499 lines
22 KiB
Python
499 lines
22 KiB
Python
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import os,sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import multiprocessing
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class Harvest(multiprocessing.Process):
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def __init__(self,inp_q,opt_q):
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multiprocessing.Process.__init__(self)
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self.inp_q=inp_q
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self.opt_q=opt_q
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def run(self):
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import numpy as np, pyworld
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while(1):
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idx, x, res_f0,n_cpu,ts=self.inp_q.get()
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f0,t=pyworld.harvest(
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x.astype(np.double),
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fs=16000,
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f0_ceil=1100,
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f0_floor=50,
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frame_period=10,
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)
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res_f0[idx]=f0
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if(len(res_f0.keys())>=n_cpu):
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self.opt_q.put(ts)
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if __name__ == '__main__':
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from multiprocessing import Queue
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from queue import Empty
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import numpy as np
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import multiprocessing
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import traceback, re
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import json
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import PySimpleGUI as sg
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import sounddevice as sd
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import noisereduce as nr
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from multiprocessing import cpu_count
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import librosa, torch, time, threading
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import torch.nn.functional as F
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import torchaudio.transforms as tat
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from i18n import I18nAuto
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i18n = I18nAuto()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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current_dir = os.getcwd()
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inp_q = Queue()
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opt_q=Queue()
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n_cpu=min(cpu_count(),8)
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for _ in range(n_cpu):
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Harvest(inp_q,opt_q).start()
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from rvc_for_realtime import RVC
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class GUIConfig:
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def __init__(self) -> None:
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self.pth_path: str = ""
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self.index_path: str = ""
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self.pitch: int = 12
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self.samplerate: int = 40000
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self.block_time: float = 1.0 # s
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self.buffer_num: int = 1
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self.threhold: int = -30
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self.crossfade_time: float = 0.08
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self.extra_time: float = 0.04
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self.I_noise_reduce = False
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self.O_noise_reduce = False
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self.index_rate = 0.3
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self.n_cpu=min(n_cpu,8)
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self.f0method="harvest"
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class GUI:
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def __init__(self) -> None:
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self.config = GUIConfig()
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self.flag_vc = False
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self.launcher()
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def load(self):
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input_devices, output_devices, _, _ = self.get_devices()
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try:
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with open("values1.json", "r") as j:
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data = json.load(j)
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data["pm"]=data["f0method"]=="pm"
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data["harvest"]=data["f0method"]=="harvest"
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data["crepe"]=data["f0method"]=="crepe"
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except:
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with open("values1.json", "w") as j:
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data = {
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"pth_path": " ",
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"index_path": " ",
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"sg_input_device": input_devices[sd.default.device[0]],
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"sg_output_device": output_devices[sd.default.device[1]],
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"threhold": "-45",
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"pitch": "0",
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"index_rate": "0",
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"block_time": "1",
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"crossfade_length": "0.04",
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"extra_time": "1",
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"f0method": "harvest",
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}
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return data
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def launcher(self):
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data = self.load()
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sg.theme("LightBlue3")
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input_devices, output_devices, _, _ = self.get_devices()
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layout = [
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[
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sg.Frame(
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title=i18n("加载模型"),
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layout=[
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[
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sg.Input(
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default_text=data.get("pth_path", ""),
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key="pth_path",
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),
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sg.FileBrowse(
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i18n("选择.pth文件"),
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initial_folder=os.path.join(os.getcwd(), "weights"),
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file_types=((". pth"),),
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),
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],
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[
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sg.Input(
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default_text=data.get("index_path", ""),
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key="index_path",
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),
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sg.FileBrowse(
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i18n("选择.index文件"),
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initial_folder=os.path.join(os.getcwd(), "logs"),
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file_types=((". index"),),
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),
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],
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],
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)
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],
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[
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("输入设备")),
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sg.Combo(
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input_devices,
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key="sg_input_device",
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default_value=data.get("sg_input_device", ""),
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),
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],
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[
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sg.Text(i18n("输出设备")),
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sg.Combo(
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output_devices,
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key="sg_output_device",
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default_value=data.get("sg_output_device", ""),
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),
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],
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],
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title=i18n("音频设备(请使用同种类驱动)"),
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)
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],
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[
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("响应阈值")),
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sg.Slider(
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range=(-60, 0),
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key="threhold",
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resolution=1,
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orientation="h",
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default_value=data.get("threhold", ""),
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),
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],
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[
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sg.Text(i18n("音调设置")),
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sg.Slider(
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range=(-24, 24),
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key="pitch",
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resolution=1,
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orientation="h",
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default_value=data.get("pitch", ""),
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),
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],
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[
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sg.Text(i18n("Index Rate")),
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sg.Slider(
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range=(0.0, 1.0),
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key="index_rate",
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resolution=0.01,
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orientation="h",
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default_value=data.get("index_rate", ""),
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),
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],
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[
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sg.Text(i18n("音高算法")),
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sg.Radio("pm","f0method",key="pm",default=data.get("pm","")==True),
