Cool_Tools/Retrieval-based-Voice-Conversion-WebUI
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go-realtime-gui.bat
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runtime\python.exe gui.py runtime\python.exe gui_v1.py
pause pause

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gui_v1.py Normal file
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import os,sys
now_dir = os.getcwd()
sys.path.append(now_dir)
import multiprocessing
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, 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__':
from multiprocessing import Queue
from queue import Empty
import numpy as np
import multiprocessing
import traceback, re
import json
import PySimpleGUI as sg
import sounddevice as sd
import noisereduce as nr
from multiprocessing import cpu_count
import librosa, torch, time, threading
import torch.nn.functional as F
import torchaudio.transforms as tat
from i18n import I18nAuto
i18n = I18nAuto()
device = torch.device("cuda" if torch.cuda.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()
from rvc_for_realtime import RVC
class GUIConfig:
def __init__(self) -> None:
self.pth_path: str = ""
self.index_path: str = ""
self.pitch: int = 12
self.samplerate: int = 40000
self.block_time: float = 1.0 # s
self.buffer_num: int = 1
self.threhold: int = -30
self.crossfade_time: float = 0.08
self.extra_time: float = 0.04
self.I_noise_reduce = False
self.O_noise_reduce = False
self.index_rate = 0.3
self.n_cpu=min(n_cpu,8)
self.f0method="harvest"
class GUI:
def __init__(self) -> None:
self.config = GUIConfig()
self.flag_vc = False
self.launcher()
def load(self):
input_devices, output_devices, _, _ = self.get_devices()
try:
with open("values1.json", "r") as j:
data = json.load(j)
data["pm"]=data["f0method"]=="pm"
data["harvest"]=data["f0method"]=="harvest"
data["crepe"]=data["f0method"]=="crepe"
except:
with open("values1.json", "w") as j:
data = {
"pth_path": " ",
"index_path": " ",
"sg_input_device": input_devices[sd.default.device[0]],
"sg_output_device": output_devices[sd.default.device[1]],
"threhold": "-45",
"pitch": "0",
"index_rate": "0",
"block_time": "1",
"crossfade_length": "0.04",
"extra_time": "1",
"f0method": "harvest",
}
return data
def launcher(self):
data = self.load()
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(), "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", ""),
),
],
],
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", ""),
),
],
[
sg.Text(i18n("音调设置")),
sg.Slider(
range=(-24, 24),
key="pitch",
resolution=1,
orientation="h",
default_value=data.get("pitch", ""),
),
],
[
sg.Text(i18n("Index Rate")),
sg.Slider(
range=(0.0, 1.0),
key="index_rate",
resolution=0.01,
orientation="h",
default_value=data.get("index_rate", ""),
),
],
[
sg.Text(i18n("音高算法")),
sg.Radio("pm","f0method",key="pm",default=data.get("pm","")==True),
sg.Radio("harvest","f0method",key="harvest",default=data.get("harvest","")==True),
sg.Radio("crepe","f0method",key="crepe",default=data.get("crepe","")==True),
],
],
title=i18n("常规设置"),
),
sg.Frame(
layout=[
[
sg.Text(i18n("采样长度")),
sg.Slider(
range=(0.12, 2.4),
key="block_time",
resolution=0.03,
orientation="h",
default_value=data.get("block_time", ""),
),
],
[
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.config.n_cpu,n_cpu)),
),
],
[
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", ""),
),
],
[
sg.Text(i18n("额外推理时长")),
sg.Slider(
range=(0.05, 3.00),
key="extra_time",
resolution=0.01,
orientation="h",
default_value=data.get("extra_time", ""),
),
],
[
sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
],
],
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.Text(i18n("推理时间(ms):")),
