328 lines
14 KiB
Python
328 lines
14 KiB
Python
import PySimpleGUI as sg
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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 torch.nn.functional as F
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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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i18n = I18nAuto()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class RVC:
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def __init__(self,key,pth_path,index_path,npy_path) -> None:
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'''
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初始化
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'''
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self.f0_up_key=key
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self.time_step = 160 / 16000 * 1000
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self.f0_min = 50
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self.f0_max = 1100
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self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
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self.index=faiss.read_index(index_path)
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self.big_npy=np.load(npy_path)
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model_path = "TEMP\\hubert_base.pt"
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print("load model(s) from {}".format(model_path))
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[model_path],
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suffix="",
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)
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self.model = models[0]
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self.model = self.model.to(device)
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self.model = self.model.half()
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self.model.eval()
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cpt = torch.load(pth_path, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
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if_f0=cpt.get("f0",1)
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if(if_f0==1):
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self.net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=True)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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del self.net_g.enc_q
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print(self.net_g.load_state_dict(cpt["weight"], strict=False))
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self.net_g.eval().to(device)
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self.net_g.half()
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def get_f0_coarse(self,f0):
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (self.f0_mel_max - self.f0_mel_min) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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# f0_mel[f0_mel > 188] = 188
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse
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def get_f0(self,x, p_len,f0_up_key=0):
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f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
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time_step=self.time_step / 1000, voicing_threshold=0.6,
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pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
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pad_size=(p_len - len(f0) + 1) // 2
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if(pad_size>0 or p_len - len(f0) - pad_size>0):
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f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
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f0 *= pow(2, f0_up_key / 12)
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# f0=suofang(f0)
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f0bak = f0.copy()
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f0_coarse=self.get_f0_coarse(f0)
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return f0_coarse, f0bak
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def infer(self,audio:np.ndarray,sampling_rate:int) -> np.ndarray:
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'''
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推理函数。
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:param audio: ndarray(n,2)
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:sampling_rate: 采样率
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'''
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# f0_up_key=12
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if len(audio.shape) > 1:
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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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'''padding'''
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feats = torch.from_numpy(audio).float()
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.half().to(device),
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"padding_mask": padding_mask.to(device),
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"output_layer": 9, # layer 9
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}
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torch.cuda.synchronize()
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with torch.no_grad():
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logits = self.model.extract_features(**inputs)
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feats = self.model.final_proj(logits[0])
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####索引优化
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npy = feats[0].cpu().numpy().astype("float32")
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D, I = self.index.search(npy, 1)
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# feats = torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
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index_rate=0.5
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feats = torch.from_numpy(npy).unsqueeze(0).to(device) * index_rate + (1 - index_rate) * feats
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feats=feats.half()
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feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
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torch.cuda.synchronize()
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# p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
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p_len = min(feats.shape[1],12000)#
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pitch, pitchf = self.get_f0(audio, p_len,self.f0_up_key)
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p_len = min(feats.shape[1],12000,pitch.shape[0])#太大了爆显存
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torch.cuda.synchronize()
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# print(feats.shape,pitch.shape)
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feats = feats[:,:p_len, :]
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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p_len = torch.LongTensor([p_len]).to(device)
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pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
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pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
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ii=0#sid
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sid=torch.LongTensor([ii]).to(device)
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with torch.no_grad():
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audio = self.net_g.infer(feats, p_len,pitch,pitchf,sid)[0][0, 0].data.cpu().float().numpy()#nsf
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torch.cuda.synchronize()
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return audio
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class Config:
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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.npy_path:str=''
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self.pitch:int=12
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self.samplerate:int=44100
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self.block_time:float=1.0#s
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self.buffer_num:int=1
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self.threhold:int=-30
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self.crossfade_time:float=0.08
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self.extra_time:float=0.04
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class GUI:
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def __init__(self) -> None:
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self.config=Config()
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self.flag_vc=False
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self.launcher()
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def launcher(self):
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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(title=i18n('加载模型/Load Model'),layout=[
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[sg.Input(default_text='TEMP\\atri.pth',key='pth_path'),sg.FileBrowse(i18n('选择.pth文件/.pth File'))],
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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/.index File'))],
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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/.npy File'))]
