def _cochlear_trim_sai_marginals(filename_and_indexes):
try:
filename, norm_segstart, norm_segend, audio_id, NAP_detail = filename_and_indexes
sai_video_filename = '{}_sai_video_{}'.format(filename, NAP_detail)
if os.path.isfile('{}.npy'.format(sai_video_filename)):
return sai_video_filename
if NAP_detail == 'high':
try:
NAP = utils.csv_to_array(filename + 'cochlear' + NAP_detail)
except:
NAP = brain.cochlear(filename,
stride=1,
rate=44100,
apply_filter=0,
suffix='cochlear' + NAP_detail)
if NAP_detail == 'low':
try:
NAP = utils.csv_to_array(filename + 'cochlear' + NAP_detail)
except:
NAP = brain.cochlear(
filename,
stride=IO.NAP_STRIDE,
rate=IO.NAP_RATE,
apply_filter=0,
suffix='cochlear' + NAP_detail
) # Seems to work best, in particular when they are all the same.
num_channels = NAP.shape[1]
input_segment_width = 2048
sai_params = CreateSAIParams(num_channels=num_channels,
input_segment_width=input_segment_width,
trigger_window_width=input_segment_width,
sai_width=1024)
sai = pysai.SAI(sai_params)
NAP = utils.trim_right(NAP[np.int(np.rint(NAP.shape[0] *
norm_segstart)):np.
int(np.rint(NAP.shape[0] * norm_segend))],
threshold=.05)
sai_video = [
np.copy(sai.RunSegment(input_segment.T))
for input_segment in utils.chunks(NAP, input_segment_width)
]
del NAP
np.save(sai_video_filename,
np.array([sai_rectangles(frame) for frame in sai_video]))
return sai_video_filename
except:
print utils.print_exception(
'Calculation SAI video failed for file {}, NAP detail {}'.format(
filename, NAP_detail))
return False
def _cochlear_trim_sai_marginals(filename_and_indexes):
try:
filename, norm_segstart, norm_segend, audio_id, NAP_detail = filename_and_indexes
sai_video_filename = '{}_sai_video_{}'.format(filename, NAP_detail)
if os.path.isfile('{}.npy'.format(sai_video_filename)):
return sai_video_filename
if NAP_detail == 'high':
try:
NAP = utils.csv_to_array(filename+'cochlear'+NAP_detail)
except:
NAP = brain.cochlear(filename, stride=1, rate=44100, apply_filter=0, suffix='cochlear'+NAP_detail)
if NAP_detail == 'low':
try:
NAP = utils.csv_to_array(filename+'cochlear'+NAP_detail)
except:
NAP = brain.cochlear(filename, stride=IO.NAP_STRIDE, rate=IO.NAP_RATE, apply_filter=0, suffix='cochlear'+NAP_detail) # Seems to work best, in particular when they are all the same.
num_channels = NAP.shape[1]
input_segment_width = 2048
sai_params = CreateSAIParams(num_channels=num_channels,
input_segment_width=input_segment_width,
trigger_window_width=input_segment_width,
sai_width=1024)
sai = pysai.SAI(sai_params)
NAP = utils.trim_right(NAP[ np.int(np.rint(NAP.shape[0]*norm_segstart)) : np.int(np.rint(NAP.shape[0]*norm_segend)) ], threshold=.05)
sai_video = [ np.copy(sai.RunSegment(input_segment.T)) for input_segment in utils.chunks(NAP, input_segment_width) ]
del NAP
np.save(sai_video_filename, np.array([ sai_rectangles(frame) for frame in sai_video ]))
return sai_video_filename
except:
print utils.print_exception('Calculation SAI video failed for file {}, NAP detail {}'.format(filename, NAP_detail))
return False
def parse(self, message):
print '[self.] received: {}'.format(message)
black_list = []
try:
# if 'learnwav' in message or 'respondwav_single' in message or 'respondwav_sentence' in message:
# _, filename = message.split()
# if filename in black_list:
# print 'SKIPPING BAD FILE {}'.format(filename)
# return
if message == 'dream':
self.state['memoryRecording'] = False
self.state['autorespond_sentence'] = False
self.state['ambientSound'] = False
self.state['autolearn'] = False
self.state['autorespond_single'] = False
self.state['_audioLearningStatus'] = False
self.state['record'] = False
self.publisher.send_json(self.state)
self.event.send_json({'dream': True})
if message == 'reboot':
utils.reboot()
if message == 'appendCurrentSettings':
self.association.send_pyobj([message])
self.association.recv_pyobj()
if 'getCurrentSettings' in message:
msg, value = message.split()
self.association.send_pyobj([msg, value])
self.association.recv_pyobj()
if 'i_am_speaking' in message:
_, value = message.split()
self.state['i_am_speaking'] = value in ['True', '1']
if 'enable_say_something' in message:
_, value = message.split()
self.state['enable_say_something'] = value in ['True', '1']
if 'last_segment_ids' in message:
the_ids = message[17:]
self.event.send_json({'last_segment_ids': loads(the_ids) })
if 'last_most_significant_audio_id' in message:
audio_id = message[31:]
self.event.send_json({'last_most_significant_audio_id': audio_id })
if message == 'clear play_events':
self.event.send_json({'clear play_events' : 'True'})
if 'calculate_cochlear' in message:
_, wav_file = message.split()
t0 = time.time()
try:
