diff --git a/.github/workflows/export-wenet-to-onnx.yaml b/.github/workflows/export-wenet-to-onnx.yaml
new file mode 100644
index 00000000..191e6a6b
--- /dev/null
+++ b/.github/workflows/export-wenet-to-onnx.yaml
@@ -0,0 +1,293 @@
+name: export-wenet-to-onnx
+
+on:
+  push:
+    branches:
+      - master
+    paths:
+      - 'scripts/wenet/**'
+      - '.github/workflows/export-wenet-to-onnx.yaml'
+  pull_request:
+    paths:
+      - 'scripts/wenet/**'
+      - '.github/workflows/export-wenet-to-onnx.yaml'
+
+  workflow_dispatch:
+
+concurrency:
+  group: export-wenet-to-onnx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  export-wenet-to-onnx:
+    if: github.repository_owner == 'k2-fsa' || github.repository_owner == 'csukuangfj'
+    name: export wenet
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.8"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Run
+        shell: bash
+        run: |
+          sudo apt-get install tree sox
+          cd scripts/wenet
+          ./run.sh
+
+      - name: Publish to huggingface (aishell)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v2
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+
+            git clone https://huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-aishell huggingface
+            cd huggingface
+            git fetch
+            git pull
+
+            cp -v ../scripts/wenet/aishell_u2pp_conformer_exp/*.onnx .
+            cp -v ../scripts/wenet/aishell_u2pp_conformer_exp/units.txt tokens.txt
+            cp -v ../scripts/wenet/aishell_u2pp_conformer_exp/README.md .
+
+            if [ ! -d test_wavs ]; then
+              mkdir test_wavs
+              cd test_wavs
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/0.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/1.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/8k.wav
+              cd ..
+            fi
+            git lfs track "*.onnx"
+            git add .
+
+            git commit -m "add aishell models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-aishell main || true
+
+            cd ..
+            rm -rf huggingface
+
+      - name: Publish to huggingface (aishell2)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v2
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+
+            git clone https://huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-aishell2 huggingface
+            cd huggingface
+            git fetch
+            git pull
+
+            cp -v ../scripts/wenet/aishell2_u2pp_conformer_exp/*.onnx .
+            cp -v ../scripts/wenet/aishell2_u2pp_conformer_exp/units.txt tokens.txt
+            cp -v ../scripts/wenet/aishell2_u2pp_conformer_exp/README.md .
+
+            if [ ! -d test_wavs ]; then
+              mkdir test_wavs
+              cd test_wavs
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/0.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/1.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/8k.wav
+              cd ..
+            fi
+            git lfs track "*.onnx"
+            git add .
+
+            git commit -m "add aishell2 models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-aishell2 main || true
+
+            cd ..
+            rm -rf huggingface
+
+      - name: Publish to huggingface (multi_cn)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v2
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+
+            git clone https://huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-multi-cn huggingface
+            cd huggingface
+            git fetch
+            git pull
+
+            cp -v ../scripts/wenet/multi_cn_unified_conformer_exp/*.onnx .
+            cp -v ../scripts/wenet/multi_cn_unified_conformer_exp/units.txt tokens.txt
+            cp -v ../scripts/wenet/multi_cn_unified_conformer_exp/README.md .
+
+            if [ ! -d test_wavs ]; then
+              mkdir test_wavs
+              cd test_wavs
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/0.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/1.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/8k.wav
+              cd ..
+            fi
+            git lfs track "*.onnx"
+            git add .
+
+            git commit -m "add multi_cn models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-multi-cn main || true
+
+            cd ..
+            rm -rf huggingface
+
+      - name: Publish to huggingface (wenetspeech)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v2
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+
+            git clone https://huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-wenetspeech huggingface
+            cd huggingface
+            git fetch
+            git pull
+
+            cp -v ../scripts/wenet/20220506_u2pp_conformer_exp/*.onnx .
+            cp -v ../scripts/wenet/20220506_u2pp_conformer_exp/units.txt tokens.txt
+            cp -v ../scripts/wenet/20220506_u2pp_conformer_exp/README.md .
+
+            if [ ! -d test_wavs ]; then
+              mkdir test_wavs
+              cd test_wavs
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/0.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/1.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23/resolve/main/test_wavs/8k.wav
+              cd ..
+            fi
+            git lfs track "*.onnx"
+            git add .
+
+            git commit -m "add wenetspeech models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-zh-wenet-wenetspeech main || true
+
+            cd ..
+            rm -rf huggingface
+
+      - name: Publish to huggingface (librispeech)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v2
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+
+            git clone https://huggingface.co/csukuangfj/sherpa-onnx-en-wenet-librispeech huggingface
+            cd huggingface
+            git fetch
+            git pull
+
+            cp -v ../scripts/wenet/librispeech_u2pp_conformer_exp/*.onnx .
+            cp -v ../scripts/wenet/librispeech_u2pp_conformer_exp/units.txt tokens.txt
+            cp -v ../scripts/wenet/librispeech_u2pp_conformer_exp/README.md .
+
+            if [ ! -d test_wavs ]; then
+              mkdir test_wavs
+              cd test_wavs
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/0.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/1.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/8k.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/trans.txt
+              cd ..
+            fi
+            git lfs track "*.onnx"
+            git add .
+
+            git commit -m "add librispeech models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-en-wenet-librispeech main || true
+
+            cd ..
+            rm -rf huggingface
+
+      - name: Publish to huggingface (gigaspeech)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v2
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+
+            git clone https://huggingface.co/csukuangfj/sherpa-onnx-en-wenet-gigaspeech huggingface
+            cd huggingface
+            git fetch
+            git pull
+
+            cp -v ../scripts/wenet/20210728_u2pp_conformer_exp/*.onnx .
+            cp -v ../scripts/wenet/20210728_u2pp_conformer_exp/units.txt tokens.txt
+            cp -v ../scripts/wenet/20210728_u2pp_conformer_exp/README.md .
+
+            if [ ! -d test_wavs ]; then
+              mkdir test_wavs
+              cd test_wavs
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/0.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/1.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/8k.wav
+              wget -q https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-02-21/resolve/main/test_wavs/trans.txt
+              cd ..
+            fi
+            git lfs track "*.onnx"
+            git add .
+
+            git commit -m "add gigaspeech models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-en-wenet-gigaspeech main || true
+
+            cd ..
+            rm -rf huggingface
diff --git a/scripts/wenet/README.md b/scripts/wenet/README.md
new file mode 100644
index 00000000..1741a049
--- /dev/null
+++ b/scripts/wenet/README.md
@@ -0,0 +1,10 @@
+# Introduction
+
+This folder contains script for exporting models
+from [wenet](https://github.com/wenet-e2e/wenet)
+to onnx. You can use the exported models in sherpa-onnx.
+
+Note that both **streaming** and **non-streaming** models are supported.
+
+We only use the CTC branch. Rescore with the attention decoder
+is not supported, though decoding with H, HL, and HLG is supported.
diff --git a/scripts/wenet/export-onnx-streaming.py b/scripts/wenet/export-onnx-streaming.py
new file mode 100755
index 00000000..bc384d69
--- /dev/null
+++ b/scripts/wenet/export-onnx-streaming.py
@@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+# pip install git+https://github.com/wenet-e2e/wenet.git
+# pip install onnxruntime onnx pyyaml
+# cp -a ~/open-source/wenet/wenet/transducer/search .
+# cp -a ~/open-source//wenet/wenet/e_branchformer .
+# cp -a ~/open-source/wenet/wenet/ctl_model .
+
+import os
+from typing import Dict
+
+import onnx
+import torch
+import yaml
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+from wenet.utils.init_model import init_model
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+
+    onnx.save(model, filename)
+
+
+class OnnxModel(torch.nn.Module):
+    def __init__(self, encoder: torch.nn.Module, ctc: torch.nn.Module):
+        super().__init__()
+        self.encoder = encoder
+        self.ctc = ctc
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        offset: torch.Tensor,
+        required_cache_size: torch.Tensor,
+        attn_cache: torch.Tensor,
+        conv_cache: torch.Tensor,
+        attn_mask: torch.Tensor,
+    ):
+        """
+        Args:
+          x:
+            A 3-D float32 tensor of shape (N, T, C). It supports only N == 1.
+          offset:
+            A scalar of dtype torch.int64.
+          required_cache_size:
+            A scalar of dtype torch.int64.
+          attn_cache:
+            A 4-D float32 tensor of shape (num_blocks, head, required_cache_size, encoder_output_size / head /2).
