print '-' * 120
gmm_speech = GMM(n_features=0)
gmm_speech.load_model('model_voip/vad_speech_sds_mfcc.gmm')
gmm_sil = GMM(n_features=0)
gmm_sil.load_model('model_voip/vad_sil_sds_mfcc.gmm')
vta = test
print "Length of test data:", len(vta)
print datetime.datetime.now()
accuracy = 0.0
n = 0
for frame, label in vta:
log_prob_speech = gmm_speech.score(frame)
log_prob_sil = gmm_sil.score(frame)
ratio = log_prob_speech - log_prob_sil
if ratio >= 0:
rec_label = 'speech'
else:
rec_label = 'sil'
if rec_label == label:
accuracy += 1.0
n += 1
accuracy = accuracy * 100.0 / n
print datetime.datetime.now()
print '-' * 120
gmm_speech = GMM(n_features=0)
gmm_speech.load_model('model_voip/vad_speech_sds_mfcc.gmm')
gmm_sil = GMM(n_features=0)
gmm_sil.load_model('model_voip/vad_sil_sds_mfcc.gmm')
vta = test
print "Length of test data:", len(vta)
print datetime.datetime.now()
accuracy = 0.0
n = 0
for frame, label in vta:
log_prob_speech = gmm_speech.score(frame)
log_prob_sil = gmm_sil.score(frame)
ratio = log_prob_speech - log_prob_sil
if ratio >= 0:
rec_label = 'speech'
else:
rec_label = 'sil'
if rec_label == label:
accuracy += 1.0
n += 1
accuracy = accuracy * 100.0 / n
class GMMVAD():
""" This is implementation of a GMM based voice activity detector.
It only implements decisions whether input frame is speech of non speech.
It returns the posterior probability of speech for N last input frames.
"""
def __init__(self, cfg):
self.cfg = cfg
self.audio_recorded_in = []
self.gmm_speech = GMM()
self.gmm_speech.load_model(self.cfg['VAD']['gmm']['speech_model'])
self.gmm_sil = GMM()
self.gmm_sil.load_model(self.cfg['VAD']['gmm']['sil_model'])
self.log_probs_speech = deque(
maxlen=self.cfg['VAD']['gmm']['filter_length'])
self.log_probs_sil = deque(
maxlen=self.cfg['VAD']['gmm']['filter_length'])
self.last_decision = 0.0
if self.cfg['VAD']['gmm']['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['Audio']['sample_rate'],
self.cfg['VAD']['gmm']['framesize'],
self.cfg['VAD']['gmm']['usehamming'],
self.cfg['VAD']['gmm']['preemcoef'],
self.cfg['VAD']['gmm']['numchans'],
self.cfg['VAD']['gmm']['ceplifter'],
self.cfg['VAD']['gmm']['numceps'],
self.cfg['VAD']['gmm']['enormalise'],
self.cfg['VAD']['gmm']['zmeansource'],
self.cfg['VAD']['gmm']['usepower'],
self.cfg['VAD']['gmm']['usec0'],
self.cfg['VAD']['gmm']['usecmn'],
self.cfg['VAD']['gmm']['usedelta'],
self.cfg['VAD']['gmm']['useacc'],
self.cfg['VAD']['gmm']['n_last_frames'],
self.cfg['VAD']['gmm']['lofreq'],
self.cfg['VAD']['gmm']['hifreq'])
else:
raise ASRException('Unsupported frontend: %s' %
(self.cfg['VAD']['gmm']['frontend'], ))
def decide(self, data):
"""Processes the input frame whether the input segment is speech or non speech.
The returned values can be in range from 0.0 to 1.0.
It returns 1.0 for 100% speech segment and 0.0 for 100% non speech segment.
