diff --git a/gui.py b/gui.py
new file mode 100644
index 0000000..8987197
--- /dev/null
+++ b/gui.py
@@ -0,0 +1,330 @@
+import PySimpleGUI as sg
+import sounddevice as sd
+#import noisereduce as nr
+import numpy as np
+from fairseq import checkpoint_utils
+import librosa,torch,parselmouth,faiss,time,threading
+import torch.nn.functional as F
+
+#import matplotlib.pyplot as plt
+from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class RVC:
+    def __init__(self,key,pth_path,index_path,npy_path) -> None:
+        '''
+        初始化
+        '''
+        self.f0_up_key=key
+        self.time_step = 160 / 16000 * 1000
+        self.f0_min = 50
+        self.f0_max = 1100
+        self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
+        self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
+        self.index=faiss.read_index(index_path)
+        self.big_npy=np.load(npy_path)
+        model_path = "TEMP\\hubert_base.pt"
+        print("load model(s) from {}".format(model_path))
+        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+            [model_path],
+            suffix="",
+        )
+        self.model = models[0]
+        self.model = self.model.to(device)
+        self.model = self.model.half()
+        self.model.eval()
+        cpt = torch.load(pth_path, 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):
+            self.net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=True)
+        else:
+            self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+        del self.net_g.enc_q
+        print(self.net_g.load_state_dict(cpt["weight"], strict=False))
+        self.net_g.eval().to(device)
+        self.net_g.half()
+
+
+    def get_f0_coarse(self,f0):
+        f0_mel = 1127 * np.log(1 + f0 / 700)
+        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (self.f0_mel_max - self.f0_mel_min) + 1
+        f0_mel[f0_mel <= 1] = 1
+        f0_mel[f0_mel > 255] = 255
+        # f0_mel[f0_mel > 188] = 188
+        f0_coarse = np.rint(f0_mel).astype(np.int)
+        return f0_coarse
+    
+    def get_f0(self,x, p_len,f0_up_key=0):
+        f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
+            time_step=self.time_step / 1000, voicing_threshold=0.6,
+            pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
+
+        pad_size=(p_len - len(f0) + 1) // 2
+        if(pad_size>0 or p_len - len(f0) - pad_size>0):
+            f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
+        f0 *= pow(2, f0_up_key / 12)
+        # f0=suofang(f0)
+        f0bak = f0.copy()
+        f0_coarse=self.get_f0_coarse(f0)
+        return f0_coarse, f0bak
+    
+
+    def infer(self,audio:np.ndarray,sampling_rate:int) -> np.ndarray:
+        '''
+        推理函数。
+        :param audio: ndarray(n,2)
+        :sampling_rate: 采样率
+        '''
+        
+        # f0_up_key=12
+        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)
+            print('test:audio:'+str(audio.shape))
+        '''padding'''
+        
+        
+        feats = torch.from_numpy(audio).float()
+        if feats.dim() == 2:  # double channels
+            feats = feats.mean(-1)
+        assert feats.dim() == 1, feats.dim()
+        feats = feats.view(1, -1)
+        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+        inputs = {
+            "source": feats.half().to(device),
+            "padding_mask": padding_mask.to(device),
+            "output_layer": 9,  # layer 9
+        }
+        torch.cuda.synchronize()
+        with torch.no_grad():
+            logits = self.model.extract_features(**inputs)
+            feats = self.model.final_proj(logits[0])
+
+        ####索引优化
+        npy = feats[0].cpu().numpy().astype("float32")
+        D, I = self.index.search(npy, 1)
+        # feats = torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
+        index_rate=0.5
+        feats = torch.from_numpy(npy).unsqueeze(0).to(device) * index_rate + (1 - index_rate) * feats
+        feats=feats.half()
+
+        feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
+        torch.cuda.synchronize()
+        # p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
+        p_len = min(feats.shape[1],12000)#
+        pitch, pitchf = self.get_f0(audio, p_len,self.f0_up_key)
+        p_len = min(feats.shape[1],12000,pitch.shape[0])#太大了爆显存
+        torch.cuda.synchronize()
+        # print(feats.shape,pitch.shape)
+        feats = feats[:,:p_len, :]
+        pitch = pitch[:p_len]
+        pitchf = pitchf[:p_len]
+        p_len = torch.LongTensor([p_len]).to(device)
+        pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
+        pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
+        ii=0#sid
+        sid=torch.LongTensor([ii]).to(device)
+        with torch.no_grad():
+            audio = self.net_g.infer(feats, p_len,pitch,pitchf,sid)[0][0, 0].data.cpu().float().numpy()#nsf
+        torch.cuda.synchronize()
+        return audio
+
+
+class Config:
+    def __init__(self) -> None:
+        self.pth_path:str=''
+        self.index_path:str=''
+        self.npy_path:str=''
+        self.pitch:int=12
