diff --git a/egs/aishell/ASR/pruned_transducer_stateless7/export.py b/egs/aishell/ASR/pruned_transducer_stateless7/export.py
deleted file mode 100755
index 1b0e8d3b9..000000000
--- a/egs/aishell/ASR/pruned_transducer_stateless7/export.py
+++ /dev/null
@@ -1,321 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This script converts several saved checkpoints
-# to a single one using model averaging.
-"""
-
-Usage:
-
-(1) Export to torchscript model using torch.jit.script()
-
-./pruned_transducer_stateless7/export.py \
-  --exp-dir ./pruned_transducer_stateless7/exp \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9 \
-  --jit 1
-
-It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
-load it by `torch.jit.load("cpu_jit.pt")`.
-
-Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
-are on CPU. You can use `to("cuda")` to move them to a CUDA device.
-
-Check
-https://github.com/k2-fsa/sherpa
-for how to use the exported models outside of icefall.
-
-(2) Export `model.state_dict()`
-
-./pruned_transducer_stateless7/export.py \
-  --exp-dir ./pruned_transducer_stateless7/exp \
-  --lang-dir data/lang_char \
-  --epoch 20 \
-  --avg 10
-
-It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
-load it by `icefall.checkpoint.load_checkpoint()`.
-
-To use the generated file with `pruned_transducer_stateless7/decode.py`,
-you can do:
-
-    cd /path/to/exp_dir
-    ln -s pretrained.pt epoch-9999.pt
-
-    cd /path/to/egs/librispeech/ASR
-    ./pruned_transducer_stateless7/decode.py \
-        --exp-dir ./pruned_transducer_stateless7/exp \
-        --epoch 9999 \
-        --avg 1 \
-        --max-duration 600 \
-        --decoding-method greedy_search \
-        --lang-dir data/lang_char
-
-Check ./pretrained.py for its usage.
-
-Note: If you don't want to train a model from scratch, we have
-provided one for you. You can get it at
-
-https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21
-
-with the following commands:
-
-    sudo apt-get install git-lfs
-    git lfs install
-    git clone https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21
-    # You will find the pre-trained model in icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21exp
-"""
-
-import argparse
-import logging
-from pathlib import Path
-
-import sentencepiece as spm
-import torch
-import torch.nn as nn
-from scaling_converter import convert_scaled_to_non_scaled
-from train2 import add_model_arguments, get_params, get_transducer_model
-
-from icefall.checkpoint import (
-    average_checkpoints,
-    average_checkpoints_with_averaged_model,
-    find_checkpoints,
-    load_checkpoint,
-)
-from icefall.lexicon import Lexicon
-from icefall.utils import str2bool
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--epoch",
-        type=int,
-        default=30,
-        help="""It specifies the checkpoint to use for decoding.
-        Note: Epoch counts from 1.
-        You can specify --avg to use more checkpoints for model averaging.""",
-    )
-
-    parser.add_argument(
-        "--iter",
-        type=int,
-        default=0,
-        help="""If positive, --epoch is ignored and it
-        will use the checkpoint exp_dir/checkpoint-iter.pt.
-        You can specify --avg to use more checkpoints for model averaging.
-        """,
-    )
-
-    parser.add_argument(
-        "--avg",
-        type=int,
-        default=9,
-        help="Number of checkpoints to average. Automatically select "
-        "consecutive checkpoints before the checkpoint specified by "
-        "'--epoch' and '--iter'",
-    )
-
-    parser.add_argument(
-        "--use-averaged-model",
-        type=str2bool,
-        default=True,
-        help="Whether to load averaged model. Currently it only supports "
-        "using --epoch. If True, it would decode with the averaged model "
-        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
-        "Actually only the models with epoch number of `epoch-avg` and "
-        "`epoch` are loaded for averaging. ",
-    )
-
-    parser.add_argument(
-        "--exp-dir",
-        type=str,
-        default="pruned_transducer_stateless7/exp",
-        help="""It specifies the directory where all training related
-        files, e.g., checkpoints, log, etc, are saved
-        """,
-    )
-
-    parser.add_argument(
-        "--lang-dir",
-        type=str,
-        default="data/lang_char",
-        help="""The lang dir
-        It contains language related input files such as
-        "lexicon.txt"
-        """,
-    )
-
-    parser.add_argument(
-        "--jit",
-        type=str2bool,
-        default=False,
-        help="""True to save a model after applying torch.jit.script.
-        It will generate a file named cpu_jit.pt
-
-        Check ./jit_pretrained.py for how to use it.
-        """,
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-@torch.no_grad()
-def main():
-    args = get_parser().parse_args()
-    args.exp_dir = Path(args.exp_dir)
-
-    params = get_params()
-    params.update(vars(args))
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = 0
-    params.vocab_size = max(lexicon.tokens) + 1
-
