def callback(recognizer, audio): try: sents = recognizer.recognize_google(audio , language = "en") print(sents) sents = precompute(sents) print(sents) for relation in listeRelation: rels = relation.extract(sents) for rel in rels: rel.post(); except sr.UnknownValueError: print("Google Speech Recognition could not understand audio") except sr.RequestError: print("Could not request results from Google Speech Recognition service")
import speech_recognition as sr import nltk from server import post_request from precompute import precompute from supportedRelations import * from supportedRelations import listeRelation, dic # sents = "The capital of France is Paris." # sents = "France's capital is Paris." # sents = "France's capital is Palaiseau." # sents = "Paris is the capital of France." # sents = "There are 70 million people in France." # sents = "There are more than 10 million people in France." # sents = "There are less than 10 million people in France." # sents = "population density of France is 100 inhabitants per square kilometer" # sents = "The gdp in France is 10 billion dollars" # sents = "France's import is 10 billion dollars" sents = "France has population density greater than 100 people per kilometer square" print("Analysed sentence : ", sents) sents = precompute(sents) print("Precomputed sentence : ", sents) for relation in listeRelation: rels = relation.extract(sents) for rel in rels: rel.post();
params_file_name = "{}_params_{}_{}_{}.npy".format(file_name, NUM_FOV, NUM_H, NUM_V) t1 = time.time() if os.path.exists(params_file_name): params = np.load(params_file_name) else: params = get_params(MIN_FOV, MAX_FOV, NUM_FOV, NUM_H, NUM_V) np.save(params_file_name, params) t2 = time.time() print(params.shape) print("Got params") table_file_name = "{}_table_{}_{}_{}_{}_{}.npy".format(file_name, table_type, SAMPLE_RATE, NUM_FOV, NUM_H, NUM_V) if os.path.exists(table_file_name): heat_table = np.load(table_file_name) elif table_type == 0: heat_table = precompute(data, file_name, SAMPLE_RATE, params, write_masks=True) np.save(file_name, heat_table) elif table_type == 1: temp_name = "{}_table_{}_{}_{}_{}_{}.npy".format(file_name, REGULAR, SAMPLE_RATE, NUM_FOV, NUM_H, NUM_V) if os.path.exists(temp_name): print("triggered") heat_table = np.load(temp_name) else: heat_table = precompute(data, file_name, SAMPLE_RATE, params, write_masks=True) np.save(temp_name, heat_table) heat_table = normalize_table(heat_table) np.save(table_file_name, heat_table) t3 = time.time() print("Parameter time: {}".format(t2 - t1))
if process_type == "OPF+OPF" or process_type == "OPF+SVM": distance_type = str(data[7]) distance_param = str(data[8]) print "Num K: ", num_k print "Thumbnail size: ", thumbnail_size print "Feature type: ", feature_type print "Descriptor type: ", descriptor_type print "Precomputing descriptors" pool = None if feature_type == "OVERFEAT" or descriptor_type == "OVERFEAT": pool = multiprocessing.Pool(processes=1) else: pool = multiprocessing.Pool() precompute.precompute(train_path, test_path, feature_type, descriptor_type, thumbnail_size, pool) pool.close() pool.terminate() if process_type == "KMeans+SVM": process_kmeans_svm(num_k=num_k, n_sample_images=n_sample_images, n_sample_descriptors=n_sample_descriptors, thumbnail_size=thumbnail_size, feature_type=feature_type, descriptor_type=descriptor_type) elif process_type == "KMeans+OPF": process_kmeans_opf(num_k=num_k, n_sample_images=n_sample_images, n_sample_descriptors=n_sample_descriptors, thumbnail_size=thumbnail_size, feature_type=feature_type, descriptor_type=descriptor_type) elif process_type == "OPF+OPF": process_opf_opf(num_k=num_k, n_sample_images=n_sample_images, n_sample_descriptors=n_sample_descriptors, thumbnail_size=thumbnail_size, feature_type=feature_type, descriptor_type=descriptor_type,
if process_type == "OPF+OPF" or process_type == "OPF+SVM": distance_type = str(data[7]) distance_param = str(data[8]) print "Num K: ", num_k print "Thumbnail size: ", thumbnail_size print "Feature type: ", feature_type print "Descriptor type: ", descriptor_type print "Precomputing descriptors" pool = None if feature_type == "OVERFEAT" or descriptor_type == "OVERFEAT": pool = multiprocessing.Pool(processes=1) else: pool = multiprocessing.Pool() precompute.precompute(train_path, test_path, feature_type, descriptor_type, thumbnail_size, pool) pool.close() pool.terminate() if process_type == "KMeans+SVM": process_kmeans_svm(num_k=num_k, n_sample_images=n_sample_images, n_sample_descriptors=n_sample_descriptors, thumbnail_size=thumbnail_size, feature_type=feature_type, descriptor_type=descriptor_type) elif process_type == "KMeans+OPF": process_kmeans_opf(num_k=num_k, n_sample_images=n_sample_images, n_sample_descriptors=n_sample_descriptors, thumbnail_size=thumbnail_size,