def test(self, inputs, label, timestep):
'''Test model.
param timestep: split into segments of length timestep.
'''
for embed in self.embeds:
embed.eval()
input = Variable(inputs[0].cuda(), requires_grad=False)
label = Variable(label.cuda(), requires_grad=False)
length = input.size(1)
# Split video into segments
input, start_indices = utils.get_segments(input, timestep)
inputs = [input]
logits, _ = self._forward(inputs)
logits = utils.to_numpy(logits).squeeze(0)
all_logits = [[] for i in range(length)]
for i in range(len(start_indices)):
s = start_indices[i]
for j in range(timestep):
all_logits[s + j].append(logits[i][j])
# Average logits for each time step.
final_logits = np.zeros((length, self.n_classes + 1))
for i in range(length):
final_logits[i] = np.mean(all_logits[i], axis=0)
logits = final_logits
info_acc = self._get_acc([torch.Tensor(logits)], label)
scores = utils.softmax(logits, axis=1)
return OrderedDict(info_acc), logits, scores
def perform_vad(wav_file, segs_file, speech_label, **kwargs):
"""Perform VAD for WAV file.
If an exception is raised during processing, it returns the exception as well as
the full traceback. Otherwise, returns ``None``.
Parameters
----------
wav_file : str
Path to WAV file to perform VAD for.
segs_file : str
Path to output segments file.
speech_label : str
Label for speech segments.
kwargs
Keyword arguments to pass to ``utils.vad``.
"""
try:
data, fs = librosa.load(wav_file, sr=None)
vad_info = utils.vad(data, fs, **kwargs)
segments = utils.get_segments(vad_info, fs)
utils.write_segments(segs_file, segments, label=speech_label)
return None
except Exception as e:
tb = traceback.format_exc()
return e, tb
def main_vad(wav_dir ,mode, hop):
if not os.path.exists(wav_dir):
raise RuntimeError("cannot locate the original dictionary !")
wav_files = [os.path.join(wav_dir,line) for line in os.listdir(wav_dir) ]
for wav in wav_files:
if wav.endswith('.wav'):
data, fs = load(wav, sr=16000)
vad_info = utils.vad(data, fs, fs_vad = 16000, hoplength = hop, vad_mode=mode)
segments = utils.get_segments(vad_info,fs)
output_file = open(wav.replace('.wav','.sad' ),'w')
for i in range(segments.shape[0]):
start_time = segments[i][0]
end_time = segments[i][1]
output_file.write( "%.3f %.3f \n" %(start_time, end_time) )
def preprocess(file, BATCH_SIZE, max_length, tokenizer):
train_dataset = []
input_vocab_size = len(tokenizer.vocab)
f = open(file, 'r')
words = f.read()
words = words.replace('\n\n', '.')
words = words.replace('\n', ' ')
words = re.split('[;:.!?]', words)
i = 0
for _ in range(len(words) // BATCH_SIZE + 1):
if i + 1 >= len(words):
break
input_ids_list = []
segment_list = []
is_masked_list = []
is_next_list = []
for j in range(BATCH_SIZE):
if i + 1 >= len(words):
break
now = int(
random.random() > 0.5
) # decide if the 2nd sentence has to be next sentence or not
if now == 1:
res = ["[CLS]"] + tokenizer.tokenize(words[i]) + [
"[SEP]"
] + tokenizer.tokenize(words[i + 1]) + ["[SEP]"]
else:
res = ["[CLS]"] + tokenizer.tokenize(
words[i]) + ["[SEP]"] + tokenizer.tokenize(
words[random.randint(0,
len(words) - 1)]) + ["[SEP]"]
input_ids = get_ids(res, tokenizer, max_length)
segment_list.append(get_segments(res, max_length))
is_next_list.append(now)
is_masked = [0] * max_length
for ind in range(max_length):
if input_ids[ind] == 0: # is padding token appears, then break
break
if input_ids[ind] == 101 or input_ids[
ind] == 102: # don't mask [CLS] and [SEP] tokens
continue
if random.random() < 0.15: # mask 15% of tokens
is_masked[ind] = input_ids[ind]
if random.random() < 0.8: # out of 15%, mask 80%
input_ids[ind] = 103
elif random.random(
) < 0.5: # replace 10% with random token