+          conv_cache:
+            A 4-D float32 tensor of shape (num_blocks, N, encoder_output_size, cnn_module_kernel - 1).
+          attn_mask:
+            A 3-D bool tensor of shape (N, 1, required_cache_size + chunk_size)
+        Returns:
+          Return a tuple of 3 tensors:
+            - A 3-D float32 tensor of shape (N, T, C) containing log_probs
+            - next_attn_cache
+            - next_conv_cache
+        """
+        encoder_out, next_att_cache, next_conv_cache = self.encoder.forward_chunk(
+            xs=x,
+            offset=offset,
+            required_cache_size=required_cache_size,
+            att_cache=attn_cache,
+            cnn_cache=conv_cache,
+            att_mask=attn_mask,
+        )
+        log_probs = self.ctc.log_softmax(encoder_out)
+
+        return log_probs, next_att_cache, next_conv_cache
+
+
+class Foo:
+    pass
+
+
+@torch.no_grad()
+def main():
+    args = Foo()
+    args.checkpoint = "./final.pt"
+    config_file = "./train.yaml"
+
+    with open(config_file, "r") as fin:
+        configs = yaml.load(fin, Loader=yaml.FullLoader)
+    torch_model, configs = init_model(args, configs)
+    torch_model.eval()
+
+    head = configs["encoder_conf"]["attention_heads"]
+    num_blocks = configs["encoder_conf"]["num_blocks"]
+    output_size = configs["encoder_conf"]["output_size"]
+    cnn_module_kernel = configs["encoder_conf"].get("cnn_module_kernel", 1)
+
+    right_context = torch_model.right_context()
+    subsampling_factor = torch_model.encoder.embed.subsampling_rate
+    chunk_size = 16
+    left_chunks = 4
+
+    decoding_window = (chunk_size - 1) * subsampling_factor + right_context + 1
+
+    required_cache_size = chunk_size * left_chunks
+
+    offset = required_cache_size
+
+    attn_cache = torch.zeros(
+        num_blocks,
+        head,
+        required_cache_size,
+        output_size // head * 2,
+        dtype=torch.float32,
+    )
+
+    attn_mask = torch.ones(1, 1, required_cache_size + chunk_size, dtype=torch.bool)
+    attn_mask[:, :, :required_cache_size] = 0
+
+    conv_cache = torch.zeros(
+        num_blocks, 1, output_size, cnn_module_kernel - 1, dtype=torch.float32
+    )
+
+    sos = torch_model.sos_symbol()
+    eos = torch_model.eos_symbol()
+
+    onnx_model = OnnxModel(
+        encoder=torch_model.encoder,
+        ctc=torch_model.ctc,
+    )
+    filename = "model-streaming.onnx"
+
+    N = 1
+    T = decoding_window
+    C = 80
+    x = torch.rand(N, T, C, dtype=torch.float32)
+    offset = torch.tensor([offset], dtype=torch.int64)
+    required_cache_size = torch.tensor([required_cache_size], dtype=torch.int64)
+
+    opset_version = 13
+    torch.onnx.export(
+        onnx_model,
+        (x, offset, required_cache_size, attn_cache, conv_cache, attn_mask),
+        filename,
+        opset_version=opset_version,
+        input_names=[
+            "x",
+            "offset",
+            "required_cache_size",
+            "attn_cache",
+            "conv_cache",
+            "attn_mask",
+        ],
+        output_names=["log_probs", "next_att_cache", "next_conv_cache"],
+        dynamic_axes={
+            "x": {0: "N", 1: "T"},
+            "attn_cache": {2: "T"},
+            "log_probs": {0: "N"},
+            "new_attn_cache": {2: "T"},
+        },
+    )
+
+    # https://wenet.org.cn/downloads?models=wenet&version=aishell_u2pp_conformer_exp.tar.gz
+    url = os.environ.get("WENET_URL", "")
+    meta_data = {
+        "model_type": "wenet-ctc",
+        "version": "1",
+        "model_author": "wenet",
+        "comment": "streaming",
+        "url": "https://wenet.org.cn/downloads?models=wenet&version=aishell_u2pp_conformer_exp.tar.gz",
+        "chunk_size": chunk_size,
+        "left_chunks": left_chunks,
+        "head": head,
+        "num_blocks": num_blocks,
+        "output_size": output_size,
+        "cnn_module_kernel": cnn_module_kernel,
+        "right_context": right_context,
+        "subsampling_factor": subsampling_factor,
+    }
+    add_meta_data(filename=filename, meta_data=meta_data)
+
+    print("Generate int8 quantization models")
+
+    filename_int8 = f"model-streaming.int8.onnx"
+    quantize_dynamic(
+        model_input=filename,
+        model_output=filename_int8,
+        op_types_to_quantize=["MatMul"],
+        weight_type=QuantType.QInt8,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/wenet/export-onnx.py b/scripts/wenet/export-onnx.py
new file mode 100755
index 00000000..791afbd5
--- /dev/null
+++ b/scripts/wenet/export-onnx.py
@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+# pip install git+https://github.com/wenet-e2e/wenet.git
+# pip install onnxruntime onnx pyyaml
+# cp -a ~/open-source/wenet/wenet/transducer/search .
