Rewrite GUI audio processor with torch. Improve speed. (#43)
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EntropyRiser
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gui.py
72
gui.py
@ -3,8 +3,10 @@ import sounddevice as sd
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import noisereduce as nr
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import numpy as np
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from fairseq import checkpoint_utils
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import librosa,torch,parselmouth,faiss,time,threading
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import librosa,torch,parselmouth,faiss,time,threading,math
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import torch.nn.functional as F
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import torchaudio.transforms as tat
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#import matplotlib.pyplot as plt
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from webui_locale import I18nAuto
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@ -85,7 +87,7 @@ class RVC:
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audio = librosa.to_mono(audio.transpose(1, 0))
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if sampling_rate != 16000:
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
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print('test:audio:'+str(audio.shape))
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#print('test:audio:'+str(audio.shape))
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'''padding'''
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@ -147,7 +149,8 @@ class Config:
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self.threhold:int=-30
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self.crossfade_time:float=0.08
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self.extra_time:float=0.04
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self.noise_reduce=False
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self.I_noise_reduce=False
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self.O_noise_reduce=False
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class GUI:
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def __init__(self) -> None:
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@ -162,6 +165,7 @@ class GUI:
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layout=[
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[
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sg.Frame(title=i18n('加载模型'),layout=[
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[sg.Input(default_text='TEMP\\hubert_base.pt',key='hubert_path'),sg.FileBrowse(i18n('Hubert File'))],
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[sg.Input(default_text='TEMP\\atri.pth',key='pth_path'),sg.FileBrowse(i18n('选择.pth文件'))],
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[sg.Input(default_text='TEMP\\added_IVF512_Flat_atri_baseline_src_feat.index',key='index_path'),sg.FileBrowse(i18n('选择.index文件'))],
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[sg.Input(default_text='TEMP\\big_src_feature_atri.npy',key='npy_path'),sg.FileBrowse(i18n('选择.npy文件'))]
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@ -183,10 +187,10 @@ class GUI:
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[sg.Text(i18n("采样长度")),sg.Slider(range=(0.1,3.0),key='block_time',resolution=0.1,orientation='h',default_value=1.0)],
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[sg.Text(i18n("淡入淡出长度")),sg.Slider(range=(0.01,0.15),key='crossfade_length',resolution=0.01,orientation='h',default_value=0.08)],
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[sg.Text(i18n("额外推理时长")),sg.Slider(range=(0.05,3.00),key='extra_time',resolution=0.01,orientation='h',default_value=0.05)],
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[sg.Checkbox(i18n('输出降噪/Output Noisereduce'),key='noise_reduce')]
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[sg.Checkbox(i18n('Input Noisereduce'),key='I_noise_reduce'),sg.Checkbox(i18n('Output Noisereduce'),key='O_noise_reduce')]
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],title=i18n("性能设置"))
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],
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[sg.Button(i18n("开始音频转换"),key='start_vc'),sg.Button(i18n("停止音频转换"),key='stop_vc')]
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[sg.Button(i18n("开始音频转换"),key='start_vc'),sg.Button(i18n("停止音频转换"),key='stop_vc'),sg.Text(i18n("Infer Time(ms):")),sg.Text("0",key='infer_time')]
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]
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self.window=sg.Window("RVC - GUI",layout=layout)
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@ -219,11 +223,13 @@ class GUI:
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self.config.block_time=values['block_time']
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self.config.crossfade_time=values['crossfade_length']
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self.config.extra_time=values['extra_time']
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self.config.noise_reduce=values['noise_reduce']
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self.config.I_noise_reduce=values['I_noise_reduce']
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self.config.O_noise_reduce=values['O_noise_reduce']
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def start_vc(self):
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torch.cuda.empty_cache()
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self.flag_vc=True
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self.RMS_threhold=math.e**(float(self.config.threhold)/10)
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self.block_frame=int(self.config.block_time*self.config.samplerate)
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self.crossfade_frame=int(self.config.crossfade_time*self.config.samplerate)
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self.sola_search_frame=int(0.012*self.config.samplerate)
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@ -231,11 +237,15 @@ class GUI:
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self.extra_frame=int(self.config.extra_time*self.config.samplerate)#往后预留0.04s
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self.rvc=None
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self.rvc=RVC(self.config.pitch,self.config.pth_path,self.config.index_path,self.config.npy_path)
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self.input_wav:np.ndarray=np.zeros(self.extra_frame+self.crossfade_frame+self.sola_search_frame+self.block_frame)
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self.output_wav:np.ndarray=np.zeros(self.block_frame)
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self.sola_buffer:np.ndarray=np.zeros(self.crossfade_frame,dtype='float32')
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self.fade_in_window:np.ndarray = np.linspace(0, 1, self.crossfade_frame)
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self.fade_out_window:np.ndarray = 1 - self.fade_in_window
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self.input_wav:np.ndarray=np.zeros(self.extra_frame+self.crossfade_frame+self.sola_search_frame+self.block_frame,dtype='float32')
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self.output_wav:torch.Tensor=torch.zeros(self.block_frame,device=device,dtype=torch.float32)
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#self.sola_buffer:np.ndarray=np.zeros(self.crossfade_frame,dtype='float32')
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self.sola_buffer:torch.Tensor=torch.zeros(self.crossfade_frame,device=device,dtype=torch.float32)
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#self.fade_in_window:np.ndarray = np.linspace(0, 1, self.crossfade_frame)
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self.fade_in_window:torch.Tensor=torch.linspace(0.0,1.0,steps=self.crossfade_frame,device=device,dtype=torch.float32)
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self.fade_out_window:torch.Tensor = 1 - self.fade_in_window