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sg.Radio("harvest","f0method",key="harvest",default=data.get("harvest","")==True),
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sg.Radio("crepe","f0method",key="crepe",default=data.get("crepe","")==True),
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],
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],
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title=i18n("常规设置"),
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),
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("采样长度")),
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sg.Slider(
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range=(0.12, 2.4),
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key="block_time",
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resolution=0.03,
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orientation="h",
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default_value=data.get("block_time", ""),
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),
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],
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[
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sg.Text(i18n("harvest进程数")),
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sg.Slider(
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range=(1, n_cpu),
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key="n_cpu",
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resolution=1,
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orientation="h",
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default_value=data.get("n_cpu", min(self.config.n_cpu,n_cpu)),
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),
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],
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[
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sg.Text(i18n("淡入淡出长度")),
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sg.Slider(
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range=(0.01, 0.15),
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key="crossfade_length",
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resolution=0.01,
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orientation="h",
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default_value=data.get("crossfade_length", ""),
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),
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],
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[
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sg.Text(i18n("额外推理时长")),
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sg.Slider(
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range=(0.05, 3.00),
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key="extra_time",
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resolution=0.01,
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orientation="h",
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default_value=data.get("extra_time", ""),
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),
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],
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[
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sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
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sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
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],
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],
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title=i18n("性能设置"),
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),
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],
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[
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sg.Button(i18n("开始音频转换"), key="start_vc"),
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sg.Button(i18n("停止音频转换"), key="stop_vc"),
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sg.Text(i18n("推理时间(ms):")),
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sg.Text("0", key="infer_time"),
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],
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]
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self.window = sg.Window("RVC - GUI", layout=layout)
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self.event_handler()
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def event_handler(self):
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while True:
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event, values = self.window.read()
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if event == sg.WINDOW_CLOSED:
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self.flag_vc = False
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exit()
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if event == "start_vc" and self.flag_vc == False:
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if self.set_values(values) == True:
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print("using_cuda:" + str(torch.cuda.is_available()))
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self.start_vc()
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settings = {
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"pth_path": values["pth_path"],
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"index_path": values["index_path"],
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"sg_input_device": values["sg_input_device"],
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"sg_output_device": values["sg_output_device"],
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"threhold": values["threhold"],
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"pitch": values["pitch"],
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"index_rate": values["index_rate"],
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"block_time": values["block_time"],
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"crossfade_length": values["crossfade_length"],
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"extra_time": values["extra_time"],
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"n_cpu": values["n_cpu"],
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"f0method": ["pm","harvest","crepe"][[values["pm"],values["harvest"],values["crepe"]].index(True)],
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}
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with open("values1.json", "w") as j:
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json.dump(settings, j)
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if event == "stop_vc" and self.flag_vc == True:
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self.flag_vc = False
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def set_values(self, values):
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if len(values["pth_path"].strip()) == 0:
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sg.popup(i18n("请选择pth文件"))
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return False
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if len(values["index_path"].strip()) == 0:
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sg.popup(i18n("请选择index文件"))
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return False
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pattern = re.compile("[^\x00-\x7F]+")
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if pattern.findall(values["pth_path"]):
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sg.popup(i18n("pth文件路径不可包含中文"))
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return False
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if pattern.findall(values["index_path"]):
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sg.popup(i18n("index文件路径不可包含中文"))
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return False
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self.set_devices(values["sg_input_device"], values["sg_output_device"])
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self.config.pth_path = values["pth_path"]
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self.config.index_path = values["index_path"]
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self.config.threhold = values["threhold"]
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self.config.pitch = values["pitch"]
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self.config.block_time = values["block_time"]
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self.config.crossfade_time = values["crossfade_length"]
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self.config.extra_time = values["extra_time"]
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self.config.I_noise_reduce = values["I_noise_reduce"]
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self.config.O_noise_reduce = values["O_noise_reduce"]
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self.config.index_rate = values["index_rate"]
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self.config.n_cpu = values["n_cpu"]
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self.config.f0method = ["pm","harvest","crepe"][[values["pm"],values["harvest"],values["crepe"]].index(True)]
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return True
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def start_vc(self):
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torch.cuda.empty_cache()
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self.flag_vc = True
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self.rvc = RVC(
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self.config.pitch,
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self.config.pth_path,
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self.config.index_path,
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self.config.index_rate,
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self.config.n_cpu,inp_q,opt_q,device
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)
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self.config.samplerate=self.rvc.tgt_sr
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self.config.crossfade_time=min(self.config.crossfade_time,self.config.block_time)
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self.block_frame = int(self.config.block_time * self.config.samplerate)
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self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
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self.sola_search_frame = int(0.01 * self.config.samplerate)
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self.extra_frame = int(self.config.extra_time * self.config.samplerate)
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self.zc=self.rvc.tgt_sr//100
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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",)
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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)
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self.pitch: np.ndarray = np.zeros(self.input_wav.shape[0]//self.zc,dtype="int32",)
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self.pitchf: np.ndarray = np.zeros(self.input_wav.shape[0]//self.zc,dtype="float64",)
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self.output_wav: torch.Tensor = torch.zeros(self.block_frame, device=device, dtype=torch.float32)
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self.sola_buffer: torch.Tensor = torch.zeros(
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self.crossfade_frame, device=device, dtype=torch.float32
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)
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self.fade_in_window: torch.Tensor = torch.linspace(
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0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
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)
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self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
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self.resampler = tat.Resample(
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orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
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)
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thread_vc = threading.Thread(target=self.soundinput)
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thread_vc.start()
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||
|
|
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()
|