diff --git a/egs/aishell2/ASR/README.md b/egs/aishell2/ASR/README.md
new file mode 100644
index 000000000..9f18b8b51
--- /dev/null
+++ b/egs/aishell2/ASR/README.md
@@ -0,0 +1,19 @@
+
+# Introduction
+
+This recipe includes some different ASR models trained with Aishell2.
+
+[./RESULTS.md](./RESULTS.md) contains the latest results.
+
+# Transducers
+
+There are various folders containing the name `transducer` in this folder.
+The following table lists the differences among them.
+
+|                                       | Encoder             | Decoder            | Comment                     |
+|---------------------------------------|---------------------|--------------------|-----------------------------|
+| `pruned_transducer_stateless5`        | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner  |
+
+The decoder in `transducer_stateless` is modified from the paper
+[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
+We place an additional Conv1d layer right after the input embedding layer.
diff --git a/egs/aishell2/ASR/RESULTS.md b/egs/aishell2/ASR/RESULTS.md
new file mode 100644
index 000000000..7ae3b3082
--- /dev/null
+++ b/egs/aishell2/ASR/RESULTS.md
@@ -0,0 +1,72 @@
+## Results
+
+### Aishell2 char-based training results (Pruned Transducer 5)
+
+#### 2022-07-11
+
+Using the codes from this commit https://github.com/k2-fsa/icefall/pull/461.
+
+When training with context size equals to 1, the WERs are
+
+|                                    |  dev-ios  | test-ios | comment                      |
+|------------------------------------|-------|----------|----------------------------------|
+|          greedy search             |   |      | --epoch 10, --avg 2, --max-duration 100  |
+| modified beam search (beam size 4) |   |      | --epoch 10, --avg 2, --max-duration 100  |
+| fast beam search (set as default)  |   |      | --epoch 10, --avg 2, --max-duration 1500 |
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./pruned_transducer_stateless5/train.py \
+  --world-size 4 \
+  --lang-dir data/lang_char \
+  --num-epochs 40 \
+  --start-epoch 1 \
+  --exp-dir /result \
+  --max-duration 300 \
+  --use-fp16 0 \
+  --num-encoder-layers 24 \
+  --dim-feedforward 1536 \
+  --nhead 8 \
+  --encoder-dim 384 \
+  --decoder-dim 512 \
+  --joiner-dim 512
+```
+
+The decoding command is:
+```
+for method in greedy_search modified_beam_search fast_beam_search; do
+  ./pruned_transducer_stateless5/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --exp-dir /result \
+    --max-duration 600 \
+    --decoding-method $method \
+    --max-sym-per-frame 1 \
+    --num-encoder-layers 24 \
+    --dim-feedforward 1536 \
+    --nhead 8 \
+    --encoder-dim 384 \
+    --decoder-dim 512 \
+    --joiner-dim 512 \
+    --context-size 1 \
+    --use-averaged-model True
+done
+```
+The tensorboard training log can be found at
+https:
+
+A pre-trained model and decoding logs can be found at <https:>
+
+When training with context size equals to 2, the WERs are
+
+|                                    |  dev-ios  | test-ios | comment                      |
+|------------------------------------|-------|----------|----------------------------------|
+|          greedy search             | 5.47  |  5.81    | --epoch 25, --avg 5, --max-duration 600  |
+| modified beam search (beam size 4) | 5.38  |  5.61    | --epoch 25, --avg 5, --max-duration 600  |
+| fast beam search (set as default)  | 5.36  |  5.61    | --epoch 25, --avg 5, --max-duration 600  |
+
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/5AxJ8LHoSre8kDAuLp4L7Q/#scalars
diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py b/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py
index f87d23cc9..eac169fff 100755
--- a/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py
+++ b/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py
@@ -20,77 +20,32 @@
 Usage:
 (1) greedy search
 ./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method greedy_search
-
-(2) beam search (not recommended)
+        --epoch 25 \
+        --avg 5 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --lang-dir data/lang_char \
+        --max-duration 100 \
+        --decoding-method greedy_search
+(2) modified beam search
 ./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method beam_search \
-    --beam-size 4
-
-(3) modified beam search
+        --epoch 25 \
+        --avg 5 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --lang-dir data/lang_char \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+(3) fast beam search
 ./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method modified_beam_search \
-    --beam-size 4
-
-(4) fast beam search (one best)
-./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method fast_beam_search \
-    --beam 20.0 \
-    --max-contexts 8 \
-    --max-states 64
-
-(5) fast beam search (nbest)
-./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method fast_beam_search_nbest \
-    --beam 20.0 \
-    --max-contexts 8 \
-    --max-states 64 \
-    --num-paths 200 \
-    --nbest-scale 0.5
-
-(6) fast beam search (nbest oracle WER)
-./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method fast_beam_search_nbest_oracle \
-    --beam 20.0 \
-    --max-contexts 8 \
-    --max-states 64 \
-    --num-paths 200 \
-    --nbest-scale 0.5
-
-(7) fast beam search (with LG)
-./pruned_transducer_stateless5/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --exp-dir ./pruned_transducer_stateless5/exp \
-    --max-duration 600 \
-    --decoding-method fast_beam_search_nbest_LG \
-    --beam 20.0 \
-    --max-contexts 8 \
-    --max-states 64
+        --epoch 25 \
+        --avg 5 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --lang-dir data/lang_char \
+        --max-duration 1500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
 """
 
