def __init__(self, model, filter_length, sample_rate, framesize,
frameshift, usehamming, preemcoef, numchans, ceplifter,
numceps, enormalise, zmeansource, usepower, usec0, usecmn,
usedelta, useacc, n_last_frames, n_prev_frames, lofreq,
hifreq, mel_banks_only):
self.audio_recorded_in = []
self.ffnn = TheanoFFNN()
self.ffnn.load(model)
self.log_probs_speech = deque(maxlen=filter_length)
self.log_probs_sil = deque(maxlen=filter_length)
self.last_decision = 0.0
self.front_end = MFCCFrontEnd(sample_rate, framesize, usehamming,
preemcoef, numchans, ceplifter, numceps,
enormalise, zmeansource, usepower, usec0,
usecmn, usedelta, useacc,
n_last_frames + n_prev_frames, lofreq,
hifreq, mel_banks_only)
self.samplerate = sample_rate
self.framesize = framesize
self.frameshift = frameshift
def __init__(self, cfg):
self.cfg = cfg
self.audio_recorded_in = []
self.ffnn = TheanoFFNN()
self.ffnn.load(self.cfg['model'])
self.log_probs_speech = deque(maxlen=self.cfg['filter_length'])
self.log_probs_sil = deque(maxlen=self.cfg['filter_length'])
self.last_decision = 0.0
if self.cfg['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['sample_rate'], self.cfg['framesize'],
self.cfg['usehamming'], self.cfg['preemcoef'],
self.cfg['numchans'], self.cfg['ceplifter'],
self.cfg['numceps'], self.cfg['enormalise'],
self.cfg['zmeansource'], self.cfg['usepower'],
self.cfg['usec0'], self.cfg['usecmn'], self.cfg['usedelta'],
self.cfg['useacc'],
self.cfg['n_last_frames'] + self.cfg['n_prev_frames'],
self.cfg['lofreq'], self.cfg['hifreq'],
self.cfg['mel_banks_only'])
else:
raise ASRException('Unsupported frontend: %s' %
(self.cfg['frontend'], ))
def __init__(self, cfg):
self.cfg = cfg
self.audio_recorded_in = []
self.ffnn = TheanoFFNN()
self.ffnn.load(self.cfg['model'])
self.log_probs_speech = deque(maxlen=self.cfg['filter_length'])
self.log_probs_sil = deque(maxlen=self.cfg['filter_length'])
self.last_decision = 0.0
if self.cfg['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['sample_rate'], self.cfg['framesize'],
self.cfg['usehamming'], self.cfg['preemcoef'],
self.cfg['numchans'], self.cfg['ceplifter'],
self.cfg['numceps'], self.cfg['enormalise'],
self.cfg['zmeansource'], self.cfg['usepower'],
self.cfg['usec0'], self.cfg['usecmn'],
self.cfg['usedelta'], self.cfg['useacc'],
self.cfg['n_last_frames']+self.cfg['n_prev_frames'],
self.cfg['lofreq'], self.cfg['hifreq'],
self.cfg['mel_banks_only'])
else:
raise ASRException('Unsupported frontend: %s' % (self.cfg['frontend'], ))
def __init__(self, model, filter_length, sample_rate, framesize, frameshift,
usehamming, preemcoef, numchans, ceplifter, numceps,
enormalise, zmeansource, usepower, usec0, usecmn, usedelta,
useacc, n_last_frames, n_prev_frames, lofreq, hifreq,
mel_banks_only):
self.audio_recorded_in = []
self.ffnn = TheanoFFNN()
self.ffnn.load(model)
self.log_probs_speech = deque(maxlen=filter_length)
self.log_probs_sil = deque(maxlen=filter_length)
self.last_decision = 0.0
self.front_end = MFCCFrontEnd(
sample_rate, framesize,
usehamming, preemcoef,
numchans, ceplifter,
numceps, enormalise,
zmeansource, usepower,
usec0, usecmn,
usedelta, useacc,
n_last_frames + n_prev_frames,
lofreq, hifreq,
mel_banks_only)
self.framesize = framesize
self.frameshift = frameshift
class FFNNVAD():
""" This is implementation of a FFNN 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.ffnn = TheanoFFNN()
self.ffnn.load(self.cfg['model'])
self.log_probs_speech = deque(maxlen=self.cfg['filter_length'])
self.log_probs_sil = deque(maxlen=self.cfg['filter_length'])
self.last_decision = 0.0
if self.cfg['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['sample_rate'], self.cfg['framesize'],
self.cfg['usehamming'], self.cfg['preemcoef'],
self.cfg['numchans'], self.cfg['ceplifter'],
self.cfg['numceps'], self.cfg['enormalise'],
self.cfg['zmeansource'], self.cfg['usepower'],
self.cfg['usec0'], self.cfg['usecmn'],
self.cfg['usedelta'], self.cfg['useacc'],
self.cfg['n_last_frames']+self.cfg['n_prev_frames'],
self.cfg['lofreq'], self.cfg['hifreq'],
self.cfg['mel_banks_only'])
else:
raise ASRException('Unsupported frontend: %s' % (self.cfg['frontend'], ))
def reset(self):
self.log_probs_speech.clear()
self.log_probs_sil.clear()
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['framesize']:
frame = self.audio_recorded_in[:self.cfg['framesize']]
self.audio_recorded_in = self.audio_recorded_in[self.cfg['frameshift']:]
mfcc = self.front_end.param(frame)
prob_sil, prob_speech = self.ffnn.predict_normalise(mfcc.reshape(1,len(mfcc)))[0]
# print prob_sil, prob_speech
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 FFNNVADGeneral(object):
""" This is implementation of a FFNN 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, model, filter_length, sample_rate, framesize,
frameshift, usehamming, preemcoef, numchans, ceplifter,
numceps, enormalise, zmeansource, usepower, usec0, usecmn,
usedelta, useacc, n_last_frames, n_prev_frames, lofreq,
hifreq, mel_banks_only):
self.audio_recorded_in = []
self.ffnn = TheanoFFNN()
self.ffnn.load(model)
self.log_probs_speech = deque(maxlen=filter_length)
self.log_probs_sil = deque(maxlen=filter_length)
self.last_decision = 0.0
self.front_end = MFCCFrontEnd(sample_rate, framesize, usehamming,
preemcoef, numchans, ceplifter, numceps,
enormalise, zmeansource, usepower, usec0,
usecmn, usedelta, useacc,
n_last_frames + n_prev_frames, lofreq,
hifreq, mel_banks_only)
self.samplerate = sample_rate
self.framesize = framesize
self.frameshift = frameshift
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.
