def main(args): # get model model = load_obj(os.path.join(args.folder_model, 'model')) feature_extractor = load_obj(os.path.join(args.folder_model, 'feature_extractor')) if not os.path.exists(args.folder_out): os.makedirs(args.folder_out) dic_forward = dict() dic_forward['model_used'] = args.folder_model dic_forward['data_forwarded'] = args.folder_audio with open(os.path.join(args.folder_out, 'forward_info.json'), 'w') as fp: json.dump(dic_forward, fp) # plot result forward_model(args.folder_out, args.folder_audio, model, feature_extractor)
def main(args): # get model with open(os.path.join(args.folder_forwarded, 'forward_info.json')) as config_description: config_forward = ast.literal_eval(config_description.read()) values_possible = load_obj(os.path.join(config_forward['model_used'], 'values_possible')) # plot result plot_forwarded(args.folder_out, config_forward['data_forwarded'], os.path.join(args.folder_forwarded, 'clusters'), values_possible, args.freq_max)