Update app.py

app.py

@@ -1,378 +1,195 @@
-import torch, os, traceback, sys, warnings, shutil, numpy as np
+import os
+import glob
+import json
+import traceback
+import logging
 import gradio as gr
+import numpy as np
 import librosa
+import torch
 import asyncio
-import rarfile
 import edge_tts
 import yt_dlp
 import ffmpeg
-import gdown
 import subprocess
+import sys
+import io
 import wave
-import soundfile as sf
-from scipy.io import wavfile
 from datetime import datetime
-from urllib.parse import urlparse
-from mega import Mega
-
-now_dir = os.getcwd()
-tmp = os.path.join(now_dir, "TEMP")
-shutil.rmtree(tmp, ignore_errors=True)
-os.makedirs(tmp, exist_ok=True)
-os.environ["TEMP"] = tmp
+from fairseq import checkpoint_utils
 from lib.infer_pack.models import (
     SynthesizerTrnMs256NSFsid,
     SynthesizerTrnMs256NSFsid_nono,
     SynthesizerTrnMs768NSFsid,
     SynthesizerTrnMs768NSFsid_nono,
 )
-from fairseq import checkpoint_utils
 from vc_infer_pipeline import VC
 from config import Config
 config = Config()
+logging.getLogger("numba").setLevel(logging.WARNING)
+spaces = os.getenv("SYSTEM") == "spaces"
+force_support = None
+if config.unsupported is False:
+    if config.device == "mps" or config.device == "cpu":
+        force_support = False
+else:
+    force_support = True
 
-tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
+audio_mode = []
+f0method_mode = []
+f0method_info = ""
 
-hubert_model = None
-
-f0method_mode = ["pm", "harvest", "crepe"]
-f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"
+if force_support is False or spaces is True:
+    if spaces is True:
+        audio_mode = ["Upload audio", "TTS Audio"]
+    else:
+        audio_mode = ["Input path", "Upload audio", "TTS Audio"]
+    f0method_mode = ["pm", "harvest"]
+    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
+else:
+    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
+    f0method_mode = ["pm", "harvest", "crepe"]
+    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
 
 if os.path.isfile("rmvpe.pt"):
     f0method_mode.insert(2, "rmvpe")
-    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
 
-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()
-
-load_hubert()
-
-weight_root = "weights"
-index_root = "weights/index"
-weights_model = []
-weights_index = []
-for _, _, model_files in os.walk(weight_root):
-    for file in model_files:
-        if file.endswith(".pth"):
-            weights_model.append(file)
-for _, _, index_files in os.walk(index_root):
-    for file in index_files:
-        if file.endswith('.index') and "trained" not in file:
-            weights_index.append(os.path.join(index_root, file))
-
-def check_models():
-    weights_model = []
-    weights_index = []
-    for _, _, model_files in os.walk(weight_root):
-        for file in model_files:
-            if file.endswith(".pth"):
-                weights_model.append(file)
-    for _, _, index_files in os.walk(index_root):
-        for file in index_files:
-            if file.endswith('.index') and "trained" not in file:
-                weights_index.append(os.path.join(index_root, file))
-    return (
-        gr.Dropdown.update(choices=sorted(weights_model), value=weights_model[0]),
-        gr.Dropdown.update(choices=sorted(weights_index))
-    )
-
-def clean():
-    return (
-        gr.Dropdown.update(value=""),
-        gr.Slider.update(visible=False)
-    )
-
-def vc_single(
-    sid,
-    vc_audio_mode,
-    input_audio_path,
-    input_upload_audio,
-    vocal_audio,
-    tts_text,
-    tts_voice,
-    f0_up_key,
-    f0_file,
-    f0_method,
-    file_index,
-    index_rate,
-    filter_radius,
-    resample_sr,
-    rms_mix_rate,
-    protect
-):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
-    global tgt_sr, net_g, vc, hubert_model, version, cpt
-    try:
-        logs = []
-        print(f"Converting...")
-        logs.append(f"Converting...")
-        yield "\n".join(logs), None
-        if vc_audio_mode == "Input path" or "Youtube" and input_audio_path != "":
-            audio, sr = librosa.load(input_audio_path, sr=16000, mono=True)
-        elif vc_audio_mode == "Upload audio":
-            selected_audio = input_upload_audio
-            if vocal_audio:
-                selected_audio = vocal_audio
-            elif input_upload_audio:
-                selected_audio = input_upload_audio
-            sampling_rate, audio = selected_audio
-            duration = audio.shape[0] / sampling_rate
-            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-            if len(audio.shape) > 1:
-                audio = librosa.to_mono(audio.transpose(1, 0))
-            if sampling_rate != 16000:
-                audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-        elif vc_audio_mode == "TTS Audio":
-            if tts_text is None or tts_voice is None:
-                return "You need to enter text and select a voice", None
-            asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-            audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            input_audio_path = "tts.mp3"
-        f0_up_key = int(f0_up_key)
-        times = [0, 0, 0]
-        if hubert_model == None:
-            load_hubert()
-        if_f0 = cpt.get("f0", 1)
-        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,
-            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."
