diff --git a/egs/reazonspeech/ASR/zipformer/export.py b/egs/reazonspeech/ASR/zipformer/export.py
deleted file mode 100644
index 666bcc831..000000000
--- a/egs/reazonspeech/ASR/zipformer/export.py
+++ /dev/null
@@ -1,250 +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.
-
-import argparse
-import logging
-from pathlib import Path
-
-import torch
-from scaling_converter import convert_scaled_to_non_scaled
-from tokenizer import Tokenizer
-from train 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.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_streaming/exp",
-        help="""It specifies the directory where all training related
-        files, e.g., checkpoints, log, etc, are saved
-        """,
-    )
-
-    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=2,
-        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():
-    parser = get_parser()
-    Tokenizer.add_arguments(parser)
-    args = 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}")
-
-    sp = Tokenizer.load(params.lang, params.lang_type)
-
-    # <blk> is defined in local/prepare_lang_char.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
-
-    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/reazonspeech/ASR/zipformer/export.py b/egs/reazonspeech/ASR/zipformer/export.py
new file mode 120000
index 000000000..dfc1bec08
--- /dev/null
+++ b/egs/reazonspeech/ASR/zipformer/export.py
@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export.py
\ No newline at end of file
diff --git a/egs/reazonspeech/ASR/zipformer/pretrained.py b/egs/reazonspeech/ASR/zipformer/pretrained.py
deleted file mode 100644
index 932026868..000000000
--- a/egs/reazonspeech/ASR/zipformer/pretrained.py
+++ /dev/null
@@ -1,347 +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_streaming/export.py \
-  --exp-dir ./pruned_transducer_stateless7_streaming/exp \
-  --lang data/lang_char \
-  --epoch 20 \
-  --avg 10
-
-Usage of this script:
-
-(1) greedy search
-./pruned_transducer_stateless7_streaming/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7_streaming/exp/pretrained.pt \
-    --lang data/lang_char \
-    --method greedy_search \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(2) beam search
-./pruned_transducer_stateless7_streaming/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7_streaming/exp/pretrained.pt \
-    --lang data/lang_char \
-    --method beam_search \
-    --beam-size 4 \
-    /path/to/foo.wav \
-    /path/to/bar.wav
-
-(3) modified beam search
-./pruned_transducer_stateless7_streaming/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7_streaming/exp/pretrained.pt \
-    --lang 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_streaming/pretrained.py \
-    --checkpoint ./pruned_transducer_stateless7_streaming/exp/pretrained.pt \
-    --lang 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_streaming/exp/epoch-xx.pt`.
-
-Note: ./pruned_transducer_stateless7_streaming/exp/pretrained.pt is generated by
-./pruned_transducer_stateless7_streaming/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 tokenizer import Tokenizer
-from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
-
-
-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(
-        "--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()
-    Tokenizer.add_arguments(parser)
-    args = parser.parse_args()
-
-    params = get_params()
-
-    params.update(vars(args))
-
-    sp = Tokenizer.load(params.lang, params.lang_type)
-
-    # <blk> is defined in local/prepare_lang_char.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
-
-    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,
-        )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
-    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 sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
-    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())
-    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(sp.decode(hyp).split())
-
-    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/reazonspeech/ASR/zipformer/pretrained.py b/egs/reazonspeech/ASR/zipformer/pretrained.py
new file mode 120000
index 000000000..0bd71dde4
--- /dev/null
+++ b/egs/reazonspeech/ASR/zipformer/pretrained.py
@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/pretrained.py
\ No newline at end of file