Upload app.py

app.py

@@ -0,0 +1,1017 @@
+import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
+from mega import Mega
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+import threading
+from time import sleep
+from subprocess import Popen
+import faiss
+from random import shuffle
+import json, datetime, requests
+from gtts import gTTS
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
+os.makedirs(tmp, exist_ok=True)
+os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
+os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
+os.environ["TEMP"] = tmp
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
+from i18n import I18nAuto
+
+import signal
+
+import math
+
+from utils import load_audio, CSVutil
+
+global DoFormant, Quefrency, Timbre
+
+if not os.path.isdir('csvdb/'):
+    os.makedirs('csvdb')
+    frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
+    frmnt.close()
+    stp.close()
+
+try:
+    DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
+    DoFormant = (
+        lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant)
+    )(DoFormant)
+except (ValueError, TypeError, IndexError):
+    DoFormant, Quefrency, Timbre = False, 1.0, 1.0
+    CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre)
+
+def download_models():
+    # Download hubert base model if not present
+    if not os.path.isfile('./hubert_base.pt'):
+        response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')
+
+        if response.status_code == 200:
+            with open('./hubert_base.pt', 'wb') as f:
+                f.write(response.content)
+            print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.")
+        else:
+            raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".")
+        
+    # Download rmvpe model if not present
+    if not os.path.isfile('./rmvpe.pt'):
+        response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058')
+
+        if response.status_code == 200:
+            with open('./rmvpe.pt', 'wb') as f:
+                f.write(response.content)
+            print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.")
+        else:
+            raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".")
+
+download_models()
+
+print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n")
+
+i18n = I18nAuto()
+#i18n.print()
+# 判断是否有能用来训练和加速推理的N卡
+ngpu = torch.cuda.device_count()
+gpu_infos = []
+mem = []
+if (not torch.cuda.is_available()) or ngpu == 0:
+    if_gpu_ok = False
+else:
+    if_gpu_ok = False
+    for i in range(ngpu):
+        gpu_name = torch.cuda.get_device_name(i)
+        if (
+            "10" in gpu_name
+            or "16" in gpu_name
+            or "20" in gpu_name
+            or "30" in gpu_name
+            or "40" in gpu_name
+            or "A2" in gpu_name.upper()
+            or "A3" in gpu_name.upper()
+            or "A4" in gpu_name.upper()
+            or "P4" in gpu_name.upper()
+            or "A50" in gpu_name.upper()
+            or "A60" in gpu_name.upper()
+            or "70" in gpu_name
+            or "80" in gpu_name
+            or "90" in gpu_name
+            or "M4" in gpu_name.upper()
+            or "T4" in gpu_name.upper()
+            or "TITAN" in gpu_name.upper()
+        ):  # A10#A100#V100#A40#P40#M40#K80#A4500
+            if_gpu_ok = True  # 至少有一张能用的N卡
+            gpu_infos.append("%s\t%s" % (i, gpu_name))
+            mem.append(
+                int(
+                    torch.cuda.get_device_properties(i).total_memory
+                    / 1024
+                    / 1024
+                    / 1024
+                    + 0.4
+                )
+            )
+if if_gpu_ok == True and len(gpu_infos) > 0:
+    gpu_info = "\n".join(gpu_infos)
+    default_batch_size = min(mem) // 2
+else:
+    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
+    default_batch_size = 1
+gpus = "-".join([i[0] for i in gpu_infos])
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+import soundfile as sf
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import Config
+
+config = Config()
+# from trainset_preprocess_pipeline import PreProcess
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+hubert_model = None
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+
+weight_root = "weights"
+index_root = "logs"
+names = []
+for name in os.listdir(weight_root):
+    if name.endswith(".pth"):
+        names.append(name)
+index_paths = []
+for root, dirs, files in os.walk(index_root, topdown=False):
+    for name in files:
+        if name.endswith(".index") and "trained" not in name:
+            index_paths.append("%s/%s" % (root, name))
+
+
+
+def vc_single(
+    sid,
+    input_audio_path,
+    f0_up_key,
+    f0_file,
+    f0_method,
+    file_index,
+    #file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    crepe_hop_length,
+):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+    global tgt_sr, net_g, vc, hubert_model, version
+    if input_audio_path is None:
+        return "You need to upload an audio", None
+    f0_up_key = int(f0_up_key)
+    try:
+        audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre)
+        audio_max = np.abs(audio).max() / 0.95
+        if audio_max > 1:
+            audio /= audio_max
+        times = [0, 0, 0]
+        if hubert_model == None:
+            load_hubert()
+        if_f0 = cpt.get("f0", 1)
+        file_index = (
+            (
+                file_index.strip(" ")
+                .strip('"')
+                .strip("\n")
+                .strip('"')
+                .strip(" ")
+                .replace("trained", "added")
+            )
+        )  # 防止小白写错，自动帮他替换掉
+        # file_big_npy = (
+        #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        # )
+        audio_opt = vc.pipeline(
+            hubert_model,
+            net_g,
+            sid,
+            audio,
+            input_audio_path,
+            times,
+            f0_up_key,
+            f0_method,
+            file_index,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            crepe_hop_length,
+            f0_file=f0_file,
+        )
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
+            tgt_sr = resample_sr
+        index_info = (
+            "Using index:%s." % file_index
+            if os.path.exists(file_index)
+            else "Index not used."