"""
# print chardet.detect(data[0])
# print(struct.calcsize('%dh' % (len(data) / 2, )))
data = struct.unpack('%dh' % (len(data) / 2, ), data)
self.audio_recorded_in.extend(data)
num_frame = 0
while len(self.audio_recorded_in) > self.framesize:
num_frame += 1
frame = self.audio_recorded_in[:self.framesize]
self.audio_recorded_in = self.audio_recorded_in[self.frameshift:]
mfcc = self.front_end.param(frame)
prob_sil, prob_speech = self.ffnn.predict_normalise(
mfcc.reshape(1, len(mfcc)))[0]
# prob_sil, prob_speech = self.ffnn.predict_normalise(mfcc)[0]
print prob_sil, prob_speech
# print num_frame,self.ffnn.predict_normalise(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_probs_speech和log_probs_sil长度为2的deque，超出部分，末尾添加，起始移除。那么这里的zip将每个数据计算两次。
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 'frame: ', num_frame, 'time: ', num_frame * self.frameshift / float(
self.samplerate), 'prob_speech_avg: %5.3f' % prob_speech_avg
self.last_decision = prob_speech_avg
self.last_decision = 1
# returns a speech / non-speech decisions
return self.last_decision
class FFNNVAD():
""" This is implementation of a FFNN 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.ffnn = TheanoFFNN()
self.ffnn.load(self.cfg['model'])
self.log_probs_speech = deque(maxlen=self.cfg['filter_length'])
self.log_probs_sil = deque(maxlen=self.cfg['filter_length'])
self.last_decision = 0.0
if self.cfg['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['sample_rate'], self.cfg['framesize'],
self.cfg['usehamming'], self.cfg['preemcoef'],
self.cfg['numchans'], self.cfg['ceplifter'],
self.cfg['numceps'], self.cfg['enormalise'],
self.cfg['zmeansource'], self.cfg['usepower'],
self.cfg['usec0'], self.cfg['usecmn'], self.cfg['usedelta'],
self.cfg['useacc'],
self.cfg['n_last_frames'] + self.cfg['n_prev_frames'],
self.cfg['lofreq'], self.cfg['hifreq'],
self.cfg['mel_banks_only'])
else:
raise ASRException('Unsupported frontend: %s' %
(self.cfg['frontend'], ))
def reset(self):
self.log_probs_speech.clear()
self.log_probs_sil.clear()
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['framesize']:
frame = self.audio_recorded_in[:self.cfg['framesize']]
self.audio_recorded_in = self.audio_recorded_in[self.
cfg['frameshift']:]
mfcc = self.front_end.param(frame)
prob_sil, prob_speech = self.ffnn.predict_normalise(
mfcc.reshape(1, len(mfcc)))[0]
# print prob_sil, prob_speech
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 FFNNVAD():
""" This is implementation of a FFNN 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, model, filter_length, sample_rate, framesize, frameshift,
usehamming, preemcoef, numchans, ceplifter, numceps,
enormalise, zmeansource, usepower, usec0, usecmn, usedelta,
useacc, n_last_frames, n_prev_frames, lofreq, hifreq,
mel_banks_only):
self.audio_recorded_in = []
self.ffnn = TheanoFFNN()
self.ffnn.load(model)
self.log_probs_speech = deque(maxlen=filter_length)
self.log_probs_sil = deque(maxlen=filter_length)
self.last_decision = 0.0
self.front_end = MFCCFrontEnd(
sample_rate, framesize,
usehamming, preemcoef,
numchans, ceplifter,
numceps, enormalise,
zmeansource, usepower,
usec0, usecmn,
usedelta, useacc,
n_last_frames + n_prev_frames,
lofreq, hifreq,
mel_banks_only)
self.framesize = framesize
self.frameshift = frameshift
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.framesize:
frame = self.audio_recorded_in[:self.framesize]
self.audio_recorded_in = self.audio_recorded_in[self.frameshift:]
mfcc = self.front_end.param(frame)
prob_sil, prob_speech = self.ffnn.predict_normalise(mfcc.reshape(1,len(mfcc)))[0]
# print prob_sil, prob_speech
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