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import os |
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import logging |
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import sys |
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from dotenv import load_dotenv |
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|
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load_dotenv() |
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|
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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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|
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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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|
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logger = logging.getLogger(__name__) |
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|
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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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|
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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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|
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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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|
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if __name__ == "__main__": |
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import json |
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import re |
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import threading |
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import time |
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from multiprocessing import Queue, cpu_count |
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|
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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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|
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import tools.rvc_for_realtime as rvc_for_realtime |
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from assets.i18n.i18n import I18nAuto |
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|
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i18n = I18nAuto() |
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device = rvc_for_realtime.config.device |
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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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|
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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.samplerate: int = 40000 |
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self.block_time: float = 1.0 |
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self.buffer_num: int = 1 |
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self.threhold: int = -60 |
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self.crossfade_time: float = 0.04 |
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self.extra_time: float = 2.0 |
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self.I_noise_reduce = False |
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self.O_noise_reduce = False |
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self.rms_mix_rate = 0.0 |
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self.index_rate = 0.3 |
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self.n_cpu = min(n_cpu, 6) |
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self.f0method = "harvest" |
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self.sg_input_device = "" |
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self.sg_output_device = "" |
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|
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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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|
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self.launcher() |
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|
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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("assets/configs/config.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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data["rmvpe"] = data["f0method"] == "rmvpe" |
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except: |
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with open("assets/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_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": "-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": "0.04", |
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"extra_time": "2", |
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"f0method": "rmvpe", |
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} |
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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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return data |
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|
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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( |
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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("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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[sg.Button(i18n("重载设备列表"), key="reload_devices")], |
