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gui.py

@@ -0,0 +1,788 @@
+"""
+0416后的更新：
+    引入config中half
+    重建npy而不用填写
+    v2支持
+    无f0模型支持
+    修复
+
+    int16：
+    增加无索引支持
+    f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好
+"""
+import os, sys, traceback, re
+
+import json
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from config import Config
+
+Config = Config()
+import PySimpleGUI as sg
+import sounddevice as sd
+import noisereduce as nr
+import numpy as np
+from fairseq import checkpoint_utils
+import librosa, torch, pyworld, faiss, time, threading
+import torch.nn.functional as F
+import torchaudio.transforms as tat
+import scipy.signal as signal
+import torchcrepe
+
+# import matplotlib.pyplot as plt
+from infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from i18n import I18nAuto
+
+i18n = I18nAuto()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+current_dir = os.getcwd()
+
+
+class RVC:
+    def __init__(
+        self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
+    ) -> None:
+        """
+        初始化
+        """
+        try:
+            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.f0_method = f0_method
+            self.sr = 16000
+            self.window = 160
+
+            # Get Torch Device
+            if(torch.cuda.is_available()):
+                self.torch_device = torch.device(f"cuda:{0 % torch.cuda.device_count()}")
+            elif torch.backends.mps.is_available():
+                self.torch_device = torch.device("mps")
+            else:
+                self.torch_device = torch.device("cpu")
+
+            if index_rate != 0:
+                self.index = faiss.read_index(index_path)
+                # self.big_npy = np.load(npy_path)
+                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
+                print("index search enabled")
+            self.index_rate = index_rate
+            model_path = hubert_path
+            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)
+            if Config.is_half:
+                self.model = self.model.half()
+            else:
+                self.model = self.model.float()
+            self.model.eval()
+            cpt = torch.load(pth_path, map_location="cpu")
+            self.tgt_sr = cpt["config"][-1]
+            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+            self.if_f0 = cpt.get("f0", 1)
+            self.version = cpt.get("version", "v1")
+            if self.version == "v1":
+                if self.if_f0 == 1:
+                    self.net_g = SynthesizerTrnMs256NSFsid(
+                        *cpt["config"], is_half=Config.is_half
+                    )
+                else:
+                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            elif self.version == "v2":
+                if self.if_f0 == 1:
+                    self.net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt["config"], is_half=Config.is_half
+                    )
+                else:
+                    self.net_g = SynthesizerTrnMs768NSFsid_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)
+            if Config.is_half:
+                self.net_g = self.net_g.half()
+            else:
+                self.net_g = self.net_g.float()
+        except:
+            print(traceback.format_exc())
+
+    def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
+        batch_size = 512
+        # Compute pitch using first gpu
+        audio = torch.tensor(np.copy(x))[None].float()
+        f0, pd = torchcrepe.predict(
+            audio,
+            self.sr,
+            self.window,
+            f0_min,
+            f0_max,
+            model,
+            batch_size=batch_size,
+            device=self.torch_device,
+            return_periodicity=True,
+        )
+        pd = torchcrepe.filter.median(pd, 3)
+        f0 = torchcrepe.filter.mean(f0, 3)
+        f0[pd < 0.1] = 0
+        f0 = f0[0].cpu().numpy()
+        return f0
+
+    def get_harvest_computation(self, x, f0_min, f0_max):
+        f0, t = pyworld.harvest(
+            x.astype(np.double),
+            fs=self.sr,
+            f0_ceil=f0_max,
+            f0_floor=f0_min,
+            frame_period=10,
+        )
+        f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
+        f0 = signal.medfilt(f0, 3)
+        return f0
+
+    def get_f0(self, x, f0_up_key, inp_f0=None):
+        # Calculate Padding and f0 details here
+        p_len = x.shape[0] // 512 # For Now This probs doesn't work
+        x_pad = 1
+        f0_min = 50
+        f0_max = 1100
+        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+
+        f0 = 0
+        # Here, check f0_methods and get their computations
+        if(self.f0_method == 'harvest'):
+            f0 = self.get_harvest_computation(x, f0_min, f0_max)
+        elif(self.f0_method == 'reg-crepe'):
+            f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
+        elif(self.f0_method == 'reg-crepe-tiny'):
+            f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
