diff --git a/deploy/demo_server.py b/deploy/demo_server.py
index 53be16f7..eca13dce 100644
--- a/deploy/demo_server.py
+++ b/deploy/demo_server.py
@@ -171,11 +171,11 @@ def start_server():
             feeding_dict=data_generator.feeding)
 
         if args.decoding_method == "ctc_greedy":
-            result_transcript = ds2_model.infer_batch_greedy(
+            result_transcript = ds2_model.decode_batch_greedy(
                 probs_split=probs_split,
                 vocab_list=vocab_list)
         else:
-            result_transcript = ds2_model.infer_batch_beam_search(
+            result_transcript = ds2_model.decode_batch_beam_search(
                 probs_split=probs_split,
                 beam_alpha=args.alpha,
                 beam_beta=args.beta,
diff --git a/infer.py b/infer.py
index 5dd9b406..ff45a5dc 100644
--- a/infer.py
+++ b/infer.py
@@ -98,11 +98,11 @@ def infer():
     probs_split = ds2_model.infer_probs_batch(infer_data=infer_data,
             feeding_dict=data_generator.feeding)
     if args.decoding_method == "ctc_greedy":
-        result_transcripts = ds2_model.infer_batch_greedy(
+        result_transcripts = ds2_model.decode_batch_greedy(
             probs_split=probs_split,
             vocab_list=vocab_list)
     else:
-        result_transcripts = ds2_model.infer_batch_beam_search(
+        result_transcripts = ds2_model.decode_batch_beam_search(
             probs_split=probs_split,
             beam_alpha=args.alpha,
             beam_beta=args.beta,
diff --git a/model_utils/model.py b/model_utils/model.py
index 70ba7bb9..a8283fae 100644
--- a/model_utils/model.py
+++ b/model_utils/model.py
@@ -205,8 +205,9 @@ class DeepSpeech2Model(object):
         ]
         return probs_split
 
-    def infer_batch_greedy(self, probs_split, vocab_list):
-        """
+    def decode_batch_greedy(self, probs_split, vocab_list):
+        """Decode by best path for a batch of probs matrix input.
+
         :param probs_split: List of 2-D probability matrix, and each consists
                             of prob vectors for one speech utterancce.
         :param probs_split: List of matrix
@@ -256,11 +257,10 @@ class DeepSpeech2Model(object):
             self.logger.info("no language model provided, "
                              "decoding by pure beam search without scorer.")
 
-    def infer_batch_beam_search(self, probs_split, beam_alpha, beam_beta,
-                                beam_size, cutoff_prob, cutoff_top_n,
-                                vocab_list, num_processes):
-        """Model inference. Infer the transcription for a batch of speech
-        utterances.
+    def decode_batch_beam_search(self, probs_split, beam_alpha, beam_beta,
+                                 beam_size, cutoff_prob, cutoff_top_n,
+                                 vocab_list, num_processes):
+        """Decode by beam search for a batch of probs matrix input.
 
         :param probs_split: List of 2-D probability matrix, and each consists
                             of prob vectors for one speech utterancce.
diff --git a/test.py b/test.py
index 24ce54a2..a82893c0 100644
--- a/test.py
+++ b/test.py
@@ -102,11 +102,11 @@ def evaluate():
             feeding_dict=data_generator.feeding)
 
         if args.decoding_method == "ctc_greedy":
-            result_transcripts = ds2_model.infer_batch_greedy(
+            result_transcripts = ds2_model.decode_batch_greedy(
                 probs_split=probs_split,
                 vocab_list=vocab_list)
         else:
-            result_transcripts = ds2_model.infer_batch_beam_search(
+            result_transcripts = ds2_model.decode_batch_beam_search(
                 probs_split=probs_split,
                 beam_alpha=args.alpha,
                 beam_beta=args.beta,
diff --git a/tools/tune.py b/tools/tune.py
index 923e6c3c..d8e28c58 100644
--- a/tools/tune.py
+++ b/tools/tune.py
@@ -128,7 +128,7 @@ def tune():
         num_ins += len(target_transcripts)
         # grid search
         for index, (alpha, beta) in enumerate(params_grid):
-            result_transcripts = ds2_model.infer_batch_beam_search(
+            result_transcripts = ds2_model.decode_batch_beam_search(
                 probs_split=probs_split,
                 beam_alpha=alpha,
                 beam_beta=beta,