-    logging.info(params)
-
-    logging.info("About to create model")
-    model = get_transducer_model(params)
-
-    model.to(device)
-
-    if not params.use_averaged_model:
-        if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
-            if len(filenames) == 0:
-                raise ValueError(
-                    f"No checkpoints found for"
-                    f" --iter {params.iter}, --avg {params.avg}"
-                )
-            elif len(filenames) < params.avg:
-                raise ValueError(
-                    f"Not enough checkpoints ({len(filenames)}) found for"
-                    f" --iter {params.iter}, --avg {params.avg}"
-                )
-            logging.info(f"averaging {filenames}")
-            model.to(device)
-            model.load_state_dict(average_checkpoints(filenames, device=device))
-        elif params.avg == 1:
-            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
-        else:
-            start = params.epoch - params.avg + 1
-            filenames = []
-            for i in range(start, params.epoch + 1):
-                if i >= 1:
-                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
-            logging.info(f"averaging {filenames}")
-            model.to(device)
-            model.load_state_dict(average_checkpoints(filenames, device=device))
-    else:
-        if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
-            if len(filenames) == 0:
-                raise ValueError(
-                    f"No checkpoints found for"
-                    f" --iter {params.iter}, --avg {params.avg}"
-                )
-            elif len(filenames) < params.avg + 1:
-                raise ValueError(
-                    f"Not enough checkpoints ({len(filenames)}) found for"
-                    f" --iter {params.iter}, --avg {params.avg}"
-                )
-            filename_start = filenames[-1]
-            filename_end = filenames[0]
-            logging.info(
-                "Calculating the averaged model over iteration checkpoints"
-                f" from {filename_start} (excluded) to {filename_end}"
-            )
-            model.to(device)
-            model.load_state_dict(
-                average_checkpoints_with_averaged_model(
-                    filename_start=filename_start,
-                    filename_end=filename_end,
-                    device=device,
-                )
-            )
-        else:
-            assert params.avg > 0, params.avg
-            start = params.epoch - params.avg
-            assert start >= 1, start
-            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
-            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
-            logging.info(
-                f"Calculating the averaged model over epoch range from "
-                f"{start} (excluded) to {params.epoch}"
-            )
-            model.to(device)
-            model.load_state_dict(
-                average_checkpoints_with_averaged_model(
-                    filename_start=filename_start,
-                    filename_end=filename_end,
-                    device=device,
-                )
-            )
-
-    model.to("cpu")
-    model.eval()
-
-    if params.jit is True:
-        convert_scaled_to_non_scaled(model, inplace=True)
-        # We won't use the forward() method of the model in C++, so just ignore
-        # it here.
-        # Otherwise, one of its arguments is a ragged tensor and is not
-        # torch scriptabe.
-        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
-        logging.info("Using torch.jit.script")
-        model = torch.jit.script(model)
-        filename = params.exp_dir / "cpu_jit.pt"
-        model.save(str(filename))
-        logging.info(f"Saved to {filename}")
-    else:
-        logging.info("Not using torchscript. Export model.state_dict()")
-        # Save it using a format so that it can be loaded
-        # by :func:`load_checkpoint`
-        filename = params.exp_dir / "pretrained.pt"
-        torch.save({"model": model.state_dict()}, str(filename))
-        logging.info(f"Saved to {filename}")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()
diff --git a/egs/aishell/ASR/pruned_transducer_stateless7/export.py b/egs/aishell/ASR/pruned_transducer_stateless7/export.py
new file mode 120000
index 000000000..2713792e6
--- /dev/null
+++ b/egs/aishell/ASR/pruned_transducer_stateless7/export.py
@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/export.py
\ No newline at end of file
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/export_unified.py b/egs/aishell/ASR/pruned_transducer_stateless7/export2.py
old mode 100755
new mode 100644
similarity index 98%
rename from egs/librispeech/ASR/pruned_transducer_stateless7/export_unified.py
rename to egs/aishell/ASR/pruned_transducer_stateless7/export2.py
index e491c3762..824b619e7
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/export_unified.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless7/export2.py
@@ -46,7 +46,7 @@ for how to use the exported models outside of icefall.
 
