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'