-        )
-        print("Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
-            index_info,
-            times[0],
-            times[1],
-            times[2],
-        ))
-        info = f"{index_info}\n[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-        logs.append(info)
-        yield "\n".join(logs), (tgt_sr, audio_opt)
-    except:
-        info = traceback.format_exc()
-        print(info)
-        logs.append(info)
-        yield "\n".join(logs), None
+def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
+    def vc_fn(
+        vc_audio_mode,
+        vc_input, 
+        vc_upload,
+        tts_text,
+        tts_voice,
+        f0_up_key,
+        f0_method,
+        index_rate,
+        filter_radius,
+        resample_sr,
+        rms_mix_rate,
+        protect,
+    ):
+        try:
+            logs = []
+            print(f"Converting using {model_name}...")
+            logs.append(f"Converting using {model_name}...")
+            yield "\n".join(logs), None
+            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
+                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
+            elif vc_audio_mode == "Upload audio":
+                if vc_upload is None:
+                    return "You need to upload an audio", None
+                sampling_rate, audio = vc_upload
+                duration = audio.shape[0] / sampling_rate
+                if duration > 20 and spaces:
+                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
+                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+                if len(audio.shape) > 1:
+                    audio = librosa.to_mono(audio.transpose(1, 0))
+                if sampling_rate != 16000:
+                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+            elif vc_audio_mode == "TTS Audio":
+                if len(tts_text) > 100 and spaces:
+                    return "Text is too long", None
+                if tts_text is None or tts_voice is None:
+                    return "You need to enter text and select a voice", None
+                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
+                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
+                vc_input = "tts.mp3"
+            times = [0, 0, 0]
+            f0_up_key = int(f0_up_key)
+            audio_opt = vc.pipeline(
+                hubert_model,
+                net_g,
+                0,
+                audio,
+                vc_input,
+                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,
+                f0_file=None,
+            )
+            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
+            print(f"{model_name} | {info}")
+            logs.append(f"Successfully Convert {model_name}\n{info}")
+            yield "\n".join(logs), (tgt_sr, audio_opt)
+        except:
+            info = traceback.format_exc()
+            print(info)
+            yield info, None
+    return vc_fn
 
-def get_vc(sid, to_return_protect0):
-    global n_spk, tgt_sr, net_g, vc, cpt, version, weights_index
-    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
-                    )
+def load_model():
+    categories = []
+    if os.path.isfile("weights/folder_info.json"):
+        with open("weights/folder_info.json", "r", encoding="utf-8") as f:
+            folder_info = json.load(f)
+        for category_name, category_info in folder_info.items():
+            if not category_info['enable']:
+                continue
+            category_title = category_info['title']
+            category_folder = category_info['folder_path']
+            description = category_info['description']
+            models = []
+            with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
+                models_info = json.load(f)
+            for character_name, info in models_info.items():
+                if not info['enable']:
+                    continue
+                model_title = info['title']
+                model_name = info['model_path']
+                model_author = info.get("author", None)
+                model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
+                model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
+                cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", 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"])
+                    model_version = "V1"
+                elif version == "v2":
+                    if if_f0 == 1:
+                        net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+                    else:
+                        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+                    model_version = "V2"
+                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 = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            del net_g, cpt
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-            cpt = None
-        return (
-            gr.Slider.update(maximum=2333, visible=False),
-            gr.Slider.update(visible=True),
-            gr.Dropdown.update(choices=sorted(weights_index), value=""),
-            gr.Markdown.update(value="# <center> No model selected")
-        )
-    print(f"Loading {sid} model...")
-    selected_model = sid[:-4]
-    cpt = torch.load(os.path.join(weight_root, sid), map_location="cpu")
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-    if_f0 = cpt.get("f0", 1)
-    if if_f0 == 0:
-        to_return_protect0 = {
-            "visible": False,
-            "value": 0.5,
-            "__type__": "update",
-        }
+                    net_g = net_g.float()
+                vc = VC(tgt_sr, config)
+                print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
+                models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
+            categories.append([category_title, category_folder, description, models])
     else:
-        to_return_protect0 = {
-            "visible": True,
-            "value": to_return_protect0,
-            "__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]
-    weights_index = []
-    for _, _, index_files in os.walk(index_root):
-        for file in index_files:
-            if file.endswith('.index') and "trained" not in file:
-                weights_index.append(os.path.join(index_root, file))
-    if weights_index == []:
-        selected_index = gr.Dropdown.update(value="")
-    else:
-        selected_index = gr.Dropdown.update(value=weights_index[0])
-    for index, model_index in enumerate(weights_index):
-        if selected_model in model_index:
-            selected_index = gr.Dropdown.update(value=weights_index[index])
-            break
-    return (
-        gr.Slider.update(maximum=n_spk, visible=True),
-        to_return_protect0,
-        selected_index,
-        gr.Markdown.update(
-            f'## <center> {selected_model}\n'+
-            f'### <center> RVC {version} Model'
-        )
-    )
-
-def find_audio_files(folder_path, extensions):
-    audio_files = []
-    for root, dirs, files in os.walk(folder_path):
-        for file in files:
-            if any(file.endswith(ext) for ext in extensions):
-                audio_files.append(file)
-    return audio_files
-
-def vc_multi(
-    spk_item,
-    vc_input,
-    vc_output,
-    vc_transform0,
-    f0method0,
-    file_index,
-    index_rate,
-    filter_radius,
-    resample_sr,
-    rms_mix_rate,
-    protect,
-):
-    global tgt_sr, net_g, vc, hubert_model, version, cpt
-    logs = []
-    logs.append("Converting...")
