1024 lines
46 KiB
Python
1024 lines
46 KiB
Python
import os
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import sys
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from dotenv import load_dotenv
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load_dotenv()
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os.environ["OMP_NUM_THREADS"] = "4"
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if sys.platform == "darwin":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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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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flag_vc = False
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def printt(strr, *args):
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if len(args) == 0:
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print(strr)
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else:
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print(strr % args)
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def phase_vocoder(a, b, fade_out, fade_in):
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window = torch.sqrt(fade_out * fade_in)
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fa = torch.fft.rfft(a * window)
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fb = torch.fft.rfft(b * window)
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absab = torch.abs(fa) + torch.abs(fb)
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n = a.shape[0]
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if n % 2 == 0:
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absab[1:-1] *= 2
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else:
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absab[1:] *= 2
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phia = torch.angle(fa)
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phib = torch.angle(fb)
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deltaphase = phib - phia
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deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5)
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w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase
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t = torch.arange(n).unsqueeze(-1).to(a) / n
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result = (
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a * (fade_out**2)
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+ b * (fade_in**2)
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+ torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
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)
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return result
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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
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import 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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import json
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import multiprocessing
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import re
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import threading
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import time
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import traceback
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from multiprocessing import Queue, cpu_count
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from queue import Empty
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import librosa
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from tools.torchgate import TorchGate
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import numpy as np
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import PySimpleGUI as sg
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import sounddevice as sd
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import torch
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import torch.nn.functional as F
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import torchaudio.transforms as tat
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import tools.rvc_for_realtime as rvc_for_realtime
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from i18n.i18n import I18nAuto
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from configs.config import Config
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i18n = I18nAuto()
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# device = rvc_for_realtime.config.device
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# device = torch.device(
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# "cuda"
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# if torch.cuda.is_available()
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# else ("mps" if torch.backends.mps.is_available() else "cpu")
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# )
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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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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 = 0
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self.sr_type: str = "sr_model"
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self.block_time: float = 0.25 # s
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self.threhold: int = -60
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self.crossfade_time: float = 0.05
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self.extra_time: float = 2.5
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self.I_noise_reduce: bool = False
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self.O_noise_reduce: bool = False
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self.use_pv: bool = False
