gui.py

"""
Updates after 0416:
    Import half in config
    Rebuild npy without filling
    v2 support
    No f0 model support
    Fix

int16：
    Added support for no index
    Changed f0 algorithm to harvest (seems like this is the only thing that affects CPU usage), but the effect is not good without this change
"""
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:
        """
        initialization
        """
        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  # f0 points per second
        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:
        """
        inference function
        """
        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]
            )

        ####Index optimization
        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])  # Too big to burst video memory
        else:
            pitch, pitchf = None, None
            p_len = min(feats.shape[1], 13000)  # Too big to burst video memory
        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("Load model"),
                    layout=[
                        [
                            sg.Input(
                                default_text="hubert_base.pt",
                                key="hubert_path",
                                disabled=True,
                            ),
                            sg.FileBrowse(
                                i18n("Hubert model"),
                                initial_folder=os.path.join(os.getcwd()),
                                file_types=((". pt"),),
                            ),
                        ],
                        [
                            sg.Input(
                                default_text=data.get("pth_path", ""),
                                key="pth_path",
                            ),
                            sg.FileBrowse(
                                i18n("Select .pth file"),
                                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("Select .index file"),
                                initial_folder=os.path.join(os.getcwd(), "logs"),
                                file_types=((". index"),),
                            ),
                        ],
                        [
                            sg.Input(
                                default_text="You don't need to write this.",
                                key="npy_path",
                                disabled=True,
                            ),
                            sg.FileBrowse(
                                i18n("Select .npy file"),
                                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("input device")),
                            sg.Combo(
                                input_devices,
                                key="sg_input_device",
                                default_value=data.get("sg_input_device", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("output device")),
                            sg.Combo(
                                output_devices,
                                key="sg_output_device",
                                default_value=data.get("sg_output_device", ""),
                            ),
                        ],
                    ],
                    title=i18n("Audio device (please use the same type of driver)"),
                )
            ],
            [
                sg.Frame(
                    layout=[
                        [
                            sg.Text(i18n("response threshold")),
                            sg.Slider(
                                range=(-60, 0),
                                key="threhold",
                                resolution=1,
                                orientation="h",
                                default_value=data.get("threhold", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Tone settings")),
                            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("General settings"),
                ),
                sg.Frame(
                    layout=[
                        [
                            sg.Text(i18n("Sample length")),
                            sg.Slider(
                                range=(0.1, 3.0),
                                key="block_time",
                                resolution=0.1,
                                orientation="h",
                                default_value=data.get("block_time", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Fade Length")),
                            sg.Slider(
                                range=(0.01, 0.15),
                                key="crossfade_length",
                                resolution=0.01,
                                orientation="h",
                                default_value=data.get("crossfade_length", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Additional reasoning time")),
                            sg.Slider(
                                range=(0.05, 3.00),
                                key="extra_time",
                                resolution=0.01,
                                orientation="h",
                                default_value=data.get("extra_time", ""),
                            ),
                        ],
                        [
                            sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
                            sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
                        ],
                    ],
                    title=i18n("Performance settings"),
                ),
            ],
            [
                sg.Button(i18n("Start audio conversion"), key="start_vc"),
                sg.Button(i18n("Stop audio conversion"), key="stop_vc"),
                sg.Text(i18n("Inference time (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("Please select pth file"))
            return False
        if len(values["index_path"].strip()) == 0:
            sg.popup(i18n("Please select index file"))
            return False
        pattern = re.compile("[^\x00-\x7F]+")
        if pattern.findall(values["hubert_path"]):
            sg.popup(i18n("The hubert model path cannot contain Chinese characters"))
            return False
        if pattern.findall(values["pth_path"]):
            sg.popup(i18n("pth file path cannot contain Chinese characters"))
            return False
        if pattern.findall(values["index_path"]):
            sg.popup(i18n("The index file path cannot contain Chinese characters"))
            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)  # Reserve 0.02s forward
        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):
        """
        accept audio input
        """
        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
    ):
        """
        audio processing
        """
        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):
        """Get device list"""
        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):
        """Set up output device"""
        (
            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()