Merge remote-tracking branch 'upstream/develop' into chinese_poetry

deep_speech_2/README.md

@@ -498,13 +498,13 @@ Language Model | Training Data | Token-based | Size | Descriptions
 
 Test Set                | LibriSpeech Model | BaiduEN8K Model
 :---------------------  | ---------------:  | -------------------:
-LibriSpeech Test-Clean  |   8.06            |   6.63
-LibriSpeech Test-Other  |   24.25           |   16.59
-VoxForge American-Canadian | 13.48          |   7.46
-VoxForge Commonwealth   |   22.37           |   16.23
-VoxForge European       |   32.64           |   20.47
-VoxForge Indian         |   58.48           |   28.15
-Baidu Internal Testset  |   48.93           |   8.92
+LibriSpeech Test-Clean  |   7.77            |   6.63
+LibriSpeech Test-Other  |   23.25           |   16.59
+VoxForge American-Canadian | 12.52          |   7.46
+VoxForge Commonwealth   |   21.08           |   16.23
+VoxForge European       |   31.21           |   20.47
+VoxForge Indian         |   56.79           |   28.15
+Baidu Internal Testset  |   47.73           |   8.92
 
 #### Benchmark Results for Mandarin Model (Character Error Rate)
 

deep_speech_2/decoders/swig/ctc_beam_search_decoder.cpp

@@ -110,17 +110,17 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
           // language model scoring
           if (ext_scorer != nullptr &&
               (c == space_id || ext_scorer->is_character_based())) {
-            PathTrie *prefix_toscore = nullptr;
+            PathTrie *prefix_to_score = nullptr;
             // skip scoring the space
             if (ext_scorer->is_character_based()) {
-              prefix_toscore = prefix_new;
+              prefix_to_score = prefix_new;
             } else {
-              prefix_toscore = prefix;
+              prefix_to_score = prefix;
             }
 
-            double score = 0.0;
+            float score = 0.0;
             std::vector<std::string> ngram;
-            ngram = ext_scorer->make_ngram(prefix_toscore);
+            ngram = ext_scorer->make_ngram(prefix_to_score);
             score = ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha;
             log_p += score;
             log_p += ext_scorer->beta;
@@ -131,6 +131,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
       }  // end of loop over prefix
     }    // end of loop over vocabulary
 
+
     prefixes.clear();
     // update log probs
     root.iterate_to_vec(prefixes);
@@ -147,6 +148,23 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
     }
   }  // end of loop over time
 
+  // score the last word of each prefix that doesn't end with space
+  if (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
+    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
+      auto prefix = prefixes[i];
+      if (!prefix->is_empty() && prefix->character != space_id) {
+        float score = 0.0;
+        std::vector<std::string> ngram = ext_scorer->make_ngram(prefix);
+        score = ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha;
+        score += ext_scorer->beta;
+        prefix->score += score;
+      }
+    }
+  }
+
+  size_t num_prefixes = std::min(prefixes.size(), beam_size);
+  std::sort(prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
+
   // compute aproximate ctc score as the return score, without affecting the
   // return order of decoding result. To delete when decoder gets stable.
   for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {