def load_audio_classifier(folder): print('Loading config folder: ' + folder) config_file, caffemodel_file, net_proto = get_filenames(folder) sbd.SbdConfig(config_file) temp_proto = make_audio_temp_deploy(folder, net_proto) net = caffe.Net(temp_proto, caffemodel_file, caffe.TEST) classifier = AudioClassifier(net) return classifier
def main(model_folder, example_folder): config_file, caffemodel_file, net_proto = get_filenames(model_folder) sbd.SbdConfig(config_file) ctm_file, pitch_file, energy_file = get_audio_files(example_folder) # parse ctm_file, pitch_file and energy_file parser = AudioParser(ctm_file, pitch_file, energy_file) parser.parse() classifier = load_audio_classifier(model_folder) data = classifier.predict_audio(parser) print(data)
def load_lexical_classifier(folder, vector): print('Loading config folder: ' + folder) config_file, caffemodel_file, net_proto = get_filenames(folder) sbd.SbdConfig(config_file) temp_proto = make_lexical_temp_deploy(folder, net_proto) net = caffe.Net(temp_proto, caffemodel_file, caffe.TEST) if vector: classifier = LexicalClassifier(net, vector) else: classifier = LexicalClassifier(net, vector) return classifier
plain_text_instances_file.write(s.encode('utf8')) # write to level db level_db.write_training_instance(training_instance) plain_text_instances_file.close() if __name__ == '__main__': parser = argparse.ArgumentParser( description='create test and train datasets as a lmdb.') parser.add_argument('config_file', help="path to config file") args = parser.parse_args() # initialize config sbd.SbdConfig(args.config_file) # create proper name for the database SENTENCE_HOME = os.environ['SENTENCE_HOME'] data_folder = "/mnt/naruto/sentence/data/" LEVEL_DB_DIR = "leveldbs" database = SENTENCE_HOME + "/" + LEVEL_DB_DIR + "/" + sbd.SbdConfig.get_db_name_from_config( sbd.config) # check if database already exists if os.path.isdir(database): print("Deleting " + database + ". y/N?") sys.stdout.flush() s = raw_input() if s != "Y" and s != "y":
def load_config(model_folder, model): default_model = os.path.join(route_folder, model_folder, model) config_file, caffemodel_file, net_proto = get_filenames(default_model) sbd.SbdConfig(config_file)
parser.add_argument('vectorfile', help='the google news word vector', default='demo_data/GoogleNews-vectors-negative300.bin', nargs='?') parser.add_argument('-nd','--no-debug', help='do not use debug mode, google vector is read', action='store_false', dest='debug', default=DEBUG) args = parser.parse_args() route_folder = args.routefolder #### load lexical model #### lexical_models = get_options(route_folder, LEXICAL_MODEL_FOLDER) default_lexical_model = os.path.join(route_folder, LEXICAL_MODEL_FOLDER, lexical_models[0]) # get the caffe files config_file, caffemodel_file, net_proto = get_filenames(default_lexical_model) # read the config file config_file = sbd.SbdConfig(config_file) if not args.debug: vector = Word2VecFile(args.vectorfile) lexical_classifier = load_lexical_classifier(default_lexical_model, vector) else: vector = None lexical_classifier = load_lexical_classifier(default_lexical_model, vector) #### load audio model #### audio_models = get_options(route_folder, AUDIO_MODEL_FOLDER) default_audio_model = os.path.join(route_folder, AUDIO_MODEL_FOLDER, audio_models[0]) # get the caffe files config_file, caffemodel_file, net_proto = get_filenames(default_audio_model)
def _load_config(self, model_folder): config_file, caffemodel_file, net_proto = get_filenames(model_folder) sbd.SbdConfig(config_file)