optimize: realtime inference (#1693)
* update real-time gui * update real-time gui * update real-time gui
This commit is contained in:
yxlllc
GitHub
parent
26e2805f0e
commit
c3e65cdf96
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@ -1 +1 @@
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{"pth_path": "assets/weights/kikiV1.pth", "index_path": "logs/kikiV1.index", "sg_input_device": "VoiceMeeter Output (VB-Audio Vo (MME)", "sg_output_device": "VoiceMeeter Input (VB-Audio Voi (MME)", "sr_type": "sr_model", "threhold": -60.0, "pitch": 12.0, "rms_mix_rate": 0.5, "index_rate": 0.0, "block_time": 0.2, "crossfade_length": 0.08, "extra_time": 2.00, "n_cpu": 4.0, "use_jit": false, "use_pv": false, "f0method": "fcpe"}
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{"pth_path": "assets/weights/kikiV1.pth", "index_path": "logs/kikiV1.index", "sg_hostapi": "MME", "sg_wasapi_exclusive": false, "sg_input_device": "VoiceMeeter Output (VB-Audio Vo", "sg_output_device": "VoiceMeeter Input (VB-Audio Voi", "sr_type": "sr_device", "threhold": -60.0, "pitch": 12.0, "rms_mix_rate": 0.5, "index_rate": 0.0, "block_time": 0.15, "crossfade_length": 0.08, "extra_time": 2.0, "n_cpu": 4.0, "use_jit": false, "use_pv": false, "f0method": "fcpe"}
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170
gui_v1.py
170
gui_v1.py
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@ -125,6 +125,8 @@ if __name__ == "__main__":
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self.index_rate: float = 0.0
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self.n_cpu: int = min(n_cpu, 4)
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self.f0method: str = "fcpe"
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self.sg_hostapi: str = ""
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self.wasapi_exclusive: bool = False
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self.sg_input_device: str = ""
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self.sg_output_device: str = ""
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@ -134,6 +136,7 @@ if __name__ == "__main__":
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self.config = Config()
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self.function = "vc"
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self.delay_time = 0
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self.hostapis = None
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self.input_devices = None
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self.output_devices = None
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self.input_devices_indices = None
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@ -153,11 +156,22 @@ if __name__ == "__main__":
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data["crepe"] = data["f0method"] == "crepe"
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data["rmvpe"] = data["f0method"] == "rmvpe"
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data["fcpe"] = data["f0method"] == "fcpe"
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if data["sg_input_device"] not in self.input_devices:
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if data["sg_hostapi"] in self.hostapis:
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self.update_devices(hostapi_name=data["sg_hostapi"])
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if data["sg_input_device"] not in self.input_devices or data["sg_output_device"] not in self.output_devices:
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self.update_devices()
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data["sg_hostapi"] = self.hostapis[0]
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data["sg_input_device"] = self.input_devices[
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self.input_devices_indices.index(sd.default.device[0])
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]
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data["sg_output_device"] = self.output_devices[
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self.output_devices_indices.index(sd.default.device[1])
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]
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else:
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data["sg_hostapi"] = self.hostapis[0]
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data["sg_input_device"] = self.input_devices[
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self.input_devices_indices.index(sd.default.device[0])
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]
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if data["sg_output_device"] not in self.output_devices:
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data["sg_output_device"] = self.output_devices[
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self.output_devices_indices.index(sd.default.device[1])
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]
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@ -166,6 +180,8 @@ if __name__ == "__main__":
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data = {
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"pth_path": "",
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"index_path": "",
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"sg_hostapi": self.hostapis[0],
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"sg_wasapi_exclusive": False,
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"sg_input_device": self.input_devices[
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self.input_devices_indices.index(sd.default.device[0])
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],
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@ -233,12 +249,30 @@ if __name__ == "__main__":
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[
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("设备类型")),
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sg.Combo(
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self.hostapis,
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key="sg_hostapi",
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default_value=data.get("sg_hostapi", ""),
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enable_events=True,
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size=(20, 1)
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),
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sg.Checkbox(
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i18n("独占 WASAPI 设备"),
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key="sg_wasapi_exclusive",
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default=data.get("sg_wasapi_exclusive", False),
