adapt to the new folder structure of DS2

examples/librispeech/generate.sh

@@ -12,9 +12,9 @@ python -u infer.py \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

examples/librispeech/run_test.sh

@@ -3,7 +3,7 @@
 pushd ../..
 
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python -u evaluate.py \
+python -u test.py \
 --batch_size=128 \
 --trainer_count=8 \
 --beam_size=500 \
@@ -12,9 +12,9 @@ python -u evaluate.py \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

infer.py

@@ -84,6 +84,8 @@ def infer():
         use_gru=args.use_gru,
         pretrained_model_path=args.model_path,
         share_rnn_weights=args.share_rnn_weights)
+
+    vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
     result_transcripts = ds2_model.infer_batch(
         infer_data=infer_data,
         decoding_method=args.decoding_method,
@@ -91,7 +93,7 @@ def infer():
         beam_beta=args.beta,
         beam_size=args.beam_size,
         cutoff_prob=args.cutoff_prob,
-        vocab_list=data_generator.vocab_list,
+        vocab_list=vocab_list,
         language_model_path=args.lang_model_path,
         num_processes=args.num_proc_bsearch)
 

models/model.py

@@ -8,8 +8,9 @@ import os
 import time
 import gzip
 import paddle.v2 as paddle
-from lm.lm_scorer import LmScorer
-from models.decoder import ctc_greedy_decoder, ctc_beam_search_decoder
+from models.swig_decoders_wrapper import Scorer
+from models.swig_decoders_wrapper import ctc_greedy_decoder
+from models.swig_decoders_wrapper import ctc_beam_search_decoder_batch
 from models.network import deep_speech_v2_network
 
 
@@ -199,9 +200,12 @@ class DeepSpeech2Model(object):
         elif decoding_method == "ctc_beam_search":
             # initialize external scorer
             if self._ext_scorer == None:
-                self._ext_scorer = LmScorer(beam_alpha, beam_beta,
-                                            language_model_path)
+                self._ext_scorer = Scorer(beam_alpha, beam_beta,
+                                          language_model_path)
                 self._loaded_lm_path = language_model_path
+                self._ext_scorer.set_char_map(vocab_list)
+                if (not self._ext_scorer.is_character_based()):
+                    self._ext_scorer.fill_dictionary(True)
             else:
                 self._ext_scorer.reset_params(beam_alpha, beam_beta)
                 assert self._loaded_lm_path == language_model_path

deploy/ctc_decoders.cpp → models/swig_decoders/ctc_decoders.cpp

@@ -10,8 +10,8 @@
 #include "fst/fstlib.h"
 #include "path_trie.h"
 
-std::string ctc_best_path_decoder(std::vector<std::vector<double>> probs_seq,
-                                  std::vector<std::string> vocabulary) {
+std::string ctc_greedy_decoder(std::vector<std::vector<double>> probs_seq,
+                               std::vector<std::string> vocabulary) {
   // dimension check
   int num_time_steps = probs_seq.size();
   for (int i = 0; i < num_time_steps; i++) {

deploy/ctc_decoders.h → models/swig_decoders/ctc_decoders.h

@@ -16,7 +16,7 @@
  *     A vector that each element is a pair of score  and decoding result,
  *     in desending order.
  */
-std::string ctc_best_path_decoder(std::vector<std::vector<double>> probs_seq,
+std::string ctc_greedy_decoder(std::vector<std::vector<double>> probs_seq,
                                   std::vector<std::string> vocabulary);
 
 /* CTC Beam Search Decoder

deploy/swig_decoders_wrapper.py → models/swig_decoders_wrapper.py

@@ -23,7 +23,7 @@ class Scorer(swig_decoders.Scorer):
         swig_decoders.Scorer.__init__(self, alpha, beta, model_path)
 
 
-def ctc_best_path_decoder(probs_seq, vocabulary):
+def ctc_greedy_decoder(probs_seq, vocabulary):
     """Wrapper for ctc best path decoder in swig.
 
     :param probs_seq: 2-D list of probability distributions over each time
@@ -35,7 +35,7 @@ def ctc_best_path_decoder(probs_seq, vocabulary):
     :return: Decoding result string.
     :rtype: basestring
     """
-    return swig_decoders.ctc_best_path_decoder(probs_seq.tolist(), vocabulary)
+    return swig_decoders.ctc_greedy_decoder(probs_seq.tolist(), vocabulary)
 
 
 def ctc_beam_search_decoder(probs_seq,

test.py

@@ -85,6 +85,7 @@ def evaluate():
         pretrained_model_path=args.model_path,
         share_rnn_weights=args.share_rnn_weights)
 
+    vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
     error_rate_func = cer if args.error_rate_type == 'cer' else wer
     error_sum, num_ins = 0.0, 0
     for infer_data in batch_reader():
@@ -95,7 +96,7 @@ def evaluate():
             beam_beta=args.beta,
             beam_size=args.beam_size,
             cutoff_prob=args.cutoff_prob,
-            vocab_list=data_generator.vocab_list,
+            vocab_list=vocab_list,
             language_model_path=args.lang_model_path,
             num_processes=args.num_proc_bsearch)
         target_transcripts = [