final refining on old data provider: enable pruning & add evaluation …

…& code cleanup

deep_speech_2/decoder.py

@@ -5,7 +5,6 @@
 import os
 from itertools import groupby
 import numpy as np
-import copy
 import kenlm
 import multiprocessing
 
@@ -73,25 +72,40 @@ def word_count(self, sentence):
         return len(words)
 
     # execute evaluation
-    def __call__(self, sentence):
+    def __call__(self, sentence, log=False):
+        """
+        Evaluation function
+
+        :param sentence: The input sentence for evalutation
+        :type sentence: basestring
+        :param log: Whether return the score in log representation.
+        :type log: bool
+        :return: Evaluation score, in the decimal or log.
+        :rtype: float
+        """
         lm = self.language_model_score(sentence)
         word_cnt = self.word_count(sentence)
-        score = np.power(lm, self._alpha) \
-                * np.power(word_cnt, self._beta)
+        if log == False:
+            score = np.power(lm, self._alpha) \
+                    * np.power(word_cnt, self._beta)
+        else:
+            score = self._alpha * np.log(lm) \
+                    + self._beta * np.log(word_cnt)
         return score
 
 
 def ctc_beam_search_decoder(probs_seq,
                             beam_size,
                             vocabulary,
                             blank_id=0,
+                            cutoff_prob=1.0,
                             ext_scoring_func=None,
                             nproc=False):
     '''
     Beam search decoder for CTC-trained network, using beam search with width
     beam_size to find many paths to one label, return  beam_size labels in
-    the order of probabilities. The implementation is based on Prefix Beam
-    Search(https://arxiv.org/abs/1408.2873), and the unclear part is
+    the descending order of probabilities. The implementation is based on Prefix
+    Beam 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 num_time_steps, each element
@@ -102,22 +116,25 @@ def ctc_beam_search_decoder(probs_seq,
     :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
+    :param blank_id: ID of blank, default 0.
+    :type blank_id: int
+    :param cutoff_prob: Cutoff probability in pruning,
+                        default 1.0, no pruning.
+    :type cutoff_prob: float
     :param ext_scoring_func: External defined scoring function for
                             partially decoded sentence, e.g. word count
                             and language model.
     :type external_scoring_function: function
-    :param blank_id: id of blank, default 0.
-    :type blank_id: int
     :param nproc: Whether the decoder used in multiprocesses.
     :type nproc: bool
-    :return: Decoding log probability and result string.
+    :return: Decoding log probabilities and result sentences in descending order.
     :rtype: list
 
     '''
     # dimension check
     for prob_list in probs_seq:
         if not len(prob_list) == len(vocabulary) + 1:
-            raise ValueError("probs dimension mismatchedd with vocabulary")
+            raise ValueError("probs dimension mismatched with vocabulary")
     num_time_steps = len(probs_seq)
 
     # blank_id check
@@ -137,19 +154,35 @@ def ctc_beam_search_decoder(probs_seq,
     probs_b_prev, probs_nb_prev = {'\t': 1.0}, {'\t': 0.0}
 
     ## extend prefix in loop
-    for time_step in range(num_time_steps):
+    for time_step in xrange(num_time_steps):
         # the set containing candidate prefixes
         prefix_set_next = {}
         probs_b_cur, probs_nb_cur = {}, {}
+        prob = probs_seq[time_step]
+        prob_idx = [[i, prob[i]] for i in xrange(len(prob))]
+        cutoff_len = len(prob_idx)
+        #If pruning is enabled
+        if (cutoff_prob < 1.0):
+            prob_idx = sorted(prob_idx, key=lambda asd: asd[1], reverse=True)
+            cutoff_len = 0
+            cum_prob = 0.0
+            for i in xrange(len(prob_idx)):
+                cum_prob += prob_idx[i][1]
+                cutoff_len += 1
+                if cum_prob >= cutoff_prob:
+                    break
+            prob_idx = prob_idx[0:cutoff_len]
+
         for l in prefix_set_prev:
-            prob = probs_seq[time_step]
             if not prefix_set_next.has_key(l):
                 probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0
 
-            # extend prefix by travering vocabulary
-            for c in range(0, probs_dim):
+            # extend prefix by travering prob_idx
+            for index in xrange(cutoff_len):
+                c, prob_c = prob_idx[index][0], prob_idx[index][1]
+
                 if c == blank_id:
-                    probs_b_cur[l] += prob[c] * (
+                    probs_b_cur[l] += prob_c * (
                         probs_b_prev[l] + probs_nb_prev[l])
                 else:
                     last_char = l[-1]
@@ -159,18 +192,18 @@ def ctc_beam_search_decoder(probs_seq,
                         probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
 
                     if new_char == last_char:
-                        probs_nb_cur[l_plus] += prob[c] * probs_b_prev[l]
-                        probs_nb_cur[l] += prob[c] * probs_nb_prev[l]
+                        probs_nb_cur[l_plus] += prob_c * probs_b_prev[l]
+                        probs_nb_cur[l] += prob_c * probs_nb_prev[l]
                     elif new_char == ' ':
                         if (ext_scoring_func is None) or (len(l) == 1):
                             score = 1.0
                         else:
                             prefix = l[1:]
                             score = ext_scoring_func(prefix)
-                        probs_nb_cur[l_plus] += score * prob[c] * (
+                        probs_nb_cur[l_plus] += score * prob_c * (
                             probs_b_prev[l] + probs_nb_prev[l])
                     else:
-                        probs_nb_cur[l_plus] += prob[c] * (
+                        probs_nb_cur[l_plus] += prob_c * (
                             probs_b_prev[l] + probs_nb_prev[l])
                     # add l_plus into prefix_set_next
                     prefix_set_next[l_plus] = probs_nb_cur[
@@ -203,6 +236,7 @@ def ctc_beam_search_decoder_nproc(probs_split,
                                   beam_size,
                                   vocabulary,
                                   blank_id=0,
+                                  cutoff_prob=1.0,
                                   ext_scoring_func=None,
                                   num_processes=None):
     '''
@@ -216,16 +250,19 @@ def ctc_beam_search_decoder_nproc(probs_split,
     :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
+    :param blank_id: ID of blank, default 0.
+    :type blank_id: int
+    :param cutoff_prob: Cutoff probability in pruning,
+                        default 0, no pruning.
+    :type cutoff_prob: float
     :param ext_scoring_func: External defined scoring function for
                             partially decoded sentence, e.g. word count
                             and language model.
     :type external_scoring_function: function
-    :param blank_id: id of blank, default 0.
-    :type blank_id: int
     :param num_processes: Number of processes, default None, equal to the
                  number of CPUs.
     :type num_processes: int
-    :return: Decoding log probability and result string.
+    :return: Decoding log probabilities and result sentences in descending order.
     :rtype: list
 
     '''
@@ -243,7 +280,8 @@ def ctc_beam_search_decoder_nproc(probs_split,
     pool = multiprocessing.Pool(processes=num_processes)
     results = []
     for i, probs_list in enumerate(probs_split):
-        args = (probs_list, beam_size, vocabulary, blank_id, None, nproc)
+        args = (probs_list, beam_size, vocabulary, blank_id, cutoff_prob, None,
+                nproc)
         results.append(pool.apply_async(ctc_beam_search_decoder, args))
 
     pool.close()