
+                visible=False,
+                )
+            opt_ins_root = gr.Textbox(
+                label=i18n("指定输出非主人声文件夹"), value="opt",
+                visible=False,
+                )
+            format0 = gr.Radio(
+                label=i18n("导出文件格式"),
+                choices=["wav", "flac", "mp3", "m4a"],
+                value="wav",
+                interactive=True,
+                visible=False,
+                )
+        with gr.Row():
+            vc_output4 = gr.Textbox(label=i18n("地位"))
+        with gr.Row():
+            but2 = gr.Button(i18n("下载并分离"))
+            but2.click(
+                uvr,
+                    [
+                    input_url, 
+                    output_path,
+                    model_choose,
+                    save_root_ins,
+                    save_root_vocal,
+                    opt_ins_root,
+                    format0,
+                            ],
+                    [vc_output4],
+                )