Fix bugs for demo_server.py.

deploy/_init_paths.py

+"""Set up paths for DS2"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path
+import sys
+
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+
+this_dir = os.path.dirname(__file__)
+
+# Add project path to PYTHONPATH
+proj_path = os.path.join(this_dir, '..')
+add_path(proj_path)

deploy/demo_server.py

@@ -12,7 +12,7 @@ import paddle.v2 as paddle
 import _init_paths
 from data_utils.data import DataGenerator
 from model_utils.model import DeepSpeech2Model
-from data_utils.utils import read_manifest
+from data_utils.utility import read_manifest
 from utils.utility import add_arguments, print_arguments
 
 parser = argparse.ArgumentParser(description=__doc__)
@@ -26,6 +26,7 @@ add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
 add_arg('alpha',            float,  0.36,   "Coef of LM for beam search.")
 add_arg('beta',             float,  0.25,   "Coef of WC for beam search.")
 add_arg('cutoff_prob',      float,  0.99,   "Cutoff probability for pruning.")
+add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")
 add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
@@ -156,6 +157,8 @@ def start_server():
         pretrained_model_path=args.model_path,
         share_rnn_weights=args.share_rnn_weights)
 
+    vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
+
     # prepare ASR inference handler
     def file_to_transcript(filename):
         feature = data_generator.process_utterance(filename, "")
@@ -166,7 +169,8 @@ def start_server():
             beam_beta=args.beta,
             beam_size=args.beam_size,
             cutoff_prob=args.cutoff_prob,
-            vocab_list=data_generator.vocab_list,
+            cutoff_top_n=args.cutoff_top_n,
+            vocab_list=vocab_list,
             language_model_path=args.lang_model_path,
             num_processes=1)
         return result_transcript[0]