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gui_v1.py
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gui_v1.py
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import os
import sys
from dotenv import load_dotenv
import shutil
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
flag_vc = False
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.sr_type: str = "sr_model"
self.block_time: float = 0.25 # s
self.threhold: int = -60
self.crossfade_time: float = 0.05
self.extra_time: float = 2.5
self.I_noise_reduce: bool = False
self.O_noise_reduce: bool = False
self.use_pv: bool = False
self.rms_mix_rate: float = 0.0
self.index_rate: float = 0.0
self.n_cpu: int = min(n_cpu, 4)
self.f0method: str = "fcpe"
self.sg_hostapi: str = ""
self.wasapi_exclusive: bool = False
self.sg_input_device: str = ""
self.sg_output_device: str = ""
class GUI:
def __init__(self) -> None:
self.gui_config = GUIConfig()
self.config = Config()
self.function = "vc"
self.delay_time = 0
self.hostapis = None
self.input_devices = None
self.output_devices = None
self.input_devices_indices = None
self.output_devices_indices = None
self.stream = None
self.update_devices()
self.launcher()
def load(self):
try:
if not os.path.exists("configs/inuse/config.json"):
shutil.copy("configs/config.json", "configs/inuse/config.json")
with open("configs/inuse/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_hostapi"] in self.hostapis:
self.update_devices(hostapi_name=data["sg_hostapi"])
if (
data["sg_input_device"] not in self.input_devices
or data["sg_output_device"] not in self.output_devices
):
self.update_devices()
data["sg_hostapi"] = self.hostapis[0]
data["sg_input_device"] = self.input_devices[
self.input_devices_indices.index(sd.default.device[0])
]
data["sg_output_device"] = self.output_devices[
self.output_devices_indices.index(sd.default.device[1])
]
else:
data["sg_hostapi"] = self.hostapis[0]
data["sg_input_device"] = self.input_devices[
self.input_devices_indices.index(sd.default.device[0])
]
data["sg_output_device"] = self.output_devices[
self.output_devices_indices.index(sd.default.device[1])
]
except:
with open("configs/inuse/config.json", "w") as j:
data = {
"pth_path": "",
"index_path": "",
"sg_hostapi": self.hostapis[0],
"sg_wasapi_exclusive": False,
"sg_input_device": self.input_devices[
self.input_devices_indices.index(sd.default.device[0])
],
"sg_output_device": self.output_devices[
self.output_devices_indices.index(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,
"n_cpu": 4,
"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")
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("设备类型")),
sg.Combo(
self.hostapis,
key="sg_hostapi",
default_value=data.get("sg_hostapi", ""),
enable_events=True,
size=(20, 1),
),
sg.Checkbox(
i18n("独占 WASAPI 设备"),
key="sg_wasapi_exclusive",
default=data.get("sg_wasapi_exclusive", False),
enable_events=True,
),
],
[
sg.Text(i18n("输入设备")),
sg.Combo(
self.input_devices,
key="sg_input_device",
default_value=data.get("sg_input_device", ""),
enable_events=True,
size=(45, 1),
),
],
[
sg.Text(i18n("输出设备")),
sg.Combo(
self.output_devices,
key="sg_output_device",
default_value=data.get("sg_output_device", ""),
enable_events=True,
size=(45, 1),
),
],
[
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"),
)
],
[
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, 1.5),
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):
global flag_vc
while True:
event, values = self.window.read()
if event == sg.WINDOW_CLOSED:
self.stop_stream()
exit()
if event == "reload_devices" or event == "sg_hostapi":
self.gui_config.sg_hostapi = values["sg_hostapi"]
self.update_devices(hostapi_name=values["sg_hostapi"])
if self.gui_config.sg_hostapi not in self.hostapis:
self.gui_config.sg_hostapi = self.hostapis[0]
self.window["sg_hostapi"].Update(values=self.hostapis)
self.window["sg_hostapi"].Update(value=self.gui_config.sg_hostapi)
if (
self.gui_config.sg_input_device not in self.input_devices
and len(self.input_devices) > 0
):
self.gui_config.sg_input_device = self.input_devices[0]
self.window["sg_input_device"].Update(values=self.input_devices)
self.window["sg_input_device"].Update(
value=self.gui_config.sg_input_device
)
if self.gui_config.sg_output_device not in self.output_devices:
self.gui_config.sg_output_device = self.output_devices[0]
self.window["sg_output_device"].Update(values=self.output_devices)
self.window["sg_output_device"].Update(
value=self.gui_config.sg_output_device
)
if event == "start_vc" and not flag_vc:
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_hostapi": values["sg_hostapi"],
"sg_wasapi_exclusive": values["sg_wasapi_exclusive"],
