diff --git a/egs/himia/wuw/ctc_tdnn/graph.py b/egs/himia/wuw/ctc_tdnn/graph.py
new file mode 100644
index 000000000..184e01ed1
--- /dev/null
+++ b/egs/himia/wuw/ctc_tdnn/graph.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (Author: Weiji Zhuang,
+#                                                 Liyong Guo)
+#
+# 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.
+
+from typing import List
+
+
+def ctc_trivial_decoding_graph(wakeup_word_tokens: List[int]):
+    """
+    A graph starts with blank/unknown and follwoing by wakeup word.
+
+    Args:
+      wakeup_word_tokens: A sequence of token ids corresponding wakeup_word.
+      It should not contain 0 and 1.
+      We assume 0 is for blank and 1 is for unknown.
+    """
+    assert 0 not in wakeup_word_tokens
+    assert 1 not in wakeup_word_tokens
+    assert len(wakeup_word_tokens) >= 2
+    keyword_ilabel_start = wakeup_word_tokens[0]
+    fst_graph = ""
+    for non_wake_word_token in range(keyword_ilabel_start):
+        fst_graph += f"0 0 {non_wake_word_token} 0\n"
+    cur_state = 1
+    for token_idx in range(len(wakeup_word_tokens) - 1):
+        token = wakeup_word_tokens[token_idx]
+        fst_graph += f"{cur_state - 1} {cur_state} {token} 0\n"
+        fst_graph += f"{cur_state} {cur_state} {token} 0\n"
+        cur_state += 1
+
+    token = wakeup_word_tokens[-1]
+    fst_graph += f"{cur_state - 1} {cur_state} {token} 1\n"
+    fst_graph += f"{cur_state} {cur_state} {token} 0\n"
+    fst_graph += f"{cur_state}\n"
+    return fst_graph
diff --git a/egs/himia/wuw/ctc_tdnn/inference.py b/egs/himia/wuw/ctc_tdnn/inference.py
new file mode 100755
index 000000000..eae9c5333
--- /dev/null
+++ b/egs/himia/wuw/ctc_tdnn/inference.py
@@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+# Copyright 2023 Xiaomi Corporation (Author: Liyong Guo)
+#
+# 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.
+
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from lhotse.features.io import NumpyHdf5Writer
+
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.env import get_env_info
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+)
+
+from asr_datamodule import HiMiaWuwDataModule
+from tdnn import Tdnn
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=10,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 1.",
+    )
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=1,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="ctc_tdnn/exp",
+        help="The experiment dir",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "env_info": get_env_info(),
+            "feature_dim": 80,
+            "number_class": 9,
+        }
+    )
+    return params
+
+
+def inference_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: torch.nn.Module,
+    test_set: str,
+):
+    """Compute and save model output of each utterance.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      test_set:
+        Name of test set.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    writer = NumpyHdf5Writer(f"{params.out_dir}/{test_set}")
+    for batch_idx, batch in enumerate(dl):
+        device = params.device
+        feature = batch["inputs"]
+        assert feature.ndim == 3
+        supervisions = batch["supervisions"]
+        start_frames = supervisions["start_frame"]
+        end_frames = start_frames + supervisions["num_frames"]
+
+        feature = feature.to(device)
+        # model_output is log_softmax(logit) with shape [N, T, C]
+        model_output = model(feature)
+
+        for i in range(feature.size(0)):
+            assert start_frames[i] == 0
+            cut = batch["supervisions"]["cut"][i]
+            cur_target = model_output[i][start_frames[i] : end_frames[i]]
+            writer.store_array(key=cut.id, value=cur_target.cpu().numpy())
+
+        num_cuts += len(batch["supervisions"]["text"])
+
+        if batch_idx % 100 == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    HiMiaWuwDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    out_dir = f"{params.exp_dir}/post/epoch_{params.epoch}-avg_{params.avg}/"
+    Path(out_dir).mkdir(parents=True, exist_ok=True)
+    params.out_dir = out_dir
+    setup_logger(f"{out_dir}/log-decode")
+    logging.info("Decoding started")
+    logging.info(params)
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    model = Tdnn(params.feature_dim, params.number_class)
+
+    if params.avg == 1:
+        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=True)
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                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), strict=True
+        )
+
+    model.to(device)
+    model.eval()
+    params.device = device
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    himia = HiMiaWuwDataModule(args)
+
+    aishell_test_cuts = himia.aishell_test_cuts()
+    test_cuts = himia.test_cuts()
+    cw_test_cuts = himia.cw_test_cuts()
+
+    aishell_test_dl = himia.test_dataloaders(aishell_test_cuts)
+    test_dl = himia.test_dataloaders(test_cuts)
+    cw_test_dl = himia.test_dataloaders(cw_test_cuts)
+
+    test_sets = ["aishell_test", "test", "cw_test"]
+    test_dls = [aishell_test_dl, test_dl, cw_test_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dls):
+        inference_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            test_set=test_set,
+        )
+
+    logging.info("Done!")
