diff --git a/config.py b/config.py
new file mode 100644
index 0000000..ed538b4
--- /dev/null
+++ b/config.py
@@ -0,0 +1,109 @@
+########################硬件参数########################
+
+# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
+device = "cuda:0"
+
+# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
+is_half = True
+
+# 默认0用上所有线程，写数字限制CPU资源使用
+n_cpu = 0
+
+########################硬件参数########################
+
+
+##################下为参数处理逻辑，勿动##################
+
+########################命令行参数########################
+if __name__ == '__main__':
+  import argparse
+
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--port", type=int, default=7865, help="Listen port")
+  parser.add_argument("--pycmd", type=str, default="python", help="Python command")
+  parser.add_argument("--colab", action="store_true", help="Launch in colab")
+  parser.add_argument(
+      "--noparallel", action="store_true", help="Disable parallel processing"
+  )
+  parser.add_argument(
+      "--noautoopen", action="store_true", help="Do not open in browser automatically"
+  )
+  cmd_opts = parser.parse_args()
+
+  python_cmd = cmd_opts.pycmd
+  listen_port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
+  iscolab = cmd_opts.colab
+  noparallel = cmd_opts.noparallel
+  noautoopen = cmd_opts.noautoopen
+########################命令行参数########################
+python_cmd = "python"
+iscolab = True
+
+import sys
+import torch
+
+
+# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+# check `getattr` and try it for compatibility
+def has_mps() -> bool:
+    if sys.platform != "darwin":
+        return False
+    else:
+        if not getattr(torch, "has_mps", False):
+            return False
+        try:
+            torch.zeros(1).to(torch.device("mps"))
+            return True
+        except Exception:
+            return False
+
+
+if not torch.cuda.is_available():
+    if has_mps():
+        print("没有发现支持的N卡, 使用MPS进行推理")
+        device = "mps"
+    else:
+        print("没有发现支持的N卡, 使用CPU进行推理")
+        device = "cpu"
+        is_half = False
+
+gpu_mem=None
+if device not in ["cpu", "mps"]:
+    i_device=int(device.split(":")[-1])
+    gpu_name = torch.cuda.get_device_name(i_device)
+    if "16" in gpu_name or "P40"in gpu_name.upper() or "1070"in gpu_name or "1080"in gpu_name:
+        print("16系显卡强制单精度")
+        is_half = False
+        with open("configs/32k.json","r")as f:strr=f.read().replace("true","false")
+        with open("configs/32k.json","w")as f:f.write(strr)
+        with open("configs/40k.json","r")as f:strr=f.read().replace("true","false")
+        with open("configs/40k.json","w")as f:f.write(strr)
+        with open("configs/48k.json","r")as f:strr=f.read().replace("true","false")
+        with open("configs/48k.json","w")as f:f.write(strr)
+        with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
+        with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
+    gpu_mem=int(torch.cuda.get_device_properties(i_device).total_memory/1024/1024/1024+0.4)
+    if(gpu_mem<=4):
+        with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
+        with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
+from multiprocessing import cpu_count
+
+if n_cpu == 0:
+    n_cpu = cpu_count()
+if is_half:
+    # 6G显存配置
+    x_pad = 3
+    x_query = 10
+    x_center = 60
+    x_max = 65
+else:
+    # 5G显存配置
+    x_pad = 1
+    x_query = 6
+    x_center = 38
+    x_max = 41
+if(gpu_mem!=None and gpu_mem<=4):
+    x_pad = 1
+    x_query = 5
+    x_center = 30
+    x_max = 32
\ No newline at end of file
diff --git a/easy-infer-cell.py b/easy-infer-cell.py
new file mode 100644
index 0000000..83bd035
--- /dev/null
+++ b/easy-infer-cell.py
@@ -0,0 +1,552 @@
+%cd /content/Retrieval-based-Voice-Conversion-WebUI
+from multiprocessing import cpu_count
+import threading, pdb, librosa
+from time import sleep
+from subprocess import Popen
+from time import sleep
+import torch, os, traceback, sys, warnings, shutil, numpy as np
+import faiss
+from random import shuffle
+import scipy.io.wavfile as wavfile
+from mega import Mega
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, ignore_errors=True)
+os.makedirs(tmp, exist_ok=True)
+os.makedirs("audios",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 ffmpeg
+import datetime
+import subprocess
+
+print("Epic")
+
+i18n = I18nAuto()
+# 判断是否有能用来训练和加速推理的N卡
+ncpu = cpu_count()
+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 "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
+                )
+            )
+
+print("GPU CHeck")
+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 = "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])
+
+print("Time to import")
+from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
+from scipy.io import wavfile
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import (
+    is_half,
+    device,
+    #python_cmd,
+    #listen_port,
+    #iscolab,
+    #noparallel,
+    #noautoopen,
+)
+# FIX
+iscolab = True
+noautoopen = False
+
+from infer_uvr5 import _audio_pre_
+from my_utils import load_audio
+from train.process_ckpt import show_info, change_info, merge, extract_small_model
+
+print("Checking log")
+# from trainset_preprocess_pipeline import PreProcess
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+print("Epic2")
+
+
+class ToolButton(gr.Button, gr.components.FormComponent):
+    """Small button with single emoji as text, fits inside gradio forms"""
+
+    def __init__(self, **kwargs):
+        super().__init__(variant="tool", **kwargs)
+
+    def get_block_name(self):
+        return "button"
+
+
+hubert_model = None
+
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(device)
+    if is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+