input_ids[ind] = random.randint(1000, input_vocab_size)
#in the remaining tokens, keep the same token
input_ids_list.append(input_ids)
is_masked_list.append(is_masked)
if now == 1:
i += 2
else:
i += 1
input_ids_list = np.array(input_ids_list)
is_masked_list = np.array(is_masked_list)
masks = create_padding_mask(input_ids_list)
segment_list = np.array(segment_list)
is_next_list = np.array(is_next_list)
is_next_list = np.reshape(is_next_list, (len(is_next_list), 1))
train_dataset.append([
input_ids_list, segment_list, masks, is_next_list, is_masked_list
])
return train_dataset
FS = 300
LENGTH = 9000
LR = 1e-3
RESAMP = False
#
try:
segments = np.load('../data/segment.npy')
except:
signals, labels = get_ecg(PATH, length=LENGTH)
segments = np.zeros((245990, 1001))
k = 0
for i, record in enumerate(signals):
rp = qrs_detection(record, sample_rate=FS)
seg = get_segments(record, rp, labels[i])
if seg is not None:
segments[k:k + seg.shape[0], :] = seg
k += seg.shape[0]
del signals, labels
np.save('./data/segment.npy', segments)
X, y = segments[:, :-1], segments[:, -1][:, np.newaxis]
del segments
train, test = build_dataloader(X, y, resamp=RESAMP, batch_size=BATCH_SIZE)
del X, y
net = cnn_feed_lstm()
try:
help='path of a text file with a word list')
parser.add_argument('-c', '--config', help='path of config file')
parser.add_argument('-f',
'--frequency',
action='store_true',
help='display frequency of segments')
parser.add_argument(
'-e',
'--check-inventory',
action='store_true',
help='display if the segment belongs to the segment inventory or not')
parser.add_argument('--version', action='version', version='%(prog)s 1.0')
args = parser.parse_args()
configPath = '../config.cfg' if args.config == None else args.config
config = configparser.ConfigParser()
config.read_file(open(configPath))
lst_charGroups = config.get('SETTINGS', 'complexCharList').split()
lst_phonInventory = config.get('SETTINGS', 'validSegmentList').split()
# Run command
lst_wordList = utils.get_wordlist_from_raw_text(args.input_file)
utils.get_segments(lst_wordList=lst_wordList,
lst_charGroups=lst_charGroups,
lst_phonInventory=lst_phonInventory,
display_frequencyColumn=args.frequency,
display_isInInventoryColumn=args.check_inventory)
def _valid_file(filename, threshold=.1):
try:
return utils.get_segments(filename)[-1] > threshold
except:
return False
def new_dream(audio_memory):
#import matplotlib.pyplot as plt
#plt.ion()
try:
print 'Dreaming - removing wrongly binned filenames'
mega_filenames_and_indexes = []
for audio_id, audio_segments in audio_memory.audio_ids.iteritems():
NAP_detail = 'low'
filenames_and_indexes = []
for audio_segment in audio_segments:
segstart, segend = audio_segment.segment_idxs
audio_times = utils.get_segments(audio_segments.wav_file)
norm_segstart = segstart/audio_times[-1]
norm_segend = segend/audio_times[-1]
filenames_and_indexes.append([ soundfile, norm_segstart, norm_segend, audio_id, NAP_detail ])
mega_filenames_and_indexes.extend(filenames_and_indexes)
k = 2
print 'Examining audio_id {}'.format(audio_id)
if len(audio_segments) == 1:
print 'Just one member in this audio_id, skipping analysis'
continue
sparse_codes = mysai.experiment(filenames_and_indexes, k)
# plt.matshow(sparse_codes, aspect='auto')
# plt.colorbar()
# plt.draw()
coarse = np.mean(sparse_codes, axis=1)
coarse.shape = (len(coarse), 1)
codebook,_ = kmeans(coarse, k)
instances = [ vq(np.atleast_2d(s), codebook)[0] for s in coarse ]
freqs = itemfreq(instances)
sorted_freqs = sorted(freqs, key=lambda x: x[1])
print 'Average sparse codes: {} Class count: {}'.format(list(itertools.chain.from_iterable(coarse)), sorted_freqs)
if len(sorted_freqs) == 1:
print 'Considered to be all the same.'
