diff --git a/egs/libriheavy/ASR/zipformer_prompt_asr/pretrained.py b/egs/libriheavy/ASR/zipformer_prompt_asr/pretrained.py
new file mode 100644
index 000000000..48fd2612a
--- /dev/null
+++ b/egs/libriheavy/ASR/zipformer_prompt_asr/pretrained.py
@@ -0,0 +1,359 @@
+#!/usr/bin/env python3
+# Copyright      2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
+#
+# 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 (`pretrained.pt`) and uses it to decode waves.
+You can generate the checkpoint with the following command:
+
+./zipformer/export_PromptASR.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --epoch 50 \
+  --avg 10
+
+Utterance level context biasing:
+
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --method modified_beam_search \
+  --use-pre-text True \
+  --content-prompt "bessy random words hello k2 ASR" \
+  --use-style-prompt True \
+  librispeech.flac
+
+
+Word level context biasing:
+
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --method modified_beam_search \
+  --use-pre-text True \
+  --content-prompt "The topic is about horses." \
+  --use-style-prompt True \
+  test.wav
+
+
+"""
+
+import argparse
+import logging
+import math
+import warnings
+from typing import List
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+from beam_search import greedy_search_batch, modified_beam_search
+from text_normalization import _apply_style_transform, train_text_normalization
+from torch.nn.utils.rnn import pad_sequence
+from train_bert_encoder import (
+    _encode_texts_as_bytes_with_tokenizer,
+    add_model_arguments,
+    get_params,
+    get_tokenizer,
+    get_transducer_model,
+)
+
+from icefall.utils import make_pad_mask, num_tokens, 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(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500_fallback_coverage_0.99/bpe.model",
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_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(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-pre-text",
+        type=str2bool,
+        default=True,
+        help="Use content prompt during decoding",
+    )
+
+    parser.add_argument(
+        "--use-style-prompt",
+        type=str2bool,
+        default=True,
+        help="Use style prompt during decoding",
+    )
+
+    parser.add_argument(
+        "--pre-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of content prompt, i.e pre_text",
+    )
+
+    parser.add_argument(
+        "--style-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of style prompt, i.e style_text",
+    )
+
+    parser.add_argument(
+        "--content-prompt", type=str, default="", help="The content prompt for decoding"
+    )
+
+    parser.add_argument(
+        "--style-prompt",
+        type=str,
+        default="Mixed-cased English text with punctuations, feel free to change it.",
+        help="The style prompt for decoding",
+    )
+
+    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].contiguous())
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    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()
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    if params.causal:
+        assert (
+            "," not in params.chunk_size
+        ), "chunk_size should be one value in decoding."
+        assert (
+            "," not in params.left_context_frames
+        ), "left_context_frames should be one value in decoding."
+
+    logging.info("Creating model")
+    model = get_transducer_model(params)
+    tokenizer = get_tokenizer(params)  # for text encoder
+
+    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()
+
+    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)
+
+    assert (
+        len(params.sound_files) == 1
+    ), "Only support decoding one audio at this moment"
+    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)
+
+    # encode prompts
+    if params.use_pre_text:
+        pre_text = [train_text_normalization(params.content_prompt)]
+        pre_text = _apply_style_transform(pre_text, params.pre_text_transform)
+    else:
+        pre_text = [""]
+
+    if params.use_style_prompt:
+        style_text = [params.style_prompt]
+        style_text = _apply_style_transform(style_text, params.style_text_transform)
+    else:
+        style_text = [""]
+
+    if params.use_pre_text or params.use_style_prompt:
+        encoded_inputs, style_lens = _encode_texts_as_bytes_with_tokenizer(
+            pre_texts=pre_text,
+            style_texts=style_text,
+            tokenizer=tokenizer,
+            device=device,
+            no_limit=True,
+        )
+
+        memory, memory_key_padding_mask = model.encode_text(
+            encoded_inputs=encoded_inputs,
+            style_lens=style_lens,
+        )  # (T,B,C)
+    else:
+        memory = None
+        memory_key_padding_mask = None
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        encoder_out, encoder_out_lens = model.encode_audio(
+            feature=features,
+            feature_lens=feature_lengths,
+            memory=memory,
+            memory_key_padding_mask=memory_key_padding_mask,
+        )
+
+    hyps = []
+    msg = f"Using {params.method}"
+    logging.info(msg)
+
+    if 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,
+        )
+        hyps.append(sp.decode(hyp_tokens)[0])
+    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,
+        )
+        hyps.append(sp.decode(hyp_tokens)[0])
+    else:
+        raise ValueError(f"Unsupported method: {params.method}")
+
+    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()