import os import sys from dotenv import load_dotenv load_dotenv() os.environ["OMP_NUM_THREADS"] = "4" if sys.platform == "darwin": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" now_dir = os.getcwd() sys.path.append(now_dir) import multiprocessing stream_latency = -1 def printt(strr, *args): if len(args) == 0: print(strr) else: print(strr % args) def phase_vocoder(a, b, fade_out, fade_in): window = torch.sqrt(fade_out * fade_in) fa = torch.fft.rfft(a * window) fb = torch.fft.rfft(b * window) absab = torch.abs(fa) + torch.abs(fb) n = a.shape[0] if n % 2 == 0: absab[1:-1] *= 2 else: absab[1:] *= 2 phia = torch.angle(fa) phib = torch.angle(fb) deltaphase = phib - phia deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5) w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase t = torch.arange(n).unsqueeze(-1).to(a) / n result = ( a * (fade_out**2) + b * (fade_in**2) + torch.sum(absab * torch.cos(w * t + phia), -1) * window / n ) return result class Harvest(multiprocessing.Process): def __init__(self, inp_q, opt_q): multiprocessing.Process.__init__(self) self.inp_q = inp_q self.opt_q = opt_q def run(self): import numpy as np import pyworld while 1: idx, x, res_f0, n_cpu, ts = self.inp_q.get() f0, t = pyworld.harvest( x.astype(np.double), fs=16000, f0_ceil=1100, f0_floor=50, frame_period=10, ) res_f0[idx] = f0 if len(res_f0.keys()) >= n_cpu: self.opt_q.put(ts) if __name__ == "__main__": import json import multiprocessing import re import threading import time import traceback from multiprocessing import Queue, cpu_count from queue import Empty import librosa from tools.torchgate import TorchGate import numpy as np import PySimpleGUI as sg import sounddevice as sd import torch import torch.nn.functional as F import torchaudio.transforms as tat import tools.rvc_for_realtime as rvc_for_realtime from i18n.i18n import I18nAuto from configs.config import Config i18n = I18nAuto() # device = rvc_for_realtime.config.device # device = torch.device( # "cuda" # if torch.cuda.is_available() # else ("mps" if torch.backends.mps.is_available() else "cpu") # ) current_dir = os.getcwd() inp_q = Queue() opt_q = Queue() n_cpu = min(cpu_count(), 8) for _ in range(n_cpu): Harvest(inp_q, opt_q).start() class GUIConfig: def __init__(self) -> None: self.pth_path: str = "" self.index_path: str = "" self.pitch: int = 0 self.samplerate: int = 40000 self.block_time: float = 1.0 # s self.buffer_num: int = 1 self.threhold: int = -60 self.crossfade_time: float = 0.05 self.extra_time: float = 2.5 self.I_noise_reduce = False self.O_noise_reduce = False self.rms_mix_rate = 0.0 self.index_rate = 0.3 self.n_cpu = min(n_cpu, 6) self.f0method = "harvest" self.sg_input_device = "" self.sg_output_device = "" class GUI: def __init__(self) -> None: self.gui_config = GUIConfig() self.config = Config() self.flag_vc = False self.function = "vc" self.delay_time = 0 self.launcher() def load(self): input_devices, output_devices, _, _ = self.get_devices() try: with open("configs/config.json", "r") as j: data = json.load(j) data["sr_model"] = data["sr_type"] == "sr_model" data["sr_device"] = data["sr_type"] == "sr_device" data["pm"] = data["f0method"] == "pm" data["harvest"] = data["f0method"] == "harvest" data["crepe"] = data["f0method"] == "crepe" data["rmvpe"] = data["f0method"] == "rmvpe" data["fcpe"] = data["f0method"] == "fcpe" if data["sg_input_device"] not in input_devices: data["sg_input_device"] = input_devices[sd.default.device[0]] if data["sg_output_device"] not in output_devices: data["sg_output_device"] = output_devices[sd.default.device[1]] except: with open("configs/config.