Rename infer_probs_batch to infer_batch_probs

deploy/demo_server.py

@@ -166,7 +166,7 @@ def start_server():
     # prepare ASR inference handler
     def file_to_transcript(filename):
         feature = data_generator.process_utterance(filename, "")
-        probs_split = ds2_model.infer_probs_batch(
+        probs_split = ds2_model.infer_batch_probs(
             infer_data=[feature],
             feeding_dict=data_generator.feeding)
 

infer.py

@@ -92,7 +92,7 @@ def infer():
 
     if args.decoding_method == "ctc_greedy":
         ds2_model.logger.info("start inference ...")
-        probs_split = ds2_model.infer_probs_batch(infer_data=infer_data,
+        probs_split = ds2_model.infer_batch_probs(infer_data=infer_data,
             feeding_dict=data_generator.feeding)
         result_transcripts = ds2_model.decode_batch_greedy(
             probs_split=probs_split,
@@ -101,7 +101,7 @@ def infer():
         ds2_model.init_ext_scorer(args.alpha, args.beta, args.lang_model_path,
                                   vocab_list)
         ds2_model.logger.info("start inference ...")
-        probs_split = ds2_model.infer_probs_batch(infer_data=infer_data,
+        probs_split = ds2_model.infer_batch_probs(infer_data=infer_data,
             feeding_dict=data_generator.feeding)
         result_transcripts = ds2_model.decode_batch_beam_search(
             probs_split=probs_split,

model_utils/model.py

@@ -173,7 +173,7 @@ class DeepSpeech2Model(object):
         # run inference
         return self._loss_inferer.infer(input=infer_data)
 
-    def infer_probs_batch(self, infer_data, feeding_dict):
+    def infer_batch_probs(self, infer_data, feeding_dict):
         """Infer the prob matrices for a batch of speech utterances.
 
         :param infer_data: List of utterances to infer, with each utterance

test.py

@@ -97,7 +97,7 @@ def evaluate():
     errors_sum, len_refs, num_ins = 0.0, 0, 0
     ds2_model.logger.info("start evaluation ...")
     for infer_data in batch_reader():
-        probs_split = ds2_model.infer_probs_batch(
+        probs_split = ds2_model.infer_batch_probs(
             infer_data=infer_data,
             feeding_dict=data_generator.feeding)
 

tools/tune.py

@@ -120,7 +120,7 @@ def tune():
     for infer_data in batch_reader():
         if (args.num_batches >= 0) and (cur_batch >= args.num_batches):
             break
-        probs_split = ds2_model.infer_probs_batch(
+        probs_split = ds2_model.infer_batch_probs(
             infer_data=infer_data,
             feeding_dict=data_generator.feeding)
         target_transcripts = [ data[1] for data in infer_data ]