633 lines
26 KiB
Python
633 lines
26 KiB
Python
import os, sys
|
|
|
|
if sys.platform == "darwin":
|
|
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
|
|
|
|
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 ("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()
|
|
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"
|
|
data["rmvpe"] = data["f0method"] == "rmvpe"
|
|
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": "rmvpe",
|
|
}
|
|
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,
|
|
),
|
|
sg.Radio(
|
|
"rmvpe",
|
|
"f0method",
|
|
key="rmvpe",
|
|
default=data.get("rmvpe", "") == 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", "rmvpe"][
|
|
[
|
|
values["pm"],
|
|
values["harvest"],
|
|
values["crepe"],
|
|
values["rmvpe"],
|
|
].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", "rmvpe"][
|
|
[
|
|
values["pm"],
|
|
values["harvest"],
|
|
values["crepe"],
|
|
values["rmvpe"],
|
|
].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
|
|
).to(device)
|
|
thread_vc = threading.Thread(target=self.soundinput)
|
|
thread_vc.start()
|
|
|
|
def soundinput(self):
|
|
"""
|
|
接受音频输入
|
|
"""
|
|
channels = 1 if sys.platform == "darwin" else 2
|
|
with sd.Stream(
|
|
channels=channels,
|
|
callback=self.audio_callback,
|
|
blocksize=self.block_frame,
|
|
samplerate=self.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).to(device)
|
|
##0
|
|
res1 = self.resampler(inp)
|
|
###55%
|
|
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 :].cpu().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 :
|
|
]
|
|
# 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
|
|
)
|
|
if sys.platform == "darwin":
|
|
cor_nom = cor_nom.cpu()
|
|
cor_den = cor_den.cpu()
|
|
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:
|
|
if sys.platform == "darwin":
|
|
noise_reduced_signal = nr.reduce_noise(
|
|
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
|
|
)
|
|
outdata[:] = noise_reduced_signal[:, np.newaxis]
|
|
else:
|
|
outdata[:] = np.tile(
|
|
nr.reduce_noise(
|
|
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
|
|
),
|
|
(2, 1),
|
|
).T
|
|
else:
|
|
if sys.platform == "darwin":
|
|
outdata[:] = self.output_wav[:].cpu().numpy()[:, np.newaxis]
|
|
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()
|