 
@@ -101,15 +56,11 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import k2
-import sentencepiece as spm
 import torch
 import torch.nn as nn
-from asr_datamodule import LibriSpeechAsrDataModule
+from asr_datamodule import AiShell2AsrDataModule
 from beam_search import (
     beam_search,
-    fast_beam_search_nbest,
-    fast_beam_search_nbest_LG,
-    fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
@@ -184,17 +135,10 @@ def get_parser():
         help="The experiment dir",
     )
 
-    parser.add_argument(
-        "--bpe-model",
-        type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
-    )
-
     parser.add_argument(
         "--lang-dir",
         type=Path,
-        default="data/lang_bpe_500",
+        default="data/lang_char",
         help="The lang dir containing word table and LG graph",
     )
 
@@ -268,7 +212,7 @@ def get_parser():
     parser.add_argument(
         "--context-size",
         type=int,
-        default=2,
+        default=1,
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
@@ -306,9 +250,8 @@ def get_parser():
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    sp: spm.SentencePieceProcessor,
+    lexicon: Lexicon,
     batch: dict,
-    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
@@ -326,8 +269,6 @@ def decode_one_batch(
         It's the return value of :func:`get_params`.
       model:
         The neural model.
-      sp:
-        The BPE model.
       batch:
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
@@ -367,51 +308,8 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
-    elif params.decoding_method == "fast_beam_search_nbest_LG":
-        hyp_tokens = fast_beam_search_nbest_LG(
-            model=model,
-            decoding_graph=decoding_graph,
-            encoder_out=encoder_out,
-            encoder_out_lens=encoder_out_lens,
-            beam=params.beam,
-            max_contexts=params.max_contexts,
-            max_states=params.max_states,
-            num_paths=params.num_paths,
-            nbest_scale=params.nbest_scale,
-        )
-        for hyp in hyp_tokens:
-            hyps.append([word_table[i] for i in hyp])
-    elif params.decoding_method == "fast_beam_search_nbest":
-        hyp_tokens = fast_beam_search_nbest(
-            model=model,
-            decoding_graph=decoding_graph,
-            encoder_out=encoder_out,
-            encoder_out_lens=encoder_out_lens,
-            beam=params.beam,
-            max_contexts=params.max_contexts,
-            max_states=params.max_states,
-            num_paths=params.num_paths,
-            nbest_scale=params.nbest_scale,
-        )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
-    elif params.decoding_method == "fast_beam_search_nbest_oracle":
-        hyp_tokens = fast_beam_search_nbest_oracle(
-            model=model,
-            decoding_graph=decoding_graph,
-            encoder_out=encoder_out,
-            encoder_out_lens=encoder_out_lens,
-            beam=params.beam,
-            max_contexts=params.max_contexts,
-            max_states=params.max_states,
-            num_paths=params.num_paths,
-            ref_texts=sp.encode(supervisions["text"]),
-            nbest_scale=params.nbest_scale,
-        )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -421,8 +319,8 @@ def decode_one_batch(
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     elif params.decoding_method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -430,8 +328,8 @@ def decode_one_batch(
             encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     else:
         batch_size = encoder_out.size(0)
 
