def get_instances_from_dir(path):
filenames = os.listdir(path)
arrays = []
for filename in filenames:
if filename.endswith('.wav'):
f_name = path + filename
try:
vect = get_normalised_vector(filename=f_name)
except Exception, e:
continue
arrays.append(vect)
from sound_recorder import get_raw_wav_data
from yes_no_test import get_labelled_yn_data_for_person
from predictor import train_with_data
from vectoriser import get_normalised_vector
#1 - train classifier
training_vectors, training_labels = [], []
for person_index in xrange(1, 38):
vectors, labels = get_labelled_yn_data_for_person(person_index)
training_vectors += vectors
training_labels += labels
print 'training...'
classifier = train_with_data(training_vectors, training_labels)
while True:
#2 - get raw data
sample_rate, data = get_raw_wav_data()
#3 vectorise data
vector = get_normalised_vector(sample_rate, data)
#4 test data
prediction = classifier.predict(vector)[0]
print '\n\n\n\n\n'
if prediction == 1:
print 'yes'
else:
print 'no'
print '\n\n\n\n\n'