"""
data = struct.unpack('%dh' % (len(data) / 2, ), data)
self.audio_recorded_in.extend(data)
while len(
self.audio_recorded_in) > self.cfg['VAD']['gmm']['framesize']:
frame = self.audio_recorded_in[:self.
cfg['VAD']['gmm']['framesize']]
self.audio_recorded_in = self.audio_recorded_in[
self.cfg['VAD']['gmm']['frameshift']:]
mfcc = self.front_end.param(frame)
log_prob_speech = self.gmm_speech.score(mfcc)
log_prob_sil = self.gmm_sil.score(mfcc)
self.log_probs_speech.append(log_prob_speech)
self.log_probs_sil.append(log_prob_sil)
log_prob_speech_avg = 0.0
for log_prob_speech, log_prob_sil in zip(self.log_probs_speech,
self.log_probs_sil):
log_prob_speech_avg += log_prob_speech - logsumexp(
[log_prob_speech, log_prob_sil])
log_prob_speech_avg /= len(self.log_probs_speech)
prob_speech_avg = np.exp(log_prob_speech_avg)
# print 'prob_speech_avg: %5.3f' % prob_speech_avg
self.last_decision = prob_speech_avg
# returns a speech / non-speech decisions
return self.last_decision
class GMMVAD():
""" This is implementation of a GMM based voice activity detector.
It only implements decisions whether input frame is speech of non speech.
It returns the posterior probability of speech for N last input frames.
"""
def __init__(self, cfg):
self.cfg = cfg
self.audio_recorded_in = []
self.gmm_speech = GMM()
self.gmm_speech.load_model(self.cfg['VAD']['gmm']['speech_model'])
self.gmm_sil = GMM()
self.gmm_sil.load_model(self.cfg['VAD']['gmm']['sil_model'])
self.log_probs_speech = deque(maxlen=self.cfg['VAD']['gmm']['filter_length'])
self.log_probs_sil = deque(maxlen=self.cfg['VAD']['gmm']['filter_length'])
self.last_decision = 0.0
if self.cfg['VAD']['gmm']['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['Audio']['sample_rate'], self.cfg['VAD']['gmm']['framesize'],
self.cfg['VAD']['gmm']['usehamming'], self.cfg['VAD']['gmm']['preemcoef'],
self.cfg['VAD']['gmm']['numchans'], self.cfg['VAD']['gmm']['ceplifter'],
self.cfg['VAD']['gmm']['numceps'], self.cfg['VAD']['gmm']['enormalise'],
self.cfg['VAD']['gmm']['zmeansource'], self.cfg['VAD']['gmm']['usepower'],
self.cfg['VAD']['gmm']['usec0'], self.cfg['VAD']['gmm']['usecmn'],
self.cfg['VAD']['gmm']['usedelta'], self.cfg['VAD']['gmm']['useacc'],
self.cfg['VAD']['gmm']['n_last_frames'],
self.cfg['VAD']['gmm']['lofreq'], self.cfg['VAD']['gmm']['hifreq'])
else:
raise ASRException('Unsupported frontend: %s' % (self.cfg['VAD']['gmm']['frontend'], ))
def decide(self, data):
"""Processes the input frame whether the input segment is speech or non speech.
The returned values can be in range from 0.0 to 1.0.
It returns 1.0 for 100% speech segment and 0.0 for 100% non speech segment.
"""
data = struct.unpack('%dh' % (len(data) / 2, ), data)
self.audio_recorded_in.extend(data)
while len(self.audio_recorded_in) > self.cfg['VAD']['gmm']['framesize']:
frame = self.audio_recorded_in[:self.cfg['VAD']['gmm']['framesize']]
self.audio_recorded_in = self.audio_recorded_in[self.cfg['VAD']['gmm']['frameshift']:]
mfcc = self.front_end.param(frame)
log_prob_speech = self.gmm_speech.score(mfcc)
log_prob_sil = self.gmm_sil.score(mfcc)
self.log_probs_speech.append(log_prob_speech)
self.log_probs_sil.append(log_prob_sil)
log_prob_speech_avg = 0.0
for log_prob_speech, log_prob_sil in zip(self.log_probs_speech, self.log_probs_sil):
log_prob_speech_avg += log_prob_speech - logsumexp([log_prob_speech, log_prob_sil])
log_prob_speech_avg /= len(self.log_probs_speech)
prob_speech_avg = np.exp(log_prob_speech_avg)
# print 'prob_speech_avg: %5.3f' % prob_speech_avg
self.last_decision = prob_speech_avg
# returns a speech / non-speech decisions
return self.last_decision