import os
import logging
import sys
from dotenv import load_dotenv
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
logger = logging.getLogger(__name__)
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
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.samplerate: int = 40000
self.block_time: float = 1.0 # s
self.buffer_num: int = 1
self.threhold: int = -60
self.crossfade_time: float = 0.04
self.extra_time: float = 2.0
self.I_noise_reduce = False
self.O_noise_reduce = False
self.rms_mix_rate = 0.0
self.index_rate = 0.3
self.n_cpu = min(n_cpu, 6)
self.f0method = "harvest"
self.sg_input_device = ""
self.sg_output_device = ""
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("configs/config.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("configs/config.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": "-60",
"pitch": "0",
"index_rate": "0",
"rms_mix_rate": "0",
"block_time": "0.25",
"crossfade_length": "0.04",
"extra_time": "2",
"f0method": "rmvpe",
}
data["pm"] = data["f0method"] == "pm"
data["harvest"] = data["f0method"] == "harvest"
data["crepe"] = data["f0method"] == "crepe"
data["rmvpe"] = data["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(), "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("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", ""),
),
],
[sg.Button(i18n("重载设备列表"), key="reload_devices")],
],
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", "-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", "") == True,
enable_events=True,
),
sg.Radio(
"harvest",
"f0method",
key="harvest",
default=data.get("harvest", "") == True,
enable_events=True,
),
sg.Radio(
"crepe",
"f0method",
key="crepe",
default=data.get("crepe", "") == True,
enable_events=True,
),
sg.Radio(
"rmvpe",
"f0method",
key="rmvpe",
default=data.get("rmvpe", "") == True,
enable_events=True,
),
],
],
title=i18n("常规设置"),
),
sg.Frame(
layout=[
[
sg.Text(i18n("采样长度")),
sg.Slider(
range=(0.05, 2.4),
key="block_time",
resolution=0.01,
orientation="h",
default_value=data.get("block_time", "0.25"),
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.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", "0.04"),
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", "2.0"),
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,
),
],
],
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, finalize=True)
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 == "reload_devices":
prev_input = self.window["sg_input_device"].get()
prev_output = self.window["sg_output_device"].get()
input_devices, output_devices, _, _ = self.get_devices(update=True)
if prev_input not in input_devices:
self.config.sg_input_device = input_devices[0]
else:
self.config.sg_input_device = prev_input
self.window["sg_input_device"].Update(values=input_devices)
self.window["sg_input_device"].Update(
value=self.config.sg_input_device
)
if prev_output not in output_devices:
self.config.sg_output_device = output_devices[0]
else:
self.config.sg_output_device = prev_output
self.window["sg_output_device"].Update(values=output_devices)
self.window["sg_output_device"].Update(
value=self.config.sg_output_device
)
if event == "start_vc" and self.flag_vc == False:
if self.set_values(values) == True:
logger.info("Use CUDA: %s", 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"],
"rms_mix_rate": values["rms_mix_rate"],
"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("configs/config.json", "w") as j:
json.dump(settings, j)
if event == "stop_vc" and self.flag_vc == True:
self.flag_vc = False
# Parameter hot update
if event == "threhold":
self.config.threhold = values["threhold"]
elif event == "pitch":
self.config.pitch = values["pitch"]
if hasattr(self, "rvc"):
self.rvc.change_key(values["pitch"])
elif event == "index_rate":
self.config.index_rate = values["index_rate"]
if hasattr(self, "rvc"):
self.rvc.change_index_rate(values["index_rate"])
elif event == "rms_mix_rate":
self.config.rms_mix_rate = values["rms_mix_rate"]
elif event in ["pm", "harvest", "crepe", "rmvpe"]:
self.config.f0method = event
elif event == "I_noise_reduce":
self.config.I_noise_reduce = values["I_noise_reduce"]
elif event == "O_noise_reduce":
self.config.O_noise_reduce = values["O_noise_reduce"]
elif event != "start_vc" and self.flag_vc == True:
# Other parameters do not support hot update
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.rms_mix_rate = values["rms_mix_rate"]
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_for_realtime.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.rvc if hasattr(self, "rvc") else None
