Update argument naming following Yibing's reviews.

deep_speech_2/demo_server.py

@@ -25,7 +25,7 @@ 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_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.")
@@ -51,9 +51,9 @@ add_arg('model_path',       str,
 add_arg('lang_model_path',  str,
         'lm/data/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
-add_arg('decoder_method',   str,
+add_arg('decoding_method',  str,
         'ctc_beam_search',
-        "Decoder method. Options: ctc_beam_search, ctc_greedy",
+        "Decoding method. Options: ctc_beam_search, ctc_greedy",
         choices = ['ctc_beam_search', 'ctc_greedy'])
 add_arg('specgram_type',    str,
         'linear',
@@ -160,7 +160,7 @@ def start_server():
         feature = data_generator.process_utterance(filename, "")
         result_transcript = ds2_model.infer_batch(
             infer_data=[feature],
-            decoder_method=args.decoder_method,
+            decoding_method=args.decoding_method,
             beam_alpha=args.alpha,
             beam_beta=args.beta,
             beam_size=args.beam_size,

deep_speech_2/evaluate.py

@@ -17,15 +17,15 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('batch_size',       int,    128,    "Minibatch size.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('parallels_bsearch',int,    12,     "# of CPUs for beam search.")
-add_arg('parallels_data',   int,    12,     "# of CPUs for data preprocessing.")
+add_arg('num_proc_bsearch', int,    12,     "# of CPUs for beam search.")
+add_arg('num_proc_data',    int,    12,     "# of CPUs for data preprocessing.")
 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_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.")
@@ -45,9 +45,9 @@ add_arg('model_path',       str,
 add_arg('lang_model_path',  str,
         'lm/data/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
-add_arg('decoder_method',   str,
+add_arg('decoding_method',  str,
         'ctc_beam_search',
-        "Decoder method. Options: ctc_beam_search, ctc_greedy",
+        "Decoding method. Options: ctc_beam_search, ctc_greedy",
         choices = ['ctc_beam_search', 'ctc_greedy'])
 add_arg('error_rate_type',  str,
         'wer',
@@ -68,7 +68,7 @@ def evaluate():
         mean_std_filepath=args.mean_std_path,
         augmentation_config='{}',
         specgram_type=args.specgram_type,
-        num_threads=args.parallels_data)
+        num_threads=args.num_proc_data)
     batch_reader = data_generator.batch_reader_creator(
         manifest_path=args.test_manifest,
         batch_size=args.batch_size,
@@ -90,14 +90,14 @@ def evaluate():
     for infer_data in batch_reader():
         result_transcripts = ds2_model.infer_batch(
             infer_data=infer_data,
-            decoder_method=args.decoder_method,
+            decoding_method=args.decoding_method,
             beam_alpha=args.alpha,
             beam_beta=args.beta,
             beam_size=args.beam_size,
             cutoff_prob=args.cutoff_prob,
             vocab_list=data_generator.vocab_list,
             language_model_path=args.lang_model_path,
-            num_processes=args.parallels_bsearch)
+            num_processes=args.num_proc_bsearch)
         target_transcripts = [
             ''.join([data_generator.vocab_list[token] for token in transcript])
             for _, transcript in infer_data

deep_speech_2/infer.py

@@ -17,14 +17,14 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('num_samples',      int,    10,     "# of samples to infer.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('parallels_bsearch',int,    12,     "# of CPUs for beam search.")
+add_arg('num_proc_bsearch', int,    12,     "# of CPUs for beam search.")
 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_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.")
@@ -44,9 +44,9 @@ 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,
+add_arg('decoding_method',  str,
         'ctc_beam_search',
-        "Decoder method. Options: ctc_beam_search, ctc_greedy",
+        "Decoding method. Options: ctc_beam_search, ctc_greedy",
         choices = ['ctc_beam_search', 'ctc_greedy'])
 add_arg('error_rate_type',  str,
         'wer',
@@ -86,14 +86,14 @@ def infer():
         share_rnn_weights=args.share_rnn_weights)
     result_transcripts = ds2_model.infer_batch(
         infer_data=infer_data,
-        decoder_method=args.decoder_method,
+        decoding_method=args.decoding_method,
         beam_alpha=args.alpha,
         beam_beta=args.beta,
         beam_size=args.beam_size,
         cutoff_prob=args.cutoff_prob,
         vocab_list=data_generator.vocab_list,
         language_model_path=args.lang_model_path,
-        num_processes=args.parallels_bsearch)
+        num_processes=args.num_proc_bsearch)
 
     error_rate_func = cer if args.error_rate_type == 'cer' else wer
     target_transcripts = [

deep_speech_2/model.py

@@ -146,7 +146,7 @@ class DeepSpeech2Model(object):
         # run inference
         return self._loss_inferer.infer(input=infer_data)
 