continue
fewest_class = sorted_freqs[0][0]
ousted_audio_segments = [ audio_segment for audio_segment, i in zip(audio_segments, instances) if i == fewest_class ]
print 'Class {} has fewest members, deleting audio_segments {}'.format(fewest_class, ousted_audio_segments)
filter(audio_memory.forget, ousted_audio_segments)
print 'Creating mega super self-organized class'
for row in mega_filenames_and_indexes:
row[-1] = 'high'
high_resolution_k = 256
clusters = 24
sparse_codes = mysai.experiment(mega_filenames_and_indexes, high_resolution_k)
sparse_codes = np.array(sparse_codes)
# plt.matshow(sparse_codes, aspect='auto')
# plt.colorbar()
# plt.draw()
codebook,_ = kmeans(sparse_codes, clusters)
instances = [ vq(np.atleast_2d(s), codebook)[0] for s in sparse_codes ]
cluster_list = {}
for mega, instance in zip(mega_filenames_and_indexes, instances):
soundfile,_,_,audio_id,_ = mega
cluster_list[(soundfile, audio_id)] = instance
print cluster_list
except:
utils.print_exception('NIGHTMARE!')
def new_learn_audio(host, debug=False):
context = zmq.Context()
mic = context.socket(zmq.SUB)
mic.connect('tcp://{}:{}'.format(host, IO.MIC))
mic.setsockopt(zmq.SUBSCRIBE, b'')
dreamQ = context.socket(zmq.PUSH)
dreamQ.connect('tcp://{}:{}'.format(host, IO.DREAM))
stateQ, eventQ, brainQ = _three_amigos(context, host)
sender = context.socket(zmq.PUSH)
sender.connect('tcp://{}:{}'.format(host, IO.EXTERNAL))
counterQ = context.socket(zmq.REQ)
counterQ.connect('tcp://{}:{}'.format(host, IO.COUNTER))
poller = zmq.Poller()
poller.register(mic, zmq.POLLIN)
poller.register(stateQ, zmq.POLLIN)
poller.register(eventQ, zmq.POLLIN)
audio = deque()
NAPs = []
wavs = []
wav_audio_ids = {}
NAP_hashes = {}
audio_classifier = []
audio_recognizer = []
global_audio_recognizer = []
mixture_audio_recognizer = []
maxlen = []
deleted_ids = []
state = stateQ.recv_json()
black_list = open('black_list.txt', 'a')
audio_memory = AudioMemory()
if debug:
import matplotlib.pyplot as plt
plt.ion()
while True:
events = dict(poller.poll())
if stateQ in events:
state = stateQ.recv_json()
if mic in events:
new_audio = utils.recv_array(mic)
if state['record']:
audio.append(new_audio)
if eventQ in events:
pushbutton = eventQ.recv_json()
if 'learn' in pushbutton:
try:
t0 = time.time()
filename = pushbutton['filename']
audio_segments = utils.get_segments(filename)
print 'Learning {} duration {} seconds with {} segments'.format(filename, audio_segments[-1], len(audio_segments)-1)
new_sentence = utils.csv_to_array(filename + 'cochlear')
norm_segments = np.rint(new_sentence.shape[0]*audio_segments/audio_segments[-1]).astype('int')
audio_ids = []
new_audio_hash = []
amps = utils.get_amps(filename)
most_significant_value = -np.inf
most_significant_audio_id = []
original_NAP_length = len(NAPs)
for segment, new_sound in enumerate([ utils.trim_right(new_sentence[norm_segments[i]:norm_segments[i+1]]) for i in range(len(norm_segments)-1) ]):