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self.resampler=tat.Resample(orig_freq=40000,new_freq=self.config.samplerate,dtype=torch.float32)
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self.RMS=lambda y:torch.sqrt(torch.mean(torch.square(y))).item()#RMS calculator
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thread_vc=threading.Thread(target=self.soundinput)
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thread_vc.start()
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@ -257,46 +267,48 @@ class GUI:
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'''
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start_time=time.perf_counter()
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indata=librosa.to_mono(indata.T)
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self.input_wav[:]=np.roll(self.input_wav,-self.block_frame)
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#TODO:Convert all numpy calculation to torch
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if self.config.I_noise_reduce:
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indata[:]=nr.reduce_noise(y=indata,sr=self.config.samplerate)
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'''noise gate'''
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frame_length=1024
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hop_length=512
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frame_length=2048
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hop_length=1024
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rms=librosa.feature.rms(y=indata,frame_length=frame_length,hop_length=hop_length)
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db_threhold=librosa.amplitude_to_db(rms,ref=1.0)[0]<self.config.threhold
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#print(rms.shape,db.shape,db)
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for i in range(db_threhold.shape[0]):
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if db_threhold[i]:
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indata[i*hop_length:(i+1)*hop_length]=0
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self.input_wav[-self.block_frame:]=indata[:]
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self.input_wav[:]=np.append(self.input_wav[self.block_frame:],indata)
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#infer
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print('input_wav:'+str(self.input_wav.shape))
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infer_wav=librosa.resample(y=self.rvc.infer(self.input_wav[:],self.config.samplerate),orig_sr=40000,target_sr=self.config.samplerate)[-self.crossfade_frame-self.sola_search_frame-self.block_frame:]
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print('infered_wav:'+str(infer_wav.shape))
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#print('infered_wav:'+str(infer_wav.shape))
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infer_wav:torch.Tensor=self.resampler(torch.from_numpy(self.rvc.infer(self.input_wav,self.config.samplerate)))[-self.crossfade_frame-self.sola_search_frame-self.block_frame:].to(device)
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print('infer_wav:'+str(infer_wav.shape))
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
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cor_nom = np.convolve(infer_wav[ : self.crossfade_frame + self.sola_search_frame], np.flip(self.sola_buffer), 'valid')
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cor_den = np.sqrt(np.convolve(infer_wav[ : self.crossfade_frame + self.sola_search_frame] ** 2, np.ones(self.crossfade_frame), 'valid') + 1e-3)
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sola_offset = np.argmax( cor_nom / cor_den)
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print('sola offset: ' + str(sola_offset))
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cor_nom=F.conv1d(infer_wav[None,None,:self.crossfade_frame + self.sola_search_frame],self.sola_buffer[None,None,:])
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cor_den=torch.sqrt(F.conv1d(infer_wav[None,None,:self.crossfade_frame + self.sola_search_frame]**2,torch.ones(1, 1,self.crossfade_frame,device=device))+1e-8)
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sola_offset = torch.argmax( cor_nom[0, 0] / cor_den[0, 0])
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print('sola offset: ' + str(int(sola_offset)))
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# crossfade
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self.output_wav[:]=infer_wav[sola_offset : sola_offset + self.block_frame]
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self.output_wav[:self.crossfade_frame] *= self.fade_in_window
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self.output_wav[:self.crossfade_frame] += self.sola_buffer[:]
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if sola_offset < self.sola_search_frame:
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self.sola_buffer[:] = infer_wav[-self.sola_search_frame - self.crossfade_frame + sola_offset: -self.sola_search_frame + sola_offset]* self.fade_out_window
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else:
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self.sola_buffer[:] = infer_wav[- self.crossfade_frame :]* self.fade_out_window
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if self.config.noise_reduce:
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self.output_wav[:]=nr.reduce_noise(y=self.output_wav,sr=self.config.samplerate)
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outdata[:]=np.array([self.output_wav,self.output_wav]).T
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print('infer time:'+str(time.perf_counter()-start_time))
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if self.config.O_noise_reduce:
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outdata[:]=np.tile(nr.reduce_noise(y=self.output_wav[:].cpu().numpy(),sr=self.config.samplerate),(2,1)).T
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else:
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outdata[:]=self.output_wav[:].repeat(2, 1).t().cpu().numpy()
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total_time=time.perf_counter()-start_time
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print('infer time:'+str(total_time))
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self.window['infer_time'].update(int(total_time*1000))
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def get_devices(self,update: bool = True):
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'''获取设备列表'''
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@ -76,6 +76,7 @@
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"点击查看交流、问题反馈群号": "点击查看交流、问题反馈群号",
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"xxxxx": "xxxxx",
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"加载模型": "加载模型",
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"Hubert File":"Hubert模型",
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"选择.pth文件": "选择.pth文件",
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"选择.index文件": "选择.index文件",
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"选择.npy文件": "选择.npy文件",
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@ -88,8 +89,10 @@
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"采样长度": "采样长度",
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"淡入淡出长度": "淡入淡出长度",
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"额外推理时长": "额外推理时长",
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"输出降噪/Output Noisereduce": "输出降噪/Output Noisereduce",
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"Input Noisereduce":"输入降噪",
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"Output Noisereduce": "输出降噪",
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"性能设置": "性能设置",
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"开始音频转换": "开始音频转换",
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"停止音频转换": "停止音频转换"
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"停止音频转换": "停止音频转换",
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"Infer Time(ms):":"推理时间(ms):"
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}
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