-    def infer_batch(self, infer_data, decoder_method, beam_alpha, beam_beta,
+    def infer_batch(self, infer_data, decoding_method, beam_alpha, beam_beta,
                     beam_size, cutoff_prob, vocab_list, language_model_path,
                     num_processes):
         """Model inference. Infer the transcription for a batch of speech
@@ -156,9 +156,9 @@ class DeepSpeech2Model(object):
                            consisting of a tuple of audio features and
                            transcription text (empty string).
         :type infer_data: list
-        :param decoder_method: Decoding method name, 'ctc_greedy' or
+        :param decoding_method: Decoding method name, 'ctc_greedy' or
                                 'ctc_beam_search'.
-        :param decoder_method: string
+        :param decoding_method: string
         :param beam_alpha: Parameter associated with language model.
         :type beam_alpha: float
         :param beam_beta: Parameter associated with word count.
@@ -190,13 +190,13 @@ class DeepSpeech2Model(object):
         ]
         # run decoder
         results = []
-        if decoder_method == "ctc_greedy":
+        if decoding_method == "ctc_greedy":
             # best path decode
             for i, probs in enumerate(probs_split):
                 output_transcription = ctc_greedy_decoder(
                     probs_seq=probs, vocabulary=vocab_list)
                 results.append(output_transcription)
-        elif decoder_method == "ctc_beam_search":
+        elif decoding_method == "ctc_beam_search":
             # initialize external scorer
             if self._ext_scorer == None:
                 self._ext_scorer = LmScorer(beam_alpha, beam_beta,
@@ -217,8 +217,8 @@ class DeepSpeech2Model(object):
 
             results = [result[0][1] for result in beam_search_results]
         else:
-            raise ValueError("Decoder method [%s] is not supported." %
-                             decoder_method)
+            raise ValueError("Decoding method [%s] is not supported." %
+                             decoding_method)
         return results
 
     def _create_parameters(self, model_path=None):

deep_speech_2/train.py

@@ -16,7 +16,7 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('batch_size',       int,    256,    "Minibatch size.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
 add_arg('num_passes',       int,    200,    "# of training epochs.")
-add_arg('parallels_data',   int,    12,     "# of CPUs for data preprocessing.")
+add_arg('num_proc_data',    int,    12,     "# of CPUs for data preprocessing.")
 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.")
@@ -28,7 +28,7 @@ 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('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.")
 add_arg('train_manifest',   str,
@@ -74,13 +74,13 @@ def train():
         max_duration=args.max_duration,
         min_duration=args.min_duration,
         specgram_type=args.specgram_type,
-        num_threads=args.parallels_data)
+        num_threads=args.num_proc_data)
     dev_generator = DataGenerator(
         vocab_filepath=args.vocab_path,
         mean_std_filepath=args.mean_std_path,
         augmentation_config="{}",
         specgram_type=args.specgram_type,
-        num_threads=args.parallels_data)
+        num_threads=args.num_proc_data)
     train_batch_reader = train_generator.batch_reader_creator(
         manifest_path=args.train_manifest,
         batch_size=args.batch_size,

deep_speech_2/tune.py

@@ -18,7 +18,7 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('num_samples',      int,    100,    "# of samples to infer.")
 add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
 add_arg('beam_size',        int,    500,    "Beam search width.")
-add_arg('parallels_bsearch',int,    12,     "# of CPUs for beam search.")
+add_arg('num_proc_bsearch', int,    12,     "# of CPUs for beam search.")
 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.")
@@ -29,7 +29,7 @@ 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_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.")
@@ -104,14 +104,14 @@ def tune():
     for alpha, beta in params_grid:
         result_transcripts = ds2_model.infer_batch(
             infer_data=tune_data,
-            decoder_method='ctc_beam_search',
+            decoding_method='ctc_beam_search',
             beam_alpha=alpha,
             beam_beta=beta,
             beam_size=args.beam_size,
             cutoff_prob=args.cutoff_prob,
             vocab_list=data_generator.vocab_list,
             language_model_path=args.lang_model_path,
-            num_processes=args.parallels_bsearch)
+            num_processes=args.num_proc_bsearch)
         wer_sum, num_ins = 0.0, 0
         for target, result in zip(target_transcripts, result_transcripts):
             wer_sum += wer(target, result)