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self.rms_mix_rate: float = 0.0
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self.index_rate: float = 0.0
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self.n_cpu: int = min(n_cpu, 4)
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self.f0method: str = "fcpe"
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self.sg_hostapi: str = ""
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self.wasapi_exclusive: bool = False
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self.sg_input_device: str = ""
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self.sg_output_device: str = ""
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class GUI:
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def __init__(self) -> None:
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self.gui_config = GUIConfig()
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self.config = Config()
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self.function = "vc"
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self.delay_time = 0
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self.hostapis = None
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self.input_devices = None
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self.output_devices = None
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self.input_devices_indices = None
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self.output_devices_indices = None
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self.stream = None
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self.update_devices()
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self.launcher()
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def load(self):
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try:
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with open("configs/config.json", "r") as j:
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data = json.load(j)
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data["sr_model"] = data["sr_type"] == "sr_model"
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data["sr_device"] = data["sr_type"] == "sr_device"
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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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data["rmvpe"] = data["f0method"] == "rmvpe"
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data["fcpe"] = data["f0method"] == "fcpe"
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if data["sg_hostapi"] in self.hostapis:
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self.update_devices(hostapi_name=data["sg_hostapi"])
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if data["sg_input_device"] not in self.input_devices or data["sg_output_device"] not in self.output_devices:
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self.update_devices()
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data["sg_hostapi"] = self.hostapis[0]
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data["sg_input_device"] = self.input_devices[
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self.input_devices_indices.index(sd.default.device[0])
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]
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data["sg_output_device"] = self.output_devices[
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self.output_devices_indices.index(sd.default.device[1])
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]
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else:
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data["sg_hostapi"] = self.hostapis[0]
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data["sg_input_device"] = self.input_devices[
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self.input_devices_indices.index(sd.default.device[0])
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]
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data["sg_output_device"] = self.output_devices[
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self.output_devices_indices.index(sd.default.device[1])
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]
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except:
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with open("configs/config.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_hostapi": self.hostapis[0],
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"sg_wasapi_exclusive": False,
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"sg_input_device": self.input_devices[
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self.input_devices_indices.index(sd.default.device[0])
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],
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"sg_output_device": self.output_devices[
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self.output_devices_indices.index(sd.default.device[1])
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],
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"sr_type": "sr_model",
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"threhold": -60,
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"pitch": 0,
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"index_rate": 0,
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"rms_mix_rate": 0,
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"block_time": 0.25,
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"crossfade_length": 0.05,
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"extra_time": 2.5,
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"n_cpu": 4,
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"f0method": "rmvpe",
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"use_jit": False,
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"use_pv": False,