+
+        # Calculate f0_course and f0_bak here
+        f0 *= pow(2, f0_up_key / 12)
+        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
+        tf0 = self.sr // self.window  # 每秒f0点数
+        if inp_f0 is not None:
+            delta_t = np.round(
+                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
+            ).astype("int16")
+            replace_f0 = np.interp(
+                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
+            )
+            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
+            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
+        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
+        f0bak = f0.copy()
+        f0_mel = 1127 * np.log(1 + f0 / 700)
+        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
+            f0_mel_max - f0_mel_min
+        ) + 1
+        f0_mel[f0_mel <= 1] = 1
+        f0_mel[f0_mel > 255] = 255
+        f0_coarse = np.rint(f0_mel).astype(np.int)
+        return f0_coarse, f0bak  # 1-0
+
+    def infer(self, feats: torch.Tensor) -> np.ndarray:
+        """
+        推理函数
+        """
+        audio = feats.clone().cpu().numpy()
+        assert feats.dim() == 1, feats.dim()
+        feats = feats.view(1, -1)
+        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+        if Config.is_half:
+            feats = feats.half()
+        else:
+            feats = feats.float()
+        inputs = {
+            "source": feats.to(device),
+            "padding_mask": padding_mask.to(device),
+            "output_layer": 9 if self.version == "v1" else 12,
+        }
+        torch.cuda.synchronize()
+        with torch.no_grad():
+            logits = self.model.extract_features(**inputs)
+            feats = (
+                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
+            )
+
+        ####索引优化
+        try:
+            if (
+                hasattr(self, "index")
+                and hasattr(self, "big_npy")
+                and self.index_rate != 0
+            ):
+                npy = feats[0].cpu().numpy().astype("float32")
+                score, ix = self.index.search(npy, k=8)
+                weight = np.square(1 / score)
+                weight /= weight.sum(axis=1, keepdims=True)
+                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
+                if Config.is_half:
+                    npy = npy.astype("float16")
+                feats = (
+                    torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
+                    + (1 - self.index_rate) * feats
+                )
+            else:
+                print("index search FAIL or disabled")
+        except:
+            traceback.print_exc()
+            print("index search FAIL")
+        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+        torch.cuda.synchronize()
+        print(feats.shape)
+        if self.if_f0 == 1:
+            pitch, pitchf = self.get_f0(audio, self.f0_up_key)
+            p_len = min(feats.shape[1], 13000, pitch.shape[0])  # 太大了爆显存
+        else:
+            pitch, pitchf = None, None
+            p_len = min(feats.shape[1], 13000)  # 太大了爆显存
+        torch.cuda.synchronize()
+        # print(feats.shape,pitch.shape)
+        feats = feats[:, :p_len, :]
+        if self.if_f0 == 1:
+            pitch = pitch[:p_len]
+            pitchf = pitchf[:p_len]
+            pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
+            pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
+        p_len = torch.LongTensor([p_len]).to(device)
+        ii = 0  # sid
+        sid = torch.LongTensor([ii]).to(device)
+        with torch.no_grad():
+            if self.if_f0 == 1:
+                infered_audio = (
+                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
+                    .data.cpu()
+                    .float()
+                )
+            else:
+                infered_audio = (
+                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
+                )
+        torch.cuda.synchronize()
+        return infered_audio
+
+
+class GUIConfig:
+    def __init__(self) -> None:
+        self.hubert_path: str = ""
+        self.pth_path: str = ""
+        self.index_path: str = ""
+        self.npy_path: str = ""
+        self.f0_method: 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
+        self.I_noise_reduce = False
+        self.O_noise_reduce = False
+        self.index_rate = 0.3
+
+
+class GUI:
+    def __init__(self) -> None:
+        self.config = GUIConfig()
+        self.flag_vc = False
+
+        self.launcher()
+
+    def load(self):
+        input_devices, output_devices, _, _ = self.get_devices()
+        try:
+            with open("values1.json", "r") as j:
+                data = json.load(j)
+        except:
+            # Injecting f0_method into the json data 
+            with open("values1.json", "w") as j:
+                data = {
+                    "pth_path": " ",
+                    "index_path": " ",