# We filter out short, abrupt sounds with lots of noise.
if np.mean(new_sound) < 2 or new_sound.shape[0] == 0:
black_list.write('{} {}\n'.format(filename, segment))
print 'BLACKLISTED segment {} in file {}'.format(segment, filename)
continue
if debug:
utils.plot_NAP_and_energy(new_sound, plt)
audio_id = audio_memory.learn(new_sound, filename, [ audio_segments[segment], audio_segments[segment+1] ])
# START LEGACY
try:
wavs[audio_id].append(filename)
except:
wavs.append([filename])
wav_audio_ids[(filename, audio_id)] = [ audio_segments[segment], audio_segments[segment+1] ]
# END LEGACY
audio_ids.append(audio_id)
if amps[segment] > most_significant_value:
most_significant_audio_id = audio_id
most_significant_value = amps[segment]
black_list.flush()
print 'AUDIO IDs after blacklisting {}'. format(audio_ids)
if len(audio_ids):
# while len(NAPs) - len(deleted_ids) > AUDIO_MEMORY_SIZE:
# utils.delete_loner(counterQ, NAPs, 'audio_ids_counter', int(AUDIO_MEMORY_SIZE*PROTECT_PERCENTAGE), deleted_ids)
# maxlen = max([ m.shape[0] for memory in NAPs for m in memory if len(m) ])
# memories = [ np.ndarray.flatten(utils.zero_pad(m, maxlen)) for memory in NAPs for m in memory if len(m) ]
# targets = [ i for i,f in enumerate(NAPs) for k in f if len(k) ]
# audio_classifier = train_rPCA_SVM(memories, targets)
# all_hammings = [ utils.hamming_distance(new_audio_hash[i], new_audio_hash[j])
# for i in range(len(new_audio_hash)) for j in range(len(new_audio_hash)) if i > j ]
# print 'RHYME VALUE', np.mean(sorted(all_hammings)[int(len(all_hammings)/2):])
# rhyme = np.mean(sorted(all_hammings)[int(len(all_hammings)/2):]) < RHYME_HAMMERTIME
# sender.send_json('rhyme {}'.format(rhyme))
brainQ.send_pyobj(['audio_learn', filename, audio_ids, audio_memory, most_significant_audio_id, wavs, wav_audio_ids])
print 'Audio learned from {} in {} seconds'.format(filename, time.time() - t0)
else:
print 'SKIPPING fully blacklisted file {}'.format(filename)
except:
utils.print_exception('Audio learning aborted.')
audio.clear()
if 'dream' in pushbutton:
new_dream(audio_memory)
if 'save' in pushbutton:
utils.save('{}.{}'.format(pushbutton['save'], mp.current_process().name), [ deleted_ids, NAPs, wavs, wav_audio_ids, NAP_hashes, audio_classifier, maxlen, audio_memory ])
if 'load' in pushbutton:
deleted_ids, NAPs, wavs, wav_audio_ids, NAP_hashes, audio_classifier, maxlen, audio_memory = utils.load('{}.{}'.format(pushbutton['load'], mp.current_process().name))
def new_respond(control_host, learn_host, debug=False):
context = zmq.Context()
eventQ = context.socket(zmq.SUB)
eventQ.connect('tcp://{}:{}'.format(control_host, IO.EVENT))
eventQ.setsockopt(zmq.SUBSCRIBE, b'')
projector = context.socket(zmq.PUSH)
projector.connect('tcp://{}:{}'.format(control_host, IO.PROJECTOR))
sender = context.socket(zmq.PUSH)
sender.connect('tcp://{}:{}'.format(control_host, IO.EXTERNAL))
brainQ = context.socket(zmq.PULL)
brainQ.bind('tcp://*:{}'.format(IO.BRAIN))
counterQ = context.socket(zmq.REQ)
counterQ.connect('tcp://{}:{}'.format(control_host, IO.COUNTER))
cognitionQ = context.socket(zmq.PUSH)
cognitionQ.connect('tcp://{}:{}'.format(control_host, IO.COGNITION))
association = context.socket(zmq.REQ)
association.connect('tcp://{}:{}'.format(learn_host, IO.ASSOCIATION))
snapshot = context.socket(zmq.REQ)
snapshot.connect('tcp://{}:{}'.format(control_host, IO.SNAPSHOT))
scheduler = context.socket(zmq.PUSH)
scheduler.connect('tcp://{}:{}'.format(control_host, IO.SCHEDULER))
dreamQ = context.socket(zmq.PULL)
dreamQ.bind('tcp://*:{}'.format(IO.DREAM))
snapshot.send_json('Give me state!')