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}
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data["sr_model"] = data["sr_type"] == "sr_model"
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data["sr_device"] = data["sr_type"] == "sr_device"
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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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data["rmvpe"] = data["f0method"] == "rmvpe"
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data["fcpe"] = data["f0method"] == "fcpe"
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return data
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def launcher(self):
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data = self.load()
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self.config.use_jit = False # data.get("use_jit", self.config.use_jit)
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sg.theme("LightBlue3")
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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(
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os.getcwd(), "assets/weights"
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),
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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("设备类型")),
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sg.Combo(
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self.hostapis,
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key="sg_hostapi",
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default_value=data.get("sg_hostapi", ""),
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enable_events=True,
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size=(20, 1)
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),
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sg.Checkbox(
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i18n("独占 WASAPI 设备"),
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key="sg_wasapi_exclusive",
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default=data.get("sg_wasapi_exclusive", False),
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enable_events=True,
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),
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],
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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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self.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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enable_events=True,
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size=(45, 1),
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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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self.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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enable_events=True,
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size=(45, 1),
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),
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],
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[
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sg.Button(i18n("重载设备列表"), key="reload_devices"),
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sg.Radio(
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i18n("使用模型采样率"),
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"sr_type",
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key="sr_model",
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default=data.get("sr_model", True),
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enable_events=True,
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),
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sg.Radio(
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i18n("使用设备采样率"),
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"sr_type",
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key="sr_device",
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default=data.get("sr_device", False),
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enable_events=True,
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),
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sg.Text(i18n("采样率:")),
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sg.Text("", key="sr_stream"),
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],
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],
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title=i18n("音频设备"),
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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", -60),
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enable_events=True,
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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", 0),
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enable_events=True,
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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", 0),
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enable_events=True,
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),
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],
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[
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sg.Text(i18n("响度因子")),
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sg.Slider(
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range=(0.0, 1.0),
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key="rms_mix_rate",
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resolution=0.01,
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orientation="h",
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default_value=data.get("rms_mix_rate", 0),
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enable_events=True,
