diff --git a/paddlespeech/vector/__init__.py b/paddlespeech/vector/__init__.py
index 185a92b8..2a3588ec 100644
--- a/paddlespeech/vector/__init__.py
+++ b/paddlespeech/vector/__init__.py
@@ -11,3 +11,34 @@
 # 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.
+
+"""
+__init__ file for sidt package.
+"""
+
+import logging as sidt_logging
+import colorlog
+
+LOG_COLOR_CONFIG = {
+    'DEBUG': 'white',
+    'INFO': 'white',
+    'WARNING': 'yellow',
+    'ERROR': 'red',
+    'CRITICAL': 'purple',
+}
+
+# 设置全局的logger
+colored_formatter = colorlog.ColoredFormatter(
+    '%(log_color)s [%(levelname)s] [%(asctime)s] [%(filename)s:%(lineno)d] - %(message)s',
+    datefmt="%Y-%m-%d %H:%M:%S",
+    log_colors=LOG_COLOR_CONFIG)  # 日志输出格式
+_logger = sidt_logging.getLogger("sidt")
+handler = colorlog.StreamHandler()
+handler.setLevel(sidt_logging.INFO)
+handler.setFormatter(colored_formatter)
+_logger.addHandler(handler)
+_logger.setLevel(sidt_logging.INFO)
+
+from .trainer.trainer import Trainer
+from .dataset.ark_dataset import create_kaldi_ark_dataset
+from .dataset.egs_dataset import create_kaldi_egs_dataset
diff --git a/paddlespeech/vector/datasets/ark_dataset.py b/paddlespeech/vector/datasets/ark_dataset.py
new file mode 100755
index 00000000..7a00e7ba
--- /dev/null
+++ b/paddlespeech/vector/datasets/ark_dataset.py
@@ -0,0 +1,142 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 sys
+import random
+import numpy as np
+import kaldi_python_io as k_io
+from paddle.io import Dataset
+from paddlespeech.vector.utils.data_utils import batch_pad_right
+import paddlespeech.vector.utils as utils
+from paddlespeech.vector.utils.utils import read_map_file
+from paddlespeech.vector import _logger as log
+
+def ark_collate_fn(batch):
+    """
+    Custom collate function] for kaldi feats dataset
+
+    Args:
+        min_chunk_size: min chunk size of a utterance
+        max_chunk_size: max chunk size of a utterance
+
+    Returns:
+        ark_collate_fn: collate funtion for dataloader
+    """
+
+    data = []
+    target = []
+    for items in batch:
+        for x, y in zip(items[0], items[1]):
+            data.append(np.array(x))
+            target.append(y)
+
+    data, lengths = batch_pad_right(data)
+    return np.array(data, dtype=np.float32), \
+           np.array(lengths, dtype=np.float32), \
+           np.array(target, dtype=np.long).reshape((len(target), 1))
+
+
+class KaldiArkDataset(Dataset):
+    """
+    Dataset used to load kaldi ark/scp files.
+    """
+    def __init__(self, scp_file, label2utt, min_item_size=1,
+                 max_item_size=1, repeat=50, min_chunk_size=200,
+                 max_chunk_size=400, select_by_speaker=True):
+        self.scp_file = scp_file
+        self.scp_reader = None
+        self.repeat = repeat
+        self.min_item_size = min_item_size
+        self.max_item_size = max_item_size
+        self.min_chunk_size = min_chunk_size
+        self.max_chunk_size = max_chunk_size
+        self._collate_fn = ark_collate_fn
+        self._is_select_by_speaker = select_by_speaker
+        if utils.is_exist(self.scp_file):
+            self.scp_reader = k_io.ScriptReader(self.scp_file)
+
+        label2utts, utt2label = read_map_file(label2utt, key_func=int)
+        self.utt_info = list(label2utts.items()) if self._is_select_by_speaker else list(utt2label.items())
+
+    @property
+    def collate_fn(self):
+        """
+        Return a collate funtion.
