with open(filename, "wb") as f:
f.write(total_data)
# print(total_data.decode())
# total_data = total_data.decode()
# total_data = total_data.split('+@+')
# if len(total_data) == 2:
# result = "N"
# else:
# result = "N"
# print(result)
# conn.send(result.encode()) #然后再发送数据
result1 = ''
success = True
flag = identifier[1]
voiceprint_a = extraction.ertract_voiceprint(filename, sr=16000)
if (flag == '0'):
# save(identifier[0],voiceprint=voiceprint_a)
voice = voiceprint_a
print('注册')
result1 = 'Y'
else:
print('identifier', identifier[0])
# voiceprint_e = findvoice(identifier[0])
Euclideandist = PairwiseDistance(2)
print('voice', type(voice))
distance = Euclideandist(voice, voiceprint_a)
print(distance)
if distance <= 0.4:
result1 = "Y"
filename_e = '/AttackSample/zzl1.wav'
filename_a = '/AttackSample/liqi&&0.wav'
y1,sr = librosa.load(filename_e,sr=16000,mono=True)
y2,sr = librosa.load(filename_a,sr=16000,mono=True)
print('y1:',len(y1))
print('y2:',len(y2))
#print('================')
#y变成80000
#print(len(y1))
#with torch.no_grad():
# voiceprint_e = extraction.ertract_voiceprint(filename_e,sr=16000)
# voiceprint_a = extraction.ertract_voiceprint(filename_a,sr=16000)
#print(voiceprint_e)
Euclideandist = PairwiseDistance(2)
distance = 0
for i in range(20):
voiceprint_e = extraction.ertract_voiceprint(filename_e, sr=16000)
voiceprint_a = extraction.ertract_voiceprint(filename_a, sr=16000)
distance += Euclideandist(voiceprint_e, voiceprint_a).item()
print("distance:",distance/20)
#fake_noise = attacker(voiceprint_e).squeeze()
#while len(y2)>len(fake_noise):
# fake_noise = np.concatenate((y1,y1),axis = 0)#y1 = y1 + y1
# y1 = y1[0:80000]
# while len(y2)<80000:
# y2 = np.concatenate((y2,y2),axis = 0)
# #print(len(y2))
# y2 = y2[0:80000]
import InnerProduct
import extraction
import torch
secu = InnerProduct.InnerProduct()
voiceprint_a = extraction.ertract_voiceprint("Hjjgg&&0.wav", sr=16000)
voiceprint_b = extraction.ertract_voiceprint("Hjjgg&&1.wav", sr=16000)
A = torch.norm(voiceprint_a).item()
B = torch.norm(voiceprint_b).item()
print(voiceprint_a)
print('torch.norm(voiceprint_a)', torch.norm(voiceprint_a))
print('A', A)
print('type(A)', type(A))
a = voiceprint_a.cpu().numpy().tolist()[0]
print(len(a))
print(type(a))
print('a', a)
b = voiceprint_b.cpu().numpy().tolist()[0]
b = list(b)
print(len(b))
print(type(b))
print('b', b)
print(max(b))
print(min(b))
s, C = secu.Step1(a)
DSum = secu.Step2(b, C)
innerproduct = secu.Step3(DSum, s)
print('innerproduct', innerproduct)
cos = innerproduct / (A * B)
print('cos', cos)