+# cp -a ~/open-source//wenet/wenet/e_branchformer .
+# cp -a ~/open-source/wenet/wenet/ctl_model .
+
+import os
+from typing import Dict
+
+import onnx
+import torch
+import yaml
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+from wenet.utils.init_model import init_model
+
+
+class Foo:
+    pass
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+
+    onnx.save(model, filename)
+
+
+class OnnxModel(torch.nn.Module):
+    def __init__(self, encoder: torch.nn.Module, ctc: torch.nn.Module):
+        super().__init__()
+        self.encoder = encoder
+        self.ctc = ctc
+
+    def forward(self, x, x_lens):
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+          x_lens:
+            A 1-D tensor of shape (N,) containing valid lengths in x before
+            padding. Its type is torch.int64
+        """
+        encoder_out, encoder_out_mask = self.encoder(
+            x,
+            x_lens,
+            decoding_chunk_size=-1,
+            num_decoding_left_chunks=-1,
+        )
+        log_probs = self.ctc.log_softmax(encoder_out)
+        log_probs_lens = encoder_out_mask.int().squeeze(1).sum(1)
+
+        return log_probs, log_probs_lens
+
+
+@torch.no_grad()
+def main():
+    args = Foo()
+    args.checkpoint = "./final.pt"
+    config_file = "./train.yaml"
+
+    with open(config_file, "r") as fin:
+        configs = yaml.load(fin, Loader=yaml.FullLoader)
+    torch_model, configs = init_model(args, configs)
+    torch_model.eval()
+
+    onnx_model = OnnxModel(encoder=torch_model.encoder, ctc=torch_model.ctc)
+    filename = "model.onnx"
+
+    N = 1
+    T = 1000
+    C = 80
+    x = torch.rand(N, T, C, dtype=torch.float)
+    x_lens = torch.full((N,), fill_value=T, dtype=torch.int64)
+
+    opset_version = 13
+    onnx_model = torch.jit.script(onnx_model)
+    torch.onnx.export(
+        onnx_model,
+        (x, x_lens),
+        filename,
+        opset_version=opset_version,
+        input_names=["x", "x_lens"],
+        output_names=["log_probs", "log_probs_lens"],
+        dynamic_axes={
+            "x": {0: "N", 1: "T"},
+            "x_lens": {0: "N"},
+            "log_probs": {0: "N", 1: "T"},
+            "log_probs_lens": {0: "N"},
+        },
+    )
+
+    # https://wenet.org.cn/downloads?models=wenet&version=aishell_u2pp_conformer_exp.tar.gz
+    url = os.environ.get("WENET_URL", "")
+    meta_data = {
+        "model_type": "wenet-ctc",
+        "version": "1",
+        "model_author": "wenet",
+        "comment": "non-streaming",
+        "url": url,
+    }
+    add_meta_data(filename=filename, meta_data=meta_data)
+
+    print("Generate int8 quantization models")
+
+    filename_int8 = f"model.int8.onnx"
+    quantize_dynamic(
+        model_input=filename,
+        model_output=filename_int8,
+        op_types_to_quantize=["MatMul"],
+        weight_type=QuantType.QInt8,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/wenet/run.sh b/scripts/wenet/run.sh
new file mode 100755
index 00000000..03336ff3
--- /dev/null
+++ b/scripts/wenet/run.sh
@@ -0,0 +1,249 @@
+#!/usr/bin/env bash
+#
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# Please refer to
+# https://github.com/wenet-e2e/wenet/blob/main/docs/pretrained_models.en.md
+# for a table of pre-trained models.
