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RVC-Boss
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runtime\python.exe gui.py
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runtime\python.exe gui_v1.py
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pause
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498
gui_v1.py
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498
gui_v1.py
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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):
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"""
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接受音频输入
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"""
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with sd.Stream(
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channels=2,
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callback=self.audio_callback,
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blocksize=self.block_frame,
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samplerate=self.config.samplerate,
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dtype="float32",
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):
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while self.flag_vc:
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time.sleep(self.config.block_time)
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print("Audio block passed.")
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print("ENDing VC")
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def audio_callback(
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self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
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):
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"""
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音频处理
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"""
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start_time = time.perf_counter()
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indata = librosa.to_mono(indata.T)
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if self.config.I_noise_reduce:
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indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
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"""noise gate"""
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frame_length = 2048
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hop_length = 1024
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rms = librosa.feature.rms(
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y=indata, frame_length=frame_length, hop_length=hop_length
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)
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if(self.config.threhold>-60):
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db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
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for i in range(db_threhold.shape[0]):
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if db_threhold[i]:
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indata[i * hop_length : (i + 1) * hop_length] = 0
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self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
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# infer
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inp=torch.from_numpy(self.input_wav)
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res1=self.resampler(inp)
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rate1=self.block_frame/(self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)
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rate2=(self.crossfade_frame + self.sola_search_frame + self.block_frame)/(self.extra_frame+ self.crossfade_frame+ self.sola_search_frame+ self.block_frame)
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res2=self.rvc.infer(res1,res1[-self.block_frame:].numpy(),rate1,rate2,self.pitch,self.pitchf,self.config.f0method)
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self.output_wav_cache[-res2.shape[0]:]=res2
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infer_wav = self.output_wav_cache[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(device)
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
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cor_nom = F.conv1d(
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infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
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self.sola_buffer[None, None, :],
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)
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cor_den = torch.sqrt(
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F.conv1d(
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infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
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** 2,
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torch.ones(1, 1, self.crossfade_frame, device=device),
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)
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+ 1e-8
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)
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sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
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print("sola offset: " + str(int(sola_offset)))
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self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
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self.output_wav[: self.crossfade_frame] *= self.fade_in_window
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self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
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# crossfade
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if sola_offset < self.sola_search_frame:
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self.sola_buffer[:] = (
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infer_wav[
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-self.sola_search_frame
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- self.crossfade_frame
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+ sola_offset : -self.sola_search_frame
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+ sola_offset
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]
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* self.fade_out_window
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)
|
||||
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()
|
||||
257
rvc_for_realtime.py
Normal file
257
rvc_for_realtime.py
Normal file
@ -0,0 +1,257 @@
|
||||
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
|
||||
Loading…
Reference in New Issue
Block a user