+weight_root = "weights"
+weight_uvr5_root = "uvr5_weights"
+names = []
+for name in os.listdir(weight_root):
+    if name.endswith(".pth"):
+        names.append(name)
+        
+uvr5_names = []
+for name in os.listdir(weight_uvr5_root):
+    if name.endswith(".pth"):
+        uvr5_names.append(name.replace(".pth", ""))
+
+def find_parent(search_dir, file_name):
+    for dirpath, dirnames, filenames in os.walk(search_dir):
+        if file_name in filenames:
+            return os.path.abspath(dirpath)
+    return None
+
+def vc_single(
+    sid,
+    input_audio,
+    f0_up_key,
+    f0_file,
+    f0_method,
+    file_index,
+    # file_big_npy,
+    index_rate,
+):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+    global tgt_sr, net_g, vc, hubert_model
+    if input_audio is None:
+        return "You need to upload an audio", None
+    f0_up_key = int(f0_up_key)
+    try:
+        parent_dir = find_parent(".",input_audio)
+        audio = load_audio(parent_dir+'/'+input_audio, 16000)
+        times = [0, 0, 0]
+        if hubert_model == None:
+            load_hubert()
+        if_f0 = cpt.get("f0", 1)
+        try:
+            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
+        except:
+            file_index=''
+            print("Skipped index.")
+        # file_big_npy = (
+        #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        # )
+        audio_opt = vc.pipeline(
+            hubert_model,
+            net_g,
+            sid,
+            audio,
+            times,
+            f0_up_key,
+            f0_method,
+            file_index,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            f0_file=f0_file,
+        )
+        print(
+            "npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep=""
+        )
+        return "Success", (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_big_npy,
+    index_rate,
+):
+    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 = []
+        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
+        for path in paths:
+            info, opt = vc_single(
+                sid,
+                path,
+                f0_up_key,
+                None,
+                f0_method,
+                file_index,
+                # file_big_npy,
+                index_rate,
+            )
+            if info == "Success":
+                try:
+                    tgt_sr, audio_opt = opt
+                    wavfile.write(
+                        "%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt
+                    )
+                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
+    if 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)
+            if if_f0 == 1:
+                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+            else:
+                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            del net_g, cpt
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            cpt = None
+        return {"visible": False, "__type__": "update"}
+    person = "%s/%s" % (weight_root, sid)
+    print("loading %s" % person)
+    cpt = torch.load(person, map_location="cpu")
+    tgt_sr = cpt["config"][-1]
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    if_f0 = cpt.get("f0", 1)
+    if if_f0 == 1:
+        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+    else:
+        net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    del net_g.enc_q
+    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
+    net_g.eval().to(device)
+    if is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, device, is_half)
+    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)
+    return {"choices": sorted(names), "__type__": "update"}
+
+def change_choices2():
+    audio_files=[]
+    for filename in os.listdir("./audios"):
+        if filename.endswith(('.wav','.mp3')):
+            audio_files.append(filename)
+    return {"choices": sorted(audio_files), "__type__": "update"}
+
+def clean():
+    return {"value": "", "__type__": "update"}
+
+def change_sr2(sr2, if_f0_3):
+    if if_f0_3 == "是":
+        return "pretrained/f0G%s.pth" % sr2, "pretrained/f0D%s.pth" % sr2
+    else:
+        return "pretrained/G%s.pth" % sr2, "pretrained/D%s.pth" % sr2
+        
+def get_index():
+    if check_for_name() != '':
+        if iscolab:
+            chosen_model=sorted(names)[0].split(".")[0]
+            logs_path="/content/Retrieval-based-Voice-Conversion-WebUI/logs/"+chosen_model
+            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=[]
+    if iscolab:
+        for dirpath, dirnames, filenames in os.walk("/content/Retrieval-based-Voice-Conversion-WebUI/logs/"):
+            for filename in filenames:
+                if filename.endswith(".index"):
+                    indexes_list.append(os.path.join(dirpath,filename))
+        return indexes_list
+    else:
+        return ''
+        
+audio_files=[]
+for filename in os.listdir("./audios"):
+    if filename.endswith(('.wav','.mp3')):
+        audio_files.append(filename)
+        
+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_name
+    
+def save_to_wav2(dropbox):
+    file_path=dropbox.name
+    shutil.move(file_path,'./audios')
+    return os.path.basename(file_path)
+    
+def match_index(speaker):
+    folder=speaker.split(".")[0]
+    parent_dir="/content/Retrieval-based-Voice-Conversion-WebUI/logs/"+folder
+    for filename in os.listdir(parent_dir):
+        if filename.endswith(".index"):
+            index_path=os.path.join(parent_dir,filename)
+            return index_path
+            
+def download_from_url(url, model):
+    url = url.strip()
+    if url == '':
+        return "URL cannot be left empty."