+                    "sg_input_device": input_devices[sd.default.device[0]],
+                    "sg_output_device": output_devices[sd.default.device[1]],
+                    "threhold": "-45",
+                    "pitch": "0",
+                    "index_rate": "0",
+                    "block_time": "1",
+                    "crossfade_length": "0.04",
+                    "extra_time": "1",
+                }
+        return data
+
+    def launcher(self):
+        data = self.load()
+        sg.theme("DarkTeal12")
+        input_devices, output_devices, _, _ = self.get_devices()
+        layout = [
+            [
+                sg.Frame(
+                    title="Proudly forked by Mangio621",
+                    layout=[
+                        [
+                            sg.Image('./mangio_utils/lol.png')
+                        ]
+                    ]
+                ),
+                sg.Frame(
+                    title=i18n("加载模型"),
+                    layout=[
+                        [
+                            sg.Input(
+                                default_text="hubert_base.pt",
+                                key="hubert_path",
+                                disabled=True,
+                            ),
+                            sg.FileBrowse(
+                                i18n("Hubert模型"),
+                                initial_folder=os.path.join(os.getcwd()),
+                                file_types=((". pt"),),
+                            ),
+                        ],
+                        [
+                            sg.Input(
+                                default_text=data.get("pth_path", ""),
+                                key="pth_path",
+                            ),
+                            sg.FileBrowse(
+                                i18n("选择.pth文件"),
+                                initial_folder=os.path.join(os.getcwd(), "weights"),
+                                file_types=((". pth"),),
+                            ),
+                        ],
+                        [
+                            sg.Input(
+                                default_text=data.get("index_path", ""),
+                                key="index_path",
+                            ),
+                            sg.FileBrowse(
+                                i18n("选择.index文件"),
+                                initial_folder=os.path.join(os.getcwd(), "logs"),
+                                file_types=((". index"),),
+                            ),
+                        ],
+                        [
+                            sg.Input(
+                                default_text="你不需要填写这个You don't need write this.",
+                                key="npy_path",
+                                disabled=True,
+                            ),
+                            sg.FileBrowse(
+                                i18n("选择.npy文件"),
+                                initial_folder=os.path.join(os.getcwd(), "logs"),
+                                file_types=((". npy"),),
+                            ),
+                        ],
+                    ],
+                )
+            ],
+            [
+                # Mangio f0 Selection frame Here
+                sg.Frame(
+                    layout=[
+                        [
+                            sg.Radio("Harvest", "f0_method", key="harvest", default=True),
+                            sg.Radio("Crepe", "f0_method", key="reg-crepe"),
+                            sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
+                        ]
+                    ],
+                    title="Select an f0 Method",
+                )
+            ],
+            [
+                sg.Frame(
+                    layout=[
+                        [
+                            sg.Text(i18n("输入设备")),
+                            sg.Combo(
+                                input_devices,
+                                key="sg_input_device",
+                                default_value=data.get("sg_input_device", ""),
+                            ),
+                        ],
+                        [
+                            sg.Text(i18n("输出设备")),
+                            sg.Combo(
+                                output_devices,
+                                key="sg_output_device",
+                                default_value=data.get("sg_output_device", ""),
+                            ),
+                        ],
+                    ],
+                    title=i18n("音频设备(请使用同种类驱动)"),
+                )
+            ],
+            [
+                sg.Frame(
+                    layout=[
+                        [
+                            sg.Text(i18n("响应阈值")),
+                            sg.Slider(
+                                range=(-60, 0),
+                                key="threhold",
+                                resolution=1,
+                                orientation="h",