@@ -455,7 +353,7 @@ def decode_one_batch(
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"
                 )
-            hyps.append(sp.decode(hyp).split())
+            hyps.append([lexicon.token_table[idx] for idx in hyp])
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
@@ -478,8 +376,7 @@ def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
     model: nn.Module,
-    sp: spm.SentencePieceProcessor,
-    word_table: Optional[k2.SymbolTable] = None,
+    lexicon: Lexicon,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -491,10 +388,6 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
-      sp:
-        The BPE model.
-      word_table:
-        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
         only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
@@ -525,9 +418,8 @@ def decode_dataset(
         hyps_dict = decode_one_batch(
             params=params,
             model=model,
-            sp=sp,
+            lexicon=lexicon,
             decoding_graph=decoding_graph,
-            word_table=word_table,
             batch=batch,
         )
 
@@ -535,8 +427,7 @@ def decode_dataset(
             this_batch = []
             assert len(hyps) == len(texts)
             for hyp_words, ref_text in zip(hyps, texts):
-                ref_words = ref_text.split()
-                this_batch.append((ref_words, hyp_words))
+                this_batch.append((ref_text, hyp_words))
 
             results[name].extend(this_batch)
 
@@ -598,7 +489,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    LibriSpeechAsrDataModule.add_arguments(parser)
+    AiShell2AsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -607,11 +498,7 @@ def main():
 
     assert params.decoding_method in (
         "greedy_search",
-        "beam_search",
         "fast_beam_search",
-        "fast_beam_search_nbest",
-        "fast_beam_search_nbest_LG",
-        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / params.decoding_method
@@ -650,13 +537,10 @@ def main():
 
     logging.info(f"Device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
-
-    # <blk> and <unk> are defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
 
     logging.info(params)
 
@@ -744,45 +628,31 @@ def main():
     model.eval()
 
     if "fast_beam_search" in params.decoding_method:
-        if params.decoding_method == "fast_beam_search_nbest_LG":
-            lexicon = Lexicon(params.lang_dir)
-            word_table = lexicon.word_table
-            lg_filename = params.lang_dir / "LG.pt"
-            logging.info(f"Loading {lg_filename}")
-            decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
-            )
-            decoding_graph.scores *= params.ngram_lm_scale
-        else:
-            word_table = None
-            decoding_graph = k2.trivial_graph(
-                params.vocab_size - 1, device=device
-            )
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
     else:
         decoding_graph = None
-        word_table = None
 
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    librispeech = LibriSpeechAsrDataModule(args)
+    aishell2 = AiShell2AsrDataModule(args)
 
-    test_clean_cuts = librispeech.test_clean_cuts()
-    test_other_cuts = librispeech.test_other_cuts()
+    valid_cuts = aishell2.valid_cuts()
+    test_cuts = aishell2.test_cuts()
 
-    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
-    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+    # use ios sets for dev and test
+    dev_dl = aishell2.valid_dataloaders(valid_cuts)
+    test_dl = aishell2.test_dataloaders(test_cuts)
 