+    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
+    MODELEPOCH = ''
+    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", f"./zips/{zipfile}"])
+    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:
+            if "G_" in file:
+                MODELEPOCH = file.split("G_")[1].split(".")[0]
+        if MODELEPOCH == '':
+            MODELEPOCH = '404'
+        for file in files:
+            file_path = os.path.join(root, file)
+            if file.endswith(".npy") or file.endswith(".index"):
+                subprocess.run(["mkdir", "-p", f"./logs/{model}"])
+                subprocess.run(["mv", file_path, f"./logs/{model}/"])
+            elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
+                subprocess.run(["mv", file_path, f"./weights/{model}.pth"])
+    shutil.rmtree("zips")
+    shutil.rmtree("unzips")
+    return "Success."
+
+def check_for_name():
+    if len(names) > 0:
+        return sorted(names)[0]
+    else:
+        return ''
+print(check_for_name())        
+#with gr.Blocks() as app
+with gr.Blocks(theme=gr.themes.Base()) as app:
+    with gr.Tab("Inference"):
+        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", size='sm')
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0])
+            if check_for_name() != '':
+                get_vc(sorted(names)[0])
+            else:
+                print("Starting without preloaded Model.")
+            vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0)
+            #clean_button = gr.Button("Unload Voice to Save Memory", variant="primary")
+            spk_item = gr.Slider(minimum=0,maximum=2333,step=1,label="Please select speaker id",value=0,visible=False,interactive=True)
+            #clean_button.click(fn=clean, inputs=[], outputs=[sid0])
+            sid0.change(
+                fn=get_vc,
+                inputs=[sid0],
+                outputs=[],
+            )
+            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.Textbox(label="Enter the Path to the Audio File to be Processed (e.g. /content/youraudio.wav)",value="/content/youraudio.wav")
+                    input_audio0 = gr.Dropdown(choices=sorted(audio_files), label="2.Choose your audio.", value=get_name())
+                    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')
+                    refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                    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.Column():
+                with gr.Accordion(label="Feature Settings", open=False):
+                    file_index1 = gr.Dropdown(
+                        label="3. Path to your added.index file (if it didn't automatically find it.)",
+                        value=get_index(),
+                        choices=get_indexes(),
+                        interactive=True,
+                        visible=True
+                    )
+                    index_rate1 = gr.Slider(
+                    minimum=0,
+                    maximum=1,
+                    label="Strength:",
+                    value=0.69,
+                    interactive=True,
+                    )
+                sid0.change(fn=match_index, inputs=[sid0], outputs=[file_index1])
+                
+                with gr.Row():
+                    vc_output2 = gr.Audio(label="Output Audio (Click on the Three Dots in the Right Corner to Download)") 
+                with gr.Row():
+                    f0method0 = gr.Radio(
+                    label="Optional: Change the Pitch Extraction Algorithm. Use PM for fast results or Harvest for better low range (but it's extremely slow)",
+                    choices=["pm", "harvest"],
+                    value="pm",
+                    interactive=True,
+                    )
+        with gr.Row():
+            vc_output1 = gr.Textbox(label="")
+        with gr.Row():
+            instructions = gr.Markdown("""
+                This is simply a modified version of the RVC GUI found here: 
+                https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
+                """)
+        f0_file = gr.File(label="F0 Curve File (Optional, One Pitch Per Line, Replaces Default F0 and Pitch Shift)", visible=False)        
+        but0.click(
+            vc_single,
+            [
+                spk_item,
+                input_audio0,
+                vc_transform0,
+                f0_file,
+                f0method0,
+                file_index1,
+                index_rate1,
+            ],
+            [vc_output1, vc_output2]
+        )
+    with gr.Tab("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(label="Download")
+        with gr.Row():
+            status_bar=gr.Textbox(label="")
+            download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])
+    if iscolab:
+        app.launch(debug=True)
+    else:
+        app.queue(concurrency_count=511, max_size=1022).launch(
+            server_name="0.0.0.0",
+            inbrowser=not noautoopen,
+            server_port=listen_port,
+            quiet=True,
+        )