def record(self, button):
text, ok = QtWidgets.QInputDialog().getText(self,
"Emotion Recognition",
"Enter Your Name:")
if ok and text:
self.speaker = text
print "Speaker: " + self.speaker
wFile = "Test/" + self.speaker + ".wav"
start_record(self, wFile, 5, self.state)
state = "File saved as " + self.speaker
self.repaint()
print 'File saved as ' + self.speaker + ".wav"
self.add_files()
fFile = "feat/Test/" + self.speaker + ".htk"
try:
# call MFCC feature extraction subroutine
f, E, fs = mfcc(wFile, self.winlen, self.ovrlen, self.pre_coef,
self.nfilter, self.nftt)
# VAD part
if self.opts == 1:
f = vad_thr(f, E)
# Energy threshold based VAD [comment this line if you would like to plugin the rVAD labels]
elif self.opts == 0:
l = numpy.loadtxt('..corresponding vad label file')
# [Plugin the VAD label generated by rVAD matlab]
if (len(f) - len(l)) == 1:
# 1-[end-frame] correction [matlab/python]
l = numpy.append(l, l[-1:, ])
elif (len(f) - len(l)) == -1:
l = numpy.delete(l, -1)
if (len(l)
== len(f)) and (len(numpy.argwhere(l == 1)) != 0):
idx = numpy.where(l == 1)
f = f[idx]
else:
print "mismatch frames between: label and feature files or no voice-frame in VAD"
exit()
# Zero mean unit variance normalize after VAD
f = cmvn(f)
# write the VAD+normalized features in file
if not os.path.exists(os.path.dirname(fFile)):
# create director for the feature file
os.makedirs(os.path.dirname(fFile))
writehtk(fFile, f, 0.01)
except:
print("Fail1..%s ---> %s\n" % (wFile, fFile))
if button.text() == "Live Detection":
self.test_emotion(name=self.speaker)
else:
self.state = "Not valid Name"
self.repaint()
return
if (len(f) - len(l)) ==1: #1-[end-frame] correction [matlab/python]
l= numpy.append(l,l[-1:,])
elif (len(f) -len(l)) == -1:
l=numpy.delete(l,-1)
if (len(l) == len(f)) and (len(numpy.argwhere(l==1)) !=0):
idx=numpy.where(l==1)
f=f[idx]
else:
print "mismatch frames between: label and feature files or no voice-frame in VAD"
exit()
# Zero mean unit variance normalize after VAD
f=cmvn(f)
#write the VAD+normalized features in file
if not os.path.exists(os.path.dirname(fFile)): # create director for the feature file
os.makedirs(os.path.dirname(fFile))
#print("%s --> %s\n" %(wFile,fFile))
writehtk(fFile, f , 0.01)
except:
print("Fail ..%s ---> %s\n" %(wFile, fFile))
decision=test_decision(fFile,'DEC3_Tau10.0',ubm_mu,ubm_cov, ubm_w)
if(decision=="YES"):
def test_emotion(self, name):
if name:
value = name
else:
file = self.files.currentItem()
print file.text()
value = file.text().split(".")[0]
fFile = "feat/Test/" + value + ".htk"
self.emotion = test(fFile, self.Tardest, self.ubm_mu, self.ubm_cov,
self.ubm_w)
self.result = "Detected as: "
self.repaint()
start_play(self.emotion + "_dec.wav")
self.state = "Say Yes or No"
self.repaint()
wFile = "Test_dec.wav"
start_record(self, wFile, 3, self.state)
fFile = "feat/Test/" + wFile + ".htk"
try:
# call MFCC feature extraction subroutine
f, E, fs = mfcc(wFile, self.winlen, self.ovrlen, self.pre_coef,
self.nfilter, self.nftt)
# VAD part
if self.opts == 1:
f = vad_thr(f, E)
# Energy threshold based VAD [comment this line if you would like to plugin the rVAD labels]
elif self.opts == 0:
l = numpy.loadtxt('..corresponding vad label file')
# [Plugin the VAD label generated by rVAD matlab]
if (len(f) - len(l)) == 1:
# 1-[end-frame] correction [matlab/python]
l = numpy.append(l, l[-1:, ])
elif (len(f) - len(l)) == -1:
l = numpy.delete(l, -1)
if (len(l) == len(f)) and (len(numpy.argwhere(l == 1)) != 0):
idx = numpy.where(l == 1)
f = f[idx]
else:
print "mismatch frames between: label and feature files or no voice-frame in VAD"
exit()
# Zero mean unit variance normalize after VAD
f = cmvn(f)
# write the VAD+normalized features in file
if not os.path.exists(os.path.dirname(fFile)):
# create director for the feature file
os.makedirs(os.path.dirname(fFile))
#print("%s --> %s\n" %(wFile,fFile))
writehtk(fFile, f, 0.01)
except:
print("Fail ..%s ---> %s\n" % (wFile, fFile))
decision = test_decision(fFile, 'DEC3_Tau10.0', self.ubm_mu,
self.ubm_cov, self.ubm_w)
if decision == "YES":
start_play(self.emotion + ".wav")
self.result = ""
self.emotion = ""
self.state = ""
self.repaint()
return
#[Pluggin the VAD label generated by rVAD matlab]
if (len(f) - len(l)) == 1: #1-[end-frame] correction [matlab/python]
l = numpy.append(l, l[-1:, ])
elif (len(f) - len(l)) == -1:
l = numpy.delete(l, -1)
if (len(l) == len(f)) and (len(numpy.argwhere(l == 1)) != 0):
idx = numpy.where(l == 1)
f = f[idx]
else:
print "mismatch frames between: label and feature files or no voice-frame in VAD"
exit()
# Zero mean unit variance normalize after VAD
f = cmvn(f)
#write the VAD+normalized features in file
if not os.path.exists(
os.path.dirname(fFile)): # create director for the feature file
os.makedirs(os.path.dirname(fFile))
#print("%s --> %s\n" %(wFile,fFile))
writehtk(fFile, f, 0.01)
except:
print("Fail ..%s ---> %s\n" % (wFile, fFile))
emotion = test(fFile, Tardest, ubm_mu, ubm_cov, ubm_w)