 ./pruned_transducer_stateless7/export.py \
   --exp-dir ./pruned_transducer_stateless7/exp \
-  --tokens data/lang_bpe_500/tokens.txt \
+  --tokens data/lang_char/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -66,7 +66,7 @@ you can do:
         --avg 1 \
         --max-duration 600 \
         --decoding-method greedy_search \
-        --tokens data/lang_bpe_500/tokens.txt \
+        --tokens data/lang_char/tokens.txt
 
 Check ./pretrained.py for its usage.
 
@@ -89,11 +89,10 @@ from pathlib import Path
 import re
 
 import k2
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from scaling_converter import convert_scaled_to_non_scaled
-from train import add_model_arguments, get_params, get_transducer_model
+from train2 import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py b/egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py
deleted file mode 100644
index cc54027d6..000000000
--- a/egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py
+++ /dev/null
@@ -1,348 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script loads a checkpoint and uses it to decode waves.
-You can generate the checkpoint with the following command:
-
-./pruned_transducer_stateless7/export.py \
-  --exp-dir ./pruned_transducer_stateless7/exp \
-  --lang-dir data/lang_char \
-  --epoch 20 \
-  --avg 10
-
-Usage of this script:
-
-(1) greedy search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --lang-dir ./data/lang_char \
-    --method greedy_search \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(2) beam search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --lang-dir ./data/lang_char \
-    --method beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(3) modified beam search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --lang-dir ./data/lang_char \
-    --method modified_beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(4) fast beam search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --lang-dir ./data/lang_char \
-    --method fast_beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-You can also use `./pruned_transducer_stateless7/exp/epoch-xx.pt`.
-
-Note: ./pruned_transducer_stateless7/exp/pretrained.pt is generated by
-./pruned_transducer_stateless7/export.py
-"""
-
-
-import argparse
-import logging
-import math
-from typing import List
-
-import k2
-import kaldifeat
-import sentencepiece as spm
-import torch
-import torchaudio
-from beam_search import (
-    beam_search,
-    fast_beam_search_one_best,
-    greedy_search,
-    greedy_search_batch,
-    modified_beam_search,
-)
-from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
-
-from icefall.lexicon import Lexicon
-from icefall.utils import str2bool
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--checkpoint",
-        type=str,
-        required=True,
-        help="Path to the checkpoint. "
-        "The checkpoint is assumed to be saved by "
-        "icefall.checkpoint.save_checkpoint().",
-    )
-
-    parser.add_argument(
-        "--lang-dir",
-        type=str,
-        help="""The lang dir
-        It contains language related input files such as
-        "lexicon.txt"
-        """,
-    )
-
-    parser.add_argument(
-        "--method",
-        type=str,
-        default="greedy_search",
-        help="""Possible values are:
-          - greedy_search
-          - beam_search
-          - modified_beam_search
-          - fast_beam_search
-        """,
-    )
-
-    parser.add_argument(
-        "sound_files",
-        type=str,
-        nargs="+",
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    parser.add_argument(
-        "--beam-size",
-        type=int,
-        default=4,
-        help="""An integer indicating how many candidates we will keep for each
-        frame. Used only when --method is beam_search or
-        modified_beam_search.""",
-    )
-
-    parser.add_argument(
-        "--beam",
-        type=float,
-        default=4,
-        help="""A floating point value to calculate the cutoff score during beam
-        search (i.e., `cutoff = max-score - beam`), which is the same as the
-        `beam` in Kaldi.
-        Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--max-contexts",
-        type=int,
-        default=4,
-        help="""Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--max-states",
-        type=int,
-        default=8,
-        help="""Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
-    )
-    parser.add_argument(
-        "--max-sym-per-frame",
-        type=int,
-        default=1,
-        help="""Maximum number of symbols per frame. Used only when
-        --method is greedy_search.
-        """,
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def read_sound_files(
