Update default config params and result display for evaluator.py and infer.py for DS2.

deep_speech_2/evaluate.py

@@ -4,6 +4,7 @@ from __future__ import division
 from __future__ import print_function
 
 import distutils.util
+import sys
 import argparse
 import gzip
 import paddle.v2 as paddle
@@ -12,13 +13,19 @@ from model import deep_speech2
 from decoder import *
 from lm.lm_scorer import LmScorer
 from error_rate import wer
+import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--batch_size",
-    default=100,
+    default=128,
     type=int,
     help="Minibatch size for evaluation. (default: %(default)s)")
+parser.add_argument(
+    "--trainer_count",
+    default=8,
+    type=int,
+    help="Trainer number. (default: %(default)s)")
 parser.add_argument(
     "--num_conv_layers",
     default=2,
@@ -58,8 +65,8 @@ parser.add_argument(
     "--decode_method",
     default='beam_search',
     type=str,
-    help="Method for ctc decoding, best_path or beam_search. (default: %(default)s)"
-)
+    help="Method for ctc decoding, best_path or beam_search. "
+    "(default: %(default)s)")
 parser.add_argument(
     "--language_model_path",
     default="lm/data/common_crawl_00.prune01111.trie.klm",
@@ -67,12 +74,12 @@ parser.add_argument(
     help="Path for language model. (default: %(default)s)")
 parser.add_argument(
     "--alpha",
-    default=0.26,
+    default=0.36,
     type=float,
     help="Parameter associated with language model. (default: %(default)f)")
 parser.add_argument(
     "--beta",
-    default=0.1,
+    default=0.25,
     type=float,
     help="Parameter associated with word count. (default: %(default)f)")
 parser.add_argument(
@@ -191,7 +198,7 @@ def evaluate():
                 blank_id=len(data_generator.vocab_list),
                 num_processes=args.num_processes_beam_search,
                 ext_scoring_func=ext_scorer,
-                cutoff_prob=args.cutoff_prob, )
+                cutoff_prob=args.cutoff_prob)
             for i, beam_search_result in enumerate(beam_search_results):
                 wer_sum += wer(target_transcription[i],
                                beam_search_result[0][1])
@@ -199,12 +206,15 @@ def evaluate():
         else:
             raise ValueError("Decoding method [%s] is not supported." %
                              decode_method)
+        print("WER (%d/?) = %f" % (wer_counter, wer_sum / wer_counter))
 
-    print("Final WER = %f" % (wer_sum / wer_counter))
+    print("Final WER (%d/%d) = %f" % (wer_counter, wer_counter,
+                                      wer_sum / wer_counter))
 
 
 def main():
-    paddle.init(use_gpu=args.use_gpu, trainer_count=1)
+    utils.print_arguments(args)
+    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
     evaluate()
 
 

deep_speech_2/infer.py

@@ -57,6 +57,11 @@ parser.add_argument(
     type=str,
     help="Feature type of audio data: 'linear' (power spectrum)"
     " or 'mfcc'. (default: %(default)s)")
+parser.add_argument(
+    "--trainer_count",
+    default=8,
+    type=int,
+    help="Trainer number. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -208,7 +213,7 @@ def infer():
             wer_cur = wer(target_transcription[i], beam_search_result[0][1])
             wer_sum += wer_cur
             wer_counter += 1
-            print("cur wer = %f , average wer = %f" %
+            print("Current WER = %f , Average WER = %f" %
                   (wer_cur, wer_sum / wer_counter))
     else:
         raise ValueError("Decoding method [%s] is not supported." %
@@ -217,7 +222,7 @@ def infer():
 
 def main():
     utils.print_arguments(args)
-    paddle.init(use_gpu=args.use_gpu, trainer_count=1)
+    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
     infer()