-    yield "\n".join(logs)
-    print()
-    try:
-        if os.path.exists(vc_input):
-            folder_path = vc_input
-            extensions = [".mp3", ".wav", ".flac", ".ogg"]
-            audio_files = find_audio_files(folder_path, extensions)
-            for index, file in enumerate(audio_files, start=1):
-                audio, sr = librosa.load(os.path.join(folder_path, file), sr=16000, mono=True)
-                input_audio_path = folder_path, file
-                f0_up_key = int(vc_transform0)
-                times = [0, 0, 0]
-                if hubert_model == None:
-                    load_hubert()
-                if_f0 = cpt.get("f0", 1)
-                audio_opt = vc.pipeline(
-                    hubert_model,
-                    net_g,
-                    spk_item,
-                    audio,
-                    input_audio_path,
-                    times,
-                    f0_up_key,
-                    f0method0,
-                    file_index,
-                    index_rate,
-                    if_f0,
-                    filter_radius,
-                    tgt_sr,
-                    resample_sr,
-                    rms_mix_rate,
-                    version,
-                    protect,
-                    f0_file=None
-                )
-                if resample_sr >= 16000 and tgt_sr != resample_sr:
-                    tgt_sr = resample_sr
-                output_path = f"{os.path.join(vc_output, file)}"
-                os.makedirs(os.path.join(vc_output), exist_ok=True)
-                sf.write(
-                    output_path,
-                    audio_opt,
-                    tgt_sr,
-                )
-                info = f"{index} / {len(audio_files)} | {file}"
-                print(info)
-                logs.append(info)
-                yield "\n".join(logs)
-        else:
-            logs.append("Folder not found or path doesn't exist.")
-            yield "\n".join(logs)
-    except:
-        info = traceback.format_exc()
-        print(info)
-        logs.append(info)
-        yield "\n".join(logs)
+        categories = []
+    return categories
 
 def download_audio(url, audio_provider):
     logs = []
-    os.makedirs("dl_audio", exist_ok=True)
     if url == "":
-        logs.append("URL required!")
-        yield None, "\n".join(logs)
-        return None, "\n".join(logs)
+        raise gr.Error("URL Required!")
+        return "URL Required"
+    if not os.path.exists("dl_audio"):
+        os.mkdir("dl_audio")
     if audio_provider == "Youtube":
         logs.append("Downloading the audio...")
         yield None, "\n".join(logs)
@@ -383,132 +200,58 @@ def download_audio(url, audio_provider):
                 'key': 'FFmpegExtractAudio',
                 'preferredcodec': 'wav',
             }],
-            "outtmpl": 'result/dl_audio/audio',
+            "outtmpl": 'dl_audio/audio',
         }
-        audio_path = "result/dl_audio/audio.wav"
+        audio_path = "dl_audio/audio.wav"
         with yt_dlp.YoutubeDL(ydl_opts) as ydl:
             ydl.download([url])
         logs.append("Download Complete.")
         yield audio_path, "\n".join(logs)
 
-def cut_vocal_and_inst_yt(split_model):
+def cut_vocal_and_inst(split_model):
     logs = []
     logs.append("Starting the audio splitting process...")
-    yield "\n".join(logs), None, None, None
-    command = f"demucs --two-stems=vocals -n {split_model} result/dl_audio/audio.wav -o output"
+    yield "\n".join(logs), None, None, None, None
+    command = f"demucs --two-stems=vocals -n {split_model} dl_audio/audio.wav -o output"
     result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
     for line in result.stdout:
         logs.append(line)
-        yield "\n".join(logs), None, None, None
+        yield "\n".join(logs), None, None, None, None
     print(result.stdout)
     vocal = f"output/{split_model}/audio/vocals.wav"
     inst = f"output/{split_model}/audio/no_vocals.wav"
     logs.append("Audio splitting complete.")
     yield "\n".join(logs), vocal, inst, vocal
 
-def cut_vocal_and_inst(split_model, audio_data):
-    logs = []
-    vocal_path = "output/result/audio.wav"
-    os.makedirs("output/result", exist_ok=True)
-    wavfile.write(vocal_path, audio_data[0], audio_data[1])
-    logs.append("Starting the audio splitting process...")
-    yield "\n".join(logs), None, None
-    command = f"demucs --two-stems=vocals -n {split_model} {vocal_path} -o output"
-    result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
-    for line in result.stdout:
-        logs.append(line)
-        yield "\n".join(logs), None, None
-    print(result.stdout)
-    vocal = f"output/{split_model}/audio/vocals.wav"
-    inst = f"output/{split_model}/audio/no_vocals.wav"
-    logs.append("Audio splitting complete.")
