mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-12-18 20:49:48 +01:00
Reformat and rewrite _get_name_params (#57)
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
This commit is contained in:
Ftps
572
gui.py
572
gui.py
@@ -3,32 +3,36 @@ 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
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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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# 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,hubert_path,pth_path,index_path,npy_path,index_rate) -> None:
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'''
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def __init__(
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self, key, hubert_path, pth_path, index_path, npy_path, index_rate
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) -> 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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"""
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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.index_rate=index_rate
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'''NOT YET USED'''
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self.big_npy=np.load(npy_path)
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self.index = faiss.read_index(index_path)
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self.index_rate = index_rate
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"""NOT YET USED"""
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self.big_npy = np.load(npy_path)
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model_path = hubert_path
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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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@@ -41,9 +45,9 @@ class RVC:
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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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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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@@ -52,36 +56,43 @@ class RVC:
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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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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 > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
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self.f0_mel_max - self.f0_mel_min
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) + 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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def get_f0(self, x, p_len, f0_up_key=0):
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f0 = (
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parselmouth.Sound(x, 16000)
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.to_pitch_ac(
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time_step=self.time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=self.f0_min,
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pitch_ceiling=self.f0_max,
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)
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.selected_array["frequency"]
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)
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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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f0_coarse = self.get_f0_coarse(f0)
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return f0_coarse, f0bak
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def infer(self,feats:torch.Tensor) -> np.ndarray:
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'''
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def infer(self, feats: torch.Tensor) -> np.ndarray:
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"""
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推理函数
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'''
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audio=feats.clone().cpu().numpy()
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"""
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audio = feats.clone().cpu().numpy()
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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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@@ -96,209 +107,389 @@ class RVC:
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feats = self.model.final_proj(logits[0])
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####索引优化
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if(isinstance(self.index,type(None))==False and isinstance(self.big_npy,type(None))==False and self.index_rate!=0):
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if (
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isinstance(self.index, type(None)) == False
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and isinstance(self.big_npy, type(None)) == False
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and self.index_rate != 0
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):
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npy = feats[0].cpu().numpy().astype("float32")
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_, I = self.index.search(npy, 1)
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npy=self.big_npy[I.squeeze()].astype("float16")
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feats = torch.from_numpy(npy).unsqueeze(0).to(device)*self.index_rate + (1-self.index_rate)*feats
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npy = self.big_npy[I.squeeze()].astype("float16")
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
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+ (1 - self.index_rate) * feats
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)
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feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
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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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p_len = min(feats.shape[1], 12000) #
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print(feats.shape)
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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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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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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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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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infered_audio = self.net_g.infer(feats, p_len,pitch,pitchf,sid)[0][0, 0].data.cpu().float()#nsf
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infered_audio = (
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self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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.data.cpu()
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.float()
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) # nsf
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torch.cuda.synchronize()
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return infered_audio
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class Config:
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def __init__(self) -> None:
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self.hubert_path:str=''
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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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self.I_noise_reduce=False
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self.O_noise_reduce=False
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self.index_rate=0.3
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self.hubert_path: str = ""
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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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self.I_noise_reduce = False
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self.O_noise_reduce = False
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self.index_rate = 0.3
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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.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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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'),layout=[
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[sg.Input(default_text='TEMP\\hubert_base.pt',key='hubert_path'),sg.FileBrowse(i18n('Hubert模型'))],
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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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])
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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="TEMP\\hubert_base.pt", key="hubert_path"
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),
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sg.FileBrowse(i18n("Hubert模型")),
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],
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[
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sg.Input(default_text="TEMP\\atri.pth", key="pth_path"),
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sg.FileBrowse(i18n("选择.pth文件")),