+        )
+        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
+            index_info,
+            times[0],
+            times[1],
+            times[2],
+        ), (tgt_sr, audio_opt)
+    except:
+        info = traceback.format_exc()
+        print(info)
+        return info, (None, None)
+
+
+def vc_multi(
+    sid,
+    dir_path,
+    opt_root,
+    paths,
+    f0_up_key,
+    f0_method,
+    file_index,
+    file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    format1,
+    crepe_hop_length,
+):
+    try:
+        dir_path = (
+            dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # 防止小白拷路径头尾带了空格和"和回车
+        opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        os.makedirs(opt_root, exist_ok=True)
+        try:
+            if dir_path != "":
+                paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
+            else:
+                paths = [path.name for path in paths]
+        except:
+            traceback.print_exc()
+            paths = [path.name for path in paths]
+        infos = []
+        for path in paths:
+            info, opt = vc_single(
+                sid,
+                path,
+                f0_up_key,
+                None,
+                f0_method,
+                file_index,
+                # file_big_npy,
+                index_rate,
+                filter_radius,
+                resample_sr,
+                rms_mix_rate,
+                protect,
+                crepe_hop_length
+            )
+            if "Success" in info:
+                try:
+                    tgt_sr, audio_opt = opt
+                    if format1 in ["wav", "flac"]:
+                        sf.write(
+                            "%s/%s.%s" % (opt_root, os.path.basename(path), format1),
+                            audio_opt,
+                            tgt_sr,
+                        )
+                    else:
+                        path = "%s/%s.wav" % (opt_root, os.path.basename(path))
+                        sf.write(
+                            path,
+                            audio_opt,
+                            tgt_sr,
+                        )
+                        if os.path.exists(path):
+                            os.system(
+                                "ffmpeg -i %s -vn %s -q:a 2 -y"
+                                % (path, path[:-4] + ".%s" % format1)
+                            )
+                except:
+                    info += traceback.format_exc()
+            infos.append("%s->%s" % (os.path.basename(path), info))
+            yield "\n".join(infos)
+        yield "\n".join(infos)
+    except:
+        yield traceback.format_exc()
+
+# 一个选项卡全局只能有一个音色
+def get_vc(sid):
+    global n_spk, tgt_sr, net_g, vc, cpt, version
+    if sid == "" or sid == []:
+        global hubert_model
+        if hubert_model != None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+            print("clean_empty_cache")
+            del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
+            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            ###楼下不这么折腾清理不干净
+            if_f0 = cpt.get("f0", 1)
+            version = cpt.get("version", "v1")
+            if version == "v1":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs256NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            elif version == "v2":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+            del net_g, cpt
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            cpt = None
+        return {"visible": False, "__type__": "update"}
+    person = "%s/%s" % (weight_root, sid)
+    print("loading %s" % person)
+    cpt = torch.load(person, map_location="cpu")
+    tgt_sr = cpt["config"][-1]
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    if_f0 = cpt.get("f0", 1)
+    version = cpt.get("version", "v1")
+    if version == "v1":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif version == "v2":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+    del net_g.enc_q
+    print(net_g.load_state_dict(cpt["weight"], strict=False))
+    net_g.eval().to(config.device)
+    if config.is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, config)
+    n_spk = cpt["config"][-3]
+    return {"visible": False, "maximum": n_spk, "__type__": "update"}
+
+
+def change_choices():
+    names = []
+    for name in os.listdir(weight_root):
+        if name.endswith(".pth"):
+            names.append(name)
+    index_paths = []
+    for root, dirs, files in os.walk(index_root, topdown=False):
+        for name in files:
+            if name.endswith(".index") and "trained" not in name:
+                index_paths.append("%s/%s" % (root, name))
+    return {"choices": sorted(names), "__type__": "update"}, {
+        "choices": sorted(index_paths),
+        "__type__": "update",
+    }
+
+
+def clean():
+    return {"value": "", "__type__": "update"}
+
+
+sr_dict = {
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+
+def if_done(done, p):
+    while 1:
+        if p.poll() == None:
+            sleep(0.5)
+        else:
+            break
+    done[0] = True
+
+
+def if_done_multi(done, ps):
+    while 1:
+        # poll==None代表进程未结束
+        # 只要有一个进程未结束都不停
+        flag = 1
+        for p in ps:
+            if p.poll() == None:
+                flag = 0
+                sleep(0.5)
+                break
+        if flag == 1:
+            break
+    done[0] = True
+
+
+
+
+
+
+# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
+
+
+def whethercrepeornah(radio):
+    mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False
+    return ({"visible": mango, "__type__": "update"})
+
+#                    ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
+
+
+#region RVC WebUI App
+
+
+def change_choices2():
+    audio_files=[]
+    for filename in os.listdir("./audios"):
+        if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
+            audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
+    return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}
+    
+audio_files=[]
+for filename in os.listdir("./audios"):
+    if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
+        audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
+        
+def get_index():
+    if check_for_name() != '':
+        chosen_model=sorted(names)[0].split(".")[0]
+        logs_path="./logs/"+chosen_model
+        if os.path.exists(logs_path):
+            for file in os.listdir(logs_path):
+                if file.endswith(".index"):
+                    return os.path.join(logs_path, file)
+            return ''
+        else:
+            return ''
+        
+def get_indexes():
+    indexes_list=[]
+    for dirpath, dirnames, filenames in os.walk("./logs/"):
+        for filename in filenames:
+            if filename.endswith(".index"):
+                indexes_list.append(os.path.join(dirpath,filename))
+    if len(indexes_list) > 0:
+        return indexes_list
+    else:
+        return ''
+        
+def get_name():
+    if len(audio_files) > 0:
+        return sorted(audio_files)[0]
+    else:
+        return ''
+        
+def save_to_wav(record_button):
+    if record_button is None:
+        pass
+    else:
+        path_to_file=record_button
+        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
+        new_path='./audios/'+new_name
+        shutil.move(path_to_file,new_path)
+        return new_path
+    
+def save_to_wav2(dropbox):
+    file_path=dropbox.name
+    shutil.move(file_path,'./audios')
+    return os.path.join('./audios',os.path.basename(file_path))
+    
+def match_index(sid0):
+    folder=sid0.split(".")[0]
+    parent_dir="./logs/"+folder
+    if os.path.exists(parent_dir):
+        for filename in os.listdir(parent_dir):
+            if filename.endswith(".index"):
+                index_path=os.path.join(parent_dir,filename)
+                return index_path
+    else:
+        return ''
+                
+def check_for_name():
+    if len(names) > 0:
+        return sorted(names)[0]
+    else:
+        return ''
+            
+def download_from_url(url, model):
+    if url == '':
+        return "URL cannot be left empty."
+    if model =='':
+        return "You need to name your model. For example: My-Model"
+    url = url.strip()
+    zip_dirs = ["zips", "unzips"]
+    for directory in zip_dirs:
+        if os.path.exists(directory):
+            shutil.rmtree(directory)
+    os.makedirs("zips", exist_ok=True)
+    os.makedirs("unzips", exist_ok=True)
+    zipfile = model + '.zip'
+    zipfile_path = './zips/' + zipfile
+    try:
+        if "drive.google.com" in url:
+            subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
+        elif "mega.nz" in url:
+            m = Mega()
+            m.download_url(url, './zips')
+        else:
+            subprocess.run(["wget", url, "-O", zipfile_path])
+        for filename in os.listdir("./zips"):
+            if filename.endswith(".zip"):
+                zipfile_path = os.path.join("./zips/",filename)
+                shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
+            else:
+                return "No zipfile found."