-    yield "\n".join(logs), vocal, inst
-    
 def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume, split_model):
-    os.makedirs("output/result", exist_ok=True)
+    if not os.path.exists("output/result"):
+        os.mkdir("output/result")
     vocal_path = "output/result/output.wav"
     output_path = "output/result/combine.mp3"
     inst_path = f"output/{split_model}/audio/no_vocals.wav"
-    wavfile.write(vocal_path, audio_data[0], audio_data[1])
+    with wave.open(vocal_path, "w") as wave_file:
+        wave_file.setnchannels(1) 
+        wave_file.setsampwidth(2)
+        wave_file.setframerate(audio_data[0])
+        wave_file.writeframes(audio_data[1].tobytes())
     command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}'
     result = subprocess.run(command.split(), stdout=subprocess.PIPE)
     print(result.stdout.decode())
     return output_path
 
-def download_and_extract_models(urls):
-    logs = []
-    os.makedirs("zips", exist_ok=True)
-    os.makedirs(os.path.join("zips", "extract"), exist_ok=True)
-    os.makedirs(os.path.join(weight_root), exist_ok=True)
-    os.makedirs(os.path.join(index_root), exist_ok=True)
-    for link in urls.splitlines():
-        url = link.strip()
-        if not url:
-            raise gr.Error("URL Required!")
-            return "No URLs provided."
-        model_zip = urlparse(url).path.split('/')[-2] + '.zip'
-        model_zip_path = os.path.join('zips', model_zip)
-        logs.append(f"Downloading...")
-        yield "\n".join(logs)
-        if "drive.google.com" in url:
-            gdown.download(url, os.path.join("zips", "extract"), quiet=False)
-        elif "mega.nz" in url:
-            m = Mega()
-            m.download_url(url, 'zips')
-        else:
-            os.system(f"wget {url} -O {model_zip_path}")
-        logs.append(f"Extracting...")
-        yield "\n".join(logs)
-        for filename in os.listdir("zips"):
-            archived_file = os.path.join("zips", filename)
-            if filename.endswith(".zip"):
-                shutil.unpack_archive(archived_file, os.path.join("zips", "extract"), 'zip')
-            elif filename.endswith(".rar"):
-                with rarfile.RarFile(archived_file, 'r') as rar:
-                    rar.extractall(os.path.join("zips", "extract"))
-        for _, dirs, files in os.walk(os.path.join("zips", "extract")):
-            logs.append(f"Searching Model and Index...")
-            yield "\n".join(logs)
-            model = False
-            index = False
-            if files:
-                for file in files:
-                    if file.endswith(".pth"):
-                        basename = file[:-4]
-                        shutil.move(os.path.join("zips", "extract", file), os.path.join(weight_root, file))
-                        model = True
-                    if file.endswith('.index') and "trained" not in file:
-                        shutil.move(os.path.join("zips", "extract", file), os.path.join(index_root, file))
-                        index = True
-            else:
-                logs.append("No model in main folder.")
-                yield "\n".join(logs)
-                logs.append("Searching in subfolders...")
-                yield "\n".join(logs)
-                for sub_dir in dirs:
-                    for _, _, sub_files in os.walk(os.path.join("zips", "extract", sub_dir)):
-                        for file in sub_files:
-                            if file.endswith(".pth"):
-                                basename = file[:-4]
-                                shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(weight_root, file))
-                                model = True
-                            if file.endswith('.index') and "trained" not in file:
-                                shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(index_root, file))
-                                index = True  
-                        shutil.rmtree(os.path.join("zips", "extract", sub_dir))
-            if index is False:
-                logs.append("Model only file, no Index file detected.")
-                yield "\n".join(logs)
-        logs.append("Download Completed!")