sg.Text("0", key="infer_time"),
],
]
self.window = sg.Window("RVC - GUI", layout=layout)
self.event_handler()
def event_handler(self):
while True:
event, values = self.window.read()
if event == sg.WINDOW_CLOSED:
self.flag_vc = False
exit()
if event == "start_vc" and self.flag_vc == False:
if self.set_values(values) == True:
print("using_cuda:" + str(torch.cuda.is_available()))
self.start_vc()
settings = {
"pth_path": values["pth_path"],
"index_path": values["index_path"],
"sg_input_device": values["sg_input_device"],
"sg_output_device": values["sg_output_device"],
"threhold": values["threhold"],
"pitch": values["pitch"],
"index_rate": values["index_rate"],
"block_time": values["block_time"],
"crossfade_length": values["crossfade_length"],
"extra_time": values["extra_time"],
"n_cpu": values["n_cpu"],
"f0method": ["pm","harvest","crepe"][[values["pm"],values["harvest"],values["crepe"]].index(True)],
}
with open("values1.json", "w") as j:
json.dump(settings, j)
if event == "stop_vc" and self.flag_vc == True:
self.flag_vc = False
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.pth_path = values["pth_path"]
self.config.index_path = values["index_path"]
self.config.threhold = values["threhold"]
self.config.pitch = values["pitch"]
self.config.block_time = values["block_time"]
self.config.crossfade_time = values["crossfade_length"]
self.config.extra_time = values["extra_time"]
self.config.I_noise_reduce = values["I_noise_reduce"]
self.config.O_noise_reduce = values["O_noise_reduce"]
self.config.index_rate = values["index_rate"]
self.config.n_cpu = values["n_cpu"]
self.config.f0method = ["pm","harvest","crepe"][[values["pm"],values["harvest"],values["crepe"]].index(True)]
return True
def start_vc(self):
torch.cuda.empty_cache()
self.flag_vc = True
self.rvc = RVC(
self.config.pitch,
self.config.pth_path,
self.config.index_path,
self.config.index_rate,
self.config.n_cpu,inp_q,opt_q,device
)
self.config.samplerate=self.rvc.tgt_sr
self.config.crossfade_time=min(self.config.crossfade_time,self.config.block_time)
self.block_frame = int(self.config.block_time * self.config.samplerate)
self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
self.sola_search_frame = int(0.01 * self.config.samplerate)
self.extra_frame = int(self.config.extra_time * self.config.samplerate)
self.zc=self.rvc.tgt_sr//100
self.input_wav: np.ndarray = np.zeros(int(np.ceil((self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)/self.zc)*self.zc),dtype="float32",)
self.output_wav_cache: torch.Tensor = torch.zeros(int(np.ceil((self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)/self.zc)*self.zc), device=device,dtype=torch.float32)
self.pitch: np.ndarray = np.zeros(self.input_wav.shape[0]//self.zc,dtype="int32",)
self.pitchf: np.ndarray = np.zeros(self.input_wav.shape[0]//self.zc,dtype="float64",)
self.output_wav: torch.Tensor = torch.zeros(self.block_frame, device=device, dtype=torch.float32)
self.sola_buffer: torch.Tensor = torch.zeros(
self.crossfade_frame, device=device, dtype=torch.float32
)
self.fade_in_window: torch.Tensor = torch.linspace(
0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
)
self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
self.resampler = tat.Resample(
orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
)
thread_vc = threading.Thread(target=self.soundinput)
thread_vc.start()
def soundinput(self):
"""
接受音频输入
"""
with sd.Stream(
channels=2,
callback=self.audio_callback,
blocksize=self.block_frame,
samplerate=self.config.samplerate,
dtype="float32",
):
while self.flag_vc:
time.sleep(self.config.block_time)
print("Audio block passed.")