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enable_events=True,
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),
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],
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[
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sg.Text(i18n("输入设备")),
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sg.Combo(
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self.input_devices,
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key="sg_input_device",
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default_value=data.get("sg_input_device", ""),
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enable_events=True,
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size=(45, 1),
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),
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],
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[
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@ -247,6 +281,8 @@ if __name__ == "__main__":
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self.output_devices,
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key="sg_output_device",
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default_value=data.get("sg_output_device", ""),
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enable_events=True,
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size=(45, 1),
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),
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],
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[
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@ -269,7 +305,7 @@ if __name__ == "__main__":
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sg.Text("", key="sr_stream"),
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],
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],
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title=i18n("音频设备(请使用同种类驱动)"),
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title=i18n("音频设备"),
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)
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],
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[
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@ -365,7 +401,7 @@ if __name__ == "__main__":
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[
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sg.Text(i18n("采样长度")),
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sg.Slider(
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range=(0.02, 2.4),
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range=(0.02, 1.5),
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key="block_time",
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resolution=0.01,
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orientation="h",
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@ -481,8 +517,15 @@ if __name__ == "__main__":
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if event == sg.WINDOW_CLOSED:
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self.stop_stream()
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exit()
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if event == "reload_devices":
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self.update_devices()
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if event == "reload_devices" or event == "sg_hostapi":
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self.gui_config.sg_hostapi = values["sg_hostapi"]
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self.update_devices(hostapi_name=values["sg_hostapi"])
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if self.gui_config.sg_hostapi not in self.hostapis:
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self.gui_config.sg_hostapi = self.hostapis[0]
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self.window["sg_hostapi"].Update(values=self.hostapis)
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self.window["sg_hostapi"].Update(
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value=self.gui_config.sg_hostapi
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)
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if self.gui_config.sg_input_device not in self.input_devices:
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self.gui_config.sg_input_device = self.input_devices[0]
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self.window["sg_input_device"].Update(values=self.input_devices)
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@ -502,6 +545,8 @@ if __name__ == "__main__":
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settings = {
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"pth_path": values["pth_path"],
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"index_path": values["index_path"],
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"sg_hostapi": values["sg_hostapi"],
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"sg_wasapi_exclusive": values["sg_wasapi_exclusive"],
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"sg_input_device": values["sg_input_device"],
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"sg_output_device": values["sg_output_device"],
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"sr_type": ["sr_model", "sr_device"][
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@ -544,7 +589,7 @@ if __name__ == "__main__":
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if values["I_noise_reduce"]:
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self.delay_time += min(values["crossfade_length"], 0.04)
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self.window["sr_stream"].update(self.gui_config.samplerate)
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self.window["delay_time"].update(int(self.delay_time * 1000))
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self.window["delay_time"].update(int(np.round(self.delay_time * 1000)))
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# Parameter hot update
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if event == "threhold":
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self.gui_config.threhold = values["threhold"]
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@ -566,7 +611,7 @@ if __name__ == "__main__":
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self.delay_time += (
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1 if values["I_noise_reduce"] else -1
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) * min(values["crossfade_length"], 0.04)
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self.window["delay_time"].update(int(self.delay_time * 1000))
