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.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
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['VAD']['ffnn']['model'])
self.log_probs_speech = deque(
maxlen=self.cfg['VAD']['ffnn']['filter_length'])
self.log_probs_sil = deque(
maxlen=self.cfg['VAD']['ffnn']['filter_length'])
self.last_decision = 0.0
if self.cfg['VAD']['ffnn']['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['Audio']['sample_rate'],
self.cfg['VAD']['ffnn']['framesize'],
self.cfg['VAD']['ffnn']['usehamming'],
self.cfg['VAD']['ffnn']['preemcoef'],
self.cfg['VAD']['ffnn']['numchans'],
self.cfg['VAD']['ffnn']['ceplifter'],
self.cfg['VAD']['ffnn']['numceps'],
self.cfg['VAD']['ffnn']['enormalise'],
self.cfg['VAD']['ffnn']['zmeansource'],
self.cfg['VAD']['ffnn']['usepower'],
self.cfg['VAD']['ffnn']['usec0'],
self.cfg['VAD']['ffnn']['usecmn'],
self.cfg['VAD']['ffnn']['usedelta'],
self.cfg['VAD']['ffnn']['useacc'],
self.cfg['VAD']['ffnn']['n_last_frames'] +
self.cfg['VAD']['ffnn']['n_prev_frames'],
self.cfg['VAD']['ffnn']['lofreq'],
self.cfg['VAD']['ffnn']['hifreq'],
self.cfg['VAD']['ffnn']['mel_banks_only'])
else:
raise ASRException('Unsupported frontend: %s' %
(self.cfg['VAD']['ffnn']['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']['ffnn']['framesize']:
frame = self.audio_recorded_in[:self.
cfg['VAD']['ffnn']['framesize']]
self.audio_recorded_in = self.audio_recorded_in[
self.cfg['VAD']['ffnn']['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.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
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['VAD']['ffnn']['model'])
self.log_probs_speech = deque(maxlen=self.cfg['VAD']['ffnn']['filter_length'])
self.log_probs_sil = deque(maxlen=self.cfg['VAD']['ffnn']['filter_length'])
self.last_decision = 0.0
if self.cfg['VAD']['ffnn']['frontend'] == 'MFCC':
self.front_end = MFCCFrontEnd(
self.cfg['Audio']['sample_rate'], self.cfg['VAD']['ffnn']['framesize'],
self.cfg['VAD']['ffnn']['usehamming'], self.cfg['VAD']['ffnn']['preemcoef'],
self.cfg['VAD']['ffnn']['numchans'], self.cfg['VAD']['ffnn']['ceplifter'],
self.cfg['VAD']['ffnn']['numceps'], self.cfg['VAD']['ffnn']['enormalise'],
self.cfg['VAD']['ffnn']['zmeansource'], self.cfg['VAD']['ffnn']['usepower'],
self.cfg['VAD']['ffnn']['usec0'], self.cfg['VAD']['ffnn']['usecmn'],
self.cfg['VAD']['ffnn']['usedelta'], self.cfg['VAD']['ffnn']['useacc'],
self.cfg['VAD']['ffnn']['n_last_frames']+self.cfg['VAD']['ffnn']['n_prev_frames'],
self.cfg['VAD']['ffnn']['lofreq'], self.cfg['VAD']['ffnn']['hifreq'],
self.cfg['VAD']['ffnn']['mel_banks_only'])
else:
raise ASRException('Unsupported frontend: %s' % (self.cfg['VAD']['ffnn']['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']['ffnn']['framesize']:
frame = self.audio_recorded_in[:self.cfg['VAD']['ffnn']['framesize']]
self.audio_recorded_in = self.audio_recorded_in[self.cfg['VAD']['ffnn']['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