+        for root, dirs, files in os.walk('./unzips'):
+            for file in files:
+                file_path = os.path.join(root, file)
+                if file.endswith(".index"):
+                    os.mkdir(f'./logs/{model}')
+                    shutil.copy2(file_path,f'./logs/{model}')
+                elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
+                    shutil.copy(file_path,f'./weights/{model}.pth')
+        shutil.rmtree("zips")
+        shutil.rmtree("unzips")
+        return "Success."
+    except:
+        return "There's been an error."
+def success_message(face):
+    return f'{face.name} has been uploaded.', 'None'
+def mouth(size, face, voice, faces):
+    if size == 'Half':
+        size = 2
+    else:
+        size = 1
+    if faces == 'None':
+        character = face.name
+    else:
+        if faces == 'Ben Shapiro':
+            character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4'
+        elif faces == 'Andrew Tate':
+            character = '/content/wav2lip-HD/inputs/tate-7.mp4'
+    command = "python inference.py " \
+            "--checkpoint_path checkpoints/wav2lip.pth " \
+            f"--face {character} " \
+            f"--audio {voice} " \
+            "--pads 0 20 0 0 " \
+            "--outfile /content/wav2lip-HD/outputs/result.mp4 " \
+            "--fps 24 " \
+            f"--resize_factor {size}"
+    process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master')
+    stdout, stderr = process.communicate()
+    return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.'
+eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli']
+eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O']
+chosen_voice = dict(zip(eleven_voices, eleven_voices_ids))
+
+def stoptraining(mim): 
+    if int(mim) == 1:
+        try:
+            CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True')
+            os.kill(PID, signal.SIGTERM)
+        except Exception as e:
+            print(f"Couldn't click due to {e}")
+    return (
+        {"visible": False, "__type__": "update"}, 
+        {"visible": True, "__type__": "update"},
+    )
+
+
+def elevenTTS(xiapi, text, id, lang):
+    if xiapi!= '' and id !='': 
+        choice = chosen_voice[id]
+        CHUNK_SIZE = 1024
+        url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}"
+        headers = {
+        "Accept": "audio/mpeg",
+        "Content-Type": "application/json",
+        "xi-api-key": xiapi
+        }
+        if lang == 'en':
+            data = {
+            "text": text,
+            "model_id": "eleven_monolingual_v1",
+            "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.5
+            }
+            }
+        else:
+            data = {
+            "text": text,
+            "model_id": "eleven_multilingual_v1",
+            "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.5
+            }
+            }
+
+        response = requests.post(url, json=data, headers=headers)
+        with open('./temp_eleven.mp3', 'wb') as f:
+          for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
+              if chunk:
+                  f.write(chunk)
+        aud_path = save_to_wav('./temp_eleven.mp3')
+        return aud_path, aud_path
+    else:
+        tts = gTTS(text, lang=lang)
+        tts.save('./temp_gTTS.mp3')
+        aud_path = save_to_wav('./temp_gTTS.mp3')
+        return aud_path, aud_path
+
+def upload_to_dataset(files, dir):
+    if dir == '':
+        dir = './dataset'
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+    count = 0
+    for file in files:
+        path=file.name
+        shutil.copy2(path,dir)
+        count += 1
+    return f' {count} files uploaded to {dir}.'     
+    
+def zip_downloader(model):
+    if not os.path.exists(f'./weights/{model}.pth'):
+        return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth'
+    index_found = False
+    for file in os.listdir(f'./logs/{model}'):
+        if file.endswith('.index') and 'added' in file:
+            log_file = file
+            index_found = True
+    if index_found:
+        return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done"
+    else:
+        return f'./weights/{model}.pth', "Could not find Index file."
