Adapt demo_server to the decoupling in infer_batch()

deploy/demo_server.py

@@ -160,22 +160,30 @@ def start_server():
 
     vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
 
+    if args.decoding_method == "ctc_beam_search":
+        ds2_model.init_ext_scorer(args.alpha, args.beta, args.lang_model_path,
+                                  vocab_list)
     # prepare ASR inference handler
     def file_to_transcript(filename):
         feature = data_generator.process_utterance(filename, "")
-
-        result_transcript = ds2_model.infer_batch(
+        probs_split = ds2_model.infer_probs_batch(
             infer_data=[feature],
-            decoding_method=args.decoding_method,
-            beam_alpha=args.alpha,
-            beam_beta=args.beta,
-            beam_size=args.beam_size,
-            cutoff_prob=args.cutoff_prob,
-            cutoff_top_n=args.cutoff_top_n,
-            vocab_list=vocab_list,
-            language_model_path=args.lang_model_path,
-            num_processes=1,
             feeding_dict=data_generator.feeding)
+
+        if args.decoding_method == "ctc_greedy":
+            result_transcript = ds2_model.infer_batch_greedy(
+                probs_split=probs_split,
+                vocab_list=vocab_list)
+        else:
+            result_transcript = ds2_model.infer_batch_beam_search(
+                probs_split=probs_split,
+                beam_alpha=args.alpha,
+                beam_beta=args.beta,
+                beam_size=args.beam_size,
+                cutoff_prob=args.cutoff_prob,
+                cutoff_top_n=args.cutoff_top_n,
+                vocab_list=vocab_list,
+                num_processes=1)
         return result_transcript[0]
 
     # warming up with utterrances sampled from Librispeech