enable loading language model in multiple format

deep_speech_2/deploy.py

@@ -14,6 +14,7 @@ from swig_ctc_beam_search_decoder import *
 from swig_scorer import Scorer
 from error_rate import wer
 import utils
+import time
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
@@ -74,7 +75,7 @@ parser.add_argument(
 )
 parser.add_argument(
     "--beam_size",
-    default=500,
+    default=200,
     type=int,
     help="Width for beam search decoding. (default: %(default)d)")
 parser.add_argument(
@@ -166,6 +167,7 @@ def infer():
     ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
 
     ## decode and print
+    time_begin = time.time()
     wer_sum, wer_counter = 0, 0
     for i, probs in enumerate(probs_split):
         beam_result = ctc_beam_search_decoder(
@@ -183,6 +185,8 @@ def infer():
         wer_counter += 1
         print("cur wer = %f , average wer = %f" %
               (wer_cur, wer_sum / wer_counter))
+    time_end = time.time()
+    print("total time = %f" % (time_end - time_begin))
 
 
 def main():

deep_speech_2/deploy/scorer.cpp

 #include <iostream>
+#include <unistd.h>
 #include "scorer.h"
 #include "lm/model.hh"
 #include "util/tokenize_piece.hh"
@@ -9,11 +10,16 @@ using namespace lm::ngram;
 Scorer::Scorer(float alpha, float beta, std::string lm_model_path) {
     this->_alpha = alpha;
     this->_beta = beta;
-    this->_language_model = new Model(lm_model_path.c_str());
+
+    if (access(lm_model_path.c_str(), F_OK) != 0) {
+        std::cout<<"Invalid language model path!"<<std::endl;
+        exit(1);
+    }
+    this->_language_model = LoadVirtual(lm_model_path.c_str());
 }
 
 Scorer::~Scorer(){
-   delete (Model *)this->_language_model;
+   delete (lm::base::Model *)this->_language_model;
 }
 
 /* Strip a input sentence
@@ -63,14 +69,14 @@ int Scorer::word_count(std::string sentence) {
 }
 
 double Scorer::language_model_score(std::string sentence) {
-    Model *model = (Model *)this->_language_model;
+    lm::base::Model *model = (lm::base::Model *)this->_language_model;
     State state, out_state;
     lm::FullScoreReturn ret;
-    state = model->BeginSentenceState();
+    model->BeginSentenceWrite(&state);
 
     for (util::TokenIter<util::SingleCharacter, true> it(sentence, ' '); it; ++it){
-        lm::WordIndex vocab = model->GetVocabulary().Index(*it);
-        ret = model->FullScore(state, vocab, out_state);
+        lm::WordIndex wid = model->BaseVocabulary().Index(*it);
+        ret = model->BaseFullScore(&state, wid, &out_state);
         state = out_state;
     }
     //log10 prob

deep_speech_2/deploy/setup.sh

@@ -3,9 +3,9 @@ echo "Run decoder setup ..."
 python decoder_setup.py install
 rm -r ./build
 
-echo "\nRun scorer setup ..."
+echo "Run scorer setup ..."
 
 python scorer_setup.py install
 rm -r ./build
 
-echo "\nFinish the installation of decoder and scorer."
+echo "Finish the installation of decoder and scorer."