"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/inuse/config.json", "w") as j:
json.dump(settings, j)
if self.stream is not None:
self.delay_time = (
self.stream.latency[-1]
+ 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(np.round(self.delay_time * 1000))
)
# 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 self.stream is not None:
self.delay_time += (
1 if values["I_noise_reduce"] else -1
) * min(values["crossfade_length"], 0.04)
self.window["delay_time"].update(
int(np.round(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 == "stop_vc" or event != "start_vc":
# Other parameters do not support hot update
self.stop_stream()
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.sg_hostapi = values["sg_hostapi"]
self.gui_config.sg_wasapi_exclusive = values["sg_wasapi_exclusive"]
self.gui_config.sg_input_device = values["sg_input_device"]
self.gui_config.sg_output_device = values["sg_output_device"]
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.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.gui_config.channels = self.get_device_channels()
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_denoise: torch.Tensor = self.input_wav.clone()
self.input_wav_res: torch.Tensor = torch.zeros(
160 * self.input_wav.shape[0] // self.zc,
device=self.config.device,
dtype=torch.float32,
)
self.rms_buffer: np.ndarray = np.zeros(4 * self.zc, dtype="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.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)
self.start_stream()
def start_stream(self):
global flag_vc
if not flag_vc:
flag_vc = True
if (
"WASAPI" in self.gui_config.sg_hostapi
and self.gui_config.sg_wasapi_exclusive
):
extra_settings = sd.WasapiSettings(exclusive=True)
else:
extra_settings = None
self.stream = sd.Stream(
callback=self.audio_callback,
blocksize=self.block_frame,
samplerate=self.gui_config.samplerate,
channels=self.gui_config.channels,
dtype="float32",
extra_settings=extra_settings,
)
self.stream.start()
def stop_stream(self):
global flag_vc
if flag_vc:
flag_vc = False
if self.stream is not None:
self.stream.abort()
self.stream.close()
self.stream = None
def audio_callback(
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
):
"""
音频处理
"""
global flag_vc
start_time = time.perf_counter()
indata = librosa.to_mono(indata.T)
if self.gui_config.threhold > -60:
indata = np.append(self.rms_buffer, indata)
rms = librosa.feature.rms(
y=indata, frame_length=4 * self.zc, hop_length=self.zc
)[:, 2:]
self.rms_buffer[:] = indata[-4 * self.zc :]
indata = indata[2 * self.zc - self.zc // 2 :]
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
indata = indata[self.zc // 2 :]
self.input_wav[: -self.block_frame] = self.input_wav[
self.block_frame :
].clone()
self.input_wav[-indata.shape[0] :] = 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:
self.input_wav_denoise[: -self.block_frame] = self.input_wav_denoise[
self.block_frame :
].clone()
input_wav = self.input_wav[-self.sola_buffer_frame - self.block_frame :]
input_wav = self.tg(
input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)
).squeeze(0)
input_wav[: self.sola_buffer_frame] *= self.fade_in_window
input_wav[: self.sola_buffer_frame] += (
self.nr_buffer * self.fade_out_window
)
self.input_wav_denoise[-self.block_frame :] = input_wav[
: self.block_frame
]
self.nr_buffer[:] = input_wav[self.block_frame :]
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
)[160:]
else:
self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = (
self.resampler(self.input_wav[-indata.shape[0] - 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)
elif self.gui_config.I_noise_reduce:
infer_wav = self.input_wav_denoise[self.extra_frame :].clone()
else:
infer_wav = self.input_wav[self.extra_frame :].clone()
# output noise reduction
if self.gui_config.O_noise_reduce and self.function == "vc":
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":
if self.gui_config.I_noise_reduce:
input_wav = self.input_wav_denoise[self.extra_frame :]
else:
input_wav = self.input_wav[self.extra_frame :]
rms1 = librosa.feature.rms(
y=input_wav[: infer_wav.shape[0]].cpu().numpy(),
frame_length=4 * self.zc,
hop_length=self.zc,
)
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
]
outdata[:] = (
infer_wav[: self.block_frame]
.repeat(self.gui_config.channels, 1)
.t()
.cpu()
.numpy()
)
total_time = time.perf_counter() - start_time
if flag_vc:
self.window["infer_time"].update(int(total_time * 1000))
printt("Infer time: %.2f", total_time)
def update_devices(self, hostapi_name=None):
"""获取设备列表"""
global flag_vc
flag_vc = False
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"]
self.hostapis = [hostapi["name"] for hostapi in hostapis]