-        yield "\n".join(logs)
-    logs.append("Successfully download all models! Refresh your model list to load the model")
-    yield "\n".join(logs)
-
-def use_microphone(microphone):
-    if microphone == True:
-        return gr.Audio.update(source="microphone")
+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:
-        return gr.Audio.update(source="upload")
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
 
 def change_audio_mode(vc_audio_mode):
     if vc_audio_mode == "Input path":
@@ -523,17 +266,16 @@ def change_audio_mode(vc_audio_mode):
             gr.Textbox.update(visible=False),
             gr.Button.update(visible=False),
             # Splitter
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Button.update(visible=True),
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
             gr.Button.update(visible=False),
             gr.Audio.update(visible=False),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Button.update(visible=True),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Button.update(visible=False),
             # TTS
             gr.Textbox.update(visible=False),
             gr.Dropdown.update(visible=False)
@@ -550,17 +292,16 @@ def change_audio_mode(vc_audio_mode):
             gr.Textbox.update(visible=False),
             gr.Button.update(visible=False),
             # Splitter
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
             gr.Button.update(visible=False),
-            gr.Button.update(visible=True),
             gr.Audio.update(visible=False),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Button.update(visible=True),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Button.update(visible=False),
             # TTS
             gr.Textbox.update(visible=False),
             gr.Dropdown.update(visible=False)
@@ -580,7 +321,6 @@ def change_audio_mode(vc_audio_mode):
             gr.Dropdown.update(visible=True),
             gr.Textbox.update(visible=True),
             gr.Button.update(visible=True),
-            gr.Button.update(visible=False),
             gr.Audio.update(visible=True),
             gr.Audio.update(visible=True),
             gr.Audio.update(visible=True),
@@ -607,7 +347,6 @@ def change_audio_mode(vc_audio_mode):
             gr.Dropdown.update(visible=False),
             gr.Textbox.update(visible=False),
             gr.Button.update(visible=False),
-            gr.Button.update(visible=False),
             gr.Audio.update(visible=False),
             gr.Audio.update(visible=False),
             gr.Audio.update(visible=False),
@@ -619,324 +358,322 @@ def change_audio_mode(vc_audio_mode):
             gr.Textbox.update(visible=True),
             gr.Dropdown.update(visible=True)
         )
-        
-with gr.Blocks() as app:
-    gr.Markdown(
-        "# <center> Advanced RVC Inference\n"
-    )
-    with gr.Row():
-        sid = gr.Dropdown(
-            label="Weight",
-            choices=sorted(weights_model),
-        )
-        file_index = gr.Dropdown(
-            label="List of index file",
-            choices=sorted(weights_index),
-            interactive=True,
-        )
-        spk_item = gr.Slider(
-            minimum=0,
-            maximum=2333,
-            step=1,
-            label="Speaker ID",
-            value=0,
-            visible=False,
-            interactive=True,
-        )
-        refresh_model = gr.Button("Refresh model list", variant="primary")
-        clean_button = gr.Button("Clear Model from memory", variant="primary")
-        refresh_model.click(
-            fn=check_models, inputs=[], outputs=[sid, file_index]
-        )
-        clean_button.click(fn=clean, inputs=[], outputs=[sid, spk_item])
-    with gr.TabItem("Inference"):
-        selected_model = gr.Markdown(value="# <center> No model selected")
-        with gr.Row():
-            with gr.Column():
-                vc_audio_mode = gr.Dropdown(label="Input voice", choices=["Input path", "Upload audio", "Youtube", "TTS Audio"], allow_custom_value=False, value="Upload audio")
-                # Input
-                vc_input = gr.Textbox(label="Input audio path", visible=False)
-                # Upload
-                vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
-                vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
-                # Youtube
-                vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
-                vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-                vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
-                vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
-                vc_audio_preview = gr.Audio(label="Downloaded Audio Preview", visible=False)
-                # TTS
-                tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
-                tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                # Splitter
-                vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=True, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                vc_split_log = gr.Textbox(label="Output Information", visible=True, interactive=False)
-                vc_split_yt = gr.Button("Split Audio", variant="primary", visible=False)
-                vc_split = gr.Button("Split Audio", variant="primary", visible=True)
-                vc_vocal_preview = gr.Audio(label="Vocal Preview", interactive=False, visible=True)
-                vc_inst_preview = gr.Audio(label="Instrumental Preview", interactive=False, visible=True)
-            with gr.Column():
-                vc_transform0 = gr.Number(
-                    label="Transpose", 
-                    info='Type "12" to change from male to female convertion or Type "-12" to change female to male convertion.',
-                    value=0
-                )
-                f0method0 = gr.Radio(
-                    label="Pitch extraction algorithm",
-                    info=f0method_info,
-                    choices=f0method_mode,
-                    value="pm",
-                    interactive=True,
-                )
-                index_rate0 = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    label="Retrieval feature ratio",
-                    value=0.7,
-                    interactive=True,
-                )
-                filter_radius0 = gr.Slider(
-                    minimum=0,
-                    maximum=7,
-                    label="Apply Median Filtering",
-                    info="The value represents the filter radius and can reduce breathiness.",
-                    value=3,
-                    step=1,
-                    interactive=True,
-                )
-                resample_sr0 = gr.Slider(
-                    minimum=0,
-                    maximum=48000,
-                    label="Resample the output audio",
-                    info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
-                    value=0,
-                    step=1,
-                    interactive=True,
-                )
-                rms_mix_rate0 = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    label="Volume Envelope",
-                    info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
-                    value=1,
-                    interactive=True,
-                )
-                protect0 = gr.Slider(
-                    minimum=0,
-                    maximum=0.5,
-                    label="Voice Protection",
-                    info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
-                    value=0.5,
-                    step=0.01,
-                    interactive=True,
-                )
-                f0_file0 = gr.File(
-                    label="F0 curve file (Optional)",
-                    info="One pitch per line, Replace the default F0 and pitch modulation"
-                )
-            with gr.Column():
-                vc_log = gr.Textbox(label="Output Information", interactive=False)
-                vc_output = gr.Audio(label="Output Audio", interactive=False)
-                vc_convert = gr.Button("Convert", variant="primary")
-                vc_vocal_volume = gr.Slider(
-                    minimum=0,
-                    maximum=10,
-                    label="Vocal volume",
-                    value=1,
-                    interactive=True,
-                    step=1,
-                    info="Adjust vocal volume (Default: 1}",
-                    visible=True
-                )
-                vc_inst_volume = gr.Slider(
-                    minimum=0,
-                    maximum=10,
-                    label="Instrument volume",
-                    value=1,
-                    interactive=True,
-                    step=1,
-                    info="Adjust instrument volume (Default: 1}",