+# Please select the column "Checkpoint Model" for downloading.
+
+set -ex
+
+function install_dependencies() {
+  pip install soundfile
+  pip install torch==2.1.0+cpu torchaudio==2.1.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+  pip install k2==1.24.4.dev20231022+cpu.torch2.1.0 -f https://k2-fsa.github.io/k2/cpu.html
+
+  pip install onnxruntime onnx kaldi-native-fbank pyyaml
+
+  pip install git+https://github.com/wenet-e2e/wenet.git
+  wenet_dir=$(dirname $(python3 -c "import wenet; print(wenet.__file__)"))
+  git clone https://github.com/wenet-e2e/wenet
+  if [ ! -d $wenet_dir/transducer/search ]; then
+    cp -av ./wenet/wenet/transducer/search $wenet_dir/transducer
+  fi
+
+  if [ ! -d $wenet_dir/e_branchformer ]; then
+    cp -a .//wenet/wenet/e_branchformer $wenet_dir
+  fi
+
+  if [ ! -d $wenet_dir/ctl_model ]; then
+    cp -a ./wenet/wenet/ctl_model $wenet_dir
+  fi
+
+  rm -rf wenet
+}
+
+function aishell() {
+  echo "aishell"
+  wget -q https://huggingface.co/openspeech/wenet-models/resolve/main/aishell_u2pp_conformer_exp.tar.gz
+  tar xvf aishell_u2pp_conformer_exp.tar.gz
+  rm -v aishell_u2pp_conformer_exp.tar.gz
+
+  pushd aishell_u2pp_conformer_exp
+  mkdir -p exp/20210601_u2++_conformer_exp
+  cp global_cmvn ./exp/20210601_u2++_conformer_exp
+  cp ../*.py .
+
+  export WENET_URL=https://wenet.org.cn/downloads?models=wenet&version=aishell_u2pp_conformer_exp.tar.gz
+  wget -O 0.wav https://huggingface.co/openspeech/wenet-models/resolve/main/zh.wav
+  soxi 0.wav
+
+  echo "Test streaming"
+  ./export-onnx-streaming.py
+  ls -lh
+  ./test-onnx-streaming.py
+
+  echo "Test non-streaming"
+  ./export-onnx.py
+  ls -lh
+  ./test-onnx.py
+
+  cat > README.md <<EOF
+# Introduction
+This model is converted from https://wenet.org.cn/downloads?models=wenet&version=aishell_u2pp_conformer_exp.tar.gz
+EOF
+
+  popd
+}
+
+function aishell2() {
+  echo "aishell2"
+  wget -q https://huggingface.co/openspeech/wenet-models/resolve/main/aishell2_u2pp_conformer_exp.tar.gz
+  tar xvf aishell2_u2pp_conformer_exp.tar.gz
+  rm -v aishell2_u2pp_conformer_exp.tar.gz
+
+  pushd aishell2_u2pp_conformer_exp
+  mkdir -p exp/u2++_conformer
+  cp global_cmvn ./exp/u2++_conformer
+  cp ../*.py .
+
+  export WENET_URL=https://wenet.org.cn/downloads?models=wenet&version=aishell2_u2pp_conformer_exp.tar.gz
+  wget -O 0.wav https://huggingface.co/openspeech/wenet-models/resolve/main/zh.wav
+  soxi 0.wav
+
+  echo "Test streaming"
+  ./export-onnx-streaming.py
+  ls -lh
+  ./test-onnx-streaming.py
+
+  echo "Test non-streaming"
+  ./export-onnx.py
+  ls -lh
+  ./test-onnx.py
+
+  cat > README.md <<EOF
+# Introduction
+This model is converted from https://wenet.org.cn/downloads?models=wenet&version=aishell2_u2pp_conformer_exp.tar.gz
+EOF
+
+  popd
+}
+
+function multi_cn() {
+  echo "multi_cn"
+  wget -q https://huggingface.co/openspeech/wenet-models/resolve/main/multi_cn_unified_conformer_exp.tar.gz
+  tar xvf multi_cn_unified_conformer_exp.tar.gz
+  rm -v multi_cn_unified_conformer_exp.tar.gz
+
+  pushd multi_cn_unified_conformer_exp
+  mkdir -p exp/20210815_unified_conformer_exp
+  cp global_cmvn ./exp/20210815_unified_conformer_exp
+  cp ../*.py .