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],
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[
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sg.Input(
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default_text="TEMP\\added_IVF512_Flat_atri_baseline_src_feat.index",
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key="index_path",
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),
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sg.FileBrowse(i18n("选择.index文件")),
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],
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[
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sg.Input(
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default_text="TEMP\\big_src_feature_atri.npy",
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key="npy_path",
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),
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sg.FileBrowse(i18n("选择.npy文件")),
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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(layout=[
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[sg.Text(i18n("输入设备")),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")),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)"))
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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=input_devices[sd.default.device[0]],
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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=output_devices[sd.default.device[1]],
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),
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],
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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(layout=[
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[sg.Text(i18n("响应阈值")),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")),sg.Slider(range=(-24,24),key='pitch',resolution=1,orientation='h',default_value=12)],
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[sg.Text(i18n('Index Rate')),sg.Slider(range=(0.0,1.0),key='index_rate',resolution=0.01,orientation='h',default_value=0.5)]
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],title=i18n("常规设置")),
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sg.Frame(layout=[
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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('输入降噪'),key='I_noise_reduce'),sg.Checkbox(i18n('输出降噪'),key='O_noise_reduce')]
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],title=i18n("性能设置"))
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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=-30,
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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=12,
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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=0.5,
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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=(0.1, 3.0),
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key="block_time",
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resolution=0.1,
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orientation="h",
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default_value=1.0,
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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=0.08,
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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, 3.00),
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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=0.05,
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),
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],
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[
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sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
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sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
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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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[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("推理时间(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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self.window = sg.Window("RVC - GUI", layout=layout)
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self.event_handler()
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||||
|
||||
def event_handler(self):
|
||||
while True:
|
||||
event, values = self.window.read()
|
||||
if event ==sg.WINDOW_CLOSED:
|
||||
self.flag_vc=False
|
||||
if event == sg.WINDOW_CLOSED:
|
||||
self.flag_vc = False
|
||||
exit()
|
||||
if event == 'start_vc' and self.flag_vc==False:
|
||||
if event == "start_vc" and self.flag_vc == False:
|
||||
self.set_values(values)
|
||||
print(str(self.config.__dict__))
|
||||
print('using_cuda:'+str(torch.cuda.is_available()))
|
||||
print("using_cuda:" + str(torch.cuda.is_available()))
|
||||
self.start_vc()
|
||||
if event=='stop_vc'and self.flag_vc==True:
|
||||
if event == "stop_vc" and self.flag_vc == True:
|
||||
self.flag_vc = False
|
||||
|
||||
def set_values(self, values):
|
||||
self.set_devices(values["sg_input_device"], values["sg_output_device"])
|
||||
self.config.hubert_path = values["hubert_path"]
|
||||
self.config.pth_path = values["pth_path"]
|
||||
self.config.index_path = values["index_path"]
|
||||
self.config.npy_path = values["npy_path"]
|
||||
self.config.threhold = values["threhold"]
|
||||
self.config.pitch = values["pitch"]
|
||||
self.config.block_time = values["block_time"]
|
||||
self.config.crossfade_time = values["crossfade_length"]
|
||||
self.config.extra_time = values["extra_time"]
|
||||
self.config.I_noise_reduce = values["I_noise_reduce"]
|
||||
self.config.O_noise_reduce = values["O_noise_reduce"]
|
||||
self.config.index_rate = values["index_rate"]
|
||||
|
||||
def set_values(self,values):
|
||||
self.set_devices(values["sg_input_device"],values['sg_output_device'])
|
||||
self.config.hubert_path=values['hubert_path']
|
||||
self.config.pth_path=values['pth_path']
|
||||
self.config.index_path=values['index_path']
|
||||
self.config.npy_path=values['npy_path']
|
||||
self.config.threhold=values['threhold']
|
||||
self.config.pitch=values['pitch']
|
||||
self.config.block_time=values['block_time']
|
||||
self.config.crossfade_time=values['crossfade_length']
|
||||
self.config.extra_time=values['extra_time']
|
||||
self.config.I_noise_reduce=values['I_noise_reduce']
|
||||
self.config.O_noise_reduce=values['O_noise_reduce']
|
||||
self.config.index_rate=values['index_rate']
|
||||
|
||||
def start_vc(self):
|
||||
torch.cuda.empty_cache()
|
||||
self.flag_vc=True
|
||||
self.block_frame=int(self.config.block_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.delay_frame=int(0.02*self.config.samplerate)#往前预留0.02s
|
||||
self.extra_frame=int(self.config.extra_time*self.config.samplerate)#往后预留0.04s
|
||||
self.rvc=None
|
||||
self.rvc=RVC(self.config.pitch,self.config.hubert_path,self.config.pth_path,self.config.index_path,self.config.npy_path,self.config.index_rate)
|
||||
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:torch.Tensor=torch.zeros(self.block_frame,device=device,dtype=torch.float32)
|
||||
self.sola_buffer:torch.Tensor=torch.zeros(self.crossfade_frame,device=device,dtype=torch.float32)
|
||||
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.resampler1=tat.Resample(orig_freq=self.config.samplerate,new_freq=16000,dtype=torch.float32)
|
||||
self.resampler2=tat.Resample(orig_freq=40000,new_freq=self.config.samplerate,dtype=torch.float32)
|
||||
thread_vc=threading.Thread(target=self.soundinput)
|
||||
self.flag_vc = True
|
||||
self.block_frame = int(self.config.block_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.delay_frame = int(0.02 * self.config.samplerate) # 往前预留0.02s
|
||||
self.extra_frame = int(
|
||||
self.config.extra_time * self.config.samplerate
|
||||
) # 往后预留0.04s
|
||||
self.rvc = None
|
||||
self.rvc = RVC(
|
||||
self.config.pitch,
|
||||
self.config.hubert_path,
|
||||
self.config.pth_path,
|
||||
self.config.index_path,
|
||||
self.config.npy_path,
|
||||
self.config.index_rate,
|
||||
)
|
||||
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: torch.Tensor = torch.zeros(
|
||||
self.block_frame, device=device, dtype=torch.float32
|
||||
)
|
||||
self.sola_buffer: torch.Tensor = torch.zeros(
|
||||
self.crossfade_frame, device=device, dtype=torch.float32
|
||||
)
|
||||
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.resampler1 = tat.Resample(
|
||||
orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
|
||||
)
|
||||
self.resampler2 = tat.Resample(
|
||||
orig_freq=40000, new_freq=self.config.samplerate, dtype=torch.float32
|
||||
)
|
||||
thread_vc = threading.Thread(target=self.soundinput)
|
||||
thread_vc.start()
|
||||
|
||||
|
||||
def soundinput(self):
|
||||
'''
|
||||
"""
|
||||
接受音频输入
|
||||
'''
|
||||
with sd.Stream(callback=self.audio_callback, blocksize=self.block_frame,samplerate=self.config.samplerate,dtype='float32'):
|
||||
"""
|
||||
with sd.Stream(
|
||||
callback=self.audio_callback,
|
||||
blocksize=self.block_frame,
|
||||
samplerate=self.config.samplerate,
|
||||
dtype="float32",
|
||||
):
|
||||
while self.flag_vc:
|
||||
time.sleep(self.config.block_time)
|
||||
print('Audio block passed.')