-    test_sets = ["test-clean", "test-other"]
-    test_dl = [test_clean_dl, test_other_dl]
+    test_sets = ["dev", "test"]
+    test_dl = [dev_dl, test_dl]
 
     for test_set, test_dl in zip(test_sets, test_dl):
         results_dict = decode_dataset(
             dl=test_dl,
             params=params,
             model=model,
-            sp=sp,
-            word_table=word_table,
+            lexicon=lexicon,
             decoding_graph=decoding_graph,
         )
 
diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/export.py b/egs/aishell2/ASR/pruned_transducer_stateless5/export.py
index 936508900..5274d06c7 100755
--- a/egs/aishell2/ASR/pruned_transducer_stateless5/export.py
+++ b/egs/aishell2/ASR/pruned_transducer_stateless5/export.py
@@ -22,9 +22,9 @@
 Usage:
 ./pruned_transducer_stateless5/export.py \
   --exp-dir ./pruned_transducer_stateless5/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 20 \
-  --avg 10
+  --lang-dir data/lang_char
+  --epoch 25 \
+  --avg 5
 
 It will generate a file exp_dir/pretrained.pt
 
@@ -34,21 +34,20 @@ you can do:
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
 
-    cd /path/to/egs/librispeech/ASR
+    cd /path/to/egs/aishell2/ASR
     ./pruned_transducer_stateless5/decode.py \
         --exp-dir ./pruned_transducer_stateless5/exp \
         --epoch 9999 \
         --avg 1 \
         --max-duration 600 \
         --decoding-method greedy_search \
-        --bpe-model data/lang_bpe_500/bpe.model
+        --lang-dir data/lang_char
 """
 
 import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
 import torch
 from train import add_model_arguments, get_params, get_transducer_model
 
@@ -58,6 +57,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import str2bool
 
 
@@ -115,10 +115,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--lang-dir",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_char",
+        help="The lang dir",
     )
 
     parser.add_argument(
@@ -132,7 +132,7 @@ def get_parser():
     parser.add_argument(
         "--context-size",
         type=int,
-        default=2,
+        default=1,
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
@@ -155,12 +155,10 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
-
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
 
     logging.info(params)
 
diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py b/egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py
index 1e100fcbd..32c47abb7 100755
--- a/egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py
+++ b/egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py
@@ -20,33 +20,24 @@ Usage:
 (1) greedy search
 ./pruned_transducer_stateless5/pretrained.py \
     --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --lang-dir ./data/lang_char \
     --method greedy_search \
     /path/to/foo.wav \
     /path/to/bar.wav
 
-(2) beam search
+(2) modified beam search
 ./pruned_transducer_stateless5/pretrained.py \
     --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
-    --method beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(3) modified beam search
-./pruned_transducer_stateless5/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --lang-dir ./data/lang_char \
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
     /path/to/bar.wav
 
-(4) fast beam search
+(3) fast beam search
 ./pruned_transducer_stateless5/pretrained.py \
     --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --lang-dir ./data/lang_char \
     --method fast_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -66,7 +57,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -79,6 +69,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall.lexicon import Lexicon
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -95,9 +87,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--lang-dir",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to lang.
+        """,
     )
 
     parser.add_argument(
@@ -165,7 +158,7 @@ def get_parser():
     parser.add_argument(
         "--context-size",
         type=int,
-        default=2,
+        default=1,
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
@@ -216,13 +209,10 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
-
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
 
     logging.info(f"{params}")
 
@@ -292,8 +282,8 @@ def main():
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     elif params.method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -302,16 +292,16 @@ def main():
             beam=params.beam_size,
         )
 
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     else:
         for i in range(num_waves):
             # fmt: off
@@ -332,11 +322,11 @@ def main():
             else:
                 raise ValueError(f"Unsupported method: {params.method}")
 
-            hyps.append(sp.decode(hyp).split())
+            hyps.append([lexicon.token_table[idx] for idx in hyp])
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
+        words = "".join(hyp)
         s += f"{filename}:\n{words}\n\n"
     logging.info(s)