state = snapshot.recv_json()
poller = zmq.Poller()
poller.register(eventQ, zmq.POLLIN)
poller.register(brainQ, zmq.POLLIN)
poller.register(dreamQ, zmq.POLLIN)
sound_to_face = []
wordFace = {}
face_to_sound = []
faceWord = {}
register = {}
video_producer = {}
voiceType1 = 1
voiceType2 = 6
wordSpace1 = 0.3
wordSpaceDev1 = 0.3
wordSpace2 = 0.1
wordSpaceDev2 = 0.3
audio_ids = []
wavs = []
wav_audio_ids = []
NAP_hashes = {}
most_significant_audio_id = []
if debug:
import matplotlib.pyplot as plt
plt.ion()
while True:
events = dict(poller.poll())
if brainQ in events:
cells = brainQ.recv_pyobj()
mode = cells[0]
wav_file = cells[1]
if wav_file not in register:
register[wav_file] = [False, False, False]
if mode == 'audio_learn':
register[wav_file][0] = cells
if mode == 'video_learn':
register[wav_file][1] = cells
if mode == 'face_learn':
register[wav_file][2] = cells
if all(register[wav_file]):
_, _, audio_ids, audio_memory, most_significant_audio_id, wavs, wav_audio_ids = register[wav_file][0]
_, _, tarantino = register[wav_file][1]
_, _, face_id, face_recognizer = register[wav_file][2]
print 'Audio - video - face recognizers related to {} arrived at responder, total processing time {} seconds'.format(wav_file, time.time() - utils.filetime(wav_file))
for audio_id in audio_ids: # If audio_ids is empty, none of this will happen
video_producer[(audio_id, face_id)] = tarantino
if audio_id < len(sound_to_face) and not face_id in sound_to_face[audio_id]: # sound heard before, but not said by this face
sound_to_face[audio_id].append(face_id)
if audio_id == len(sound_to_face):
sound_to_face.append([face_id])
wordFace.setdefault(audio_id, [[face_id,0]])
found = 0
for item in wordFace[audio_id]:
if item[0] == face_id:
item[1] += 1
found = 1
if found == 0:
wordFace[audio_id].append([face_id,1])