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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", "-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", "") == True, |
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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", "") == True, |
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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", "") == True, |
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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", "") == 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=(0.05, 2.4), |
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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(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.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", "0.04"), |
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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", "2.0"), |
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enable_events=True, |
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), |
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], |
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[ |
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sg.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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], |
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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, finalize=True) |
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self.event_handler() |
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|
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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 == "reload_devices": |
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prev_input = self.window["sg_input_device"].get() |
|
prev_output = self.window["sg_output_device"].get() |
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input_devices, output_devices, _, _ = self.get_devices(update=True) |
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if prev_input not in input_devices: |
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self.config.sg_input_device = input_devices[0] |
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else: |
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self.config.sg_input_device = prev_input |
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self.window["sg_input_device"].Update(values=input_devices) |
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self.window["sg_input_device"].Update( |
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value=self.config.sg_input_device |
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) |
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if prev_output not in output_devices: |
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self.config.sg_output_device = output_devices[0] |
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else: |
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self.config.sg_output_device = prev_output |
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self.window["sg_output_device"].Update(values=output_devices) |
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self.window["sg_output_device"].Update( |
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value=self.config.sg_output_device |
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) |
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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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logger.info("Use CUDA: %s", torch.cuda.is_available()) |
|
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"], |
|
"threhold": values["threhold"], |
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"pitch": values["pitch"], |
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"rms_mix_rate": values["rms_mix_rate"], |
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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", "rmvpe"][ |
|
[ |
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values["pm"], |
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values["harvest"], |
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values["crepe"], |
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values["rmvpe"], |
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].index(True) |
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], |
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} |
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with open("assets/configs/config.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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if event == "threhold": |
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self.config.threhold = values["threhold"] |
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elif event == "pitch": |
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self.config.pitch = values["pitch"] |
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if hasattr(self, "rvc"): |
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self.rvc.change_key(values["pitch"]) |
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elif event == "index_rate": |
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self.config.index_rate = values["index_rate"] |
|
if hasattr(self, "rvc"): |