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/egs/himia/wuw/ctc_tdnn/tokenizer.py b/egs/himia/wuw/ctc_tdnn/tokenizer.py
new file mode 100644
index 000000000..bb988da6d
--- /dev/null
+++ b/egs/himia/wuw/ctc_tdnn/tokenizer.py
@@ -0,0 +1,94 @@
+# Copyright      2023  Xiaomi Corp.        (authors: Liyong Guo)
+#
+# 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.
+
+import itertools
+import torch
+
+from typing import List, Tuple
+
+
+class WakeupWordTokenizer(object):
+    def __init__(
+        self,
+        wakeup_word: str = "",
+        wakeup_word_tokens: List[int] = None,
+    ) -> None:
+        """
+        Args:
+          wakeup_word: content of positive samples.
+            A sample will be treated as a negative sample unless its context
+            is exactly the same to key_words.
+          wakeup_word_tokens: A list if int represents token ids of wakeup_word.
+            For example: the pronunciation of "你好米雅" is
+            "n i h ao m i y a".
+            Suppose we are using following lexicon:
+              blk 0
+              unk 1
+              n   2
+              i   3
+              h   4
+              ao  5
+              m   6
+              y   7
+              a   8
+            Then wakeup_word_tokens for "你好米雅" is:
+             n  i  h  ao m  i  y  a
+            [2, 3, 4, 5, 6, 3, 7, 8]
+        """
+        super().__init__()
+        assert wakeup_word is not None
+        assert wakeup_word_tokens is not None
+        assert (
+            0 not in wakeup_word_tokens
+        ), f"0 is kept for blank. Please Remove 0 from {wakeup_word_tokens}"
+        assert 1 not in wakeup_word_tokens, (
+            f"1 is kept for unknown and negative samples. "
+            f" Please Remove 1 from {wakeup_word_tokens}"
+        )
+        self.wakeup_word = wakeup_word
+        self.wakeup_word_tokens = wakeup_word_tokens
+        self.positive_number_tokens = len(wakeup_word_tokens)
+        self.negative_word_tokens = [1]
+        self.negative_number_tokens = 1
+
+    def texts_to_token_ids(self, texts: List[str]) -> Tuple[torch.Tensor, int]:
+        """Convert a list of texts to a list of k2.Fsa based texts.
+
+        Args:
+          texts:
+            It is a list of strings.
+        Returns:
+          Return a list of k2.Fsa, one for an element in texts.
+          If the element is `wakeup_word`, a graph for positive samples is appneded
+          into resulting graph_vec, otherwise, a graph for negative samples is appended.
+
+          Number of positive samples is also returned to track its proportion.
+        """
+        batch_token_ids = []
+        target_lengths = []
+        number_positive_samples = 0
+        for utt_text in texts:
+            if utt_text == self.wakeup_word:
+                batch_token_ids.append(self.wakeup_word_tokens)
+                target_lengths.append(self.positive_number_tokens)
+                number_positive_samples += 1
+            else:
+                batch_token_ids.append(self.negative_word_tokens)
+                target_lengths.append(self.negative_number_tokens)
+
+        target = torch.tensor(list(itertools.chain.from_iterable(batch_token_ids)))
+        target_lengths = torch.tensor(target_lengths)
+        return target, target_lengths, number_positive_samples
diff --git a/egs/himia/wuw/ctc_tdnn/train.py b/egs/himia/wuw/ctc_tdnn/train.py
new file mode 100755
index 000000000..95ad6c324
--- /dev/null
+++ b/egs/himia/wuw/ctc_tdnn/train.py
@@ -0,0 +1,678 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Liyong Guo)
+#
+# 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.