|
||||
print('ENDing VC')
|
||||
print("Audio block passed.")
|
||||
print("ENDing VC")
|
||||
|
||||
|
||||
def audio_callback(self,indata:np.ndarray,outdata:np.ndarray, frames, times, status):
|
||||
'''
|
||||
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)
|
||||
"""
|
||||
start_time = time.perf_counter()
|
||||
indata = librosa.to_mono(indata.T)
|
||||
if self.config.I_noise_reduce:
|
||||
indata[:]=nr.reduce_noise(y=indata,sr=self.config.samplerate)
|
||||
|
||||
'''noise gate'''
|
||||
frame_length=2048
|
||||
hop_length=1024
|
||||
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
|
||||
#print(rms.shape,db.shape,db)
|
||||
indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
|
||||
|
||||
"""noise gate"""
|
||||
frame_length = 2048
|
||||
hop_length = 1024
|
||||
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
|
||||
# print(rms.shape,db.shape,db)
|
||||
for i in range(db_threhold.shape[0]):
|
||||
if db_threhold[i]:
|
||||
indata[i*hop_length:(i+1)*hop_length]=0
|
||||
self.input_wav[:]=np.append(self.input_wav[self.block_frame:],indata)
|
||||
|
||||
#infer
|
||||
print('input_wav:'+str(self.input_wav.shape))
|
||||
#print('infered_wav:'+str(infer_wav.shape))
|
||||
infer_wav:torch.Tensor=self.resampler2(self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav))))[-self.crossfade_frame-self.sola_search_frame-self.block_frame:].to(device)
|
||||
print('infer_wav:'+str(infer_wav.shape))
|
||||
|
||||
indata[i * hop_length : (i + 1) * hop_length] = 0
|
||||
self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
|
||||
|
||||
# infer
|
||||
print("input_wav:" + str(self.input_wav.shape))
|
||||
# print('infered_wav:'+str(infer_wav.shape))
|
||||
infer_wav: torch.Tensor = self.resampler2(
|
||||
self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
|
||||
)[-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
|
||||
cor_nom=F.conv1d(infer_wav[None,None,:self.crossfade_frame + self.sola_search_frame],self.sola_buffer[None,None,:])
|
||||
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 = torch.argmax( cor_nom[0, 0] / cor_den[0, 0])
|
||||
print('sola offset: ' + str(int(sola_offset)))
|
||||
|
||||
cor_nom = F.conv1d(
|
||||
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
|
||||
self.sola_buffer[None, None, :],
|
||||
)
|
||||
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 = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
|
||||
print("sola offset: " + str(int(sola_offset)))
|
||||
|
||||
# crossfade
|
||||
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.sola_buffer[:]
|
||||
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.sola_buffer[:]
|
||||
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:
|
||||
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.O_noise_reduce:
|
||||
outdata[:]=np.tile(nr.reduce_noise(y=self.output_wav[:].cpu().numpy(),sr=self.config.samplerate),(2,1)).T
|
||||
outdata[:] = np.tile(
|
||||
nr.reduce_noise(
|
||||
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
|
||||
),
|
||||
(2, 1),
|
||||
).T
|
||||
else:
|
||||
outdata[:]=self.output_wav[:].repeat(2, 1).t().cpu().numpy()
|
||||
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):
|
||||
'''获取设备列表'''
|
||||
outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
|
||||
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):
|
||||
"""获取设备列表"""
|
||||
if update:
|
||||
sd._terminate()
|
||||
sd._initialize()
|
||||
@@ -317,18 +508,33 @@ class GUI:
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
]
|
||||
input_devices_indices = [d["index"] for d in devices if d["max_input_channels"] > 0]
|
||||
input_devices_indices = [
|
||||
d["index"] for d in devices if d["max_input_channels"] > 0
|
||||
]
|
||||
output_devices_indices = [
|
||||
d["index"] 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)]
|
||||
print("input device:"+str(sd.default.device[0])+":"+str(input_device))
|
||||
print("output device:"+str(sd.default.device[1])+":"+str(output_device))
|
||||
|
||||
gui=GUI()
|
||||
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)
|
||||
]
|
||||
print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
|
||||
print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
|
||||
|
||||
|
||||
gui = GUI()
|
||||
|
||||
Reference in New Issue
Block a user