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self.rvc.change_index_rate(values["index_rate"]) |
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elif event == "rms_mix_rate": |
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self.config.rms_mix_rate = values["rms_mix_rate"] |
|
elif event in ["pm", "harvest", "crepe", "rmvpe"]: |
|
self.config.f0method = event |
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elif event == "I_noise_reduce": |
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self.config.I_noise_reduce = values["I_noise_reduce"] |
|
elif event == "O_noise_reduce": |
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self.config.O_noise_reduce = values["O_noise_reduce"] |
|
elif event != "start_vc" and self.flag_vc == True: |
|
|
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self.flag_vc = False |
|
|
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def set_values(self, values): |
|
if len(values["pth_path"].strip()) == 0: |
|
sg.popup(i18n("请选择pth文件")) |
|
return False |
|
if len(values["index_path"].strip()) == 0: |
|
sg.popup(i18n("请选择index文件")) |
|
return False |
|
pattern = re.compile("[^\x00-\x7F]+") |
|
if pattern.findall(values["pth_path"]): |
|
sg.popup(i18n("pth文件路径不可包含中文")) |
|
return False |
|
if pattern.findall(values["index_path"]): |
|
sg.popup(i18n("index文件路径不可包含中文")) |
|
return False |
|
self.set_devices(values["sg_input_device"], values["sg_output_device"]) |
|
self.config.pth_path = values["pth_path"] |
|
self.config.index_path = values["index_path"] |
|
self.config.threhold = values["threhold"] |
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self.config.pitch = values["pitch"] |
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self.config.block_time = values["block_time"] |
|
self.config.crossfade_time = values["crossfade_length"] |
|
self.config.extra_time = values["extra_time"] |
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self.config.I_noise_reduce = values["I_noise_reduce"] |
|
self.config.O_noise_reduce = values["O_noise_reduce"] |
|
self.config.rms_mix_rate = values["rms_mix_rate"] |
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self.config.index_rate = values["index_rate"] |
|
self.config.n_cpu = values["n_cpu"] |
|
self.config.f0method = ["pm", "harvest", "crepe", "rmvpe"][ |
|
[ |
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values["pm"], |
|
values["harvest"], |
|
values["crepe"], |
|
values["rmvpe"], |
|
].index(True) |
|
] |
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return True |
|
|
|
def start_vc(self): |
|
torch.cuda.empty_cache() |
|
self.flag_vc = True |
|
self.rvc = rvc_for_realtime.RVC( |
|
self.config.pitch, |
|
self.config.pth_path, |
|
self.config.index_path, |
|
self.config.index_rate, |
|
self.config.n_cpu, |
|
inp_q, |
|
opt_q, |
|
device, |
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self.rvc if hasattr(self, "rvc") else None |
|
) |
|
self.config.samplerate = self.rvc.tgt_sr |
|
self.zc = self.rvc.tgt_sr // 100 |
|
self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc |
|
self.block_frame_16k = 160 * self.block_frame // self.zc |
|
self.crossfade_frame = int(np.round(self.config.crossfade_time * self.config.samplerate / self.zc)) * self.zc |
|
self.sola_search_frame = self.zc |
|
self.extra_frame = int(np.round(self.config.extra_time * self.config.samplerate / self.zc)) * self.zc |
|
self.input_wav: torch.Tensor = torch.zeros( |
|
self.extra_frame |
|
+ self.crossfade_frame |
|
+ self.sola_search_frame |
|
+ self.block_frame, |
|
device=device, |
|
dtype=torch.float32, |
|
) |
|
self.input_wav_res: torch.Tensor= torch.zeros(160 * self.input_wav.shape[0] // self.zc, device=device,dtype=torch.float32) |
|
self.pitch: np.ndarray = np.zeros( |
|
self.input_wav.shape[0] // self.zc, |
|
dtype="int32", |
|
) |
|
self.pitchf: np.ndarray = np.zeros( |
|
self.input_wav.shape[0] // self.zc, |
|
dtype="float64", |
|
) |
|
self.sola_buffer: torch.Tensor = torch.zeros( |
|
self.crossfade_frame, device=device, dtype=torch.float32 |
|
) |
|
self.nr_buffer: torch.Tensor = self.sola_buffer.clone() |
|
self.output_buffer: torch.Tensor = self.input_wav.clone() |
|
self.res_buffer: torch.Tensor = torch.zeros(2 * self.zc, device=device,dtype=torch.float32) |
|
self.valid_rate = 1 - (self.extra_frame - 1) / self.input_wav.shape[0] |
|
self.fade_in_window: torch.Tensor = ( |
|
torch.sin( |
|
0.5 |
|
* np.pi |
|
* torch.linspace( |
|
0.0, |
|
1.0, |
|
steps=self.crossfade_frame, |
|
device=device, |
|
dtype=torch.float32, |
|
) |
|
) |
|
** 2 |
|
) |
|
self.fade_out_window: torch.Tensor = 1 - self.fade_in_window |
|
self.resampler = tat.Resample( |
|
orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32 |
|
).to(device) |
|
self.tg = TorchGate(sr=self.config.samplerate, n_fft=4*self.zc, prop_decrease=0.9).to(device) |
|
thread_vc = threading.Thread(target=self.soundinput) |
|
thread_vc.start() |
|
|
|
def soundinput(self): |
|
""" |
|
接受音频输入 |
|
""" |
|
channels = 1 if sys.platform == "darwin" else 2 |
|
with sd.Stream( |
|
channels=channels, |
|
callback=self.audio_callback, |
|
blocksize=self.block_frame, |
|
samplerate=self.config.samplerate, |
|
dtype="float32", |
|
): |
|
while self.flag_vc: |
|
time.sleep(self.config.block_time) |
|
logger.debug("Audio block passed.") |
|
logger.debug("ENDing VC") |
|
|
|
def audio_callback( |
|
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status |
|
): |
|
""" |
|
音频处理 |
|
""" |
|
start_time = time.perf_counter() |
|
indata = librosa.to_mono(indata.T) |
|
if self.config.threhold > -60: |
|