print("ENDing VC")
def audio_callback(
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
):
"""
音频处理
"""
start_time = time.perf_counter()
indata = librosa.to_mono(indata.T)
if self.config.I_noise_reduce:
indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
"""noise gate"""
frame_length = 2048
hop_length = 1024
rms = librosa.feature.rms(
y=indata, frame_length=frame_length, hop_length=hop_length
)
if(self.config.threhold>-60):
db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
for i in range(db_threhold.shape[0]):
if db_threhold[i]:
indata[i * hop_length : (i + 1) * hop_length] = 0
self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
# infer
inp=torch.from_numpy(self.input_wav)
res1=self.resampler(inp)
rate1=self.block_frame/(self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)
rate2=(self.crossfade_frame + self.sola_search_frame + self.block_frame)/(self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)
res2=self.rvc.infer(res1,res1[-self.block_frame:].numpy(),rate1,rate2,self.pitch,self.pitchf,self.config.f0method)
self.output_wav_cache[-res2.shape[0]:]=res2
infer_wav = self.output_wav_cache[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(device)
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
cor_nom = F.conv1d(
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
self.sola_buffer[None, None, :],
)
cor_den = torch.sqrt(
F.conv1d(
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
** 2,
torch.ones(1, 1, self.crossfade_frame, device=device),
)
+ 1e-8
)
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
print("sola offset: " + str(int(sola_offset)))
self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
self.output_wav[: self.crossfade_frame] *= self.fade_in_window
self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
# crossfade
if sola_offset < self.sola_search_frame:
self.sola_buffer[:] = (
infer_wav[
-self.sola_search_frame
- self.crossfade_frame
+ sola_offset : -self.sola_search_frame
+ sola_offset
]
* self.fade_out_window
)
else:
self.sola_buffer[:] = (
infer_wav[-self.crossfade_frame :] * self.fade_out_window
)
if self.config.O_noise_reduce:
outdata[:] = np.tile(
nr.reduce_noise(
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
),
(2, 1),
).T
else:
outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
total_time = time.perf_counter() - start_time
self.window["infer_time"].update(int(total_time * 1000))
print("infer time:" + str(total_time))
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)
]
print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
gui = GUI()

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import faiss,torch,traceback,parselmouth,numpy as np,torchcrepe,torch.nn as nn,pyworld
from fairseq import checkpoint_utils
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
import os,sys
from time import time as ttime
import torch.nn.functional as F
import scipy.signal as signal
now_dir = os.getcwd()
sys.path.append(now_dir)
from config import Config
from multiprocessing import Manager as M
mm = M()
config = Config()
class RVC:
def __init__(
self, key, pth_path, index_path, index_rate, n_cpu,inp_q,opt_q,device
) -> None:
"""
初始化
"""
try:
global config
self.inp_q=inp_q
self.opt_q=opt_q
self.device=device
self.f0_up_key = key
self.time_step = 160 / 16000 * 1000
self.f0_min = 50
self.f0_max = 1100
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
self.sr = 16000
self.window = 160
self.n_cpu = n_cpu
if index_rate != 0:
self.index = faiss.read_index(index_path)
self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
print("index search enabled")
self.index_rate = index_rate
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["hubert_base.pt"],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(config.device)
if config.is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_model.float()
hubert_model.eval()
self.model = hubert_model
cpt = torch.load(pth_path, map_location="cpu")
self.tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
self.if_f0 = cpt.get("f0", 1)
self.version = cpt.get("version", "v1")
if self.version == "v1":
if self.if_f0 == 1:
self.net_g = SynthesizerTrnMs256NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif self.version == "v2":
if self.if_f0 == 1:
self.net_g = SynthesizerTrnMs768NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del self.net_g.enc_q
print(self.net_g.load_state_dict(cpt["weight"], strict=False))