+
+"""
+Usage:
+  export CUDA_VISIBLE_DEVICES="0,1,2,3"
+  ./ctc_tdnn/train.py \
+     --exp-dir ./tdnn/exp \
+     --world-size 4 \
+     --max-duration 200 \
+     --num-epochs 20
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Optional, Tuple
+
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from asr_datamodule import HiMiaWuwDataModule
+from tdnn import Tdnn
+
+from lhotse.cut import Cut
+from lhotse.utils import fix_random_seed
+from torch import Tensor
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.tensorboard import SummaryWriter
+
+from tokenizer import WakeupWordTokenizer
+from icefall.checkpoint import load_checkpoint
+from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=20,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=1,
+        help="""Resume training from from this epoch.
+        If it is positive, it will load checkpoint from
+        ctc_tdnn/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="ctc_tdnn/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--lr-factor",
+        type=float,
+        default=0.001,
+        help="The lr_factor for optimizer",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    are saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - best_train_loss: Best training loss so far. It is used to select
+                           the model that has the lowest training loss. It is
+                           updated during the training.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval is 0
+
+        - feature_dim: The model input dim. It has to match the one used
+                       in computing features.
+
+        - number_class: Numer of classes. Each token will have a token id
+                     from [0, num_class).
+                     In this recipe, 0 is usually kept for blank,
+                     and 1 is usually kept for negative words.
+        - wakeup_word:  Text of wakeup word, i.e. positive samples.
+        - wakeup_word_tokens: A sequence of token ids corresponding wakeup_word.
+        - weight_decay:  The weight_decay for the optimizer.
+    """
+    params = AttributeDict(
+        {
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": 0,
+            "log_interval": 5,
+            "reset_interval": 200,
+            "valid_interval": 3000,
+            # parameters for model
+            "feature_dim": 80,
+            "number_class": 9,
+            # parameters for tokenizer
+            "wakeup_word": "你好米雅",
+            "wakeup_word_tokens": [2, 3, 4, 5, 6, 3, 7, 8],
+            # parameters for Optimizer
+            "weight_decay": 1e-6,
+            "env_info": get_env_info(),
+        }
+    )
+
+    return params
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
+) -> None:
+    """Load checkpoint from file.
+
+    If params.start_epoch is larger than 1, it will load the checkpoint from
+    `params.start_epoch - 1`. Otherwise, this function does nothing.
+
+    Apart from loading state dict for `model`, `optimizer` and `scheduler`,
+    it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+      optimizer:
+        The optimizer that we are using.
+      scheduler:
+        The learning rate scheduler we are using.
+    Returns:
+      Return None.
+    """
+    if params.start_epoch > 1:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
+    saved_params = load_checkpoint(
+        filename,
+        model=model,
+        optimizer=optimizer,
+        scheduler=scheduler,
+    )
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def save_checkpoint(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
+    rank: int = 0,
+) -> None:
+    """Save model, optimizer, scheduler and training stats to file.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The training model.
+    """
+    if rank != 0:
+        return
+    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+    save_checkpoint_impl(
+        filename=filename,
+        model=model,
+        params=params,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        rank=rank,
+    )
+
+    if params.best_train_epoch == params.cur_epoch:
+        best_train_filename = params.exp_dir / "best-train-loss.pt"
+        copyfile(src=filename, dst=best_train_filename)
+
+    if params.best_valid_epoch == params.cur_epoch:
+        best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+        copyfile(src=filename, dst=best_valid_filename)
+
+
+def compute_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    batch: dict,
+    tokenizer: WakeupWordTokenizer,
+    is_training: bool,
+) -> Tuple[Tensor, MetricsTracker]:
+    """
+    Compute CTC loss given the model and its inputs.
+
+    Args:
+      params:
+        Parameters for training. See :func:`get_params`.
+      model:
+        The model for training. It is an instance of Conformer in our case.
+      batch:
+        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        for the content in it.
+      tokenizer:
+        For positive samples, map their texts to corresponding token index sequence.
+        While for negative samples, map their texts to unknown no matter what they are.
+      is_training:
+        True for training. False for validation. When it is True, this
+        function enables autograd during computation; when it is False, it
+        disables autograd.