+        self.samplerate:int=44100
+        self.block_time:float=1.0#s
+        self.buffer_num:int=1
+        self.threhold:int=-30
+        self.crossfade_time:float=0.08
+        self.extra_time:float=0.04
+
+class GUI:
+    def __init__(self) -> None:
+        self.config=Config()
+        self.flag_vc=False
+        
+        self.launcher()
+    
+    def launcher(self):
+        sg.theme('LightBlue3')
+        input_devices,output_devices,_, _=self.get_devices()
+        layout=[
+            [
+                sg.Frame(title='加载模型',layout=[
+                    [sg.Input(default_text='TEMP\\atri.pth',key='pth_path'),sg.FileBrowse('选择pth文件')],
+                    [sg.Input(default_text='TEMP\\added_IVF512_Flat_atri_baseline_src_feat.index',key='index_path'),sg.FileBrowse('选择index文件')],
+                    [sg.Input(default_text='TEMP\\big_src_feature_atri.npy',key='npy_path'),sg.FileBrowse('选择npy文件')]
+                ])
+            ],
+            [
+                sg.Frame(layout=[
+                    [sg.Text("输入设备/Input Device"),sg.Combo(input_devices,key='sg_input_device',default_value=input_devices[sd.default.device[0]])],
+                    [sg.Text("输出设备/Output Device"),sg.Combo(output_devices,key='sg_output_device',default_value=output_devices[sd.default.device[1]])]
+                ],title='音频设备(请使用同种类驱动)')
+            ],
+            [
+                sg.Frame(layout=[
+                    [sg.Text('响应阈值/Silence Threhold'),sg.Slider(range=(-60,0),key='threhold',resolution=1,orientation='h',default_value=-30)],
+                    [sg.Text("音调设置/Pitch Offset"),sg.Slider(range=(-24,24),key='pitch',resolution=1,orientation='h',default_value=12)]
+                    
+                ],title='常规设置/Common'),
+                sg.Frame(layout=[
+                    [sg.Text('采样长度/Sample Length'),sg.Slider(range=(0.1,3.0),key='block_time',resolution=0.1,orientation='h',default_value=1.0)],
+                    [sg.Text('淡入淡出长度/Crossfade Length'),sg.Slider(range=(0.01,0.15),key='crossfade_length',resolution=0.01,orientation='h',default_value=0.08)],
+                    [sg.Text('额外推理时长/Extra Length'),sg.Slider(range=(0.05,3.00),key='extra_time',resolution=0.01,orientation='h',default_value=0.05)]
+                ],title='性能设置/Performance')
+            ],
+            [sg.Button('开始音频转换',key='start_vc'),sg.Button('停止音频转换',key='stop_vc')]
+        ]
+        
+        self.window=sg.Window('RVC - GUI',layout=layout)
+        self.event_handler()
+    
+    def event_handler(self):
+        while True:
+            event, values = self.window.read()
+            if event ==sg.WINDOW_CLOSED:
+                self.flag_vc=False
+                exit()
+            if event == 'start_vc' and self.flag_vc==False:
+                self.set_values(values)
+                print('pth_path:'+self.config.pth_path)
+                print('index_path:'+self.config.index_path)
+                print('npy_path:'+self.config.npy_path)
+                print('using_cuda:'+str(torch.cuda.is_available()))
+                self.start_vc()
+            if event=='stop_vc'and self.flag_vc==True:
+                self.flag_vc = False
+
+
+    def set_values(self,values):
+        self.set_devices(values["sg_input_device"],values['sg_output_device'])
+        self.config.pth_path=values['pth_path']
+        self.config.index_path=values['index_path']
+        self.config.npy_path=values['npy_path']
+        self.config.threhold=values['threhold']
+        self.config.pitch=values['pitch']
+        self.config.block_time=values['block_time']
+        self.config.crossfade_time=values['crossfade_length']
+        self.config.extra_time=values['extra_time']
+
+   
+    def start_vc(self):
+        torch.cuda.empty_cache()
+        self.flag_vc=True
+        self.block_frame=int(self.config.block_time*self.config.samplerate)
+        self.crossfade_frame=int(self.config.crossfade_time*self.config.samplerate)
+        self.sola_search_frame=int(0.012*self.config.samplerate)
+        self.delay_frame=int(0.02*self.config.samplerate)#往前预留0.02s
+        self.extra_frame=int(self.config.extra_time*self.config.samplerate)#往后预留0.04s
+        self.rvc=None
+        self.rvc=RVC(self.config.pitch,self.config.pth_path,self.config.index_path,self.config.npy_path)
+        self.input_wav:np.ndarray=np.zeros(self.extra_frame+self.crossfade_frame+self.sola_search_frame+self.block_frame)
+        self.output_wav:np.ndarray=np.zeros(self.block_frame)
+        self.sola_buffer:np.ndarray=np.zeros(self.crossfade_frame,dtype='float32')
+        self.fade_in_window:np.ndarray = np.linspace(0, 1, self.crossfade_frame)
+        self.fade_out_window:np.ndarray = 1 - self.fade_in_window
+        thread_vc=threading.Thread(target=self.soundinput)
+        thread_vc.start()
+
+
+    def soundinput(self):
+        '''
+        接受音频输入
+        '''
+        with sd.Stream(callback=self.audio_callback, blocksize=self.block_frame,samplerate=self.config.samplerate,dtype='float32'):
+            while self.flag_vc:
+                time.sleep(self.config.block_time)
+                print('Audio block passed.')
+        print('ENDing VC')
+
+
+    def audio_callback(self,indata:np.ndarray,outdata:np.ndarray, frames, times, status):
+        '''
+        音频处理
+        '''
+        start_time=time.perf_counter()
+        indata=librosa.to_mono(indata.T)
+        self.input_wav[:]=np.roll(self.input_wav,-self.block_frame)
+        
+        #TODO:Convert all numpy calculation to torch
+        '''noise gate'''
+        frame_length=1024
+        hop_length=512
+        rms=librosa.feature.rms(y=indata,frame_length=frame_length,hop_length=hop_length)
+        db_threhold=librosa.amplitude_to_db(rms,ref=1.0)[0]<self.config.threhold
+        #print(rms.shape,db.shape,db)
+        for i in range(db_threhold.shape[0]):
+            if db_threhold[i]:
+                indata[i*hop_length:(i+1)*hop_length]=0
+        self.input_wav[-self.block_frame:]=indata[:]
+        
+        #infer
+        print('input_wav:'+str(self.input_wav.shape))
+        infer_wav=librosa.resample(y=self.rvc.infer(self.input_wav[:],self.config.samplerate),orig_sr=40000,target_sr=self.config.samplerate)[-self.crossfade_frame-self.sola_search_frame-self.block_frame:]
+        print('infered_wav:'+str(infer_wav.shape))
+                
+        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
+        cor_nom = np.convolve(infer_wav[ : self.crossfade_frame + self.sola_search_frame], np.flip(self.sola_buffer), 'valid')
+        cor_den = np.sqrt(np.convolve(infer_wav[ : self.crossfade_frame + self.sola_search_frame] ** 2, np.ones(self.crossfade_frame), 'valid') + 1e-3)
+        sola_offset = np.argmax( cor_nom / cor_den)
+        print('sola offset: ' + str(sola_offset))
+        
+        # crossfade
+        self.output_wav[:]=infer_wav[sola_offset : sola_offset + self.block_frame]
+        self.output_wav[:self.crossfade_frame] *= self.fade_in_window
+        self.output_wav[:self.crossfade_frame] += self.sola_buffer[:] 
+        
+        if sola_offset < self.sola_search_frame:
+            self.sola_buffer[:] = infer_wav[-self.sola_search_frame - self.crossfade_frame + sola_offset: -self.sola_search_frame + sola_offset]* self.fade_out_window
+        else:
+            self.sola_buffer[:] = infer_wav[- self.crossfade_frame :]* self.fade_out_window
+        
+        outdata[:]=np.array([self.output_wav,self.output_wav]).T
+        print('infer time:'+str(time.perf_counter()-start_time))
+        
+    def get_devices(self,update: bool = True):
+        '''获取设备列表'''
+        if update:
+            sd._terminate()
+            sd._initialize()
+        devices = sd.query_devices()
+        hostapis = sd.query_hostapis()
+        for hostapi in hostapis:
+            for device_idx in hostapi["devices"]:
+                devices[device_idx]["hostapi_name"] = hostapi["name"]
+        input_devices = [
+            f"{d['name']} ({d['hostapi_name']})"
+            for d in devices
+            if d["max_input_channels"] > 0
+        ]
+        output_devices = [
+            f"{d['name']} ({d['hostapi_name']})"
+            for d in devices
+            if d["max_output_channels"] > 0
+        ]
+        input_devices_indices = [d["index"] for d in devices if d["max_input_channels"] > 0]
+        output_devices_indices = [
+            d["index"] for d in devices if d["max_output_channels"] > 0
+        ]
+        return input_devices, output_devices, input_devices_indices, output_devices_indices
+    
+    def set_devices(self,input_device,output_device):
+        '''设置输出设备'''
+        input_devices,output_devices,input_device_indices, output_device_indices=self.get_devices()
+        sd.default.device[0]=input_device_indices[input_devices.index(input_device)]
+        sd.default.device[1]=output_device_indices[output_devices.index(output_device)]
+        print("input device:"+str(sd.default.device[0])+":"+str(input_device))
+        print("output device:"+str(sd.default.device[1])+":"+str(output_device))
+
+gui=GUI()
\ No newline at end of file