-    filenames: List[str], expected_sample_rate: float
-) -> List[torch.Tensor]:
-    """Read a list of sound files into a list 1-D float32 torch tensors.
-    Args:
-      filenames:
-        A list of sound filenames.
-      expected_sample_rate:
-        The expected sample rate of the sound files.
-    Returns:
-      Return a list of 1-D float32 torch tensors.
-    """
-    ans = []
-    for f in filenames:
-        wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
-        # We use only the first channel
-        ans.append(wave[0])
-    return ans
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    params = get_params()
-
-    params.update(vars(args))
-
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = 0
-    params.vocab_size = max(lexicon.tokens) + 1
-    token_table = lexicon.token_table
-
-    logging.info(f"{params}")
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    logging.info("Creating model")
-    model = get_transducer_model(params)
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"Number of model parameters: {num_param}")
-
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
-    model.load_state_dict(checkpoint["model"], strict=False)
-    model.to(device)
-    model.eval()
-    model.device = device
-
-    logging.info("Constructing Fbank computer")
-    opts = kaldifeat.FbankOptions()
-    opts.device = device
-    opts.frame_opts.dither = 0
-    opts.frame_opts.snip_edges = False
-    opts.frame_opts.samp_freq = params.sample_rate
-    opts.mel_opts.num_bins = params.feature_dim
-
-    fbank = kaldifeat.Fbank(opts)
-
-    logging.info(f"Reading sound files: {params.sound_files}")
-    waves = read_sound_files(
-        filenames=params.sound_files, expected_sample_rate=params.sample_rate
-    )
-    waves = [w.to(device) for w in waves]
-
-    logging.info("Decoding started")
-    features = fbank(waves)
-    feature_lengths = [f.size(0) for f in features]
-
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
-
-    feature_lengths = torch.tensor(feature_lengths, device=device)
-
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lengths)
-
-    num_waves = encoder_out.size(0)
-    hyps = []
-    msg = f"Using {params.method}"
-    if params.method == "beam_search":
-        msg += f" with beam size {params.beam_size}"
-    logging.info(msg)
-
-    if params.method == "fast_beam_search":
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
-        hyp_tokens = fast_beam_search_one_best(
-            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,
-        )
-    elif params.method == "modified_beam_search":
-        hyp_tokens = modified_beam_search(
-            model=model,
-            encoder_out=encoder_out,
-            encoder_out_lens=encoder_out_lens,
-            beam=params.beam_size,
-        )
-    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,
-        )
-    else:
-        for i in range(num_waves):
-            # fmt: off
-            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
-            # fmt: on
-            if params.method == "greedy_search":
-                hyp_tokens = greedy_search(
-                    model=model,
-                    encoder_out=encoder_out_i,
-                    max_sym_per_frame=params.max_sym_per_frame,
-                )
-            elif params.method == "beam_search":
-                hyp_tokens = beam_search(
-                    model=model,
-                    encoder_out=encoder_out_i,
-                    beam=params.beam_size,
-                )
-            else:
-                raise ValueError(f"Unsupported method: {params.method}")
-
-    hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens]
-    s = "\n"
-    for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
-    logging.info(s)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()
diff --git a/egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py b/egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py
new file mode 120000
index 000000000..068f0f57f
--- /dev/null
+++ b/egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py
@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/pretrained.py
\ No newline at end of file
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/export.py b/egs/librispeech/ASR/pruned_transducer_stateless7/export.py
old mode 100755
new mode 100644
index 3e3160e7e..b77737a11
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/export.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/export.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 #
-# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang
+#                                            Zengrui Jin)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -26,7 +27,7 @@ Usage:
 
