OPENGIT
/
Retrieval-based-Voice-Conversion-WebUI
mirror of https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Rewrite GUI audio processor with torch. Improve speed. (#43)

This commit is contained in:
EntropyRiser 2023-04-13 10:15:11 +08:00 committed by GitHub
parent 5c92ddd1e1
commit 0135a3f71f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 47 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

72
gui.py
View File

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

7
locale/zh_CN.json
View File

@ -76,6 +76,7 @@
"点击查看交流、问题反馈群号": "点击查看交流、问题反馈群号", "点击查看交流、问题反馈群号": "点击查看交流、问题反馈群号",
"xxxxx": "xxxxx", "xxxxx": "xxxxx",
"加载模型": "加载模型", "加载模型": "加载模型",
"Hubert File":"Hubert模型",
"选择.pth文件": "选择.pth文件", "选择.pth文件": "选择.pth文件",
"选择.index文件": "选择.index文件", "选择.index文件": "选择.index文件",
"选择.npy文件": "选择.npy文件", "选择.npy文件": "选择.npy文件",
@ -88,8 +89,10 @@
"采样长度": "采样长度", "采样长度": "采样长度",
"淡入淡出长度": "淡入淡出长度", "淡入淡出长度": "淡入淡出长度",
"额外推理时长": "额外推理时长", "额外推理时长": "额外推理时长",
"输出降噪/Output Noisereduce": "输出降噪/Output Noisereduce", "Input Noisereduce":"输入降噪",
"Output Noisereduce": "输出降噪",
"性能设置": "性能设置", "性能设置": "性能设置",
"开始音频转换": "开始音频转换", "开始音频转换": "开始音频转换",
"停止音频转换": "停止音频转换" "停止音频转换": "停止音频转换",
"Infer Time(ms):":"推理时间(ms):"
} }