brain.cochlear(utils.wait_for_wav(wav_file), stride=IO.NAP_STRIDE, rate=IO.NAP_RATE)
except:
print 'SHOULD {} BE BLACKLISTED?'.format(wav_file)
black_list.append(wav_file)
print 'Calculating cochlear neural activation patterns took {} seconds'.format(time.time() - t0)
if message == 'evolve':
self.state['memoryRecording'] = False
self.state['autorespond_sentence'] = False
self.state['autolearn'] = False
self.state['autorespond_single'] = False
self.state['_audioLearningStatus'] = False
self.state['record'] = False
self.publisher.send_json(self.state)
self.association.send_pyobj(['evolve'])
self.association.recv_pyobj()
if 'register' in message and 'BRAIN' in message:
_, name, free = message.split()
self.state['brains'][name] = int(free)
if 'fullscreen' in message:
_, value = message.split()
self.event.send_json({ 'fullscreen': value in ['True', '1'] })
if 'display2' in message:
_, value = message.split()
self.event.send_json({ 'display2': value in ['True', '1'] })
if message == 'startrec':
self.state['record'] = True
if message == 'stoprec':
self.state['record'] = False
if 'facerecognition' in message:
_, value = message.split()
self.state['facerecognition'] = value in ['True', '1']
if 'print_me' in message:
self.event.send_json({ 'print_me': message[7:] })
if 'play_id' in message:
self.event.send_json({ 'play_id': message[8:] })
if 'testSentence' in message:
self.event.send_json({ 'testSentence': message[13:] })
if 'assoc_setPlotting' in message:
self.event.send_json({ 'assoc_setPlotting': message[18:] in ['True', '1'] })
if 'assoc_setParam' in message:
self.event.send_json({ 'assoc_setParam': message[15:] })
if 'respond_setParam' in message:
self.event.send_json({ 'respond_setParam': message[17:] })
if 'memoryRecording' in message:
self.state['memoryRecording'] = message[16:] in ['True', '1']
if '_audioLearningStatus' in message:
self.state['_audioLearningStatus'] = message[21:] in ['True', '1']
if 'roboActive' in message:
self.state['roboActive'] = int(message[11:])
if 'ambientSound' in message:
self.state['ambientSound'] = int(message[13:])
if 'decrement' in message:
_, name = message.split()
self.state['brains'][name] -= 1
print '{} has now {} available slots'.format(name, self.state['brains'][name])
if 'learnwav' in message:
_, filename = message.split()
self.event.send_json({ 'learn': True, 'filename': filename })
if 'respondwav_single' in message:
_, filename = message.split()
self.event.send_json({ 'respond_single': True, 'filename': filename })
if 'respondwav_sentence' in message:
_, filename = message.split()
self.event.send_json({ 'respond_sentence': True, 'filename': filename })
if 'play_sentence' in message:
print 'playSentence', message
sentence = message[13:]
self.event.send_json({ 'play_sentence':True, 'sentence': sentence })
if 'rhyme' in message:
_, value = message.split()
self.event.send_json({'rhyme': value == 'True'})
if 'urge_to_say_something' in message:
_, value = message.split()
self.event.send_json({'urge_to_say_something': value})
if 'autolearn' in message:
self.state['autolearn'] = message[10:] in ['True', '1']
if 'autorespond_single' in message:
self.state['autorespond_single'] = message[19:] in ['True', '1']
if 'autorespond_sentence' in message:
self.state['autorespond_sentence'] = message[21:] in ['True', '1']
if 'inputLevel' in message:
self.event.send_json({ 'inputLevel': message[11:] })
if 'calibrateEq' in message:
self.event.send_json({ 'calibrateEq': True })
if 'calibrateAudio' in message:
latency_ok = False
try:
lat = open('roundtrip_latency.txt', 'r')
latency = float(lat.readline())
self.event.send_json({ 'setLatency': latency })
latency_ok = True
except Exception, e:
print 'Something went wrong when reading latency from file.', e
self.event.send_json({ 'calibrateAudio': True })
if latency_ok:
self.event.send_json({ 'calibrateNoiseFloor': True })
if 'calibrateAudio memoryRecording' in message:
self.state['memoryRecording'] = True
if 'csinstr' in message:
self.event.send_json({ 'csinstr': message[8:] })
if 'selfDucking' in message:
self.event.send_json({ 'selfDucking': message[12:] })
if 'zerochannels' in message:
self.event.send_json({ 'zerochannels': message[13:] })
if 'playfile' in message:
self.event.send_json({ 'playfile': message[9:] })
if 'selfvoice' in message:
self.event.send_json({ 'selfvoice': message[10:] })
if 'save' in message:
self.event.send_json({ 'save': utils.brain_name() if len(message) == 4 else message[5:] })
if 'load' in message:
if len(message) == 4:
brain_name = utils.find_last_valid_brain()
else:
_, brain_name = message.split()
if brain_name:
self.event.send_json({ 'load': brain_name })
self.publisher.send_json(self.state)
