1
0
mirror of synced 2024-11-30 18:24:32 +01:00
Retrieval-based-Voice-Conve.../gui_v1.py

1047 lines
46 KiB
Python
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

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:
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]
if hostapi_name not in self.hostapis:
hostapi_name = self.hostapis[0]
self.input_devices = [
d["name"]
for d in devices
if d["max_input_channels"] > 0 and d["hostapi_name"] == hostapi_name
]
self.output_devices = [
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 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 and d["hostapi_name"] == hostapi_name
]
def set_devices(self, input_device, output_device):
"""设置输出设备"""
sd.default.device[0] = self.input_devices_indices[
self.input_devices.index(input_device)
]
sd.default.device[1] = self.output_devices_indices[
self.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"]
)
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()