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])
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],
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[
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sg.Frame(layout=[
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[sg.Text(i18n("输入设备/Input Device")),sg.Combo(input_devices,key='sg_input_device',default_value=input_devices[sd.default.device[0]])],
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[sg.Text(i18n("输出设备/Output Device")),sg.Combo(output_devices,key='sg_output_device',default_value=output_devices[sd.default.device[1]])]
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],title=i18n("音频设备(请使用同种类驱动)/Audio Devices"))
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],
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[
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sg.Frame(layout=[
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[sg.Text(i18n("响应阈值/Silence Threhold")),sg.Slider(range=(-60,0),key='threhold',resolution=1,orientation='h',default_value=-30)],
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[sg.Text(i18n("音调设置/Pitch Offset")),sg.Slider(range=(-24,24),key='pitch',resolution=1,orientation='h',default_value=12)]
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],title=i18n("常规设置/Common")),
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sg.Frame(layout=[
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[sg.Text(i18n("采样长度/Sample Length")),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/Crossfade Length")),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/Extra Length")),sg.Slider(range=(0.05,3.00),key='extra_time',resolution=0.01,orientation='h',default_value=0.05)]
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],title=i18n("性能设置/Performance"))
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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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]
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self.window=sg.Window("RVC - GUI",layout=layout)
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self.event_handler()
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def event_handler(self):
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while True:
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event, values = self.window.read()
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if event ==sg.WINDOW_CLOSED:
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self.flag_vc=False
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exit()
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if event == 'start_vc' and self.flag_vc==False:
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self.set_values(values)
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print('pth_path:'+self.config.pth_path)
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print('index_path:'+self.config.index_path)
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print('npy_path:'+self.config.npy_path)
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print('using_cuda:'+str(torch.cuda.is_available()))
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self.start_vc()
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if event=='stop_vc'and self.flag_vc==True:
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self.flag_vc = False
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def set_values(self,values):
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self.set_devices(values["sg_input_device"],values['sg_output_device'])
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self.config.pth_path=values['pth_path']
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self.config.index_path=values['index_path']
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self.config.npy_path=values['npy_path']
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self.config.threhold=values['threhold']
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self.config.pitch=values['pitch']
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self.config.block_time=values['block_time']
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self.config.crossfade_time=values['crossfade_length']
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self.config.extra_time=values['extra_time']
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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.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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self.delay_frame=int(0.02*self.config.samplerate)#往前预留0.02s
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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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thread_vc=threading.Thread(target=self.soundinput)
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thread_vc.start()
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def soundinput(self):
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'''
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接受音频输入
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'''
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with sd.Stream(callback=self.audio_callback, blocksize=self.block_frame,samplerate=self.config.samplerate,dtype='float32'):
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while self.flag_vc:
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time.sleep(self.config.block_time)
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print('Audio block passed.')
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print('ENDing VC')
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def audio_callback(self,indata:np.ndarray,outdata:np.ndarray, frames, times, status):
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'''
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音频处理
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'''
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start_time=time.perf_counter()
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indata=librosa.to_mono(indata.T)
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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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'''noise gate'''
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frame_length=1024
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hop_length=512
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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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#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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# 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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# 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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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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def get_devices(self,update: bool = True):
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'''获取设备列表'''
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if update:
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sd._terminate()
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sd._initialize()
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devices = sd.query_devices()
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hostapis = sd.query_hostapis()
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for hostapi in hostapis:
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for device_idx in hostapi["devices"]:
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devices[device_idx]["hostapi_name"] = hostapi["name"]
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input_devices = [
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f"{d['name']} ({d['hostapi_name']})"
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for d in devices
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if d["max_input_channels"] > 0
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]
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output_devices = [
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f"{d['name']} ({d['hostapi_name']})"
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for d in devices
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if d["max_output_channels"] > 0
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]
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input_devices_indices = [d["index"] for d in devices if d["max_input_channels"] > 0]
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output_devices_indices = [
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d["index"] for d in devices if d["max_output_channels"] > 0
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]
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return input_devices, output_devices, input_devices_indices, output_devices_indices
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def set_devices(self,input_device,output_device):
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'''设置输出设备'''
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input_devices,output_devices,input_device_indices, output_device_indices=self.get_devices()
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sd.default.device[0]=input_device_indices[input_devices.index(input_device)]
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sd.default.device[1]=output_device_indices[output_devices.index(output_device)]
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print("input device:"+str(sd.default.device[0])+":"+str(input_device))
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print("output device:"+str(sd.default.device[1])+":"+str(output_device)) |