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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(
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"pm",
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"f0method",
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key="pm",
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default=data.get("pm", False),
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enable_events=True,
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),
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sg.Radio(
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"harvest",
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"f0method",
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key="harvest",
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default=data.get("harvest", False),
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enable_events=True,
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),
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sg.Radio(
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"crepe",
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"f0method",
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key="crepe",
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default=data.get("crepe", False),
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enable_events=True,
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),
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sg.Radio(
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"rmvpe",
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"f0method",
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key="rmvpe",
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default=data.get("rmvpe", False),
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enable_events=True,
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),
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sg.Radio(
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"fcpe",
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"f0method",
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key="fcpe",
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default=data.get("fcpe", True),
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enable_events=True,
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),
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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=(0.02, 1.5),
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key="block_time",
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resolution=0.01,
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orientation="h",
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default_value=data.get("block_time", 0.25),
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enable_events=True,
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),
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],
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# [
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# sg.Text("设备延迟"),
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# sg.Slider(
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# range=(0, 1),
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# key="device_latency",
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# resolution=0.001,
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# orientation="h",
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# default_value=data.get("device_latency", 0.1),
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# enable_events=True,
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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(
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"n_cpu", min(self.gui_config.n_cpu, n_cpu)
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),
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enable_events=True,
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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", 0.05),
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enable_events=True,
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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, 5.00),
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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", 2.5),
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enable_events=True,
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),
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],
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[
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sg.Checkbox(
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i18n("输入降噪"),
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key="I_noise_reduce",
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enable_events=True,
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),
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sg.Checkbox(
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i18n("输出降噪"),
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key="O_noise_reduce",
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enable_events=True,
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),
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sg.Checkbox(
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i18n("启用相位声码器"),
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key="use_pv",
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default=data.get("use_pv", False),
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enable_events=True,
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),
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# sg.Checkbox(
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# "JIT加速",
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# default=self.config.use_jit,