-                    visible=True
-                )
-                vc_combined_output = gr.Audio(label="Output Combined Audio", visible=True)
-                vc_combine =  gr.Button("Combine",variant="primary", visible=True)
-        vc_convert.click(
-            vc_single,
-            [
-                spk_item,
-                vc_audio_mode,
-                vc_input,
-                vc_upload,
-                vc_vocal_preview,
-                tts_text,
-                tts_voice,
-                vc_transform0,
-                f0_file0,
-                f0method0,
-                file_index,
-                index_rate0,
-                filter_radius0,
-                resample_sr0,
-                rms_mix_rate0,
-                protect0,
-            ],
-            [vc_log, vc_output],
-        )
-        vc_download_button.click(
-            fn=download_audio, 
-            inputs=[vc_link, vc_download_audio], 
-            outputs=[vc_audio_preview, vc_log_yt]
-        )
-        vc_split_yt.click(
-            fn=cut_vocal_and_inst_yt, 
-            inputs=[vc_split_model], 
-            outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input]
-        )
-        vc_split.click(
-            fn=cut_vocal_and_inst, 
-            inputs=[vc_split_model, vc_upload],
-            outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview]
-        )
-        vc_combine.click(
-            fn=combine_vocal_and_inst,
-            inputs=[vc_output, vc_vocal_volume, vc_inst_volume, vc_split_model],
-            outputs=[vc_combined_output]
-        )
-        vc_microphone_mode.change(
-            fn=use_microphone,
-            inputs=vc_microphone_mode,
-            outputs=vc_upload
-        )
-        vc_audio_mode.change(
-            fn=change_audio_mode,
-            inputs=[vc_audio_mode],
-            outputs=[
-                # Input & Upload
-                vc_input,
-                vc_microphone_mode,
-                vc_upload,
-                # Youtube
-                vc_download_audio,
-                vc_link,
-                vc_log_yt,
-                vc_download_button,
-                # Splitter
-                vc_split_model,
-                vc_split_log,
-                vc_split_yt,
-                vc_split,
-                vc_audio_preview,
-                vc_vocal_preview,
-                vc_inst_preview,
-                vc_vocal_volume,
-                vc_inst_volume,
-                vc_combined_output,
-                vc_combine,
-                # TTS
-                tts_text,
-                tts_voice
-            ]
-        )
-        sid.change(fn=get_vc, inputs=[sid, protect0], outputs=[spk_item, protect0, file_index, selected_model])
-    with gr.TabItem("Batch Inference"):
-        with gr.Row():
-            with gr.Column():
-                vc_input_bat = gr.Textbox(label="Input audio path (folder)", visible=True)
-                vc_output_bat = gr.Textbox(label="Output audio path (folder)", value="result/batch", visible=True)
-            with gr.Column():
-                vc_transform0_bat = gr.Number(
-                    label="Transpose", 
-                    info='Type "12" to change from male to female convertion or Type "-12" to change female to male convertion.',
-                    value=0
-                )
-                f0method0_bat = gr.Radio(
-                    label="Pitch extraction algorithm",
-                    info=f0method_info,
-                    choices=f0method_mode,
-                    value="pm",
-                    interactive=True,
-                )
-                index_rate0_bat = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    label="Retrieval feature ratio",
-                    value=0.7,
-                    interactive=True,
-                )
-                filter_radius0_bat = gr.Slider(
-                    minimum=0,
-                    maximum=7,
-                    label="Apply Median Filtering",
-                    info="The value represents the filter radius and can reduce breathiness.",
-                    value=3,
-                    step=1,
-                    interactive=True,
-                )
-                resample_sr0_bat = gr.Slider(
-                    minimum=0,
-                    maximum=48000,
-                    label="Resample the output audio",
-                    info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
-                    value=0,
-                    step=1,
-                    interactive=True,
-                )
-                rms_mix_rate0_bat = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    label="Volume Envelope",
-                    info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
-                    value=1,
-                    interactive=True,
-                )
-                protect0_bat = gr.Slider(
-                    minimum=0,
-                    maximum=0.5,
-                    label="Voice Protection",
-                    info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
-                    value=0.5,
-                    step=0.01,
-                    interactive=True,
-                )
-            with gr.Column():
-                vc_log_bat = gr.Textbox(label="Output Information", interactive=False)
-                vc_convert_bat = gr.Button("Convert", variant="primary")
-        vc_convert_bat.click(
-            vc_multi,
-            [
-                spk_item,
-                vc_input_bat,
-                vc_output_bat,
-                vc_transform0_bat,
-                f0method0_bat,
-                file_index,
-                index_rate0_bat,
-                filter_radius0_bat,
-                resample_sr0_bat,
-                rms_mix_rate0_bat,
-                protect0_bat,
-            ],
-            [vc_log_bat],
-        )
-    with gr.TabItem("Model Downloader"):
+
+def use_microphone(microphone):
+    if microphone == True:
+        return gr.Audio.update(source="microphone")
+    else:
+        return gr.Audio.update(source="upload")
+
+if __name__ == '__main__':
+    load_hubert()
+    categories = load_model()
+    tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
+    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
+    with gr.Blocks() as app:
         gr.Markdown(
-            "# <center> Model Downloader (Beta)\n"+
-            "#### <center> To download multi link you have to put your link to the textbox and every link separated by space\n"+
-            "#### <center> Support Direct Link, Mega, Google Drive, etc"
+            "<div align='center'>\n\n"+
+            "# RVC Wuthering Waves\n\n"+
+            "### Recommended to use Google Colab to use other character and feature.\n\n"+
+            "[![Colab](https://img.shields.io/badge/Colab-RVC%20Blue%20Archives-blue?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/drive/19Eo2xO7EKcMqvJDc_yXrWmixuNA4NtEU)\n\n"+
+            "</div>\n\n"+
+            "[![Repository](https://img.shields.io/badge/Github-Multi%20Model%20RVC%20Inference-blue?style=for-the-badge&logo=github)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)\n\n"+
+            "</div>"
         )
-        with gr.Column():
-            md_text = gr.Textbox(label="URL")
-        with gr.Row():
-            md_download = gr.Button(label="Convert", variant="primary")
-            md_download_logs = gr.Textbox(label="Output information", interactive=False)
-            md_download.click(
-                fn=download_and_extract_models,
-                inputs=[md_text],
-                outputs=[md_download_logs]
+        if categories == []:
+            gr.Markdown(
+                "<div align='center'>\n\n"+
+                "## No model found, please add the model into weights folder\n\n"+
+                "</div>"
             )
-    with gr.TabItem("Settings"):
-        gr.Markdown(
-            "# <center> Settings\n"+
-            "#### <center> Work in progress"
-        )
-    app.queue(concurrency_count=1, max_size=50, api_open=config.api).launch(share=config.colab)
+        for (folder_title, folder, description, models) in categories:
+            with gr.TabItem(folder_title):
+                if description:
+                    gr.Markdown(f"### <center> {description}")
+                with gr.Tabs():
+                    if not models:
+                        gr.Markdown("# <center> No Model Loaded.")
+                        gr.Markdown("## <center> Please add the model or fix your model path.")
+                        continue
+                    for (name, title, author, cover, model_version, vc_fn) in models:
+                        with gr.TabItem(name):
+                            with gr.Row():
+                                gr.Markdown(
