Make type of error rate optional.

evaluate.py

@@ -10,6 +10,7 @@ import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
 from error_rate import wer
+from error_rate import cer
 import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
@@ -111,6 +112,14 @@ parser.add_argument(
     default='datasets/vocab/eng_vocab.txt',
     type=str,
     help="Vocabulary filepath. (default: %(default)s)")
+parser.add_argument(
+    "--error_rate_type",
+    default='wer',
+    choices=['wer', 'cer'],
+    type=str,
+    help="There are total two error rate types including wer and cer. wer "
+    "represents for word error rate while cer for character error rate. "
+    "(default: %(default)s)")
 args = parser.parse_args()
 
 
@@ -136,7 +145,14 @@ def evaluate():
         rnn_layer_size=args.rnn_layer_size,
         pretrained_model_path=args.model_filepath)
 
-    wer_sum, num_ins = 0.0, 0
+    if args.error_rate_type == 'wer':
+        error_rate_func = wer
+        error_rate_info = 'WER'
+    else:
+        error_rate_func = cer
+        error_rate_info = 'CER'
+
+    error_sum, num_ins = 0.0, 0
     for infer_data in batch_reader():
         result_transcripts = ds2_model.infer_batch(
             infer_data=infer_data,
@@ -153,10 +169,12 @@ def evaluate():
             for _, transcript in infer_data
         ]
         for target, result in zip(target_transcripts, result_transcripts):
-            wer_sum += wer(target, result)
+            error_sum += error_rate_func(target, result)
             num_ins += 1
-        print("WER (%d/?) = %f" % (num_ins, wer_sum / num_ins))
-    print("Final WER (%d/%d) = %f" % (num_ins, num_ins, wer_sum / num_ins))
+        print("%s (%d/?) = %f" % \
+                (error_rate_info, num_ins, error_sum / num_ins))
+    print("Final %s (%d/%d) = %f" % \
+            (error_rate_info, num_ins, num_ins, error_sum / num_ins))
 
 
 def main():

infer.py

@@ -10,6 +10,7 @@ import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
 from error_rate import wer
+from error_rate import cer
 import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
@@ -111,6 +112,14 @@ parser.add_argument(
     type=float,
     help="The cutoff probability of pruning"
     "in beam search. (default: %(default)f)")
+parser.add_argument(
+    "--error_rate_type",
+    default='wer',
+    choices=['wer', 'cer'],
+    type=str,
+    help="There are total two error rate types including wer and cer. wer "
+    "represents for word error rate while cer for character error rate. "
+    "(default: %(default)s)")
 args = parser.parse_args()
 
 
@@ -147,6 +156,13 @@ def infer():
         language_model_path=args.language_model_path,
         num_processes=args.num_processes_beam_search)
 
+    if args.error_rate_type == 'wer':
+        error_rate_func = wer
+        error_rate_info = 'wer'
+    else:
+        error_rate_func = cer
+        error_rate_info = 'cer'
+
     target_transcripts = [
         ''.join([data_generator.vocab_list[token] for token in transcript])
         for _, transcript in infer_data
@@ -154,7 +170,8 @@ def infer():
     for target, result in zip(target_transcripts, result_transcripts):
         print("\nTarget Transcription: %s\nOutput Transcription: %s" %
               (target, result))
-        print("Current wer = %f" % wer(target, result))
+        print("Current %s = %f" % \
+                (error_rate_info, error_rate_func(target, result)))
 
 
 def main():

model.py

@@ -185,7 +185,7 @@ class DeepSpeech2Model(object):
             # best path decode
             for i, probs in enumerate(probs_split):
                 output_transcription = ctc_best_path_decoder(
-                    probs_seq=probs, vocabulary=data_generator.vocab_list)
+                    probs_seq=probs, vocabulary=vocab_list)
                 results.append(output_transcription)
         elif decode_method == "beam_search":
             # initialize external scorer