Expose number of threads for decoding

fluid/DeepASR/decoder/pybind.cc

@@ -41,7 +41,7 @@ PYBIND11_MODULE(post_latgen_faster_mapped, m) {
           "and return the transcription.")
       .def("decode_batch",
            (std::vector<std::string> (Decoder::*)(
-               std::string,
+               std::vector<std::string>,
                const std::vector<std::vector<std::vector<kaldi::BaseFloat>>>&,
                size_t num_processes)) &
                Decoder::decode_batch,

fluid/DeepASR/examples/aishell/infer_by_ckpt.sh

-export CUDA_VISIBLE_DEVICES=0,1
-python -u ../../infer_by_ckpt.py --batch_size 48  \
-                        --checkpoint deep_asr.pass_20.checkpoint \
+export CUDA_VISIBLE_DEVICES=2,3,4,5
+python -u ../../infer_by_ckpt.py --batch_size 96  \
+                        --checkpoint checkpoints/deep_asr.pass_20.checkpoint \
                         --infer_feature_lst data/test_feature.lst  \
                         --infer_label_lst data/test_label.lst  \
                         --mean_var data/aishell/global_mean_var \

fluid/DeepASR/infer_by_ckpt.py

@@ -59,6 +59,11 @@ def parse_args():
         type=int,
         default=1749,
         help='Number of classes in label. (default: %(default)d)')
+    parser.add_argument(
+        '--num_threads',
+        type=int,
+        default=10,
+        help='The number of threads for decoding. (default: %(default)d)')
     parser.add_argument(
         '--learning_rate',
         type=float,
@@ -189,7 +194,7 @@ def infer_from_ckpt(args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    trg_trans = get_trg_trans(args)
+    #trg_trans = get_trg_trans(args)
     # load checkpoint.
     fluid.io.load_persistables(exe, args.checkpoint)
 
@@ -238,7 +243,9 @@ def infer_from_ckpt(args):
         probs, lod = lodtensor_to_ndarray(results[0])
         infer_batch = split_infer_result(probs, lod)
 
-        decoder.decode_batch(name_lst, infer_batch)
+        decoded = decoder.decode_batch(name_lst, infer_batch, args.num_threads)
+        for res in decoded:
+            print(res.encode("utf8"))
         if args.post_matrix_path is not None:
             for index, sample in enumerate(infer_batch):
                 key = name_lst[index]