+
+with gr.Blocks(theme=gr.themes.Base(), title='Mangio-RVC-Web 💻') as app:
+    with gr.Tabs():
+        with gr.TabItem("Inference"):
+            gr.HTML("<h1>  RVC V2 Huggingface Version   </h1>")    
+            gr.HTML("<h4>   Inference may take time because this space does not use GPU :(  </h4>")
+            gr.HTML("<h10>   Huggingface version made by Rekey   </h10>")   
+            gr.HTML("<h10>   Easy GUI coded by Rejekts   </h10>")
+            gr.HTML("<h4>  If you want to use this space privately, I recommend you duplicate the space.  </h4>")
+
+            # Inference Preset Row
+            # with gr.Row():
+            #     mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets()))
+            #     mangio_preset_name_save = gr.Textbox(
+            #         label="Your preset name"
+            #     )
+            #     mangio_preset_save_btn = gr.Button('Save Preset', variant="primary")
+
+            # Other RVC stuff
+            with gr.Row():
+                sid0 = gr.Dropdown(label="1.Choose your Model.", choices=sorted(names), value=check_for_name())
+                refresh_button = gr.Button("Refresh", variant="primary")
+                if check_for_name() != '':
+                    get_vc(sorted(names)[0])
+                vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0)
+                #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
+                spk_item = gr.Slider(
+                    minimum=0,
+                    maximum=2333,
+                    step=1,
+                    label=i18n("请选择说话人id"),
+                    value=0,
+                    visible=False,
+                    interactive=True,
+                )
+                #clean_button.click(fn=clean, inputs=[], outputs=[sid0])
+                sid0.change(
+                    fn=get_vc,
+                    inputs=[sid0],
+                    outputs=[spk_item],
+                )
+                but0 = gr.Button("Convert", variant="primary")
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
+                    with gr.Row():
+                        record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath")
+                    with gr.Row():
+                        input_audio0 = gr.Dropdown(
+                            label="2.Choose your audio.",
+                            value="./audios/someguy.mp3",
+                            choices=audio_files
+                            )
+                        dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
+                        dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                        refresh_button2 = gr.Button("Refresh", variant="primary", size='sm')
+                        record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
+                        record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                    with gr.Row():
+                        with gr.Accordion('Text To Speech', open=False):
+                            with gr.Column():
+                                lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en')
+                                api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='')
+                                elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices)
+                            with gr.Column():
+                                tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.")
+                                tts_button = gr.Button(value="Speak")
+                                tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0])
+                    with gr.Row():
+                        with gr.Accordion('Wav2Lip', open=False):
+                            with gr.Row():
+                                size = gr.Radio(label='Resolution:',choices=['Half','Full'])
+                                face = gr.UploadButton("Upload A Character",type='file')
+                                faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate'])
+                            with gr.Row():
+                                preview = gr.Textbox(label="Status:",interactive=False)
+                                face.upload(fn=success_message,inputs=[face], outputs=[preview, faces])
+                            with gr.Row():
+                                animation = gr.Video(type='filepath')
+                                refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation])
+                            with gr.Row():
+                                animate_button = gr.Button('Animate')
+
+                with gr.Column():
+                    with gr.Accordion("Index Settings", open=False):
+                        file_index1 = gr.Dropdown(
+                            label="3. Path to your added.index file (if it didn't automatically find it.)",
+                            choices=get_indexes(),
+                            value=get_index(),
+                            interactive=True,
+                            )
+                        sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1])
+                        refresh_button.click(
+                            fn=change_choices, inputs=[], outputs=[sid0, file_index1]
+                            )
+                        
+                        index_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("检索特征占比"),
+                            value=0.66,
+                            interactive=True,
+                            )
+                    vc_output2 = gr.Audio(
+                        label="Output Audio (Click on the Three Dots in the Right Corner to Download)",
+                        type='filepath',
+                        interactive=False,
+                    )
+                    animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview])
+                    with gr.Accordion("Advanced Settings", open=False):
+                        f0method0 = gr.Radio(
+                            label="Optional: Change the Pitch Extraction Algorithm.\nExtraction methods are sorted from 'worst quality' to 'best quality'.\nmangio-crepe may or may not be better than rmvpe in cases where 'smoothness' is more important, but rmvpe is the best overall.",
+                            choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        
+                        crepe_hop_length = gr.Slider(
+                            minimum=1,
+                            maximum=512,
+                            step=1,
+                            label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.",
+                            value=120,
+                            interactive=True,
+                            visible=False,
+                            )
+                        f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length])
+                        filter_radius0 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                            )