 ./pruned_transducer_stateless7/export.py \
   --exp-dir ./pruned_transducer_stateless7/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
   --jit 1
@@ -45,7 +46,7 @@ for how to use the exported models outside of icefall.
 
 ./pruned_transducer_stateless7/export.py \
   --exp-dir ./pruned_transducer_stateless7/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -65,7 +66,7 @@ you can do:
         --avg 1 \
         --max-duration 600 \
         --decoding-method greedy_search \
-        --bpe-model data/lang_bpe_500/bpe.model
+        --tokens data/lang_bpe_500/tokens.txt \
 
 Check ./pretrained.py for its usage.
 
@@ -85,8 +86,9 @@ with the following commands:
 import argparse
 import logging
 from pathlib import Path
+import re
 
-import sentencepiece as spm
+import k2
 import torch
 import torch.nn as nn
 from scaling_converter import convert_scaled_to_non_scaled
@@ -101,6 +103,26 @@ from icefall.checkpoint import (
 from icefall.utils import str2bool
 
 
+def num_tokens(
+    token_table: k2.SymbolTable, disambig_pattern: str = re.compile(r"^#\d+$")
+) -> int:
+    """Return the number of tokens excluding those from
+    disambiguation symbols.
+
+    Caution:
+      0 is not a token ID so it is excluded from the return value.
+    """
+    symbols = token_table.symbols
+    ans = []
+    for s in symbols:
+        if not disambig_pattern.match(s):
+            ans.append(token_table[s])
+    num_tokens = len(ans)
+    if 0 in ans:
+        num_tokens -= 1
+    return num_tokens
+
+
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -155,10 +177,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -198,12 +219,12 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    # Load id of the <blk> token and the vocab size
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 
@@ -292,7 +313,7 @@ def main():
     model.to("cpu")
     model.eval()
 
-    if params.jit is True:
+    if params.jit:
         convert_scaled_to_non_scaled(model, inplace=True)
         # We won't use the forward() method of the model in C++, so just ignore
         # it here.
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
old mode 100755
new mode 100644
index d05bafcfb..d97e1c138
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
-# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang
+#                                                    Zengrui Jin)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -29,7 +30,7 @@ Usage of this script:
 (1) greedy search
 ./pruned_transducer_stateless7/pretrained.py \
     --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     /path/to/foo.wav \
     /path/to/bar.wav
@@ -37,7 +38,7 @@ Usage of this script:
 (2) beam search
 ./pruned_transducer_stateless7/pretrained.py \
     --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -46,7 +47,7 @@ Usage of this script:
 (3) modified beam search
 ./pruned_transducer_stateless7/pretrained.py \
     --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -55,7 +56,7 @@ Usage of this script:
 (4) fast beam search
 ./pruned_transducer_stateless7/pretrained.py \
     --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method fast_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -75,7 +76,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -87,6 +87,7 @@ from beam_search import (
 )
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
+from export import num_tokens
 
 from icefall.utils import str2bool
 
@@ -106,9 +107,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -225,13 +226,13 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <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()
+    # Load id of the <blk> token and the vocab size
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -286,6 +287,12 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
     if params.method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
         hyp_tokens = fast_beam_search_one_best(
@@ -297,8 +304,8 @@ def main():
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -307,16 +314,16 @@ def main():
             beam=params.beam_size,
         )
 