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# key="use_jit",
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# enable_events=False,
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# ),
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],
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# [sg.Text("注：首次使用JIT加速时，会出现卡顿，\n 并伴随一些噪音，但这是正常现象！")],
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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.Radio(
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i18n("输入监听"),
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"function",
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key="im",
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default=False,
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enable_events=True,
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),
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sg.Radio(
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i18n("输出变声"),
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"function",
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key="vc",
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default=True,
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enable_events=True,
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),
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sg.Text(i18n("算法延迟(ms):")),
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sg.Text("0", key="delay_time"),
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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, finalize=True)
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self.event_handler()
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def event_handler(self):
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global flag_vc
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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.stop_stream()
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exit()
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if event == "reload_devices" or event == "sg_hostapi":
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self.gui_config.sg_hostapi = values["sg_hostapi"]
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self.update_devices(hostapi_name=values["sg_hostapi"])
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if self.gui_config.sg_hostapi not in self.hostapis:
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self.gui_config.sg_hostapi = self.hostapis[0]
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self.window["sg_hostapi"].Update(values=self.hostapis)
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||
self.window["sg_hostapi"].Update(
|
||
value=self.gui_config.sg_hostapi
|
||
)
|
||
if self.gui_config.sg_input_device not in self.input_devices:
|
||
self.gui_config.sg_input_device = self.input_devices[0]
|
||
self.window["sg_input_device"].Update(values=self.input_devices)
|
||
self.window["sg_input_device"].Update(
|
||
value=self.gui_config.sg_input_device
|
||
)
|
||
if self.gui_config.sg_output_device not in self.output_devices:
|
||
self.gui_config.sg_output_device = self.output_devices[0]
|
||
self.window["sg_output_device"].Update(values=self.output_devices)
|
||
self.window["sg_output_device"].Update(
|
||
value=self.gui_config.sg_output_device
|
||
)
|
||
if event == "start_vc" and not flag_vc:
|
||
if self.set_values(values) == True:
|
||
printt("cuda_is_available: %s", torch.cuda.is_available())
|
||
self.start_vc()
|
||
settings = {
|
||
"pth_path": values["pth_path"],
|
||
"index_path": values["index_path"],
|
||
"sg_hostapi": values["sg_hostapi"],
|
||
"sg_wasapi_exclusive": values["sg_wasapi_exclusive"],
|
||
"sg_input_device": values["sg_input_device"],
|
||
"sg_output_device": values["sg_output_device"],
|
||
"sr_type": ["sr_model", "sr_device"][
|
||
[
|
||
values["sr_model"],
|
||
values["sr_device"],
|
||
].index(True)
|
||
],
|
||
"threhold": values["threhold"],
|
||
"pitch": values["pitch"],
|
||
"rms_mix_rate": values["rms_mix_rate"],
|
||
"index_rate": values["index_rate"],
|
||
# "device_latency": values["device_latency"],
|
||
"block_time": values["block_time"],
|
||
"crossfade_length": values["crossfade_length"],
|
||
"extra_time": values["extra_time"],
|
||
"n_cpu": values["n_cpu"],
|
||
# "use_jit": values["use_jit"],
|
||
"use_jit": False,
|
||
"use_pv": values["use_pv"],
|
||
"f0method": ["pm", "harvest", "crepe", "rmvpe", "fcpe"][
|
||
[
|
||
values["pm"],
|
||
values["harvest"],
|
||
values["crepe"],
|
||
values["rmvpe"],
|
||
values["fcpe"],
|
||
].index(True)
|
||
],
|
||
}
|
||
with open("configs/config.json", "w") as j:
|
||
json.dump(settings, j)
|
||
if self.stream is not None:
|
||
self.delay_time = (
|
||
self.stream.latency[-1]
|
||
+ values["block_time"]
|
||
+ values["crossfade_length"]
|
||
+ 0.01
|
||
)
|
||
if values["I_noise_reduce"]:
|
||
self.delay_time += min(values["crossfade_length"], 0.04)
|
||
self.window["sr_stream"].update(self.gui_config.samplerate)
|
||
self.window["delay_time"].update(int(np.round(self.delay_time * 1000)))
|
||
# Parameter hot update
|
||
if event == "threhold":
|
||
self.gui_config.threhold = values["threhold"]
|
||
elif event == "pitch":
|
||
self.gui_config.pitch = values["pitch"]
|
||
if hasattr(self, "rvc"):
|
||
self.rvc.change_key(values["pitch"])
|
||
elif event == "index_rate":
|
||
self.gui_config.index_rate = values["index_rate"]
|
||
if hasattr(self, "rvc"):
|
||
self.rvc.change_index_rate(values["index_rate"])
|
||
elif event == "rms_mix_rate":
|
||
self.gui_config.rms_mix_rate = values["rms_mix_rate"]
|
||
elif event in ["pm", "harvest", "crepe", "rmvpe", "fcpe"]:
|
||
self.gui_config.f0method = event
|
||
elif event == "I_noise_reduce":
|
||
self.gui_config.I_noise_reduce = values["I_noise_reduce"]
|
||
if self.stream is not None:
|
||
self.delay_time += (
|
||