+                        resample_sr0 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                            visible=False
+                            )
+                        rms_mix_rate0 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=0.21,
+                            interactive=True,
+                            )
+                        protect0 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                            )
+                        formanting = gr.Checkbox(
+                            value=bool(DoFormant),
+                            label="[EXPERIMENTAL] Formant shift inference audio",
+                            info="Used for male to female and vice-versa conversions",
+                            interactive=True,
+                            visible=True,
+                        )
+                        
+                        
+                        
+                        
+                        
+            with gr.Row():
+                vc_output1 = gr.Textbox("")
+                f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False)
+                
+                but0.click(
+                    vc_single,
+                    [
+                        spk_item,
+                        input_audio0,
+                        vc_transform0,
+                        f0_file,
+                        f0method0,
+                        file_index1,
+                        # file_index2,
+                        # file_big_npy1,
+                        index_rate1,
+                        filter_radius0,
+                        resample_sr0,
+                        rms_mix_rate0,
+                        protect0,
+                        crepe_hop_length
+                    ],
+                    [vc_output1, vc_output2],
+                )
+                        
+            with gr.Accordion("Batch Conversion",open=False):
+                with gr.Row():
+                    with gr.Column():
+                        vc_transform1 = gr.Number(
+                            label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
+                        )
+                        opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
+                        f0method1 = gr.Radio(
+                            label=i18n(
+                                "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
+                            ),
+                            choices=["pm", "harvest", "crepe", "rmvpe"],
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        filter_radius1 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        file_index3 = gr.Textbox(
+                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
+                            value="",
+                            interactive=True,
+                        )
+                        file_index4 = gr.Dropdown(
+                            label=i18n("自动检测index路径,下拉式选择(dropdown)"),
+                            choices=sorted(index_paths),
+                            interactive=True,
+                        )
+                        refresh_button.click(
+                            fn=lambda: change_choices()[1],
+                            inputs=[],
+                            outputs=file_index4,
+                        )
+                        # file_big_npy2 = gr.Textbox(
+                        #     label=i18n("特征文件路径"),
+                        #     value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
+                        #     interactive=True,
+                        # )
+                        index_rate2 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("检索特征占比"),
+                            value=1,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        resample_sr1 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                        )
+                        rms_mix_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=1,
+                            interactive=True,
+                        )
+                        protect1 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n(
+                                "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
+                            ),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        dir_input = gr.Textbox(
+                            label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
+                            value="E:\codes\py39\\test-20230416b\\todo-songs",
+                        )
+                        inputs = gr.File(
+                            file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
+                        )
+                    with gr.Row():
+                        format1 = gr.Radio(
+                            label=i18n("导出文件格式"),
+                            choices=["wav", "flac", "mp3", "m4a"],
+                            value="flac",
+                            interactive=True,
+                        )
+                        but1 = gr.Button(i18n("转换"), variant="primary")
+                        vc_output3 = gr.Textbox(label=i18n("输出信息"))
+                    but1.click(
+                        vc_multi,
+                        [
+                            spk_item,
+                            dir_input,
+                            opt_input,
+                            inputs,
+                            vc_transform1,
+                            f0method1,
+                            file_index3,
+                            file_index4,
+                            # file_big_npy2,
+                            index_rate2,
+                            filter_radius1,
+                            resample_sr1,
+                            rms_mix_rate1,
+                            protect1,
+                            format1,
+                            crepe_hop_length,
+                        ],
+                        [vc_output3],
+                    )
+                    but1.click(fn=lambda: easy_uploader.clear())
+        with gr.TabItem("Download Model"):
+            with gr.Row():
+                url=gr.Textbox(label="Enter the URL to the Model:")
+            with gr.Row():
+                model = gr.Textbox(label="Name your model:")
+                download_button=gr.Button("Download")
+            with gr.Row():
+                status_bar=gr.Textbox(label="")
+                download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])
+            with gr.Row():
+                gr.Markdown(
+                """
+                 Mangio’s RVC Fork:https://github.com/Mangio621/Mangio-RVC-Fork ❤️ If you like the EasyGUI, help me keep it.❤️ https://paypal.me/lesantillan
+                """
+                )
+
+        
+                
+    app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True)
+#endregion