Simplify description and codes.

evaluate.py

@@ -9,8 +9,7 @@ import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
-from error_rate import wer
+from error_rate import wer, cer
-from error_rate import cer
 import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
@@ -117,8 +116,8 @@ parser.add_argument(
     default='wer',
     choices=['wer', 'cer'],
     type=str,
-    help="There are total two error rate types including wer and cer. wer "
+    help="Error rate type for evaluation. 'wer' for word error rate and 'cer' "
-    "represents for word error rate while cer for character error rate. "
+    "for character error rate. "
     "(default: %(default)s)")
 args = parser.parse_args()
 
@@ -145,13 +144,7 @@ def evaluate():
         rnn_layer_size=args.rnn_layer_size,
         pretrained_model_path=args.model_filepath)
 
-    if args.error_rate_type == 'wer':
+    error_rate_func = cer if args.error_rate_type == 'cer' else 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(
@@ -171,10 +164,10 @@ def evaluate():
         for target, result in zip(target_transcripts, result_transcripts):
             error_sum += error_rate_func(target, result)
             num_ins += 1
-        print("%s (%d/?) = %f" % \
+        print("Error rate [%s] (%d/?) = %f" %
-                (error_rate_info, num_ins, error_sum / num_ins))
+              (args.error_rate_type, num_ins, error_sum / num_ins))
-    print("Final %s (%d/%d) = %f" % \
+    print("Final error rate [%s] (%d/%d) = %f" %
-            (error_rate_info, num_ins, num_ins, error_sum / num_ins))
+          (args.error_rate_type, num_ins, num_ins, error_sum / num_ins))
 
 
 def main():

infer.py

@@ -9,8 +9,7 @@ import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
-from error_rate import wer
+from error_rate import wer, cer
-from error_rate import cer
 import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
@@ -117,8 +116,8 @@ parser.add_argument(
     default='wer',
     choices=['wer', 'cer'],
     type=str,
-    help="There are total two error rate types including wer and cer. wer "
+    help="Error rate type for evaluation. 'wer' for word error rate and 'cer' "
-    "represents for word error rate while cer for character error rate. "
+    "for character error rate. "
     "(default: %(default)s)")
 args = parser.parse_args()
 
@@ -156,13 +155,7 @@ 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 = cer if args.error_rate_type == 'cer' else 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
@@ -170,8 +163,8 @@ def infer():
     for target, result in zip(target_transcripts, result_transcripts):
         print("\nTarget Transcription: %s\nOutput Transcription: %s" %
               (target, result))
-        print("Current %s = %f" % \
+        print("Current error rate [%s] = %f" %
-                (error_rate_info, error_rate_func(target, result)))
+              (args.error_rate_type, error_rate_func(target, result)))
 
 
 def main():