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self.window["delay_time"].update(int(np.round(self.delay_time * 1000)))
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elif event == "O_noise_reduce":
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self.gui_config.O_noise_reduce = values["O_noise_reduce"]
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elif event == "use_pv":
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@ -594,6 +639,8 @@ if __name__ == "__main__":
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self.set_devices(values["sg_input_device"], values["sg_output_device"])
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self.config.use_jit = False # values["use_jit"]
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# self.device_latency = values["device_latency"]
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self.gui_config.sg_hostapi = values["sg_hostapi"]
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self.gui_config.sg_wasapi_exclusive = values["sg_wasapi_exclusive"]
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self.gui_config.sg_input_device = values["sg_input_device"]
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self.gui_config.sg_output_device = values["sg_output_device"]
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self.gui_config.pth_path = values["pth_path"]
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@ -644,6 +691,7 @@ if __name__ == "__main__":
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if self.gui_config.sr_type == "sr_model"
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else self.get_device_samplerate()
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)
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self.gui_config.channels = self.get_device_channels()
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self.zc = self.gui_config.samplerate // 100
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self.block_frame = (
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int(
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@ -686,19 +734,18 @@ if __name__ == "__main__":
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device=self.config.device,
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dtype=torch.float32,
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)
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self.input_wav_denoise: torch.Tensor = self.input_wav.clone()
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self.input_wav_res: torch.Tensor = torch.zeros(
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160 * self.input_wav.shape[0] // self.zc,
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device=self.config.device,
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dtype=torch.float32,
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)
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self.rms_buffer: np.ndarray = np.zeros(4 * self.zc, dtype="float32")
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self.sola_buffer: torch.Tensor = torch.zeros(
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self.sola_buffer_frame, device=self.config.device, dtype=torch.float32
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)
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self.nr_buffer: torch.Tensor = self.sola_buffer.clone()
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self.output_buffer: torch.Tensor = self.input_wav.clone()
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self.res_buffer: torch.Tensor = torch.zeros(
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2 * self.zc, device=self.config.device, dtype=torch.float32
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)
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self.skip_head = self.extra_frame // self.zc
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self.return_length = (
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self.block_frame + self.sola_buffer_frame + self.sola_search_frame
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@ -740,13 +787,17 @@ if __name__ == "__main__":
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global flag_vc
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if not flag_vc:
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flag_vc = True
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channels = 1 if sys.platform == "darwin" else 2
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if 'WASAPI' in self.gui_config.sg_hostapi and self.gui_config.sg_wasapi_exclusive:
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extra_settings = sd.WasapiSettings(exclusive=True)
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else:
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extra_settings = None
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self.stream = sd.Stream(
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channels=channels,
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callback=self.audio_callback,
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blocksize=self.block_frame,
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samplerate=self.gui_config.samplerate,
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channels=self.gui_config.channels,
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dtype="float32",
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extra_settings=extra_settings,
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)
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self.stream.start()
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@ -755,7 +806,7 @@ if __name__ == "__main__":
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if flag_vc:
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flag_vc = False
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if self.stream is not None:
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self.stream.stop()
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self.stream.abort()
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self.stream.close()
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self.stream = None
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@ -769,47 +820,51 @@ if __name__ == "__main__":
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start_time = time.perf_counter()
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indata = librosa.to_mono(indata.T)
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if self.gui_config.threhold > -60:
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indata = np.append(self.rms_buffer, indata)
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rms = librosa.feature.rms(