# We can't go from a not known face to any of the sounds, that's just the way it is.
print 'face_id for audio segment learned', face_id
if face_id is not -1:
if face_id < len(face_to_sound) and not audio_id in face_to_sound[face_id]: #face seen before, but the sound is new
face_to_sound[face_id].append(audio_id)
if face_id == len(face_to_sound):
face_to_sound.append([audio_id])
faceWord.setdefault(face_id, [[audio_id,0]])
found = 0
for item in faceWord[face_id]:
if item[0] == audio_id:
item[1] += 1
found = 1
if found == 0:
faceWord[face_id].append([audio_id,1])
del register[wav_file]
similar_ids = []
for audio_id in audio_ids:
# I SUSPECT THIS IS WRONG, SINCE THERE IS NO SORTING OF THESE HAMMING DISTANCES IN ASSOCIATION.PY
new_audio_hash = audio_memory.audio_ids[audio_id][-1].crude_hash
similar_ids_for_this_audio_id = [ utils.hamming_distance(new_audio_hash, random.choice(h).crude_hash) for h in audio_memory.audio_ids.itervalues() ]
similar_ids.append(similar_ids_for_this_audio_id)
if len(audio_ids):
association.send_pyobj(['analyze',wav_file,wav_audio_ids,audio_ids,wavs,similar_ids,wordFace,faceWord])
association.recv_pyobj()
sender.send_json('last_most_significant_audio_id {}'.format(most_significant_audio_id))
cognitionQ.send_pyobj(face_recognizer) # A possiblity of recognizing a face that is not connecting to any soundfiles
if eventQ in events:
pushbutton = eventQ.recv_json()
if 'respond_single' in pushbutton:
try:
filename = pushbutton['filename']
audio_segments = utils.get_segments(filename)
print 'Single response to {} duration {} seconds with {} segments'.format(filename, audio_segments[-1], len(audio_segments)-1)
new_sentence = utils.csv_to_array(filename + 'cochlear')
norm_segments = np.rint(new_sentence.shape[0]*audio_segments/audio_segments[-1]).astype('int')
segment_id = utils.get_most_significant_word(filename)
NAP = utils.trim_right(new_sentence[norm_segments[segment_id]:norm_segments[segment_id+1]])
if debug:
plt.imshow(NAP.T, aspect='auto')
plt.draw()
best_match,_,_,_,_ = audio_memory.find(NAP)
soundfile = best_match.wav_file
segstart, segend = best_match.segment_idxs
voiceChannel = 1
speed = 1
amp = -3 # voice amplitude in dB
_,dur,maxamp,_ = utils.getSoundInfo(soundfile)
start = 0
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(1, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
voice2 = ''
print 'Recognized as sound {}'.format(best_match.audio_id)
# sound_to_face, video_producer
projection = _project(best_match.audio_id, sound_to_face, NAP, video_producer)
scheduler.send_pyobj([[ dur, voice1, voice2, projection, FRAME_SIZE ]])
print 'Respond time from creation of wav file was {} seconds'.format(time.time() - utils.filetime(filename))
except:
utils.print_exception('Single response aborted.')
if 'play_sentence' in pushbutton:
try:
sentence = pushbutton['sentence']
sentence = eval(sentence)
print '*** (play) Play sentence', sentence
start = 0
nextTime1 = 0
play_events = []
for i in range(len(sentence)):
word_id = sentence[i]
soundfile = np.random.choice(wavs[word_id])
speed = 1
segstart, segend = wav_audio_ids[(soundfile, word_id)]
NAP = _extract_NAP(segstart, segend, soundfile)
amp = -3 # voice amplitude in dB
_,totaldur,maxamp,_ = utils.getSoundInfo(soundfile)
dur = segend-segstart
if dur <= 0: dur = totaldur
# play in both voices
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(1, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
voice2 = 'playfile {} {} {} {} {} {} {} {} {}'.format(2, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
wordSpacing1 = wordSpace1 + np.random.random()*wordSpaceDev1
print 'PLAY RESPOND SPACING', wordSpacing1
nextTime1 += (dur/speed)+wordSpacing1
projection = _project(audio_id, sound_to_face, NAP, video_producer)
play_events.append([ dur+wordSpacing1, voice1, voice2, projection, FRAME_SIZE ])
scheduler.send_pyobj(play_events)
except:
utils.print_exception('Sentence play aborted.')