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]], "sr_type": "sr_model", "threhold": "-60", "pitch": "0", "index_rate": "0", "rms_mix_rate": "0", "block_time": "0.25", "crossfade_length": "0.05", "extra_time": "2.5", "f0method": "rmvpe", "use_jit": False, "use_pv": False, } data["sr_model"] = data["sr_type"] == "sr_model" data["sr_device"] = data["sr_type"] == "sr_device" data["pm"] = data["f0method"] == "pm" data["harvest"] = data["f0method"] == "harvest" data["crepe"] = data["f0method"] == "crepe" data["rmvpe"] = data["f0method"] == "rmvpe" data["fcpe"] = data["f0method"] == "fcpe" return data def launcher(self): data = self.load() self.config.use_jit = False # data.get("use_jit", self.config.use_jit) sg.theme("LightBlue3") input_devices, output_devices, _, _ = self.get_devices() layout = [ [ sg.Frame( title=i18n("加载模型"), layout=[ [ 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(), "assets/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.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", ""), ), ], [ sg.Button(i18n("重载设备列表"), key="reload_devices"), sg.Radio( i18n("使用模型采样率"), "sr_type", key="sr_model", default=data.get("sr_model", True), enable_events=True, ), sg.Radio( i18n("使用设备采样率"), "sr_type", key="sr_device", default=data.get("sr_device", False), enable_events=True, ), sg.Text(i18n("采样率:")), sg.Text("", key="sr_stream"), ], ], 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", -60), enable_events=True, ), ], [ sg.Text(i18n("音调设置")), sg.Slider( range=(-24, 24), key="pitch", resolution=1, orientation="h", default_value=data.get("pitch", 0), enable_events=True, ), ], [ 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", 0), enable_events=True, ), ], [ sg.Text(i18n("响度因子")), sg.Slider( range=(0.0, 1.0), key="rms_mix_rate", resolution=0.01, orientation="h", default_value=data.get("rms_mix_rate", 0), enable_events=True, ), ], [ sg.Text(i18n("音高算法")), sg.Radio( "pm", "f0method", key="pm", default=data.get("pm", False), enable_events=True, ), sg.Radio( "harvest", "f0method", key="harvest", default=data.get("harvest", False), enable_events=True, ), sg.Radio( "crepe", "f0method", key="crepe", default=data.get("crepe", False), enable_events=True, ), sg.Radio( "rmvpe", "f0method", key="rmvpe", default=data.get("rmvpe", False), enable_events=True, ), sg.Radio( "fcpe", "f0method", key="fcpe", default=data.get("fcpe", True), enable_events=True, ), ], ], title=i18n("常规设置"), ), sg.Frame( layout=[ [ sg.Text(i18n("采样长度")), sg.Slider( range=(0.02, 2.4), key="block_time", resolution=0.01, orientation="h", default_value=data.get("block_time", 0.25), enable_events=True, ), ], # [ # sg.Text("设备延迟"), # sg.Slider( # range=(0, 1), # key="device_latency", # resolution=0.001, # orientation="h", # default_value=data.get("device_latency", 0.1), # enable_events=True, # ), # ], [ sg.Text(i18n("harvest进程数")), sg.Slider( range=(1, n_cpu), key="n_cpu", resolution=1, orientation="h", default_value=data.get( "n_cpu", min(self.gui_config.n_cpu, n_cpu) ), enable_events=True, ), ], [ 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", 0.05), enable_events=True, ), ], [ sg.Text(i18n("额外推理时长")), sg.Slider( range=(0.05, 5.00), key="extra_time", resolution=0.01, orientation="h", default_value=data.get("extra_time", 2.5), enable_events=True, ), ], [ sg.Checkbox( i18n("输入降噪"), key="I_noise_reduce", enable_events=True, ), sg.Checkbox( i18n("输出降噪"), key="O_noise_reduce", enable_events=True, ), sg.Checkbox( i18n("启用相位声码器"), key="use_pv", default=data.get("use_pv", False), enable_events=True, ), # sg.Checkbox( # "JIT加速", # default=self.config.use_jit, # key="use_jit", # enable_events=False, # ), ], # [sg.Text("注：首次使用JIT加速时，会出现卡顿，\n 并伴随一些噪音，但这是正常现象！")], ], 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.Radio( i18n("输入监听"), "function", key="im", default=False, enable_events=True, ), sg.Radio( i18n("输出变声"), "function", key="vc", default=True, enable_events=True, ), sg.Text(i18n("算法延迟(ms):")), sg.Text("0", key="delay_time"), sg.Text(i18n("推理时间(ms):")), sg.Text("0", key="infer_time"), ], ] self.window = sg.Window("RVC - GUI", layout=layout, finalize=True) 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 == "reload_devices": prev_input = self.window["sg_input_device"].get() prev_output = self.window["sg_output_device"].get() input_devices, output_devices, _, _ = self.get_devices(update=True) if prev_input not in input_devices: self.gui_config.sg_input_device = input_devices[0] else: self.gui_config.sg_input_device = prev_input self.window["sg_input_device"].Update(values=input_devices) self.window["sg_input_device"].Update( value=self.gui_config.sg_input_device ) if prev_output not in output_devices: self.gui_config.sg_output_device = output_devices[0] else: self.gui_config.sg_output_device = prev_output self.window["sg_output_device"].Update(values=output_devices) self.window["sg_output_device"].Update( value=self.gui_config.sg_output_device ) if event == "start_vc" and self.flag_vc == False: if self.set_values(values) == True: printt("cuda_is_available: %s", torch.cuda.is_available()) self.start_vc() settings = { "pth_path": values["pth_path"], "index_path": values["index_path"], "sg_input_device": values["sg_input_device"], "sg_output_device": values["sg_output_device"], "sr_type": ["sr_model", "sr_device"][ [ values["sr_model"], values["sr_device"], ].index(True) ], "threhold": values["threhold"], "pitch": values["pitch"], "rms_mix_rate": values["rms_mix_rate"], "index_rate": values["index_rate"], # "device_latency": values["device_latency"], "block_time": values["block_time"], "crossfade_length": values["crossfade_length"], "extra_time": values["extra_time"], "n_cpu": values["n_cpu"], # "use_jit": values["use_jit"], "use_jit": False, "use_pv": values["use_pv"], "f0method": ["pm", "harvest", "crepe", "rmvpe", "fcpe"][ [ values["pm"], values["harvest"], values["crepe"], values["rmvpe"], values["fcpe"], ].index(True) ], } with open("configs/config.json", "w") as j: json.dump(settings, j) global stream_latency while stream_latency < 0: time.sleep(0.01) self.delay_time = ( stream_latency + values["block_time"] + values["crossfade_length"] + 0.01 ) if values["I_noise_reduce"]: self.delay_time += min(values["crossfade_length"], 0.04) self.window["sr_stream"].update(self.gui_config.samplerate) self.window["delay_time"].update(int(self.delay_time * 1000)) if event == "stop_vc" and self.flag_vc == True: self.flag_vc = False stream_latency = -1 # Parameter hot update if event == "threhold": self.gui_config.threhold = values["threhold"] elif event == "pitch": self.gui_config.pitch = values["pitch"] if hasattr(self, "rvc"): self.rvc.change_key(values["pitch"]) elif event == "index_rate": self.gui_config.index_rate = values["index_rate"] if hasattr(self, "rvc"): self.rvc.change_index_rate(values["index_rate"]) elif event == "rms_mix_rate": self.gui_config.rms_mix_rate = values["rms_mix_rate"] elif event in ["pm", "harvest", "crepe", "rmvpe", "fcpe"]: self.gui_config.f0method = event elif event == "I_noise_reduce": self.gui_config.I_noise_reduce = values["I_noise_reduce"] if stream_latency > 0: self.delay_time += ( 1 if values["I_noise_reduce"] else -1 ) * min(values["crossfade_length"], 0.04) self.window["delay_time"].update(int(self.delay_time * 1000)) elif event == "O_noise_reduce": self.gui_config.O_noise_reduce = values["O_noise_reduce"] elif event == "use_pv": self.gui_config.use_pv = values["use_pv"] elif event in ["vc", "im"]: self.function = event elif event != "start_vc" and self.flag_vc == True: # Other parameters do not support hot update self.flag_vc = False stream_latency = -1 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["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.use_jit = False # values["use_jit"] # self.device_latency = values["device_latency"] self.gui_config.pth_path = values["pth_path"] self.gui_config.index_path = values["index_path"] self.gui_config.sr_type = ["sr_model", "sr_device"][ [ values["sr_model"], values["sr_device"], ].index(True) ] self.gui_config.threhold = values["threhold"] self.gui_config.pitch = values["pitch"] self.gui_config.block_time = values["block_time"] self.gui_config.crossfade_time = values["crossfade_length"] self.gui_config.extra_time = values["extra_time"] self.gui_config.I_noise_reduce = values["I_noise_reduce"] self.gui_config.O_noise_reduce = values["O_noise_reduce"] self.gui_config.use_pv = values["use_pv"] self.gui_config.rms_mix_rate = values["rms_mix_rate"] self.gui_config.index_rate = values["index_rate"] self.gui_config.n_cpu = values["n_cpu"] self.gui_config.f0method = ["pm", "harvest", "crepe", "rmvpe", "fcpe"][ [ values["pm"], values["harvest"], values["crepe"], values["rmvpe"], values["fcpe"], ].index(True) ] return True def start_vc(self): torch.cuda.empty_cache() self.flag_vc = True self.rvc = rvc_for_realtime.RVC( self.gui_config.pitch, self.gui_config.pth_path, self.gui_config.index_path, self.gui_config.index_rate, self.gui_config.n_cpu, inp_q, opt_q, self.config, self.rvc if hasattr(self, "rvc") else None, ) self.gui_config.samplerate = ( self.rvc.tgt_sr if self.gui_config.sr_type == "sr_model" else self.get_device_samplerate() ) self.zc = self.gui_config.samplerate // 100 self.block_frame = ( int( np.round( self.gui_config.block_time * self.gui_config.samplerate / self.zc ) ) * self.zc ) self.block_frame_16k = 160 * self.block_frame // self.zc self.crossfade_frame = ( int( np.round( self.gui_config.crossfade_time * self.gui_config.samplerate / self.zc ) ) * self.zc ) self.sola_buffer_frame = min(self.crossfade_frame, 4 * self.zc) self.sola_search_frame = self.zc self.extra_frame = ( int( np.round( self.gui_config.extra_time * self.gui_config.samplerate / self.zc ) ) * self.zc ) self.input_wav: torch.Tensor = torch.zeros( self.extra_frame + self.crossfade_frame + self.sola_search_frame + self.block_frame, device=self.config.device, dtype=torch.float32, ) self.input_wav_res: torch.Tensor = torch.zeros( 160 * self.input_wav.shape[0] // self.zc, device=self.config.device, dtype=torch.float32, ) self.sola_buffer: torch.Tensor = torch.zeros( self.sola_buffer_frame, device=self.config.device, dtype=torch.float32 ) self.nr_buffer: torch.Tensor = self.sola_buffer.clone() self.output_buffer: torch.Tensor = self.input_wav.clone() self.res_buffer: torch.Tensor = torch.zeros( 2 * self.zc, device=self.config.device, dtype=torch.float32 ) self.skip_head = self.extra_frame // self.zc self.return_length = ( self.block_frame + self.sola_buffer_frame + self.sola_search_frame ) // self.zc self.fade_in_window: torch.Tensor = ( torch.sin( 0.5 * np.pi * torch.linspace( 0.0, 1.0, steps=self.sola_buffer_frame, device=self.config.device, dtype=torch.float32, ) ) ** 2 ) self.fade_out_window: torch.Tensor = 1 - self.fade_in_window self.resampler = tat.Resample( orig_freq=self.gui_config.samplerate, new_freq=16000, dtype=torch.float32, ).to(self.config.device) if self.rvc.tgt_sr != self.gui_config.samplerate: self.resampler2 = tat.Resample( orig_freq=self.rvc.tgt_sr, new_freq=self.gui_config.samplerate, dtype=torch.float32, ).to(self.config.device) else: self.resampler2 = None self.tg = TorchGate( sr=self.gui_config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9 ).to(self.config.device) thread_vc = threading.Thread(target=self.soundinput) thread_vc.start() def soundinput(self): """ 接受音频输入 """ channels = 1 if sys.platform == "darwin" else 2 with sd.Stream( channels=channels, callback=self.audio_callback, blocksize=self.block_frame, samplerate=self.gui_config.samplerate, dtype="float32", ) as stream: global stream_latency stream_latency = stream.latency[-1] while self.flag_vc: time.sleep(self.gui_config.block_time) printt("Audio block passed.") printt("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.gui_config.threhold > -60: rms = librosa.feature.rms( y=indata, frame_length=4 * self.zc, hop_length=self.zc ) db_threhold = ( librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold ) for i in range(db_threhold.shape[0]): if db_threhold[i]: indata[i * self.zc : (i + 1) * self.zc] = 0 self.input_wav[: -self.block_frame] = self.input_wav[ self.block_frame : ].clone() self.input_wav[-self.block_frame :] = torch.from_numpy(indata).to( self.config.device ) self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[ self.block_frame_16k : ].clone() # input noise reduction and resampling if self.gui_config.I_noise_reduce and self.function == "vc": input_wav = self.input_wav[ -self.sola_buffer_frame - self.block_frame - 2 * self.zc : ] input_wav = self.tg( input_wav.unsqueeze(0), self.input_wav.unsqueeze(0) )[0, 2 * self.zc :] input_wav[: self.sola_buffer_frame] *= self.fade_in_window input_wav[: self.sola_buffer_frame] += ( self.nr_buffer * self.fade_out_window ) self.nr_buffer[:] = input_wav[self.block_frame :] input_wav = torch.cat( (self.res_buffer[:], input_wav[: self.block_frame]) ) self.res_buffer[:] = input_wav[-2 * self.zc :] self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler( input_wav )[160:] else: self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler( self.input_wav[-self.block_frame - 2 * self.zc :] )[160:] # infer if self.function == "vc": infer_wav = self.rvc.infer( self.input_wav_res, self.block_frame_16k, self.skip_head, self.return_length, self.gui_config.f0method, ) if self.resampler2 is not None: infer_wav = self.resampler2(infer_wav) else: infer_wav = self.input_wav[ -self.crossfade_frame - self.sola_search_frame - self.block_frame : ].clone() # output noise reduction if (self.gui_config.O_noise_reduce and self.function == "vc") or ( self.gui_config.I_noise_reduce and self.function == "im" ): self.output_buffer[: -self.block_frame] = self.output_buffer[ self.block_frame : ].clone() self.output_buffer[-self.block_frame :] = infer_wav[-self.block_frame :] infer_wav = self.tg( infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0) ).squeeze(0) # volume envelop mixing if self.gui_config.rms_mix_rate < 1 and self.function == "vc": rms1 = librosa.feature.rms( y=self.input_wav_res[ 160 * self.skip_head : 160 * (self.skip_head + self.return_length) ] .cpu() .numpy(), frame_length=640, hop_length=160, ) rms1 = torch.from_numpy(rms1).to(self.config.device) rms1 = F.interpolate( rms1.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear", align_corners=True, )[0, 0, :-1] rms2 = librosa.feature.rms( y=infer_wav[:].cpu().numpy(), frame_length=4 * self.zc, hop_length=self.zc, ) rms2 = torch.from_numpy(rms2).to(self.config.device) rms2 = F.interpolate( rms2.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear", align_corners=True, )[0, 0, :-1] rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3) infer_wav *= torch.pow( rms1 / rms2, torch.tensor(1 - self.gui_config.rms_mix_rate) ) # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC conv_input = infer_wav[ None, None, : self.sola_buffer_frame + self.sola_search_frame ] cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :]) cor_den = torch.sqrt( F.conv1d( conv_input**2, torch.ones(1, 1, self.sola_buffer_frame, device=self.config.device), ) + 1e-8 ) if sys.platform == "darwin": _, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0]) sola_offset = sola_offset.item() else: sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0]) printt("sola_offset = %d", int(sola_offset)) infer_wav = infer_wav[sola_offset:] if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv: infer_wav[: self.sola_buffer_frame] *= self.fade_in_window infer_wav[: self.sola_buffer_frame] += ( self.sola_buffer * self.fade_out_window ) else: infer_wav[: self.sola_buffer_frame] = phase_vocoder( self.sola_buffer, infer_wav[: self.sola_buffer_frame], self.fade_out_window, self.fade_in_window, ) self.sola_buffer[:] = infer_wav[ self.block_frame : self.block_frame + self.sola_buffer_frame ] if sys.platform == "darwin": outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis] else: outdata[:] = ( infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy() ) total_time = time.perf_counter() - start_time self.window["infer_time"].update(int(total_time * 1000)) printt("Infer time: %.2f", total_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"] 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) ] printt("Input device: %s:%s", str(sd.default.device[0]), input_device) printt("Output device: %s:%s", str(sd.default.device[1]), output_device) def get_device_samplerate(self): return int( sd.query_devices(device=sd.default.device[0])["default_samplerate"] ) gui = GUI()