+                                    '<div align="center">'
+                                    f'<div>{title}</div>\n'+
+                                    f'<div>RVC {model_version} Model</div>\n'+
+                                    (f'<div>Model author: {author}</div>' if author else "")+
+                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
+                                    '</div>'
+                                )
+                            with gr.Row():
+                                if spaces is False:
+                                    with gr.TabItem("Input"):
+                                        with gr.Row():
+                                            with gr.Column():
+                                                vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
+                                                # Input
+                                                vc_input = gr.Textbox(label="Input audio path", visible=False)
+                                                # Upload
+                                                vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
+                                                vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
+                                                # Youtube
+                                                vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
+                                                vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
+                                                vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                                vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
+                                                vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
+                                                # TTS
+                                                tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
+                                                tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
+                                            with gr.Column():
+                                                vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
+                                                vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                                vc_split = gr.Button("Split Audio", variant="primary", visible=False)
+                                                vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
+                                                vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
+                                    with gr.TabItem("Convert"):
+                                        with gr.Row():
+                                            with gr.Column():
+                                                vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
+                                                f0method0 = gr.Radio(
+                                                    label="Pitch extraction algorithm",
+                                                    info=f0method_info,
+                                                    choices=f0method_mode,
+                                                    value="pm",
+                                                    interactive=True
+                                                )
+                                                index_rate1 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=1,
+                                                    label="Retrieval feature ratio",
+                                                    info="(Default: 0.7)",
+                                                    value=0.7,
+                                                    interactive=True,
+                                                )
+                                                filter_radius0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=7,
+                                                    label="Apply Median Filtering",
+                                                    info="The value represents the filter radius and can reduce breathiness.",
+                                                    value=3,
+                                                    step=1,
+                                                    interactive=True,
+                                                )
+                                                resample_sr0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=48000,
+                                                    label="Resample the output audio",
+                                                    info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
+                                                    value=0,
+                                                    step=1,
+                                                    interactive=True,
+                                                )
+                                                rms_mix_rate0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=1,
+                                                    label="Volume Envelope",
+                                                    info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
+                                                    value=1,
+                                                    interactive=True,
+                                                )
+                                                protect0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=0.5,
+                                                    label="Voice Protection",
+                                                    info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
+                                                    value=0.5,
+                                                    step=0.01,
+                                                    interactive=True,
+                                                )
+                                            with gr.Column():
+                                                vc_log = gr.Textbox(label="Output Information", interactive=False)
+                                                vc_output = gr.Audio(label="Output Audio", interactive=False)
+                                                vc_convert = gr.Button("Convert", variant="primary")
+                                                vc_vocal_volume = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=10,
+                                                    label="Vocal volume",
+                                                    value=1,
+                                                    interactive=True,
+                                                    step=1,
+                                                    info="Adjust vocal volume (Default: 1}",
+                                                    visible=False
+                                                )
+                                                vc_inst_volume = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=10,
+                                                    label="Instrument volume",
+                                                    value=1,
+                                                    interactive=True,
+                                                    step=1,
+                                                    info="Adjust instrument volume (Default: 1}",
+                                                    visible=False
+                                                )
+                                                vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
+                                                vc_combine =  gr.Button("Combine",variant="primary", visible=False)
+                                else:
+                                    with gr.Column():
+                                        vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
+                                        # Input
+                                        vc_input = gr.Textbox(label="Input audio path", visible=False)
+                                        # Upload
+                                        vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
+                                        vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
+                                        # Youtube
+                                        vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
+                                        vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