+                                default_value=data.get("threhold", ""),
+                            ),
+                        ],
+                        [
+                            sg.Text(i18n("音调设置")),
+                            sg.Slider(
+                                range=(-24, 24),
+                                key="pitch",
+                                resolution=1,
+                                orientation="h",
+                                default_value=data.get("pitch", ""),
+                            ),
+                        ],
+                        [
+                            sg.Text(i18n("Index Rate")),
+                            sg.Slider(
+                                range=(0.0, 1.0),
+                                key="index_rate",
+                                resolution=0.01,
+                                orientation="h",
+                                default_value=data.get("index_rate", ""),
+                            ),
+                        ],
+                    ],
+                    title=i18n("常规设置"),
+                ),
+                sg.Frame(
+                    layout=[
+                        [
+                            sg.Text(i18n("采样长度")),
+                            sg.Slider(
+                                range=(0.1, 3.0),
+                                key="block_time",
+                                resolution=0.1,
+                                orientation="h",
+                                default_value=data.get("block_time", ""),
+                            ),
+                        ],
+                        [
+                            sg.Text(i18n("淡入淡出长度")),
+                            sg.Slider(
+                                range=(0.01, 0.15),
+                                key="crossfade_length",
+                                resolution=0.01,
+                                orientation="h",
+                                default_value=data.get("crossfade_length", ""),
+                            ),
+                        ],
+                        [
+                            sg.Text(i18n("额外推理时长")),
+                            sg.Slider(
+                                range=(0.05, 3.00),
+                                key="extra_time",
+                                resolution=0.01,
+                                orientation="h",
+                                default_value=data.get("extra_time", ""),
+                            ),
+                        ],
+                        [
+                            sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
+                            sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
+                        ],
+                    ],
+                    title=i18n("性能设置"),
+                ),
+            ],
+            [
+                sg.Button(i18n("开始音频转换"), key="start_vc"),
+                sg.Button(i18n("停止音频转换"), key="stop_vc"),
+                sg.Text(i18n("推理时间(ms):")),
+                sg.Text("0", key="infer_time"),
+            ],
+        ]
+        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:
+                if self.set_values(values) == True:
+                    print("using_cuda:" + str(torch.cuda.is_available()))
+                    self.start_vc()
+                    settings = {
+                        "pth_path": values["pth_path"],
+                        "index_path": values["index_path"],
+                        "f0_method": self.get_f0_method_from_radios(values),
+                        "sg_input_device": values["sg_input_device"],
+                        "sg_output_device": values["sg_output_device"],
+                        "threhold": values["threhold"],
+                        "pitch": values["pitch"],
+                        "index_rate": values["index_rate"],
+                        "block_time": values["block_time"],
+                        "crossfade_length": values["crossfade_length"],
+                        "extra_time": values["extra_time"],
+                    }
+                    with open("values1.json", "w") as j:
+                        json.dump(settings, j)
+            if event == "stop_vc" and self.flag_vc == True:
+                self.flag_vc = False
+
+    # Function that returns the used f0 method in string format "harvest" 
+    def get_f0_method_from_radios(self, values):
+        f0_array = [
+            {"name": "harvest", "val": values['harvest']},
+            {"name": "reg-crepe", "val": values['reg-crepe']},
+            {"name": "reg-crepe-tiny", "val": values['reg-crepe-tiny']},
+        ]
+        # Filter through to find a true value
+        used_f0 = ""
+        for f0 in f0_array:
+            if(f0['val'] == True):
+                used_f0 = f0['name']
+                break
+        if(used_f0 == ""): used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
+        return used_f0
+
+    def set_values(self, values):
+        if len(values["pth_path"].strip()) == 0:
+            sg.popup(i18n("请选择pth文件"))
+            return False
+        if len(values["index_path"].strip()) == 0:
+            sg.popup(i18n("请选择index文件"))
+            return False
+        pattern = re.compile("[^\x00-\x7F]+")
+        if pattern.findall(values["hubert_path"]):
+            sg.popup(i18n("hubert模型路径不可包含中文"))
+            return False
+        if pattern.findall(values["pth_path"]):
+            sg.popup(i18n("pth文件路径不可包含中文"))
+            return False
+        if pattern.findall(values["index_path"]):
+            sg.popup(i18n("index文件路径不可包含中文"))
+            return False
+        self.set_devices(values["sg_input_device"], values["sg_output_device"])
+        self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
+        self.config.pth_path = values["pth_path"]
+        self.config.index_path = values["index_path"]
+        self.config.npy_path = values["npy_path"]
+        self.config.f0_method = self.get_f0_method_from_radios(values)
+        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"]
+        self.config.I_noise_reduce = values["I_noise_reduce"]
+        self.config.O_noise_reduce = values["O_noise_reduce"]
+        self.config.index_rate = values["index_rate"]
+        return True
+
+    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(0.01 * self.config.samplerate)  # 往前预留0.02s
+        self.extra_frame = int(self.config.extra_time * self.config.samplerate)
+        self.rvc = None
+        self.rvc = RVC(
+            self.config.pitch,
+            self.config.f0_method,
+            self.config.hubert_path,
+            self.config.pth_path,
+            self.config.index_path,
+            self.config.npy_path,
+            self.config.index_rate,
+        )
+        self.input_wav: np.ndarray = np.zeros(
+            self.extra_frame
+            + self.crossfade_frame
+            + self.sola_search_frame
+            + self.block_frame,
+            dtype="float32",
+        )
+        self.output_wav: torch.Tensor = torch.zeros(
+            self.block_frame, device=device, dtype=torch.float32
+        )
+        self.sola_buffer: torch.Tensor = torch.zeros(
+            self.crossfade_frame, device=device, dtype=torch.float32
+        )
+        self.fade_in_window: torch.Tensor = torch.linspace(
+            0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
+        )
+        self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
+        self.resampler1 = tat.Resample(
+            orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
+        )
+        self.resampler2 = tat.Resample(
+            orig_freq=self.rvc.tgt_sr,
+            new_freq=self.config.samplerate,
+            dtype=torch.float32,
+        )
+        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)
+        if self.config.I_noise_reduce:
+            indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
+
+        """noise gate"""
+        frame_length = 2048
+        hop_length = 1024
+        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[:] = np.append(self.input_wav[self.block_frame :], indata)
+
+        # infer
+        print("input_wav:" + str(self.input_wav.shape))
+        # print('infered_wav:'+str(infer_wav.shape))
+        infer_wav: torch.Tensor = self.resampler2(
+            self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
+        )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
+            device
+        )
+        print("infer_wav:" + str(infer_wav.shape))
+
+        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
+        cor_nom = F.conv1d(
+            infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
+            self.sola_buffer[None, None, :],
+        )
+        cor_den = torch.sqrt(
+            F.conv1d(
+                infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
+                ** 2,
+                torch.ones(1, 1, self.crossfade_frame, device=device),
+            )
+            + 1e-8
+        )
+        sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
+        print("sola offset: " + str(int(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
+            )
+
+        if self.config.O_noise_reduce:
+            outdata[:] = np.tile(
+                nr.reduce_noise(
+                    y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
+                ),
+                (2, 1),
+            ).T
+        else:
+            outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
+        total_time = time.perf_counter() - start_time
+        self.window["infer_time"].update(int(total_time * 1000))
+        print("infer time:" + str(total_time))
+        print("f0_method: " + str(self.config.f0_method))
+
+    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"] if "index" in d else d["name"]
+            for d in devices
+            if d["max_input_channels"] > 0
+        ]
+        output_devices_indices = [
+            d["index"] if "index" in d else d["name"]
+            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()