)
self.config.samplerate = self.rvc.tgt_sr
self.zc = self.rvc.tgt_sr // 100
self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc
self.block_frame_16k = 160 * self.block_frame // self.zc
self.crossfade_frame = int(np.round(self.config.crossfade_time * self.config.samplerate / self.zc)) * self.zc
self.sola_search_frame = self.zc
self.extra_frame = int(np.round(self.config.extra_time * self.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=device,
dtype=torch.float32,
)
self.input_wav_res: torch.Tensor= torch.zeros(160 * self.input_wav.shape[0] // 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.sola_buffer: torch.Tensor = torch.zeros(
self.crossfade_frame, device=device, dtype=torch.float32
)
self.nr_buffer: torch.Tensor = self.sola_buffer.clone()
self.output_buffer: torch.Tensor = self.input_wav.clone()
self.res_buffer: torch.Tensor = torch.zeros(2 * self.zc, device=device,dtype=torch.float32)
self.valid_rate = 1 - (self.extra_frame - 1) / self.input_wav.shape[0]
self.fade_in_window: torch.Tensor = (
torch.sin(
0.5
* np.pi
* torch.linspace(
0.0,
1.0,
steps=self.crossfade_frame,
device=device,
dtype=torch.float32,
)
)
** 2
)
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)
self.tg = TorchGate(sr=self.config.samplerate, n_fft=4*self.zc, prop_decrease=0.9).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)
logger.debug("Audio block passed.")
logger.debug("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.threhold > -60:
rms = librosa.feature.rms(
y=indata, frame_length=4*self.zc, hop_length=self.zc
)
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 * self.zc : (i + 1) * self.zc] = 0
self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
self.input_wav[-self.block_frame: ] = torch.from_numpy(indata).to(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.config.I_noise_reduce:
input_wav = self.input_wav[-self.crossfade_frame -self.block_frame-2*self.zc: ]
input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0))[0, 2*self.zc:]
input_wav[: self.crossfade_frame] *= self.fade_in_window
input_wav[: self.crossfade_frame] += self.nr_buffer * self.fade_out_window
self.nr_buffer[:] = input_wav[-self.crossfade_frame: ]
input_wav = torch.cat((self.res_buffer[:], input_wav[: self.block_frame]))
self.res_buffer[:] = input_wav[-2*self.zc: ]
self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(input_wav)[160: ]
else:
self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(self.input_wav[-self.block_frame-2*self.zc: ])[160: ]
# infer
f0_extractor_frame = self.block_frame_16k + 800
if self.config.f0method == 'rmvpe':
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
infer_wav = self.rvc.infer(
self.input_wav_res,
self.input_wav_res[-f0_extractor_frame :].cpu().numpy(),
self.block_frame_16k,
self.valid_rate,
self.pitch,
self.pitchf,
self.config.f0method,
)
infer_wav = infer_wav[
-self.crossfade_frame - self.sola_search_frame - self.block_frame :
]
# output noise reduction
if self.config.O_noise_reduce:
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.config.rms_mix_rate < 1:
rms1 = librosa.feature.rms(
y=self.input_wav_res[-160*infer_wav.shape[0]//self.zc :].cpu().numpy(),
frame_length=640,
hop_length=160,
)
rms1 = torch.from_numpy(rms1).to(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(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.config.rms_mix_rate))
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
conv_input = infer_wav[None, None, : self.crossfade_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.crossfade_frame, device=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])
logger.debug("sola_offset = %d", int(sola_offset))
infer_wav = infer_wav[sola_offset: sola_offset + self.block_frame + self.crossfade_frame]
infer_wav[: self.crossfade_frame] *= self.fade_in_window
infer_wav[: self.crossfade_frame] += self.sola_buffer *self.fade_out_window
self.sola_buffer[:] = infer_wav[-self.crossfade_frame:]
if sys.platform == "darwin":
outdata[:] = infer_wav[:-self.crossfade_frame].cpu().numpy()[:, np.newaxis]
else:
outdata[:] = infer_wav[:-self.crossfade_frame].repeat(2, 1).t().cpu().numpy()
total_time = time.perf_counter() - start_time
self.window["infer_time"].update(int(total_time * 1000))
logger.info("Infer time: %.2f", 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)
]
logger.info(
"Input device: %s:%s", str(sd.default.device[0]), input_device
)
logger.info(
"Output device: %s:%s", str(sd.default.device[1]), output_device
)
gui = GUI()