self.net_g.eval().to(device)
if config.is_half:
self.net_g = self.net_g.half()
else:
self.net_g = self.net_g.float()
except:
print(traceback.format_exc())
def get_f0_post(self, f0):
f0_min = self.f0_min
f0_max = self.f0_max
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int)
return f0_coarse, f0bak
def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
n_cpu = int(n_cpu)
if (method == "crepe"): return self.get_f0_crepe(x, f0_up_key)
if (method == "pm"):
p_len = x.shape[0] // 160
f0 = (
parselmouth.Sound(x, 16000)
.to_pitch_ac(
time_step=0.01,
voicing_threshold=0.6,
pitch_floor=50,
pitch_ceiling=1100,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
print(pad_size, p_len - len(f0) - pad_size)
f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
f0 *= pow(2, f0_up_key / 12)
return self.get_f0_post(f0)
if (n_cpu == 1):
f0, t = pyworld.harvest(
x.astype(np.double),
fs=16000,
f0_ceil=1100,
f0_floor=50,
frame_period=10,
)
f0 = signal.medfilt(f0, 3)
f0 *= pow(2, f0_up_key / 12)
return self.get_f0_post(f0)
f0bak = np.zeros(x.shape[0] // 160, dtype=np.float64)
length = len(x)
part_length = int(length / n_cpu / 160) * 160
ts = ttime()
res_f0 = mm.dict()
for idx in range(n_cpu):
tail = part_length * (idx + 1) + 320
if (idx == 0):
self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
else:
self.inp_q.put((idx, x[part_length * idx - 320:tail], res_f0, n_cpu, ts))
while (1):
res_ts = self.opt_q.get()
if (res_ts == ts):
break
f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
for idx, f0 in enumerate(f0s):
if (idx == 0):
f0 = f0[:-3]
elif (idx != n_cpu - 1):
f0 = f0[2:-3]
else:
f0 = f0[2:-1]
f0bak[part_length * idx // 160:part_length * idx // 160 + f0.shape[0]] = f0
f0bak = signal.medfilt(f0bak, 3)
f0bak *= pow(2, f0_up_key / 12)
return self.get_f0_post(f0bak)
def get_f0_crepe(self, x, f0_up_key):
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.sr,
160,
self.f0_min,
self.f0_max,
"full",
batch_size=512,
device=self.device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0[pd < 0.1] = 0
f0 = f0[0].cpu().numpy()
f0 *= pow(2, f0_up_key / 12)
return self.get_f0_post(f0)
def infer(self, feats: torch.Tensor, indata: np.ndarray, rate1, rate2, cache_pitch, cache_pitchf, f0method) -> np.ndarray:
feats = feats.view(1, -1)
if config.is_half:
feats = feats.half()
else:
feats = feats.float()
feats = feats.to(self.device)
t1 = ttime()
with torch.no_grad():
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
inputs = {
"source": feats,
"padding_mask": padding_mask,
"output_layer": 9 if self.version == "v1" else 12,
}
logits = self.model.extract_features(**inputs)
feats = self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
t2 = ttime()
try:
if (
hasattr(self, "index")
and self.index_rate != 0
):
leng_replace_head = int(rate1 * feats[0].shape[0])
npy = feats[0][-leng_replace_head:].cpu().numpy().astype("float32")
score, ix = self.index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if config.is_half:
npy = npy.astype("float16")
feats[0][-leng_replace_head:] = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
+ (1 - self.index_rate) * feats[0][-leng_replace_head:]
)
else:
print("index search FAIL or disabled")
except:
traceback.print_exc()
print("index search FAIL")
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
t3 = ttime()
if self.if_f0 == 1:
pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method)
cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0]:], pitch[:-1])
cache_pitchf[:] = np.append(cache_pitchf[pitchf[:-1].shape[0]:], pitchf[:-1])
p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
else:
cache_pitch, cache_pitchf = None, None
p_len = min(feats.shape[1], 13000)
t4 = ttime()
feats = feats[:, :p_len, :]
if self.if_f0 == 1:
cache_pitch = cache_pitch[:p_len]
cache_pitchf = cache_pitchf[:p_len]
cache_pitch = torch.LongTensor(cache_pitch).unsqueeze(0).to(self.device)
cache_pitchf = torch.FloatTensor(cache_pitchf).unsqueeze(0).to(self.device)
p_len = torch.LongTensor([p_len]).to(self.device)
ii = 0 # sid
sid = torch.LongTensor([ii]).to(self.device)
with torch.no_grad():
if self.if_f0 == 1:
infered_audio = (
self.net_g.infer(feats, p_len, cache_pitch, cache_pitchf, sid, rate2)[0][0, 0]
.data.cpu()
.float()
)
else:
infered_audio = (
self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0].data.cpu().float()
)
t5 = ttime()
print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
return infered_audio