1 if values["I_noise_reduce"] else -1
|
||
) * min(values["crossfade_length"], 0.04)
|
||
self.window["delay_time"].update(int(np.round(self.delay_time * 1000)))
|
||
elif event == "O_noise_reduce":
|
||
self.gui_config.O_noise_reduce = values["O_noise_reduce"]
|
||
elif event == "use_pv":
|
||
self.gui_config.use_pv = values["use_pv"]
|
||
elif event in ["vc", "im"]:
|
||
self.function = event
|
||
elif event == "stop_vc" or event != "start_vc":
|
||
# Other parameters do not support hot update
|
||
self.stop_stream()
|
||
|
||
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.use_jit = False # values["use_jit"]
|
||
# self.device_latency = values["device_latency"]
|
||
self.gui_config.sg_hostapi = values["sg_hostapi"]
|
||
self.gui_config.sg_wasapi_exclusive = values["sg_wasapi_exclusive"]
|
||
self.gui_config.sg_input_device = values["sg_input_device"]
|
||
self.gui_config.sg_output_device = values["sg_output_device"]
|
||
self.gui_config.pth_path = values["pth_path"]
|
||
self.gui_config.index_path = values["index_path"]
|
||
self.gui_config.sr_type = ["sr_model", "sr_device"][
|
||
[
|
||
values["sr_model"],
|
||
values["sr_device"],
|
||
].index(True)
|
||
]
|
||
self.gui_config.threhold = values["threhold"]
|
||
self.gui_config.pitch = values["pitch"]
|
||
self.gui_config.block_time = values["block_time"]
|
||
self.gui_config.crossfade_time = values["crossfade_length"]
|
||
self.gui_config.extra_time = values["extra_time"]
|
||
self.gui_config.I_noise_reduce = values["I_noise_reduce"]
|
||
self.gui_config.O_noise_reduce = values["O_noise_reduce"]
|
||
self.gui_config.use_pv = values["use_pv"]
|
||
self.gui_config.rms_mix_rate = values["rms_mix_rate"]
|
||
self.gui_config.index_rate = values["index_rate"]
|
||
self.gui_config.n_cpu = values["n_cpu"]
|
||
self.gui_config.f0method = ["pm", "harvest", "crepe", "rmvpe", "fcpe"][
|
||
[
|
||
values["pm"],
|
||
values["harvest"],
|
||
values["crepe"],
|
||
values["rmvpe"],
|
||
values["fcpe"],
|
||
].index(True)
|
||
]
|
||
return True
|
||
|
||
def start_vc(self):
|
||
torch.cuda.empty_cache()
|
||
self.rvc = rvc_for_realtime.RVC(
|
||
self.gui_config.pitch,
|
||
self.gui_config.pth_path,
|
||
self.gui_config.index_path,
|
||
self.gui_config.index_rate,
|
||
self.gui_config.n_cpu,
|
||
inp_q,
|
||
opt_q,
|
||
self.config,
|
||
self.rvc if hasattr(self, "rvc") else None,
|
||
)
|
||
self.gui_config.samplerate = (
|
||
self.rvc.tgt_sr
|
||
if self.gui_config.sr_type == "sr_model"
|
||
else self.get_device_samplerate()
|
||
)
|
||
self.gui_config.channels = self.get_device_channels()
|
||
self.zc = self.gui_config.samplerate // 100
|
||
self.block_frame = (
|
||
int(
|
||
np.round(
|
||
self.gui_config.block_time
|
||
* self.gui_config.samplerate
|
||
/ self.zc
|
||
)
|
||
)
|
||
* self.zc
|
||
)
|
||
self.block_frame_16k = 160 * self.block_frame // self.zc
|
||
self.crossfade_frame = (
|
||
int(
|
||
np.round(
|
||
self.gui_config.crossfade_time
|
||
* self.gui_config.samplerate
|
||
/ self.zc
|
||
)
|
||
)
|
||
* self.zc
|
||
)
|
||
self.sola_buffer_frame = min(self.crossfade_frame, 4 * self.zc)
|
||
self.sola_search_frame = self.zc
|
||
self.extra_frame = (
|
||
int(
|
||
np.round(
|
||
self.gui_config.extra_time
|
||
* self.gui_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=self.config.device,
|
||
dtype=torch.float32,
|
||
)
|
||
self.input_wav_denoise: torch.Tensor = self.input_wav.clone()
|
||
self.input_wav_res: torch.Tensor = torch.zeros(
|
||
160 * self.input_wav.shape[0] // self.zc,
|
||
device=self.config.device,
|
||
dtype=torch.float32,
|
||
)
|
||
self.rms_buffer: np.ndarray = np.zeros(4 * self.zc, dtype="float32")
|
||
self.sola_buffer: torch.Tensor = torch.zeros(
|
||
self.sola_buffer_frame, device=self.config.device, dtype=torch.float32
|
||
)
|
||
self.nr_buffer: torch.Tensor = self.sola_buffer.clone()
|
||
self.output_buffer: torch.Tensor = self.input_wav.clone()
|
||
self.skip_head = self.extra_frame // self.zc
|
||
self.return_length = (
|
||
self.block_frame + self.sola_buffer_frame + self.sola_search_frame
|
||
) // self.zc
|
||
self.fade_in_window: torch.Tensor = (
|
||
torch.sin(
|
||
0.5
|
||
* np.pi
|
||
* torch.linspace(
|
||
0.0,
|
||
1.0,
|
||
steps=self.sola_buffer_frame,
|
||
device=self.config.device,
|
||
dtype=torch.float32,
|
||
)
|
||
)
|
||
** 2
|
||
)
|
||
self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
|
||
self.resampler = tat.Resample(
|
||
orig_freq=self.gui_config.samplerate,
|
||
new_freq=16000,
|
||
dtype=torch.float32,
|
||
).to(self.config.device)
|
||
if self.rvc.tgt_sr != self.gui_config.samplerate:
|
||
self.resampler2 = tat.Resample(
|
||
orig_freq=self.rvc.tgt_sr,
|
||
new_freq=self.gui_config.samplerate,
|
||
dtype=torch.float32,
|
||
).to(self.config.device)
|
||
else:
|
||
self.resampler2 = None
|
||
self.tg = TorchGate(
|
||
sr=self.gui_config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9
|
||
).to(self.config.device)
|
||
self.start_stream()
|
||
|
||
def start_stream(self):
|
||
global flag_vc
|
||
if not flag_vc:
|
||
flag_vc = True
|
||
if 'WASAPI' in self.gui_config.sg_hostapi and self.gui_config.sg_wasapi_exclusive:
|
||
extra_settings = sd.WasapiSettings(exclusive=True)
|
||
else:
|
||
extra_settings = None
|
||
self.stream = sd.Stream(
|
||
callback=self.audio_callback,
|
||
blocksize=self.block_frame,
|
||
samplerate=self.gui_config.samplerate,
|
||
channels=self.gui_config.channels,
|
||
dtype="float32",
|
||
extra_settings=extra_settings,
|
||
)
|
||
self.stream.start()
|
||
|
||
def stop_stream(self):
|
||
global flag_vc
|
||
if flag_vc:
|
||
flag_vc = False
|
||
if self.stream is not None:
|
||
self.stream.abort()
|
||
self.stream.close()
|
||
self.stream = None
|
||
|
||
def audio_callback(
|
||
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
|
||
):
|
||
"""
|
||
音频处理
|
||
"""
|
||
global flag_vc
|
||
start_time = time.perf_counter()
|
||
indata = librosa.to_mono(indata.T)
|
||