+
+  export WENET_URL=https://wenet.org.cn/downloads?models=wenet&version=multi_cn_unified_conformer_exp.tar.gz
+  wget -O 0.wav https://huggingface.co/openspeech/wenet-models/resolve/main/zh.wav
+  soxi 0.wav
+
+  echo "Test streaming"
+  ./export-onnx-streaming.py
+  ls -lh
+  ./test-onnx-streaming.py
+
+  echo "Test non-streaming"
+  ./export-onnx.py
+  ls -lh
+  ./test-onnx.py
+
+  cat > README.md <<EOF
+# Introduction
+This model is converted from https://wenet.org.cn/downloads?models=wenet&version=multi_cn_unified_conformer_exp.tar.gz
+EOF
+
+  popd
+}
+
+function wenetspeech() {
+  echo "wenetspeech"
+  wget -q https://huggingface.co/openspeech/wenet-models/resolve/main/wenetspeech_u2pp_conformer_exp.tar.gz
+  tar xvf wenetspeech_u2pp_conformer_exp.tar.gz
+  rm -v wenetspeech_u2pp_conformer_exp.tar.gz
+
+  pushd 20220506_u2pp_conformer_exp
+  mkdir -p exp/20220506_u2pp_conformer_exp
+  cp global_cmvn ./exp/20220506_u2pp_conformer_exp
+  cp ../*.py .
+
+  export WENET_URL=https://wenet.org.cn/downloads?models=wenet&version=wenetspeech_u2pp_conformer_exp.tar.gz
+  wget -O 0.wav https://huggingface.co/openspeech/wenet-models/resolve/main/zh.wav
+  soxi 0.wav
+
+  echo "Test streaming"
+  ./export-onnx-streaming.py
+  ls -lh
+  ./test-onnx-streaming.py
+
+  echo "Test non-streaming"
+  ./export-onnx.py
+  ls -lh
+  ./test-onnx.py
+
+  cat > README.md <<EOF
+# Introduction
+This model is converted from https://wenet.org.cn/downloads?models=wenet&version=wenetspeech_u2pp_conformer_exp.tar.gz
+EOF
+
+  popd
+}
+
+function librispeech() {
+  echo "librispeech"
+  wget -q https://huggingface.co/openspeech/wenet-models/resolve/main/librispeech_u2pp_conformer_exp.tar.gz
+  tar xvf librispeech_u2pp_conformer_exp.tar.gz
+  rm -v librispeech_u2pp_conformer_exp.tar.gz
+
+  pushd librispeech_u2pp_conformer_exp
+  mkdir -p data/train_960
+  cp global_cmvn ./data/train_960
+  cp ../*.py .
+
+  export WENET_URL=https://wenet.org.cn/downloads?models=wenet&version=librispeech_u2pp_conformer_exp.tar.gz
+  wget -O 0.wav https://huggingface.co/openspeech/wenet-models/resolve/main/en.wav
+  soxi 0.wav
+
+  echo "Test streaming"
+  ./export-onnx-streaming.py
+  ls -lh
+  ./test-onnx-streaming.py
+
+  echo "Test non-streaming"
+  ./export-onnx.py
+  ls -lh
+  ./test-onnx.py
+
+  cat > README.md <<EOF
+# Introduction
+This model is converted from https://wenet.org.cn/downloads?models=wenet&version=librispeech_u2pp_conformer_exp.tar.gz
+EOF
+
+  popd
+}
+
+function gigaspeech() {
+  echo "gigaspeech"
+  wget -q https://huggingface.co/openspeech/wenet-models/resolve/main/gigaspeech_u2pp_conformer_exp.tar.gz
+  tar xvf gigaspeech_u2pp_conformer_exp.tar.gz
+  rm -v gigaspeech_u2pp_conformer_exp.tar.gz
+
+  pushd 20210728_u2pp_conformer_exp
+  mkdir -p data/gigaspeech_train_xl
+  cp global_cmvn ./data/gigaspeech_train_xl
+  cp ../*.py .