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     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 hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     else:
         for i in range(num_waves):
             # fmt: off
@@ -337,12 +344,11 @@ def main():
             else:
                 raise ValueError(f"Unsupported method: {params.method}")
 
-            hyps.append(sp.decode(hyp).split())
+            hyps.append(token_ids_to_words(hyp))
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+        s += f"{filename}:\n{hyp}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained_unified.py b/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained_unified.py
deleted file mode 100755
index 9285479b6..000000000
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained_unified.py
+++ /dev/null
@@ -1,362 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang
-#                                                    Zengrui Jin)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script loads a checkpoint and uses it to decode waves.
-You can generate the checkpoint with the following command:
-
-./pruned_transducer_stateless7/export.py \
-  --exp-dir ./pruned_transducer_stateless7/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 20 \
-  --avg 10
-
-Usage of this script:
-
-(1) greedy search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --tokens ./data/lang_bpe_500/tokens.txt \
-    --method greedy_search \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(2) beam search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --tokens ./data/lang_bpe_500/tokens.txt \
-    --method beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(3) modified beam search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --tokens ./data/lang_bpe_500/tokens.txt \
-    --method modified_beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(4) fast beam search
-./pruned_transducer_stateless7/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
-    --tokens ./data/lang_bpe_500/tokens.txt \
-    --method fast_beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-You can also use `./pruned_transducer_stateless7/exp/epoch-xx.pt`.
-
-Note: ./pruned_transducer_stateless7/exp/pretrained.pt is generated by
-./pruned_transducer_stateless7/export.py
-"""
-
-
-import argparse
-import logging
-import math
-from typing import List
-
-import k2
-import kaldifeat
-import torch
-import torchaudio
-from beam_search import (
-    beam_search,
-    fast_beam_search_one_best,
-    greedy_search,
-    greedy_search_batch,
-    modified_beam_search,
-)
-from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
-from export_unified import num_tokens
-
-from icefall.utils import str2bool
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--checkpoint",
-        type=str,
-        required=True,
-        help="Path to the checkpoint. "
-        "The checkpoint is assumed to be saved by "
-        "icefall.checkpoint.save_checkpoint().",
-    )
-
-    parser.add_argument(
-        "--tokens",
-        type=str,
-        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the tokens.txt.",
-    )
-
-    parser.add_argument(
-        "--method",
-        type=str,
-        default="greedy_search",
-        help="""Possible values are:
-          - greedy_search
-          - beam_search
-          - modified_beam_search
-          - fast_beam_search
-        """,
-    )
-
-    parser.add_argument(
-        "sound_files",
-        type=str,
-        nargs="+",
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    parser.add_argument(
-        "--beam-size",
-        type=int,
-        default=4,
-        help="""An integer indicating how many candidates we will keep for each
-        frame. Used only when --method is beam_search or
-        modified_beam_search.""",
-    )
-
-    parser.add_argument(
-        "--beam",
-        type=float,
-        default=4,
-        help="""A floating point value to calculate the cutoff score during beam
-        search (i.e., `cutoff = max-score - beam`), which is the same as the
-        `beam` in Kaldi.
-        Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--max-contexts",
-        type=int,
-        default=4,
-        help="""Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--max-states",
-        type=int,
-        default=8,
-        help="""Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
-    )
-    parser.add_argument(
-        "--max-sym-per-frame",
-        type=int,
-        default=1,
-        help="""Maximum number of symbols per frame. Used only when