if self.gui_config.threhold > -60:
|
||
indata = np.append(self.rms_buffer, indata)
|
||
rms = librosa.feature.rms(
|
||
y=indata, frame_length=4 * self.zc, hop_length=self.zc
|
||
)[:, 2:]
|
||
self.rms_buffer[:] = indata[-4 * self.zc :]
|
||
indata = indata[2 * self.zc - self.zc // 2 :]
|
||
db_threhold = (
|
||
librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold
|
||
)
|
||
for i in range(db_threhold.shape[0]):
|
||
if db_threhold[i]:
|
||
indata[i * self.zc : (i + 1) * self.zc] = 0
|
||
indata = indata[self.zc // 2 :]
|
||
self.input_wav[: -self.block_frame] = self.input_wav[
|
||
self.block_frame :
|
||
].clone()
|
||
self.input_wav[-indata.shape[0] :] = torch.from_numpy(indata).to(
|
||
self.config.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.gui_config.I_noise_reduce:
|
||
self.input_wav_denoise[: -self.block_frame] = self.input_wav_denoise[
|
||
self.block_frame :
|
||
].clone()
|
||
input_wav = self.input_wav[
|
||
-self.sola_buffer_frame - self.block_frame:
|
||
]
|
||
input_wav = self.tg(
|
||
input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)
|
||
).squeeze(0)
|
||
input_wav[: self.sola_buffer_frame] *= self.fade_in_window
|
||
input_wav[: self.sola_buffer_frame] += (
|
||
self.nr_buffer * self.fade_out_window
|
||
)
|
||
self.input_wav_denoise[-self.block_frame :] = input_wav[: self.block_frame]
|
||
self.nr_buffer[:] = input_wav[self.block_frame :]
|
||
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
|
||
self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
|
||
)[160:]
|
||
else:
|
||
self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = self.resampler(
|
||
self.input_wav[-indata.shape[0] - 2 * self.zc :]
|
||
)[160:]
|
||
# infer
|
||
if self.function == "vc":
|
||
infer_wav = self.rvc.infer(
|
||
self.input_wav_res,
|
||
self.block_frame_16k,
|
||
self.skip_head,
|
||
self.return_length,
|
||
self.gui_config.f0method,
|
||
)
|
||
if self.resampler2 is not None:
|
||
infer_wav = self.resampler2(infer_wav)
|
||
elif self.gui_config.I_noise_reduce:
|
||
infer_wav = self.input_wav_denoise[self.extra_frame :].clone()
|
||
else:
|
||
infer_wav = self.input_wav[self.extra_frame :].clone()
|
||
# output noise reduction
|
||
if self.gui_config.O_noise_reduce and self.function == "vc":
|
||
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.gui_config.rms_mix_rate < 1 and self.function == "vc":
|
||
if self.gui_config.I_noise_reduce:
|
||
input_wav = self.input_wav_denoise[self.extra_frame :]
|
||
else:
|
||
input_wav = self.input_wav[self.extra_frame :]
|
||
rms1 = librosa.feature.rms(
|
||
y=input_wav[: infer_wav.shape[0]].cpu().numpy(),
|
||
frame_length=4 * self.zc,
|
||
hop_length=self.zc,
|
||
)
|
||
rms1 = torch.from_numpy(rms1).to(self.config.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(self.config.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.gui_config.rms_mix_rate)
|
||
)
|
||
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
|
||
conv_input = infer_wav[
|
||
None, None, : self.sola_buffer_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.sola_buffer_frame, device=self.config.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])
|
||
printt("sola_offset = %d", int(sola_offset))
|
||
infer_wav = infer_wav[sola_offset:]
|
||
if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
|
||
infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
|
||
infer_wav[: self.sola_buffer_frame] += (
|
||
self.sola_buffer * self.fade_out_window
|
||
)
|
||
else:
|
||
infer_wav[: self.sola_buffer_frame] = phase_vocoder(
|
||
self.sola_buffer,
|
||
infer_wav[: self.sola_buffer_frame],
|
||
self.fade_out_window,
|
||
self.fade_in_window,
|
||
)
|
||
self.sola_buffer[:] = infer_wav[
|
||
self.block_frame : self.block_frame + self.sola_buffer_frame
|
||
]
|
||
outdata[:] = (
|
||
infer_wav[: self.block_frame].repeat(self.gui_config.channels, 1).t().cpu().numpy()
|
||
)
|
||
total_time = time.perf_counter() - start_time
|
||
if flag_vc:
|
||
self.window["infer_time"].update(int(total_time * 1000))
|
||
printt("Infer time: %.2f", total_time)
|
||
|
||
def update_devices(self, hostapi_name=None):
|
||
"""获取设备列表"""
|
||
global flag_vc
|
||
flag_vc = False
|
||
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"]
|
||
self.hostapis = [hostapi["name"] for hostapi in hostapis]
|
||
if hostapi_name not in self.hostapis:
|
||
hostapi_name = self.hostapis[0]
|
||
self.input_devices = [
|
||
d['name'] for d in devices
|
||
if d["max_input_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||
]
|
||
self.output_devices = [
|
||
d['name'] for d in devices
|
||
if d["max_output_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||
]
|
||
self.input_devices_indices = [
|
||
d["index"] if "index" in d else d["name"]
|
||
for d in devices
|
||
if d["max_input_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||
]
|
||
self.output_devices_indices = [
|
||
d["index"] if "index" in d else d["name"]
|
||
for d in devices
|
||
if d["max_output_channels"] > 0 and d['hostapi_name'] == hostapi_name
|
||
]
|
||
|
||
def set_devices(self, input_device, output_device):
|
||
"""设置输出设备"""
|
||
sd.default.device[0] = self.input_devices_indices[
|
||
self.input_devices.index(input_device)
|
||
]
|
||
sd.default.device[1] = self.output_devices_indices[
|
||
self.output_devices.index(output_device)
|
||
]
|
||
printt("Input device: %s:%s", str(sd.default.device[0]), input_device)
|
||
printt("Output device: %s:%s", str(sd.default.device[1]), output_device)
|
||
|
||
def get_device_samplerate(self):
|
||
return int(
|
||
sd.query_devices(device=sd.default.device[0])["default_samplerate"]
|
||
)
|
||
|
||
def get_device_channels(self):
|
||
max_input_channels = sd.query_devices(device=sd.default.device[0])["max_input_channels"]
|
||
max_output_channels = sd.query_devices(device=sd.default.device[1])["max_output_channels"]
|
||
return min(max_input_channels, max_output_channels, 2)
|
||
|
||
gui = GUI()
|