+                                        vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                        vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
+                                        vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
+                                        # Splitter
+                                        vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
+                                        vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                        vc_split = gr.Button("Split Audio", variant="primary", visible=False)
+                                        vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
+                                        vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
+                                        # TTS
+                                        tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
+                                        tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
+                                    with gr.Column():
+                                        vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
+                                        f0method0 = gr.Radio(
+                                            label="Pitch extraction algorithm",
+                                            info=f0method_info,
+                                            choices=f0method_mode,
+                                            value="pm",
+                                            interactive=True
+                                        )
+                                        index_rate1 = gr.Slider(
+                                            minimum=0,
+                                            maximum=1,
+                                            label="Retrieval feature ratio",
+                                            info="(Default: 0.7)",
+                                            value=0.7,
+                                            interactive=True,
+                                        )
+                                        filter_radius0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=7,
+                                            label="Apply Median Filtering",
+                                            info="The value represents the filter radius and can reduce breathiness.",
+                                            value=3,
+                                            step=1,
+                                            interactive=True,
+                                        )
+                                        resample_sr0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=48000,
+                                            label="Resample the output audio",
+                                            info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
+                                            value=0,
+                                            step=1,
+                                            interactive=True,
+                                        )
+                                        rms_mix_rate0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=1,
+                                            label="Volume Envelope",
+                                            info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
+                                            value=1,
+                                            interactive=True,
+                                        )
+                                        protect0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=0.5,
+                                            label="Voice Protection",
+                                            info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
+                                            value=0.5,
+                                            step=0.01,
+                                            interactive=True,
+                                        )
+                                    with gr.Column():
+                                        vc_log = gr.Textbox(label="Output Information", interactive=False)
+                                        vc_output = gr.Audio(label="Output Audio", interactive=False)
+                                        vc_convert = gr.Button("Convert", variant="primary")
+                                        vc_vocal_volume = gr.Slider(
+                                            minimum=0,
+                                            maximum=10,
+                                            label="Vocal volume",
+                                            value=1,
+                                            interactive=True,
+                                            step=1,
+                                            info="Adjust vocal volume (Default: 1}",
+                                            visible=False
+                                        )
+                                        vc_inst_volume = gr.Slider(
+                                            minimum=0,
+                                            maximum=10,
+                                            label="Instrument volume",
+                                            value=1,
+                                            interactive=True,
+                                            step=1,
+                                            info="Adjust instrument volume (Default: 1}",
+                                            visible=False
+                                        )
+                                        vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
+                                        vc_combine =  gr.Button("Combine",variant="primary", visible=False)
+                        vc_convert.click(
+                            fn=vc_fn, 
+                            inputs=[
+                                vc_audio_mode,
+                                vc_input,
+                                vc_upload,
+                                tts_text,
+                                tts_voice,
+                                vc_transform0,
+                                f0method0,
+                                index_rate1,
+                                filter_radius0,
+                                resample_sr0,
+                                rms_mix_rate0,
+                                protect0,
+                            ], 
+                            outputs=[vc_log ,vc_output]
+                        )
+                        vc_download_button.click(
+                            fn=download_audio, 
+                            inputs=[vc_link, vc_download_audio], 
+                            outputs=[vc_audio_preview, vc_log_yt]
+                        )
+                        vc_split.click(
+                            fn=cut_vocal_and_inst, 
+                            inputs=[vc_split_model], 
+                            outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input]
+                        )
+                        vc_combine.click(
+                            fn=combine_vocal_and_inst,
+                            inputs=[vc_output, vc_vocal_volume, vc_inst_volume, vc_split_model],
+                            outputs=[vc_combined_output]
+                        )
+                        vc_microphone_mode.change(
+                            fn=use_microphone,
+                            inputs=vc_microphone_mode,
+                            outputs=vc_upload
+                        )
+                        vc_audio_mode.change(
+                            fn=change_audio_mode,
+                            inputs=[vc_audio_mode],
+                            outputs=[
+                                vc_input,
+                                vc_microphone_mode,
+                                vc_upload,
+                                vc_download_audio,
+                                vc_link,
+                                vc_log_yt,
+                                vc_download_button,
+                                vc_split_model,
+                                vc_split_log,
+                                vc_split,
+                                vc_audio_preview,
+                                vc_vocal_preview,
+                                vc_inst_preview,
+                                vc_vocal_volume,
+                                vc_inst_volume,
+                                vc_combined_output,
+                                vc_combine,
+                                tts_text,
+                                tts_voice
+                            ]
+                        )
+        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)