diff --git a/lm/lm_scorer.py b/lm/lm_scorer.py
index 1c029e97f63bd9b4db73430993d41c7b67c23ea1..de41754f9b8f5c83978a8c01a97223db03ca0614 100644
--- a/lm/lm_scorer.py
+++ b/lm/lm_scorer.py
@@ -42,6 +42,11 @@ class LmScorer(object):
         words = sentence.strip().split(' ')
         return len(words)
 
+    # reset alpha and beta
+    def reset_params(self, alpha, beta):
+        self._alpha = alpha
+        self._beta = beta
+
     # execute evaluation
     def __call__(self, sentence, log=False):
         """Evaluation function, gathering all the different scores
diff --git a/tests/test_decoders.py b/tests/test_decoders.py
index a5e19b08b8622621496cd628ccbe2f37f3d149da..99d8a8289d93574c58ced50923716c39cfb96558 100644
--- a/tests/test_decoders.py
+++ b/tests/test_decoders.py
@@ -76,7 +76,7 @@ class TestDecoders(unittest.TestCase):
             blank_id=len(self.vocab_list))
         self.assertEqual(beam_result[0][1], self.beam_search_result[1])
 
-    def test_beam_search_nproc_decoder(self):
+    def test_beam_search_decoder_batch(self):
         beam_results = ctc_beam_search_decoder_batch(
             probs_split=[self.probs_seq1, self.probs_seq2],
             beam_size=self.beam_size,
diff --git a/tune.py b/tune.py
index 9cea66b90fa7874f5a61499506a11dd6afeae6e9..e26bc45cee2f84d3336e8a4387487db99e8d95c9 100644
--- a/tune.py
+++ b/tune.py
@@ -12,6 +12,7 @@ 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(
@@ -180,10 +181,13 @@ def tune():
     params_grid = [(alpha, beta) for alpha in cand_alphas
                    for beta in cand_betas]
 
+    ext_scorer = LmScorer(args.alpha_from, args.beta_from,
+                          args.language_model_path)
     ## tune parameters in loop
-    for (alpha, beta) in params_grid:
+    for alpha, beta in params_grid:
         wer_sum, wer_counter = 0, 0
-        ext_scorer = LmScorer(alpha, beta, args.language_model_path)
+        # reset scorer
+        ext_scorer.reset_params(alpha, beta)
         # beam search using multiple processes
         beam_search_results = ctc_beam_search_decoder_batch(
             probs_split=probs_split,