Add decoder wrapper to inference

fluid/DeepASR/data_utils/util.py

@@ -50,6 +50,13 @@ def batch_to_ndarray(batch_samples, lod):
     return (batch_feature, batch_label)
 
 
+def split_infer_result(infer_seq, lod):
+    infer_batch = []
+    for i in xrange(0, len(lod[0]) - 1):
+        infer_batch.append(infer_seq[lod[0][i]:lod[0][i + 1]])
+    return infer_batch
+
+
 class DaemonProcessGroup(object):
     def __init__(self, proc_num, target, args):
         self._proc_num = proc_num

fluid/DeepASR/infer.py

@@ -10,6 +10,7 @@ import data_utils.augmentor.trans_add_delta as trans_add_delta
 import data_utils.augmentor.trans_splice as trans_splice
 import data_utils.data_reader as reader
 from data_utils.util import lodtensor_to_ndarray
+from data_utils.util import split_infer_result
 
 
 def parse_args():
@@ -58,13 +59,6 @@ def print_arguments(args):
     print('------------------------------------------------')
 
 
-def split_infer_result(infer_seq, lod):
-    infer_batch = []
-    for i in xrange(0, len(lod[0]) - 1):
-        infer_batch.append(infer_seq[lod[0][i]:lod[0][i + 1]])
-    return infer_batch
-
-
 def infer(args):
     """ Gets one batch of feature data and predicts labels for each sample.
     """

fluid/DeepASR/infer_by_ckpt.py

@@ -13,8 +13,10 @@ import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
 import data_utils.augmentor.trans_add_delta as trans_add_delta
 import data_utils.augmentor.trans_splice as trans_splice
 import data_utils.async_data_reader as reader
+import decoder.decoder as decoder
 from data_utils.util import lodtensor_to_ndarray
 from model_utils.model import stacked_lstmp_model
+from data_utils.util import split_infer_result
 
 
 def parse_args():
@@ -141,13 +143,20 @@ def infer_from_ckpt(args):
 
         infer_data_reader.recycle(features, labels, lod)
 
-        cost, acc = exe.run(infer_program,
-                            feed={"feature": feature_t,
-                                  "label": label_t},
-                            fetch_list=[avg_cost, accuracy],
-                            return_numpy=False)
-        infer_costs.append(lodtensor_to_ndarray(cost)[0])
-        infer_accs.append(lodtensor_to_ndarray(acc)[0])
+        results = exe.run(infer_program,
+                          feed={"feature": feature_t,
+                                "label": label_t},
+                          fetch_list=[prediction, avg_cost, accuracy],
+                          return_numpy=False)
+        infer_costs.append(lodtensor_to_ndarray(results[1])[0])
+        infer_accs.append(lodtensor_to_ndarray(results[2])[0])
+
+        probs, lod = lodtensor_to_ndarray(results[0])
+        infer_batch = split_infer_result(probs, lod)
+        for index, sample in enumerate(infer_batch):
+            print("Decoding %d: " % (batch_id * args.batch_size + index),
+                  decoder.decode(sample))
+
     print(np.mean(infer_costs), np.mean(infer_accs))