+    """
+    device = model.device
+    feature = batch["inputs"]
+    # at entry, feature is (N, T, C)
+    assert feature.ndim == 3
+    N, T, C = feature.shape
+    feature = feature.to(device)
+
+    supervisions = batch["supervisions"]
+    texts = supervisions["text"]
+    with torch.set_grad_enabled(is_training):
+        # model_output is log_softmax(logit) with shape [N, T, C]
+        model_output = model(feature)
+
+    assert torch.all(supervisions["start_frame"] == 0)
+    num_frames = supervisions["num_frames"].to(device)
+
+    target, target_lengths, number_positive_samples = tokenizer.texts_to_token_ids(
+        texts
+    )  # noqa E501
+    target = target.to(device)
+    target_lengths = target_lengths.to(device)
+    ctc_loss = nn.CTCLoss(reduction="sum")
+    # [N, T, C] --> [T, N, C]
+    model_output = model_output.transpose(0, 1)
+    loss = ctc_loss(model_output, target, num_frames, target_lengths)
+    loss /= num_frames.sum()
+
+    assert loss.requires_grad == is_training
+
+    info = MetricsTracker()
+    info["frames"] = num_frames.sum().item()
+
+    info["loss"] = loss.detach().cpu().item() * info["frames"]
+
+    # `utt_duration` and `utt_pad_proportion` would be normalized by `utterances`  # noqa
+    info["utterances"] = feature.size(0)
+    # averaged input duration in frames over utterances
+    info["utt_duration"] = supervisions["num_frames"].sum().item()
+    # averaged padding proportion over utterances
+    info["utt_pad_proportion"] = (
+        ((feature.size(1) - supervisions["num_frames"]) / feature.size(1)).sum().item()
+    )
+
+    info["number_positive_cuts_ratio"] = (number_positive_samples / N) * info["frames"]
+
+    return loss, info
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    tokenizer: WakeupWordTokenizer,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+) -> MetricsTracker:
+    """Run the validation process."""
+    model.eval()
+
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(valid_dl):
+        loss, loss_info = compute_loss(
+            params=params,
+            model=model,
+            batch=batch,
+            tokenizer=tokenizer,
+            is_training=False,
+        )
+        assert loss.requires_grad is False
+        tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(loss.device)
+
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    tokenizer: WakeupWordTokenizer,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      optimizer:
+        The optimizer we are using.
+      tokenizer:
+        For positive samples, map their texts to corresponding token index sequence.
+        While for negative samples, map their texts to unknown no matter what they are.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+    """
+    model.train()
+
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        batch_size = len(batch["supervisions"]["text"])
+
+        loss, loss_info = compute_loss(
+            params=params,
+            model=model,
+            batch=batch,
+            tokenizer=tokenizer,
+            is_training=True,
+        )
+        # summary stats
+        tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+
+        # NOTE: We use reduction==sum and loss is computed over utterances
+        # in the batch and there is no normalization to it so far.
+
+        optimizer.zero_grad()
+        loss.backward()
+        clip_grad_norm_(model.parameters(), 5.0, 2.0)
+        optimizer.step()
+
+        if batch_idx % params.log_interval == 0:
+            logging.info(
+                f"Epoch {params.cur_epoch}, "
+                f"batch {batch_idx}, loss[{loss_info}], "
+                f"tot_loss[{tot_loss}], batch size: {batch_size}"
+            )
+
+        if batch_idx % params.log_interval == 0:
+
+            if tb_writer is not None:
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+
+        if batch_idx > 0 and batch_idx % params.valid_interval == 0:
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                tokenizer=tokenizer,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+    logging.info(params)
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+
+    tokenizer = WakeupWordTokenizer(
+        wakeup_word=params.wakeup_word,
+        wakeup_word_tokens=params.wakeup_word_tokens,
+    )
+
+    logging.info("About to create model")
+
+    model = Tdnn(params.feature_dim, params.number_class)
+
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+    if world_size > 1:
+        model = DDP(model, device_ids=[rank])
+    model.device = device
+
+    optimizer = torch.optim.Adam(
+        model.parameters(),
+        lr=params.lr_factor,
+        weight_decay=params.weight_decay,
+    )
+
+    if checkpoints:
+        optimizer.load_state_dict(checkpoints["optimizer"])
+
+    himia = HiMiaWuwDataModule(args)
+
+    train_cuts = himia.train_cuts()
+
+    def remove_short_and_long_utt(c: Cut):
+        # Keep only utterances with duration between 1 second and 20 seconds
+        #
+        # Caution: There is a reason to select 20.0 here. Please see
+        # ../local/display_manifest_statistics.py
+        #
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
+        return 0.5 <= c.duration <= 20.0
+
+    train_cuts = train_cuts.filter(remove_short_and_long_utt)
+
+    train_dl = himia.train_dataloaders(train_cuts)
+
+    valid_cuts = himia.dev_cuts()
+    valid_dl = himia.valid_dataloaders(valid_cuts)
+
+    scan_pessimistic_batches_for_oom(
+        model=model,
+        train_dl=train_dl,
+        optimizer=optimizer,
+        tokenizer=tokenizer,
+        params=params,
+    )
+
+    for epoch in range(params.start_epoch, params.num_epochs):
+        fix_random_seed(params.seed + epoch)
+        train_dl.sampler.set_epoch(epoch)
+
+        # TODO: Support lr scheduler
+        cur_lr = params.lr_factor
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        if rank == 0:
+            logging.info("epoch {}, learning rate {}".format(epoch, cur_lr))
+
+        params.cur_epoch = epoch
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            tokenizer=tokenizer,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            tb_writer=tb_writer,
+            world_size=world_size,
+        )
+
+        save_checkpoint(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            rank=rank,
+        )
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def scan_pessimistic_batches_for_oom(
+    model: nn.Module,
+    train_dl: torch.utils.data.DataLoader,
+    optimizer: torch.optim.Optimizer,
+    tokenizer: WakeupWordTokenizer,
+    params: AttributeDict,
+):
+    from lhotse.dataset import find_pessimistic_batches
+
+    logging.info(
+        "Sanity check -- see if any of the batches in epoch 0 would cause OOM."
