update annotations

deep_speech_2/ctc_beam_search_decoder.py

@@ -10,12 +10,6 @@ import numpy as np
 vocab = ['-', '_', 'a']
 
 
-def ids_str2list(ids_str):
-    ids_str = ids_str.split(' ')
-    ids_list = [int(elem) for elem in ids_str]
-    return ids_list
-
-
 def ids_list2str(ids_list):
     ids_str = [str(elem) for elem in ids_list]
     ids_str = ' '.join(ids_str)
@@ -39,21 +33,45 @@ def ctc_beam_search_decoder(input_probs_matrix,
                             space_id=1,
                             num_results_per_sample=None):
     '''
-    beam search decoder for CTC-trained network, called outside of the recurrent group. 
-    adapted from Algorithm 1 in https://arxiv.org/abs/1408.2873.
+    Beam search decoder for CTC-trained network,  adapted from Algorithm 1 
+    in https://arxiv.org/abs/1408.2873.
+
+    :param input_probs_matrix: probs matrix for input sequence, row major
+    :type input_probs_matrix: 2D matrix. 
+    :param beam_size: width for beam search
+    :type beam_size: int
+    :max_time_steps: maximum steps' number for input sequence, 
+                     <=len(input_probs_matrix)
+    :type max_time_steps: int
+    :lang_model: language model for scoring
+    :type lang_model: function
+    :param alpha: parameter associated with language model.
+    :type alpha: float 
+    :param beta: parameter associated with word count
+    :type beta: float
+    :param blank_id: id of blank, default 0.
+    :type blank_id: int
+    :param space_id: id of space, default 1.
+    :type space_id: int
+    :param num_result_per_sample: the number of output decoding results 
+                                 per given sample, <=beam_size.
+    :type num_result_per_sample: int
+    '''
 
-    param input_probs_matrix: probs matrix for input sequence, row major
-    type input_probs_matrix: 2D matrix. 
-    param beam_size: width for beam search
-    type beam_size: int
-    max_time_steps: maximum steps' number for input sequence, <=len(input_probs_matrix)
-    type max_time_steps: int
-    lang_model: language model for scoring
-    type lang_model: function
+    # function to convert ids in string to list
+    def ids_str2list(ids_str):
+        ids_str = ids_str.split(' ')
+        ids_list = [int(elem) for elem in ids_str]
+        return ids_list
 
-    ......
+    # counting words in a character list
+    def word_count(ids_list):
+        cnt = 0
+        for elem in ids_list:
+            if elem == space_id:
+                cnt += 1
+        return cnt
 
-    '''
     if num_results_per_sample is None:
         num_results_per_sample = beam_size
     assert num_results_per_sample <= beam_size