if 'respond_sentence' in pushbutton:
print 'SENTENCE Respond to', pushbutton['filename'][-12:]
try:
filename = pushbutton['filename']
audio_segments = utils.get_segments(filename)
print 'Sentence response to {} duration {} seconds with {} segments'.format(filename, audio_segments[-1], len(audio_segments)-1)
new_sentence = utils.csv_to_array(filename + 'cochlear')
norm_segments = np.rint(new_sentence.shape[0]*audio_segments/audio_segments[-1]).astype('int')
segment_id = utils.get_most_significant_word(filename)
print '**Sentence selected to respond to segment {}'.format(segment_id)
NAP = utils.trim_right(new_sentence[norm_segments[segment_id]:norm_segments[segment_id+1]])
best_match,_,_,_,_ = audio_memory.find(NAP)
audio_id = best_match.audio_id
soundfile = best_match.wav_file
numWords = len(audio_segments)-1
print numWords
association.send_pyobj(['setParam', 'numWords', numWords ])
association.recv_pyobj()
association.send_pyobj(['makeSentence', audio_id])
print 'respond_sentence waiting for association output...',
sentence, secondaryStream = association.recv_pyobj()
print '*** (respond) Play sentence', sentence, secondaryStream
start = 0
nextTime1 = 0
nextTime2 = 0
enableVoice2 = 1
play_events = []
for i in range(len(sentence)):
word_id = sentence[i]
soundfile = np.random.choice(wavs[word_id])
voiceChannel = 1
speed = 1
# segment start and end within sound file, if zero, play whole file
segstart, segend = wav_audio_ids[(soundfile, word_id)]
NAP = _extract_NAP(segstart, segend, soundfile)
amp = -3 # voice amplitude in dB
#totaldur, maxamp = utils.getSoundParmFromFile(soundfile)
_,totaldur,maxamp,_ = utils.getSoundInfo(soundfile)
dur = segend-segstart
if dur <= 0: dur = totaldur
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(voiceChannel, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
#start += dur # if we want to create a 'score section' for Csound, update start time to make segments into a contiguous sentence
wordSpacing1 = wordSpace1 + np.random.random()*wordSpaceDev1
nextTime1 += (dur/speed)+wordSpacing1
#print 'voice 2 ready to play', secondaryStream[i], i
if enableVoice2:
word_id2 = secondaryStream[i]
#print 'voice 2 playing', secondaryStream[i]
soundfile2 = np.random.choice(wavs[word_id2])
voiceChannel2 = 2
start2 = 0.7 # set delay between voice 1 and 2
speed2 = 0.7
amp2 = -10 # voice amplitude in dB
try:
segstart2, segend2 = wav_audio_ids[(soundfile2, word_id2)]
dur2 = segend2-segstart2
#totalDur2, maxamp2 = utils.getSoundParmFromFile(soundfile2)
_,totalDur2,maxamp2,_ = utils.getSoundInfo(soundfile)
if dur2 <= 0: dur2 = totalDur2
voice2 = 'playfile {} {} {} {} {} {} {} {} {}'.format(voiceChannel2, voiceType2, start2, soundfile2, speed2, segstart2, segend2, amp2, maxamp2)
wordSpacing2 = wordSpace2 + np.random.random()*wordSpaceDev2
nextTime2 += (dur2/speed2)+wordSpacing2
except:
voice2 = ''
utils.print_exception('VOICE 2 tried to access an illegal soundfile/audio_id combination.')
#enableVoice2 = 0
# trig another word in voice 2 only if word 2 has finished playing (and sync to start of voice 1)
if nextTime1 > nextTime2: enableVoice2 = 1
projection = _project(audio_id, sound_to_face, NAP, video_producer)
print 'SENTENCE RESPOND SPACING', wordSpacing1
play_events.append([ dur+wordSpacing1, voice1, voice2, projection, FRAME_SIZE ])
scheduler.send_pyobj(play_events)
print 'Sentence respond time from creation of wav file was {} seconds'.format(time.time() - utils.filetime(filename))
except:
utils.print_exception('Sentence response aborted.')
if 'testSentence' in pushbutton:
print 'testSentence', pushbutton
association.send_pyobj(['makeSentence',int(pushbutton['testSentence'])])
print 'testSentence waiting for association output...'
sentence, secondaryStream = association.recv_pyobj()
print '*** Test sentence', sentence, secondaryStream
if 'assoc_setParam' in pushbutton:
try:
parm, value = pushbutton['assoc_setParam'].split()
association.send_pyobj(['setParam', parm, value ])
association.recv_pyobj()
except:
utils.print_exception('Assoc set param aborted.')