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y=indata, frame_length=4 * self.zc, hop_length=self.zc
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)
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)[:, 2:]
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self.rms_buffer[:] = indata[-4 * self.zc :]
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indata = indata[2 * self.zc - self.zc // 2 :]
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db_threhold = (
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librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold
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)
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for i in range(db_threhold.shape[0]):
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if db_threhold[i]:
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indata[i * self.zc : (i + 1) * self.zc] = 0
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indata = indata[self.zc // 2 :]
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self.input_wav[: -self.block_frame] = self.input_wav[
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self.block_frame :
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].clone()
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self.input_wav[-self.block_frame :] = torch.from_numpy(indata).to(
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self.input_wav[-indata.shape[0] :] = torch.from_numpy(indata).to(
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self.config.device
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)
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self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[
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self.block_frame_16k :
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].clone()
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# input noise reduction and resampling
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if self.gui_config.I_noise_reduce and self.function == "vc":
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if self.gui_config.I_noise_reduce:
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self.input_wav_denoise[: -self.block_frame] = self.input_wav_denoise[
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self.block_frame :
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].clone()
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input_wav = self.input_wav[
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-self.sola_buffer_frame - self.block_frame - 2 * self.zc :
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-self.sola_buffer_frame - self.block_frame:
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]
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input_wav = self.tg(
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input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)
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)[0, 2 * self.zc :]
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).squeeze(0)
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input_wav[: self.sola_buffer_frame] *= self.fade_in_window
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input_wav[: self.sola_buffer_frame] += (
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self.nr_buffer * self.fade_out_window
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)
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self.input_wav_denoise[-self.block_frame :] = input_wav[: self.block_frame]
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self.nr_buffer[:] = input_wav[self.block_frame :]
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input_wav = torch.cat(
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(self.res_buffer[:], input_wav[: self.block_frame])
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)
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self.res_buffer[:] = input_wav[-2 * self.zc :]
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self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
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input_wav
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self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
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)[160:]
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else:
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self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
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self.input_wav[-self.block_frame - 2 * self.zc :]
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self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = self.resampler(
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self.input_wav[-indata.shape[0] - 2 * self.zc :]
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)[160:]
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# infer
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if self.function == "vc":
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@ -822,14 +877,12 @@ if __name__ == "__main__":
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)
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if self.resampler2 is not None:
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infer_wav = self.resampler2(infer_wav)
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elif self.gui_config.I_noise_reduce:
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infer_wav = self.input_wav_denoise[self.extra_frame :].clone()
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else:
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infer_wav = self.input_wav[
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-self.crossfade_frame - self.sola_search_frame - self.block_frame :
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].clone()
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infer_wav = self.input_wav[self.extra_frame :].clone()
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# output noise reduction
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if (self.gui_config.O_noise_reduce and self.function == "vc") or (
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self.gui_config.I_noise_reduce and self.function == "im"
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):
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if self.gui_config.O_noise_reduce and self.function == "vc":
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self.output_buffer[: -self.block_frame] = self.output_buffer[
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self.block_frame :
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].clone()