+    )
+    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
+    for criterion, cuts in batches.items():
+        batch = train_dl.dataset[cuts]
+        try:
+            optimizer.zero_grad()
+            loss, _ = compute_loss(
+                params=params,
+                model=model,
+                batch=batch,
+                tokenizer=tokenizer,
+                is_training=True,
+            )
+            loss.backward()
+            clip_grad_norm_(model.parameters(), 5.0, 2.0)
+            optimizer.step()
+        except RuntimeError as e:
+            if "CUDA out of memory" in str(e):
+                logging.error(
+                    "Your GPU ran out of memory with the current "
+                    "max_duration setting. We recommend decreasing "
+                    "max_duration and trying again.\n"
+                    f"Failing criterion: {criterion} "
+                    f"(={crit_values[criterion]}) ..."
+                )
+            raise
+
+
+def main():
+    parser = get_parser()
+    HiMiaWuwDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/egs/himia/wuw/prepare.sh b/egs/himia/wuw/prepare.sh
index bb4f0f36c..a47a20682 100755
--- a/egs/himia/wuw/prepare.sh
+++ b/egs/himia/wuw/prepare.sh
@@ -2,7 +2,7 @@
 
 set -eou pipefail
 
-stage=6
+stage=0
 stop_stage=6
 
 # HI_MIA and aishell dataset are used in this experiment.
diff --git a/egs/himia/wuw/run_ctc_tdnn.sh b/egs/himia/wuw/run_ctc_tdnn.sh
new file mode 100644
index 000000000..6556eab93
--- /dev/null
+++ b/egs/himia/wuw/run_ctc_tdnn.sh
@@ -0,0 +1,55 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+# You need to execute ./prepare.sh to prepare datasets.
+stage=1
+stop_stage=2
+
+epoch=10
+avg=1
+exp_dir=./ctc_tdnn/exp/
+epoch_avg=epoch_${epoch}-avg_${avg}
+post_dir=${exp_dir}/post/${epoch_avg}
+
+. shared/parse_options.sh || exit 1
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Model training"
+  python ./ctc_tdnn/train.py \
+    --num-epochs $epoch
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Get posterior of test sets"
+  python ctc_tdnn/inference.py \
+    --avg $avg \
+    --epoch $epoch \
+    --exp-dir ${exp_dir}
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Decode and compute area under curve(AUC)"
+  for test_set in test aishell_test cw_test; do
+    python ctc_tdnn/decode.py \
+      --decoding-graph ./data/LG.int \
+      --post-h5 ${post_dir}/${test_set}.h5 \
+      --score-file ${post_dir}/fst_${test_set}_pos_h5.txt
+  done
+  python ./local/auc.py   \
+      --legend himia_cw \
+      --positive-score-file ${post_dir}/fst_test_pos_h5.txt \
+      --negative-score-file ${post_dir}/fst_cw_test_pos_h5.txt
+
+  python ./local/auc.py \
+      --legend himia_aishell \
+      --positive-score-file ${post_dir}/fst_test_pos_h5.txt \
+      --negative-score-file ${post_dir}/fst_aishell_test_pos_h5.txt
+fi