| [common] |
| # method can be one of the followings - train,predict,load |
| mode = train |
| #ex: gpu0,gpu1,gpu2,gpu3 |
| context = gpu0 |
| # checkpoint prefix, check point will be saved under checkpoints folder with prefix |
| prefix = test_fc |
| # when mode is load or predict, model will be loaded from the file name with model_file under checkpoints |
| model_file = test_fc-0001 |
| batch_size = 2 |
| # log will be saved by the log_filename |
| log_filename = test.log |
| # checkpoint set n to save checkpoints after n epoch |
| save_checkpoint_every_n_epoch = 1 |
| save_checkpoint_every_n_batch = 1000 |
| is_bi_graphemes = False |
| tensorboard_log_dir = tblog/libri_sample |
| # if random_seed is -1 then it gets random seed from timestamp |
| mx_random_seed = -1 |
| random_seed = -1 |
| |
| [data] |
| train_json = ./Libri_sample.json |
| test_json = ./Libri_sample.json |
| val_json = ./Libri_sample.json |
| |
| language = en |
| width = 161 |
| height = 1 |
| channel = 1 |
| stride = 1 |
| |
| [arch] |
| channel_num = 32 |
| conv_layer1_filter_dim = [11, 41] |
| conv_layer1_stride = [2, 2] |
| conv_layer2_filter_dim = [11, 21] |
| conv_layer2_stride = [1, 2] |
| |
| num_rnn_layer = 3 |
| num_hidden_rnn_list = [1760, 1760, 1760] |
| num_hidden_proj = 0 |
| |
| num_rear_fc_layers = 0 |
| num_hidden_rear_fc_list = [] |
| act_type_rear_fc_list = [] |
| |
| #network: lstm, bilstm, gru, bigru |
| rnn_type = bigru |
| #vanilla_lstm or fc_lstm (no effect when network_type is gru, bigru) |
| lstm_type = fc_lstm |
| is_batchnorm = True |
| |
| [train] |
| num_epoch = 70 |
| |
| learning_rate = 0.005 |
| # constant learning rate annealing by factor |
| learning_rate_annealing = 1.1 |
| # supports only sgd and adam |
| optimizer = adam |
| # for sgd |
| momentum = 0.9 |
| # set to 0 to disable gradient clipping |
| clip_gradient = 0 |
| |
| initializer = Xavier |
| init_scale = 2 |
| factor_type = in |
| weight_decay = 0.00001 |
| # show progress every nth batches |
| show_every = 1 |
| save_optimizer_states = True |
| normalize_target_k = 2 |
| # overwrite meta files(feats_mean,feats_std,unicode_en_baidu_bi_graphemes.csv) |
| overwrite_meta_files = True |
| enable_logging_train_metric = True |
| enable_logging_validation_metric = True |
| |
| [load] |
| load_optimizer_states = True |
| is_start_from_batch = False |
