rename variables in decoder

deep_speech_2/decoder.py

@@ -92,7 +92,7 @@ def ctc_beam_search_decoder(probs_seq,
     Search(https://arxiv.org/abs/1408.2873), and the unclear part is
     redesigned, need to be verified.
 
-    :param probs_seq: 2-D list with length max_time_steps, each element
+    :param probs_seq: 2-D list with length num_time_steps, each element
                       is a list of normalized probabilities over vocabulary
                       and blank for one time step.
     :type probs_seq: 2-D list
@@ -114,7 +114,7 @@ def ctc_beam_search_decoder(probs_seq,
     for prob_list in probs_seq:
         if not len(prob_list) == len(vocabulary) + 1:
             raise ValueError("probs dimension mismatchedd with vocabulary")
-    max_time_steps = len(probs_seq)
+    num_time_steps = len(probs_seq)
 
     # blank_id check
     probs_dim = len(probs_seq[0])
@@ -139,10 +139,10 @@ def ids2sentence(ids_list, vocab):
     ## initialize
     # the set containing selected prefixes
     prefix_set_prev = {'-1': 1.0}
-    probs_b, probs_nb = {'-1': 1.0}, {'-1': 0.0}
+    probs_b_prev, probs_nb_prev = {'-1': 1.0}, {'-1': 0.0}
 
     ## extend prefix in loop
-    for time_step in range(max_time_steps):
+    for time_step in range(num_time_steps):
         # the set containing candidate prefixes
         prefix_set_next = {}
         probs_b_cur, probs_nb_cur = {}, {}
@@ -158,33 +158,34 @@ def ids2sentence(ids_list, vocab):
             # extend prefix by travering vocabulary
             for c in range(0, probs_dim):
                 if c == blank_id:
-                    probs_b_cur[l] += prob[c] * (probs_b[l] + probs_nb[l])
+                    probs_b_cur[l] += prob[c] * (
+                        probs_b_prev[l] + probs_nb_prev[l])
                 else:
                     l_plus = l + ' ' + str(c)
                     if not prefix_set_next.has_key(l_plus):
                         probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
 
                     if c == end_id:
-                        probs_nb_cur[l_plus] += prob[c] * probs_b[l]
-                        probs_nb_cur[l] += prob[c] * probs_nb[l]
+                        probs_nb_cur[l_plus] += prob[c] * probs_b_prev[l]
+                        probs_nb_cur[l] += prob[c] * probs_nb_prev[l]
                     elif c == space_id:
                         if ext_scoring_func is None:
                             score = 1.0
                         else:
-                            prefix_sent = ids2sentence(ids_list, vocabulary)
-                            score = ext_scoring_func(prefix_sent)
+                            prefix = ids2sentence(ids_list, vocabulary)
+                            score = ext_scoring_func(prefix)
                         probs_nb_cur[l_plus] += score * prob[c] * (
-                            probs_b[l] + probs_nb[l])
+                            probs_b_prev[l] + probs_nb_prev[l])
                     else:
                         probs_nb_cur[l_plus] += prob[c] * (
-                            probs_b[l] + probs_nb[l])
+                            probs_b_prev[l] + probs_nb_prev[l])
                     # add l_plus into prefix_set_next
                     prefix_set_next[l_plus] = probs_nb_cur[
                         l_plus] + probs_b_cur[l_plus]
             # add l into prefix_set_next
             prefix_set_next[l] = probs_b_cur[l] + probs_nb_cur[l]
         # update probs
-        probs_b, probs_nb = copy.deepcopy(probs_b_cur), copy.deepcopy(
+        probs_b_prev, probs_nb_prev = copy.deepcopy(probs_b_cur), copy.deepcopy(
             probs_nb_cur)
 
         ## store top beam_size prefixes