+
+  export WENET_URL=https://wenet.org.cn/downloads?models=wenet&version=gigaspeech_u2pp_conformer_exp.tar.gz
+  wget -O 0.wav https://huggingface.co/openspeech/wenet-models/resolve/main/en.wav
+  soxi 0.wav
+
+  echo "Test streaming"
+  ./export-onnx-streaming.py
+  ls -lh
+  ./test-onnx-streaming.py
+
+  echo "Test non-streaming"
+  ./export-onnx.py
+  ls -lh
+  ./test-onnx.py
+
+  cat > README.md <<EOF
+# Introduction
+This model is converted from https://wenet.org.cn/downloads?models=wenet&version=gigaspeech_u2pp_conformer_exp.tar.gz
+EOF
+
+  popd
+}
+
+install_dependencies
+
+aishell
+
+aishell2
+
+multi_cn
+
+wenetspeech
+
+librispeech
+
+gigaspeech
+
+tree .
diff --git a/scripts/wenet/test-onnx-streaming.py b/scripts/wenet/test-onnx-streaming.py
new file mode 100755
index 00000000..45a4b5b7
--- /dev/null
+++ b/scripts/wenet/test-onnx-streaming.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import kaldi_native_fbank as knf
+import onnxruntime as ort
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 4
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+        meta = self.model.get_modelmeta().custom_metadata_map
+        self.left_chunks = int(meta["left_chunks"])
+        self.num_blocks = int(meta["num_blocks"])
+        self.chunk_size = int(meta["chunk_size"])
+        self.head = int(meta["head"])
+        self.output_size = int(meta["output_size"])
+        self.cnn_module_kernel = int(meta["cnn_module_kernel"])
+        self.right_context = int(meta["right_context"])
+        self.subsampling_factor = int(meta["subsampling_factor"])
+
+        self._init_cache()
+
+    def _init_cache(self):
+        required_cache_size = self.chunk_size * self.left_chunks
+
+        self.attn_cache = torch.zeros(
+            self.num_blocks,
+            self.head,
+            required_cache_size,
+            self.output_size // self.head * 2,
+            dtype=torch.float32,
+        ).numpy()
+
+        self.conv_cache = torch.zeros(
+            self.num_blocks,
+            1,
+            self.output_size,
+            self.cnn_module_kernel - 1,
+            dtype=torch.float32,
+        ).numpy()
+
+        self.offset = torch.tensor([required_cache_size], dtype=torch.int64).numpy()
+
+        self.required_cache_size = torch.tensor(
+            [self.chunk_size * self.left_chunks], dtype=torch.int64
+        ).numpy()
+
+    def __call__(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (T, C)
+        Returns:
+          Return a 2-D tensor of shape (T, C) containing log_probs.
+        """
+        attn_mask = torch.ones(
+            1, 1, int(self.required_cache_size + self.chunk_size), dtype=torch.bool
+        )
+        chunk_idx = self.offset // self.chunk_size - self.left_chunks
+        if chunk_idx < self.left_chunks:
+            attn_mask[
+                :, :, : int(self.required_cache_size - chunk_idx * self.chunk_size)
+            ] = False
+
+        log_probs, new_attn_cache, new_conv_cache = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+                self.model.get_outputs()[1].name,
+                self.model.get_outputs()[2].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x.unsqueeze(0).numpy(),
+                self.model.get_inputs()[1].name: self.offset,
+                self.model.get_inputs()[2].name: self.required_cache_size,
+                self.model.get_inputs()[3].name: self.attn_cache,
+                self.model.get_inputs()[4].name: self.conv_cache,
+                self.model.get_inputs()[5].name: attn_mask.numpy(),
+            },
+        )
+
+        self.attn_cache = new_attn_cache
+        self.conv_cache = new_conv_cache
+
+        log_probs = torch.from_numpy(log_probs)
+
+        self.offset += log_probs.shape[1]
+
+        return log_probs.squeeze(0)
+
+
+def get_features(test_wav_filename):
+    wave, sample_rate = torchaudio.load(test_wav_filename)
+    audio = wave[0].contiguous()  # only use the first channel
+    if sample_rate != 16000:
+        audio = torchaudio.functional.resample(
+            audio, orig_freq=sample_rate, new_freq=16000
+        )
+    audio *= 372768
+
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.mel_opts.num_bins = 80