if 'respond_setParam' in pushbutton:
items = pushbutton['respond_setParam'].split()
if items[0] == 'voiceType':
chan = items[1]
if chan == '1': voiceType1 = int(items[2])
if chan == '2': voiceType2 = int(items[2])
if items[0] == 'wordSpace':
chan = items[1]
print 'wordSpace chan', chan, items
if chan == '1': wordSpace1 = float(items[2])
if chan == '2': wordSpace2 = float(items[2])
if items[0] == 'wordSpaceDev':
chan = items[1]
print 'wordSpaceDev1 chan', chan, items
if chan == '1': wordSpaceDev1 = float(items[2])
if chan == '2': wordSpaceDev2 = float(items[2])
if 'play_id' in pushbutton:
try:
items = pushbutton['play_id'].split(' ')
if len(items) < 3: print 'PARAMETER ERROR: play_id audio_id voiceChannel voiceType'
play_audio_id = int(items[0])
voiceChannel = int(items[1])
voiceType = int(items[2])
print 'play_audio_id', play_audio_id, 'voice', voiceChannel
print 'wavs[play_audio_id]', wavs[play_audio_id]
#print wavs
soundfile = np.random.choice(wavs[play_audio_id])
speed = 1
#print 'wav_audio_ids', wav_audio_ids
segstart, segend = wav_audio_ids[(soundfile, play_audio_id)]
#segstart = 0 # segment start and end within sound file
#segend = 0 # if zero, play whole file
amp = -3 # voice amplitude in dB
#dur, maxamp = utils.getSoundParmFromFile(soundfile)
_,dur,maxamp,_ = utils.getSoundInfo(soundfile)
start = 0
sender.send_json('playfile {} {} {} {} {} {} {} {} {}'.format(voiceChannel, voiceType, start, soundfile, speed, segstart, segend, amp, maxamp))
except:
utils.print_exception('play_id aborted.')
if 'print_me' in pushbutton:
# just for inspecting the contents of objects while running
print 'printing '+pushbutton['print_me']
if 'brain ' in pushbutton['print_me']:
print_variable = pushbutton['print_me'].split('brain ')[-1]
try:
print eval(print_variable)
except Exception, e:
print e, 'print_me in brain failed.'
elif 'association ' in pushbutton['print_me']:
print_variable = pushbutton['print_me'].split('association ')[-1]
association.send_pyobj(['print_me',print_variable])
if 'dream' in pushbutton:
play_events = []
for audio_segment in audio_memory.all_segments():
segstart, segend = audio_segment.segment_idxs
dur = segend - segstart
NAP = _extract_NAP(segstart, segend, audio_segment.wav_file)
speed = 1
amp = -3
maxamp = 1
start = 0
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(1, 6, np.random.rand()/3, audio_segment.wav_file, speed, segstart, segend, amp, maxamp)
projection = _project(audio_segment.audio_id, sound_to_face, NAP, video_producer)
voice2 = 'playfile {} {} {} {} {} {} {} {} {}'.format(2, 6, np.random.randint(3,6), audio_segment.wav_file, speed, segstart, segend, amp, maxamp)
play_events.append([ dur, voice1, voice2, projection, FRAME_SIZE ])
print 'Dream mode playing back {} memories'.format(len(play_events))
scheduler.send_pyobj(play_events)
if 'save' in pushbutton:
utils.save('{}.{}'.format(pushbutton['save'], mp.current_process().name), [ sound_to_face, wordFace, face_to_sound, faceWord, video_producer, wavs, wav_audio_ids, audio_classifier, maxlen, NAP_hashes, face_id, face_recognizer, audio_memory ])
if 'load' in pushbutton:
sound_to_face, wordFace, face_to_sound, faceWord, video_producer, wavs, wav_audio_ids, audio_classifier, maxlen, NAP_hashes, face_id, face_recognizer, audio_memory = utils.load('{}.{}'.format(pushbutton['load'], mp.current_process().name))
def _valid_file(filename, threshold=.1):
try:
return utils.get_segments(filename)[-1] > threshold
except:
return False