rms = librosa.feature.rms( |
|
y=indata, frame_length=4*self.zc, hop_length=self.zc |
|
) |
|
db_threhold = ( |
|
librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold |
|
) |
|
for i in range(db_threhold.shape[0]): |
|
if db_threhold[i]: |
|
indata[i * self.zc : (i + 1) * self.zc] = 0 |
|
self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone() |
|
self.input_wav[-self.block_frame: ] = torch.from_numpy(indata).to(device) |
|
self.input_wav_res[ : -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone() |
|
|
|
if self.config.I_noise_reduce: |
|
input_wav = self.input_wav[-self.crossfade_frame -self.block_frame-2*self.zc: ] |
|
input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0))[0, 2*self.zc:] |
|
input_wav[: self.crossfade_frame] *= self.fade_in_window |
|
input_wav[: self.crossfade_frame] += self.nr_buffer * self.fade_out_window |
|
self.nr_buffer[:] = input_wav[-self.crossfade_frame: ] |
|
input_wav = torch.cat((self.res_buffer[:], input_wav[: self.block_frame])) |
|
self.res_buffer[:] = input_wav[-2*self.zc: ] |
|
self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(input_wav)[160: ] |
|
else: |
|
self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(self.input_wav[-self.block_frame-2*self.zc: ])[160: ] |
|
|
|
f0_extractor_frame = self.block_frame_16k + 800 |
|
if self.config.f0method == 'rmvpe': |
|
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) |
|
infer_wav = self.rvc.infer( |
|
self.input_wav_res, |
|
self.input_wav_res[-f0_extractor_frame :].cpu().numpy(), |
|
self.block_frame_16k, |
|
self.valid_rate, |
|
self.pitch, |
|
self.pitchf, |
|
self.config.f0method, |
|
) |
|
infer_wav = infer_wav[ |
|
-self.crossfade_frame - self.sola_search_frame - self.block_frame : |
|
] |
|
|
|
if self.config.O_noise_reduce: |
|
self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone() |
|
self.output_buffer[-self.block_frame: ] = infer_wav[-self.block_frame:] |
|
infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0) |
|
|
|
if self.config.rms_mix_rate < 1: |
|
rms1 = librosa.feature.rms( |
|
y=self.input_wav_res[-160*infer_wav.shape[0]//self.zc :].cpu().numpy(), |
|
frame_length=640, |
|
hop_length=160, |
|
) |
|
rms1 = torch.from_numpy(rms1).to(device) |
|
rms1 = F.interpolate( |
|
rms1.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear",align_corners=True, |
|
)[0,0,:-1] |
|
rms2 = librosa.feature.rms( |
|
y=infer_wav[:].cpu().numpy(), frame_length=4*self.zc, hop_length=self.zc |
|
) |
|
rms2 = torch.from_numpy(rms2).to(device) |
|
rms2 = F.interpolate( |
|
rms2.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear",align_corners=True, |
|
)[0,0,:-1] |
|
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3) |
|
infer_wav *= torch.pow(rms1 / rms2, torch.tensor(1 - self.config.rms_mix_rate)) |
|
|
|
conv_input = infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame] |
|
cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :]) |
|
cor_den = torch.sqrt( |
|
F.conv1d(conv_input ** 2, torch.ones(1, 1, self.crossfade_frame, device=device)) + 1e-8) |
|
if sys.platform == "darwin": |
|
_, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0]) |
|
sola_offset = sola_offset.item() |
|
else: |
|
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0]) |
|
logger.debug("sola_offset = %d", int(sola_offset)) |
|
infer_wav = infer_wav[sola_offset: sola_offset + self.block_frame + self.crossfade_frame] |
|
infer_wav[: self.crossfade_frame] *= self.fade_in_window |
|
infer_wav[: self.crossfade_frame] += self.sola_buffer *self.fade_out_window |
|
self.sola_buffer[:] = infer_wav[-self.crossfade_frame:] |
|
if sys.platform == "darwin": |
|
outdata[:] = infer_wav[:-self.crossfade_frame].cpu().numpy()[:, np.newaxis] |
|
else: |
|
outdata[:] = infer_wav[:-self.crossfade_frame].repeat(2, 1).t().cpu().numpy() |
|
total_time = time.perf_counter() - start_time |
|
self.window["infer_time"].update(int(total_time * 1000)) |
|
logger.info("Infer time: %.2f", total_time) |
|
|
|
def get_devices(self, update: bool = True): |
|
"""获取设备列表""" |
|
if update: |
|
sd._terminate() |
|
sd._initialize() |
|
devices = sd.query_devices() |
|
hostapis = sd.query_hostapis() |
|
for hostapi in hostapis: |
|
for device_idx in hostapi["devices"]: |
|
devices[device_idx]["hostapi_name"] = hostapi["name"] |
|
input_devices = [ |
|
f"{d['name']} ({d['hostapi_name']})" |
|
for d in devices |
|
if d["max_input_channels"] > 0 |
|
] |
|
output_devices = [ |
|
f"{d['name']} ({d['hostapi_name']})" |
|
for d in devices |
|
if d["max_output_channels"] > 0 |
|
] |
|
input_devices_indices = [ |
|
d["index"] if "index" in d else d["name"] |
|
for d in devices |
|
if d["max_input_channels"] > 0 |
|
] |
|
output_devices_indices = [ |
|
d["index"] if "index" in d else d["name"] |
|
for d in devices |
|
if d["max_output_channels"] > 0 |
|
] |
|
return ( |
|
input_devices, |
|
output_devices, |
|
input_devices_indices, |
|
output_devices_indices, |
|
) |
|
|
|
def set_devices(self, input_device, output_device): |
|
"""设置输出设备""" |
|
( |
|
input_devices, |
|
output_devices, |
|
input_device_indices, |
|
output_device_indices, |
|
) = self.get_devices() |
|
sd.default.device[0] = input_device_indices[ |
|
input_devices.index(input_device) |
|
] |
|
sd.default.device[1] = output_device_indices[ |
|
output_devices.index(output_device) |
|
] |
|
logger.info( |
|
"Input device: %s:%s", str(sd.default.device[0]), input_device |
|
) |
|
logger.info( |
|
"Output device: %s:%s", str(sd.default.device[1]), output_device |
|
) |
|
|
|
gui = GUI() |