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@ -839,16 +892,14 @@ if __name__ == "__main__":
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).squeeze(0)
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# volume envelop mixing
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if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
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if self.gui_config.I_noise_reduce:
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input_wav = self.input_wav_denoise[self.extra_frame :]
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else:
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input_wav = self.input_wav[self.extra_frame :]
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rms1 = librosa.feature.rms(
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y=self.input_wav_res[
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160
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* self.skip_head : 160
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* (self.skip_head + self.return_length)
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]
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.cpu()
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.numpy(),
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frame_length=640,
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hop_length=160,
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y=input_wav[: infer_wav.shape[0]].cpu().numpy(),
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frame_length=4 * self.zc,
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hop_length=self.zc,
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)
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rms1 = torch.from_numpy(rms1).to(self.config.device)
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rms1 = F.interpolate(
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@ -907,19 +958,18 @@ if __name__ == "__main__":
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self.sola_buffer[:] = infer_wav[
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self.block_frame : self.block_frame + self.sola_buffer_frame
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]
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if sys.platform == "darwin":
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outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
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else:
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outdata[:] = (
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infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy()
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)
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outdata[:] = (
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infer_wav[: self.block_frame].repeat(self.gui_config.channels, 1).t().cpu().numpy()
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)
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total_time = time.perf_counter() - start_time
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if flag_vc:
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self.window["infer_time"].update(int(total_time * 1000))
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printt("Infer time: %.2f", total_time)
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def update_devices(self):
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def update_devices(self, hostapi_name=None):
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"""获取设备列表"""
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global flag_vc
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flag_vc = False
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sd._terminate()
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sd._initialize()
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devices = sd.query_devices()
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@ -927,25 +977,26 @@ if __name__ == "__main__":
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for hostapi in hostapis:
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for device_idx in hostapi["devices"]:
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devices[device_idx]["hostapi_name"] = hostapi["name"]
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self.hostapis = [hostapi["name"] for hostapi in hostapis]
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if hostapi_name not in self.hostapis:
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hostapi_name = self.hostapis[0]
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self.input_devices = [
|
||||
f"{d['name']} ({d['hostapi_name']})"
|
||||
for d in devices
|
||||
if d["max_input_channels"] > 0
|
||||
d['name'] for d in devices
|
||||
if d["max_input_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||||
]
|
||||
self.output_devices = [
|
||||
f"{d['name']} ({d['hostapi_name']})"
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
d['name'] for d in devices
|
||||
if d["max_output_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||||
]
|
||||
self.input_devices_indices = [
|
||||
d["index"] if "index" in d else d["name"]
|
||||
for d in devices
|
||||
if d["max_input_channels"] > 0
|
||||
if d["max_input_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||||
]
|
||||
self.output_devices_indices = [
|
||||
d["index"] if "index" in d else d["name"]
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
if d["max_output_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||||
]
|
||||
|
||||
def set_devices(self, input_device, output_device):
|
||||
|
|
@ -963,5 +1014,10 @@ if __name__ == "__main__":
|
|||
return int(
|
||||
sd.query_devices(device=sd.default.device[0])["default_samplerate"]
|
||||
)
|
||||
|
||||
def get_device_channels(self):
|
||||
max_input_channels = sd.query_devices(device=sd.default.device[0])["max_input_channels"]
|
||||
max_output_channels = sd.query_devices(device=sd.default.device[1])["max_output_channels"]
|
||||
return min(max_input_channels, max_output_channels, 2)
|
||||
|
||||
gui = GUI()
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "Sample length",
|
||||
"重载设备列表": "Reload device list",
|
||||
"音调设置": "Pitch settings",
|
||||
"音频设备(请使用同种类驱动)": "Audio device (please use the same type of driver)",
|
||||
"音频设备": "Audio device",
|
||||
"音高算法": "pitch detection algorithm",
|
||||
"额外推理时长": "Extra inference time"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "Longitud de muestreo",
|
||||
"重载设备列表": "Actualizar lista de dispositivos",
|
||||
"音调设置": "Ajuste de tono",
|
||||
"音频设备(请使用同种类驱动)": "Dispositivo de audio (utilice el mismo tipo de controlador)",