-        --method is greedy_search.
-        """,
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def read_sound_files(
-    filenames: List[str], expected_sample_rate: float
-) -> List[torch.Tensor]:
-    """Read a list of sound files into a list 1-D float32 torch tensors.
-    Args:
-      filenames:
-        A list of sound filenames.
-      expected_sample_rate:
-        The expected sample rate of the sound files.
-    Returns:
-      Return a list of 1-D float32 torch tensors.
-    """
-    ans = []
-    for f in filenames:
-        wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
-        # We use only the first channel
-        ans.append(wave[0])
-    return ans
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    params = get_params()
-
-    params.update(vars(args))
-
-    # Load tokens.txt here
-    token_table = k2.SymbolTable.from_file(params.tokens)
-
-    # Load id of the <blk> token and the vocab size
-    params.blank_id = token_table["<blk>"]
-    params.unk_id = token_table["<unk>"]
-    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
-
-    logging.info(f"{params}")
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    logging.info("Creating model")
-    model = get_transducer_model(params)
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"Number of model parameters: {num_param}")
-
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
-    model.load_state_dict(checkpoint["model"], strict=False)
-    model.to(device)
-    model.eval()
-    model.device = device
-
-    logging.info("Constructing Fbank computer")
-    opts = kaldifeat.FbankOptions()
-    opts.device = device
-    opts.frame_opts.dither = 0
-    opts.frame_opts.snip_edges = False
-    opts.frame_opts.samp_freq = params.sample_rate
-    opts.mel_opts.num_bins = params.feature_dim
-
-    fbank = kaldifeat.Fbank(opts)
-
-    logging.info(f"Reading sound files: {params.sound_files}")
-    waves = read_sound_files(
-        filenames=params.sound_files, expected_sample_rate=params.sample_rate
-    )
-    waves = [w.to(device) for w in waves]
-
-    logging.info("Decoding started")
-    features = fbank(waves)
-    feature_lengths = [f.size(0) for f in features]
-
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
-
-    feature_lengths = torch.tensor(feature_lengths, device=device)
-
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lengths)
-
-    num_waves = encoder_out.size(0)
-    hyps = []
-    msg = f"Using {params.method}"
-    if params.method == "beam_search":
-        msg += f" with beam size {params.beam_size}"
-    logging.info(msg)
-
-    def token_ids_to_words(token_ids: List[int]) -> str:
-        text = ""
-        for i in token_ids:
-            text += token_table[i]
-        return text.replace("▁", " ").strip()
-
-    if params.method == "fast_beam_search":
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
-        hyp_tokens = fast_beam_search_one_best(
-            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,
-        )
-        for hyp in hyp_tokens:
-            hyps.append(token_ids_to_words(hyp))
-    elif params.method == "modified_beam_search":
-        hyp_tokens = modified_beam_search(
-            model=model,
-            encoder_out=encoder_out,
-            encoder_out_lens=encoder_out_lens,
-            beam=params.beam_size,
-        )
-
-        for hyp in hyp_tokens:
-            hyps.append(token_ids_to_words(hyp))
-    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 hyp_tokens:
-            hyps.append(token_ids_to_words(hyp))
-    else:
-        for i in range(num_waves):
-            # fmt: off
-            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
-            # fmt: on
-            if params.method == "greedy_search":
-                hyp = greedy_search(
-                    model=model,
-                    encoder_out=encoder_out_i,
-                    max_sym_per_frame=params.max_sym_per_frame,
-                )
-            elif params.method == "beam_search":
-                hyp = beam_search(
-                    model=model,
-                    encoder_out=encoder_out_i,
-                    beam=params.beam_size,
-                )
-            else:
-                raise ValueError(f"Unsupported method: {params.method}")
-
-            hyps.append(token_ids_to_words(hyp))
-
-    s = "\n"
-    for filename, hyp in zip(params.sound_files, hyps):
-        s += f"{filename}:\n{hyp}\n\n"
-    logging.info(s)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()