Sort the config lines to make it look better.

demo_server.py

@@ -27,41 +27,25 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
-# configurations of overall
 add_arg('host_port',        int,    8086,    "Server's IP port.")
-add_arg('host_ip',          str,
-        'localhost',
-        "Server's IP address.")
-add_arg('speech_save_dir',  str,
-        'demo_cache',
-        "Directory to save demo audios.")
-add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
-# configurations of decoder
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
-add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
-add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
-add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
-        "Filepath for language model.")
-add_arg('decoder_method',   str,
-        'ctc_beam_search',
-        "Decoder method. Options: ctc_beam_search, ctc_greedy",
-        choices = ['ctc_beam_search', 'ctc_greedy'])
-# configurations of data preprocess
-add_arg('specgram_type',    str,
-        'linear',
-        "Audio feature type. Options: linear, mfcc.",
-        choices=['linear', 'mfcc'])
-# configurations of model structure
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
+add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
+add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
+add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of Simple RNNs.")
+add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
                                             "bi-directional RNNs. Not for GRU.")
-# configurations of data io
-add_arg('warmup_manifest',   str,
+add_arg('host_ip',          str,
+        'localhost',
+        "Server's IP address.")
+add_arg('speech_save_dir',  str,
+        'demo_cache',
+        "Directory to save demo audios.")
+add_arg('warmup_manifest',  str,
         'datasets/manifest.test',
         "Filepath of manifest to warm up.")
 add_arg('mean_std_path',    str,
@@ -70,11 +54,21 @@ add_arg('mean_std_path',    str,
 add_arg('vocab_path',       str,
         'datasets/vocab/eng_vocab.txt',
         "Filepath of vocabulary.")
-# configurations of model io
 add_arg('model_path',       str,
         './checkpoints/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
+add_arg('lang_model_path',  str,
+        'lm/data/common_crawl_00.prune01111.trie.klm',
+        "Filepath for language model.")
+add_arg('decoder_method',   str,
+        'ctc_beam_search',
+        "Decoder method. Options: ctc_beam_search, ctc_greedy",
+        choices = ['ctc_beam_search', 'ctc_greedy'])
+add_arg('specgram_type',    str,
+        'linear',
+        "Audio feature type. Options: linear, mfcc.",
+        choices=['linear', 'mfcc'])
 args = parser.parse_args()
 # yapf: disable
 

evaluate.py

@@ -26,39 +26,21 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
-# configurations of overall
 add_arg('batch_size',       int,    128,    "Minibatch size.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
-add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
-add_arg('error_rate_type',  str,    'wer',  "Error rate type for evaluation.",
-        choices=['wer', 'cer'])
-# configurations of decoder
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
-add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
-add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('parallels_bsearch',int,    NUM_CPU,"# of CPUs for beam search.")
-add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
-        "Filepath for language model.")
-add_arg('decoder_method',   str,
-        'ctc_beam_search',
-        "Decoder method. Options: ctc_beam_search, ctc_greedy",
-        choices = ['ctc_beam_search', 'ctc_greedy'])
-# configurations of data preprocess
 add_arg('parallels_data',   int,    NUM_CPU,"# of CPUs for data preprocessing.")
-add_arg('specgram_type',    str,
-        'linear',
-        "Audio feature type. Options: linear, mfcc.",
-        choices=['linear', 'mfcc'])
-# configurations of model structure
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
+add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
+add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
+add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of Simple RNNs.")
+add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
                                             "bi-directional RNNs. Not for GRU.")
-# configurations of data io
 add_arg('test_manifest',   str,
         'datasets/manifest.test',
         "Filepath of manifest to evaluate.")
@@ -68,11 +50,25 @@ add_arg('mean_std_path',    str,
 add_arg('vocab_path',       str,
         'datasets/vocab/eng_vocab.txt',
         "Filepath of vocabulary.")
-# configurations of model io
 add_arg('model_path',       str,
         './checkpoints/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
+add_arg('lang_model_path',  str,
+        'lm/data/common_crawl_00.prune01111.trie.klm',
+        "Filepath for language model.")
+add_arg('decoder_method',   str,
+        'ctc_beam_search',
+        "Decoder method. Options: ctc_beam_search, ctc_greedy",
+        choices = ['ctc_beam_search', 'ctc_greedy'])
+add_arg('error_rate_type',  str,
+        'wer',
+        "Error rate type for evaluation.",
+        choices=['wer', 'cer'])
+add_arg('specgram_type',    str,
+        'linear',
+        "Audio feature type. Options: linear, mfcc.",
+        choices=['linear', 'mfcc'])
 args = parser.parse_args()
 # yapf: disable
 

infer.py

@@ -29,35 +29,18 @@ def add_arg(argname, type, default, help, **kwargs):
 # configurations of overall
 add_arg('num_samples',      int,    10,     "# of samples to infer.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
-add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
-add_arg('error_rate_type',  str,    'wer',  "Error rate type for evaluation.",
-        choices=['wer', 'cer'])
-# configurations of decoder
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
-add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
-add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('parallels_bsearch',int,    NUM_CPU,"# of CPUs for beam search.")
-add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
-        "Filepath for language model.")
-add_arg('decoder_method',   str,
-        'ctc_beam_search',
-        "Decoder method. Options: ctc_beam_search, ctc_greedy",
-        choices = ['ctc_beam_search', 'ctc_greedy'])
-# configurations of data preprocess
-add_arg('specgram_type',    str,
-        'linear',
-        "Audio feature type. Options: linear, mfcc.",
-        choices=['linear', 'mfcc'])
-# configurations of model structure
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
+add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
+add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
+add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of Simple RNNs.")
+add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
                                             "bi-directional RNNs. Not for GRU.")
-# configurations of data io
 add_arg('infer_manifest',   str,
         'datasets/manifest.dev',
         "Filepath of manifest to infer.")
@@ -67,11 +50,25 @@ add_arg('mean_std_path',    str,
 add_arg('vocab_path',       str,
         'datasets/vocab/eng_vocab.txt',
         "Filepath of vocabulary.")
-# configurations of model io
+add_arg('lang_model_path',  str,
+        'lm/data/common_crawl_00.prune01111.trie.klm',
+        "Filepath for language model.")
 add_arg('model_path',       str,
         './checkpoints/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
+add_arg('decoder_method',   str,
+        'ctc_beam_search',
+        "Decoder method. Options: ctc_beam_search, ctc_greedy",
+        choices = ['ctc_beam_search', 'ctc_greedy'])
+add_arg('error_rate_type',  str,
+        'wer',
+        "Error rate type for evaluation.",
+        choices=['wer', 'cer'])
+add_arg('specgram_type',    str,
+        'linear',
+        "Audio feature type. Options: linear, mfcc.",
+        choices=['linear', 'mfcc'])
 args = parser.parse_args()
 # yapf: disable
 

train.py

@@ -25,39 +25,24 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
-# configurations of optimization
 add_arg('batch_size',       int,    256,    "Minibatch size.")
-add_arg('learning_rate',    float,  5e-4,   "Learning rate.")
-add_arg('use_sortagrad',    bool,   True,   "Use SortaGrad or not.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
-add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('num_passes',       int,    200,    "# of training epochs.")
-add_arg('is_local',         bool,   True,   "Use pserver or not.")
-add_arg('num_iter_print',   int,    100,    "Every # iterations for printing "
-                                            "train cost.")
-# configurations of data preprocess
-add_arg('max_duration',     float,  27.0,   "Longest audio duration allowed.")
-add_arg('min_duration',     float,  0.0,    "Shortest audio duration allowed.")
 add_arg('parallels_data',   int,    NUM_CPU,"# of CPUs for data preprocessing.")
-add_arg('specgram_type',    str,
-        'linear',
-        "Audio feature type. Options: linear, mfcc.",
-        choices=['linear', 'mfcc'])
-add_arg('augment_conf_path',str,
-        'conf/augmentation.config',
-        "Filepath of augmentation configuration file (json-format).")
-add_arg('shuffle_method',   str,
-        'batch_shuffle_clipped',
-        "Shuffle method.",
-        choices=['instance_shuffle', 'batch_shuffle', 'batch_shuffle_clipped'])
-# configurations of model structure
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
+add_arg('num_iter_print',   int,    100,    "Every # iterations for printing "
+                                            "train cost.")
+add_arg('learning_rate',    float,  5e-4,   "Learning rate.")
+add_arg('max_duration',     float,  27.0,   "Longest audio duration allowed.")
+add_arg('min_duration',     float,  0.0,    "Shortest audio duration allowed.")
+add_arg('use_sortagrad',    bool,   True,   "Use SortaGrad or not.")
+add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
+add_arg('is_local',         bool,   True,   "Use pserver or not.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of Simple RNNs.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
                                             "bi-directional RNNs. Not for GRU.")
-# configurations of data io
 add_arg('train_manifest',   str,
         'datasets/manifest.train',
         "Filepath of train manifest.")
@@ -70,7 +55,6 @@ add_arg('mean_std_path',    str,
 add_arg('vocab_path',       str,
         'datasets/vocab/eng_vocab.txt',
         "Filepath of vocabulary.")
-# configurations of model io
 add_arg('init_model_path',  str,
         None,
         "If None, the training starts from scratch, "
@@ -78,6 +62,17 @@ add_arg('init_model_path',  str,
 add_arg('output_model_dir', str,
         "./checkpoints",
         "Directory for saving checkpoints.")
+add_arg('augment_conf_path',str,
+        'conf/augmentation.config',
+        "Filepath of augmentation configuration file (json-format).")
+add_arg('specgram_type',    str,
+        'linear',
+        "Audio feature type. Options: linear, mfcc.",
+        choices=['linear', 'mfcc'])
+add_arg('shuffle_method',   str,
+        'batch_shuffle_clipped',
+        "Shuffle method.",
+        choices=['instance_shuffle', 'batch_shuffle', 'batch_shuffle_clipped'])
 args = parser.parse_args()
 # yapf: disable
 

