def inference(self, file_path):
with torch.no_grad():
for key, egs in tqdm.tqdm(self.mix):
#self.logger.info("Compute on utterance {}...".format(key))
egs=egs.to(self.device)
norm = torch.norm(egs,float('inf'))
if len(self.gpuid) != 0:
if egs.dim() == 1:
egs = torch.unsqueeze(egs, 0)
ests=self.net(egs)
spks=[torch.squeeze(s.detach().cpu()) for s in ests]
else:
if egs.dim() == 1:
egs = torch.unsqueeze(egs, 0)
ests=self.net(egs)
spks=[torch.squeeze(s.detach()) for s in ests]
index=0
for s in spks:
s = s[:egs.shape[1]]
s = s - torch.mean(s)
s = s/torch.max(torch.abs(s))
#norm
#s = s*norm/torch.max(torch.abs(s))
s = s.unsqueeze(0)
index += 1
os.makedirs(file_path+'/spk'+str(index), exist_ok=True)
filename=file_path+'/spk'+str(index)+'/'+key
write_wav(filename, s, 8000)
break
self.logger.info("Compute over {:d} utterances".format(len(self.mix)))
def inference(self, file_path):
self.net.eval()
with torch.no_grad():
egs=self.mix
norm = torch.norm(egs,float('inf'))
if len(self.gpuid) != 0:
if egs.dim() == 1:
egs = torch.unsqueeze(egs, 0)
ests=self.net(egs)
spks=[torch.squeeze(s.detach().cpu()) for s in ests]
else:
if egs.dim() == 1:
egs = torch.unsqueeze(egs, 0)
ests=self.net(egs)
print(ests[0].shape)
spks=[torch.squeeze(s.detach()) for s in ests]
index=0
for s in spks:
#norm
s = s - torch.mean(s)
s = s*norm/torch.max(torch.abs(s))
index += 1
os.makedirs(file_path+'/spk'+str(index), exist_ok=True)
filename=file_path+'/spk'+str(index)+'/'+'test.wav'
write_wav(filename, s, 16000)
self.logger.info("Compute over {:d} utterances".format(len(self.mix)))