|
||||
"音频设备": "Dispositivo de audio",
|
||||
"音高算法": "Algoritmo de tono",
|
||||
"额外推理时长": "Tiempo de inferencia adicional"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "Longueur de l'échantillon",
|
||||
"重载设备列表": "Recharger la liste des dispositifs",
|
||||
"音调设置": "Réglages de la hauteur",
|
||||
"音频设备(请使用同种类驱动)": "Périphérique audio (veuillez utiliser le même type de pilote)",
|
||||
"音频设备": "Périphérique audio",
|
||||
"音高算法": "algorithme de détection de la hauteur",
|
||||
"额外推理时长": "Temps d'inférence supplémentaire"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "Lunghezza del campione",
|
||||
"重载设备列表": "Ricaricare l'elenco dei dispositivi",
|
||||
"音调设置": "Impostazioni del tono",
|
||||
"音频设备(请使用同种类驱动)": "Dispositivo audio (utilizzare lo stesso tipo di driver)",
|
||||
"音频设备": "Dispositivo audio",
|
||||
"音高算法": "音高算法",
|
||||
"额外推理时长": "Tempo di inferenza extra"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "サンプル長",
|
||||
"重载设备列表": "デバイスリストをリロードする",
|
||||
"音调设置": "音程設定",
|
||||
"音频设备(请使用同种类驱动)": "オーディオデバイス(同じ種類のドライバーを使用してください)",
|
||||
"音频设备": "オーディオデバイス",
|
||||
"音高算法": "ピッチアルゴリズム",
|
||||
"额外推理时长": "追加推論時間"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "Длина сэмпла",
|
||||
"重载设备列表": "Обновить список устройств",
|
||||
"音调设置": "Настройка высоты звука",
|
||||
"音频设备(请使用同种类驱动)": "Аудиоустройство (пожалуйста, используйте такой же тип драйвера)",
|
||||
"音频设备": "Аудиоустройство",
|
||||
"音高算法": "Алгоритм оценки высоты звука",
|
||||
"额外推理时长": "Доп. время переработки"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "Örnekleme uzunluğu",
|
||||
"重载设备列表": "Cihaz listesini yeniden yükle",
|
||||
"音调设置": "Pitch ayarları",
|
||||
"音频设备(请使用同种类驱动)": "Ses cihazı (aynı tür sürücüyü kullanın)",
|
||||
"音频设备": "Ses cihazı",
|
||||
"音高算法": "音高算法",
|
||||
"额外推理时长": "Ekstra çıkartma süresi"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -106,6 +106,8 @@
|
|||
"请选择pth文件": "请选择pth文件",
|
||||
"请选择说话人id": "请选择说话人id",
|
||||
"转换": "转换",
|
||||
"设备类型": "设备类型",
|
||||
"独占 WASAPI 设备": "独占 WASAPI 设备",
|
||||
"输入实验名": "输入实验名",
|
||||
"输入待处理音频文件夹路径": "输入待处理音频文件夹路径",
|
||||
"输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)",
|
||||
|
|
@ -129,7 +131,7 @@
|
|||
"采样长度": "采样长度",
|
||||
"重载设备列表": "重载设备列表",
|
||||
"音调设置": "音调设置",
|
||||
"音频设备(请使用同种类驱动)": "音频设备(请使用同种类驱动)",
|
||||
"音频设备": "音频设备",
|
||||
"音高算法": "音高算法",
|
||||
"额外推理时长": "额外推理时长"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "取樣長度",
|
||||
"重载设备列表": "重載設備列表",
|
||||
"音调设置": "音調設定",
|
||||
"音频设备(请使用同种类驱动)": "音訊設備 (請使用同種類驅動)",
|
||||
"音频设备": "音訊設備",
|
||||
"音高算法": "音高演算法",
|
||||
"额外推理时长": "額外推理時長"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "取樣長度",
|
||||
"重载设备列表": "重載設備列表",
|
||||
"音调设置": "音調設定",
|
||||
"音频设备(请使用同种类驱动)": "音訊設備 (請使用同種類驅動)",
|
||||
"音频设备": "音訊設備",
|
||||
"音高算法": "音高演算法",
|
||||
"额外推理时长": "額外推理時長"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@
|
|||
"采样长度": "取樣長度",
|
||||
"重载设备列表": "重載設備列表",
|
||||
"音调设置": "音調設定",
|
||||
"音频设备(请使用同种类驱动)": "音訊設備 (請使用同種類驅動)",
|
||||
"音频设备": "音訊設備",
|
||||
"音高算法": "音高演算法",
|
||||
"额外推理时长": "額外推理時長"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -91,8 +91,8 @@ class RVC:
|
|||
self.pth_path: str = pth_path
|
||||
self.index_path = index_path
|
||||
self.index_rate = index_rate
|
||||
self.cache_pitch: np.ndarray = np.zeros(1024, dtype="int32")
|
||||
self.cache_pitchf = np.zeros(1024, dtype="float32")
|
||||
self.cache_pitch: torch.Tensor = torch.zeros(1024, device=self.device, dtype=torch.long)
|
||||
self.cache_pitchf = torch.zeros(1024, device=self.device, dtype=torch.float32)
|
||||
|
||||
if last_rvc is None:
|
||||
models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
|
||||
|
|
@ -199,15 +199,17 @@ class RVC:
|
|||
self.index_rate = new_index_rate
|
||||
|
||||
def get_f0_post(self, f0):
|
||||
f0bak = f0.copy()
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
if not torch.is_tensor(f0):
|
||||
f0 = torch.from_numpy(f0)
|
||||
f0 = f0.float().to(self.device).squeeze()
|
||||
f0_mel = 1127 * torch.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_coarse = np.rint(f0_mel).astype(np.int32)
|
||||
return f0_coarse, f0bak
|
||||
f0_coarse = torch.round(f0_mel).long()
|
||||
return f0_coarse, f0
|
||||
|
||||
def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
|
||||
n_cpu = int(n_cpu)
|
||||
|
|
@ -299,14 +301,12 @@ class RVC:
|
|||
pd = torchcrepe.filter.median(pd, 3)
|
||||
f0 = torchcrepe.filter.mean(f0, 3)
|
||||
f0[pd < 0.1] = 0
|
||||
f0 = f0[0].cpu().numpy()
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def get_f0_rmvpe(self, x, f0_up_key):
|
||||
if hasattr(self, "model_rmvpe") == False:
|
||||
from infer.lib.rmvpe import RMVPE
|
||||
|
||||
printt("Loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE(
|
||||
"assets/rmvpe/rmvpe.pt",
|
||||
|
|
@ -335,7 +335,6 @@ class RVC:
|
|||
threshold=0.006,
|
||||
)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
f0 = f0.squeeze().cpu().numpy()
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def infer(
|
||||
|
|
@ -383,6 +382,7 @@ class RVC:
|
|||
traceback.print_exc()
|
||||
printt("Index search FAILED")
|
||||
t3 = ttime()
|
||||
p_len = input_wav.shape[0] // 160
|
||||
if self.if_f0 == 1:
|
||||
f0_extractor_frame = block_frame_16k + 800
|
||||
if f0method == "rmvpe":
|
||||
|
|
@ -390,25 +390,14 @@ class RVC:
|
|||
pitch, pitchf = self.get_f0(
|
||||
input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
|
||||
)
|
||||
start_frame = block_frame_16k // 160
|
||||
end_frame = len(self.cache_pitch) - (pitch.shape[0] - 4) + start_frame
|
||||
self.cache_pitch[:] = np.append(
|
||||
self.cache_pitch[start_frame:end_frame], pitch[3:-1]
|
||||
)
|
||||
self.cache_pitchf[:] = np.append(
|
||||
self.cache_pitchf[start_frame:end_frame], pitchf[3:-1]
|
||||
)
|
||||
shift = block_frame_16k // 160
|
||||
self.cache_pitch[: -shift] = self.cache_pitch[shift :].clone()
|
||||
self.cache_pitchf[: -shift] = self.cache_pitchf[shift :].clone()
|
||||
self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
|
||||
self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
|
||||
cache_pitch = self.cache_pitch[None, -p_len:]
|
||||
cache_pitchf = self.cache_pitchf[None, -p_len:]
|
||||
t4 = ttime()
|
||||
p_len = input_wav.shape[0] // 160
|
||||
if self.if_f0 == 1:
|
||||
cache_pitch = (
|
||||
torch.LongTensor(self.cache_pitch[-p_len:]).to(self.device).unsqueeze(0)
|
||||
)
|
||||
cache_pitchf = (
|
||||
torch.FloatTensor(self.cache_pitchf[-p_len:])
|
||||
.to(self.device)
|
||||
.unsqueeze(0)
|
||||
)
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
feats = feats[:, :p_len, :]
|
||||
p_len = torch.LongTensor([p_len]).to(self.device)
|
||||
|
|
|
|||
Loading…
Reference in New Issue