+    opts.frame_opts.snip_edges = False
+    opts.mel_opts.debug_mel = False
+
+    fbank = knf.OnlineFbank(opts)
+    fbank.accept_waveform(16000, audio.numpy())
+    frames = []
+    for i in range(fbank.num_frames_ready):
+        frames.append(torch.from_numpy(fbank.get_frame(i)))
+    frames = torch.stack(frames)
+    return frames
+
+
+def main():
+    model_filename = "./model-streaming.onnx"
+    model = OnnxModel(model_filename)
+
+    filename = "./0.wav"
+    x = get_features(filename)
+
+    padding = torch.zeros(int(16000 * 0.5), 80)
+    x = torch.cat([x, padding], dim=0)
+
+    chunk_length = (
+        (model.chunk_size - 1) * model.subsampling_factor + model.right_context + 1
+    )
+    chunk_length = int(chunk_length)
+    chunk_shift = int(model.required_cache_size)
+    print(chunk_length, chunk_shift)
+
+    num_frames = x.shape[0]
+    n = (num_frames - chunk_length) // chunk_shift + 1
+    tokens = []
+    for i in range(n):
+        start = i * chunk_shift
+        end = start + chunk_length
+        frames = x[start:end, :]
+        log_probs = model(frames)
+
+        indexes = log_probs.argmax(dim=1)
+        indexes = torch.unique_consecutive(indexes)
+        indexes = indexes[indexes != 0].tolist()
+        if indexes:
+            tokens.extend(indexes)
+
+    id2word = dict()
+    with open("./units.txt", encoding="utf-8") as f:
+        for line in f:
+            word, idx = line.strip().split()
+            id2word[int(idx)] = word
+    text = "".join([id2word[i] for i in tokens])
+    print(text)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/wenet/test-onnx.py b/scripts/wenet/test-onnx.py
new file mode 100755
index 00000000..988fef4b
--- /dev/null
+++ b/scripts/wenet/test-onnx.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import kaldi_native_fbank as knf
+import onnxruntime as ort
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 4
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+    def __call__(self, x: torch.Tensor, x_lens: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+          x_lens:
+            A 1-D tensor of shape (N,). Its dtype is torch.int64
+        Returns:
+          Return a 3-D tensor of shape (N, T, C) containing log_probs.
+        """
+        log_probs, log_probs_lens = self.model.run(
+            [self.model.get_outputs()[0].name, self.model.get_outputs()[1].name],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+                self.model.get_inputs()[1].name: x_lens.numpy(),
+            },
+        )
+        return torch.from_numpy(log_probs), torch.from_numpy(log_probs_lens)
+
+
+def get_features(test_wav_filename):
+    wave, sample_rate = torchaudio.load(test_wav_filename)
+    audio = wave[0].contiguous()  # only use the first channel
+    if sample_rate != 16000:
+        audio = torchaudio.functional.resample(
+            audio, orig_freq=sample_rate, new_freq=16000
+        )
+    audio *= 372768
+
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.mel_opts.num_bins = 80
+    opts.frame_opts.snip_edges = False
+    opts.mel_opts.debug_mel = False
+
+    fbank = knf.OnlineFbank(opts)
+    fbank.accept_waveform(16000, audio.numpy())
+    frames = []
+    for i in range(fbank.num_frames_ready):
+        frames.append(torch.from_numpy(fbank.get_frame(i)))
+    frames = torch.stack(frames)
+    return frames
+
+
+def main():
+    model_filename = "./model.onnx"
+    model = OnnxModel(model_filename)
+
+    filename = "./0.wav"
+    x = get_features(filename)
+    x = x.unsqueeze(0)
+
+    # Note: It supports only batch size == 1
+    x_lens = torch.tensor([x.shape[1]], dtype=torch.int64)
+
+    print(x.shape, x_lens)
+
+    log_probs, log_probs_lens = model(x, x_lens)
+    log_probs = log_probs[0]
+    print(log_probs.shape)
+
+    indexes = log_probs.argmax(dim=1)
+    print(indexes)
+    indexes = torch.unique_consecutive(indexes)
+    indexes = indexes[indexes != 0].tolist()
+
+    id2word = dict()
+    with open("./units.txt", encoding="utf-8") as f:
+        for line in f:
+            word, idx = line.strip().split()
+            id2word[int(idx)] = word
+    text = "".join([id2word[i] for i in indexes])
+    print(text)
+
+
+if __name__ == "__main__":
+    main()