tune.py

@@ -27,40 +27,25 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
-# configurations of overall
 add_arg('num_samples',      int,    100,    "# of samples to infer.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
-add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
-add_arg('error_rate_type',  str,    'wer',  "Error rate type for evaluation.",
-        choices=['wer', 'cer'])
-# configurations of tuning parameters
-add_arg('alpha_from',       float,  0.1,    "Where alpha starts tuning from.")
-add_arg('alpha_to',         float,  0.36,   "Where alpha ends tuning with.")
-add_arg('num_alphas',       int,    14,     "# of alpha candidates for tuning.")
-add_arg('beta_from',        float,  0.05,   "Where beta starts tuning from.")
-add_arg('beta_to',          float,  0.36,   "Where beta ends tuning with.")
-add_arg('num_betas',        int,    20,     "# of beta candidates for tuning.")
-# configurations of decoder
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('parallels_bsearch',int,    NUM_CPU,"# of CPUs for beam search.")
-add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
-        "Filepath for language model.")
-# configurations of data preprocess
-add_arg('specgram_type',    str,
-        'linear',
-        "Audio feature type. Options: linear, mfcc.",
-        choices=['linear', 'mfcc'])
-# configurations of model structure
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
+add_arg('num_alphas',       int,    14,     "# of alpha candidates for tuning.")
+add_arg('num_betas',        int,    20,     "# of beta candidates for tuning.")
+add_arg('alpha_from',       float,  0.1,    "Where alpha starts tuning from.")
+add_arg('alpha_to',         float,  0.36,   "Where alpha ends tuning with.")
+add_arg('beta_from',        float,  0.05,   "Where beta starts tuning from.")
+add_arg('beta_to',          float,  0.36,   "Where beta ends tuning with.")
+add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of Simple RNNs.")
+add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
                                             "bi-directional RNNs. Not for GRU.")
-# configurations of data io
-add_arg('tune_manifest',   str,
+add_arg('tune_manifest',    str,
         'datasets/manifest.test',
         "Filepath of manifest to tune.")
 add_arg('mean_std_path',    str,
@@ -69,11 +54,21 @@ add_arg('mean_std_path',    str,
 add_arg('vocab_path',       str,
         'datasets/vocab/eng_vocab.txt',
         "Filepath of vocabulary.")
-# configurations of model io
+add_arg('lang_model_path',  str,
+        'lm/data/common_crawl_00.prune01111.trie.klm',
+        "Filepath for language model.")
 add_arg('model_path',       str,
         './checkpoints/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
+add_arg('error_rate_type',  str,
+        'wer',
+        "Error rate type for evaluation.",
+        choices=['wer', 'cer'])
+add_arg('specgram_type',    str,
+        'linear',
+        "Audio feature type. Options: linear, mfcc.",
+        choices=['linear', 'mfcc'])
 args = parser.parse_args()
 # yapf: disable