+        """
+        return self._collate_fn
+
+    def _random_chunk(self, length):
+        chunk_size = random.randint(self.min_chunk_size, self.max_chunk_size)
+        if chunk_size >= length:
+            return 0, length
+        start = random.randint(0, length - chunk_size)
+        end = start + chunk_size
+
+        return start, end
+
+    def _select_by_speaker(self, index):
+        if self.scp_reader is None or not self.utt_info:
+            return []
+        index = index % (len(self.utt_info))
+        inputs = []
+        labels = []
+        item_size = random.randint(self.min_item_size, self.max_item_size)
+        for loop_idx in range(item_size):
+            try:
+                utt_index = random.randint(0, len(self.utt_info[index][1])) \
+                        % len(self.utt_info[index][1])
+                key = self.utt_info[index][1][utt_index]
+            except:
+                print(index, utt_index, len(self.utt_info[index][1]))
+                sys.exit(-1)
+            x = self.scp_reader[key]
+            x = np.transpose(x)
+            bg, end = self._random_chunk(x.shape[-1])
+            inputs.append(x[:, bg: end])
+            labels.append(self.utt_info[index][0])
+        return inputs, labels
+
+    def _select_by_utt(self, index):
+        if self.scp_reader is None or len(self.utt_info) == 0:
+            return {}
+        index = index % (len(self.utt_info))
+        key = self.utt_info[index][0]
+        x = self.scp_reader[key]
+        x = np.transpose(x)
+        bg, end = self._random_chunk(x.shape[-1])
+
+        y = self.utt_info[index][1]
+
+        return [x[:, bg: end]], [y]
+
+    def __getitem__(self, index):
+        if self._is_select_by_speaker:
+            return self._select_by_speaker(index)
+        else:
+            return self._select_by_utt(index)
+
+    def __len__(self):
+        return len(self.utt_info) * self.repeat
+
+    def __iter__(self):
+        self._start = 0
+        return self
+
+    def __next__(self):
+        if self._start < len(self):
+            ret = self[self._start]
+            self._start += 1
+            return ret
+        else:
+            raise StopIteration
diff --git a/paddlespeech/vector/datasets/egs_dataset.py b/paddlespeech/vector/datasets/egs_dataset.py
new file mode 100644
index 00000000..53130d5f
--- /dev/null
+++ b/paddlespeech/vector/datasets/egs_dataset.py
@@ -0,0 +1,91 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+"""
+Load nnet3 training egs which generated by kaldi
+"""
+
+import random
+import numpy as np
+import kaldi_python_io as k_io
+from paddle.io import Dataset
+import paddlespeech.vector.utils.utils as utils
+from paddlespeech.vector import _logger as log
+class KaldiEgsDataset(Dataset):
+    """
+    Dataset used to load kaldi nnet3 egs files.
+    """
+    def __init__(self, egs_list_file, egs_idx, transforms=None):
+        self.scp_reader = None
+        self.subset_idx = egs_idx - 1
+        self.transforms = transforms
+        if not utils.is_exist(egs_list_file):
+            return
+
+        self.egs_files = []
+        with open(egs_list_file, 'r') as in_fh:
+            for line in in_fh:
+                if line.strip():
+                    self.egs_files.append(line.strip())
+
+        self.next_subset()
+
+    def next_subset(self, target_index=None, delta_index=None):
+        """
+        Use next specific subset
+
+        Args:
+            target_index: target egs index
+            delta_index: incremental value of egs index
+        """
+        if self.egs_files:
+            if target_index:
+                self.subset_idx = target_index
+            else:
+                delta_index = delta_index if delta_index else 1
+                self.subset_idx += delta_index
+            log.info("egs dataset subset index: %d" % (self.subset_idx))
+            egs_file = self.egs_files[self.subset_idx % len(self.egs_files)]
+            if utils.is_exist(egs_file):
+                self.scp_reader = k_io.Nnet3EgsScriptReader(egs_file)
+            else:
+                log.warning("No such file or directory: %s" % (egs_file))
+
+    def __getitem__(self, index):
+        if self.scp_reader is None:
+            return {}
+        index %= len(self)
+        in_dict, out_dict = self.scp_reader[index]
+        x = np.array(in_dict['matrix'])
+        x = np.transpose(x)
+        y = np.array(out_dict['matrix'][0][0][0], dtype=np.int).reshape((1,))
+        if self.transforms is not None:
+            idx = random.randint(0, len(self.transforms) - 1)
+            x = self.transforms[idx](x)
+        return x, y
+
+    def __len__(self):
+        return len(self.scp_reader)
+
+    def __iter__(self):
+        self._start = 0
+        return self
+
+    def __next__(self):
+        if self._start < len(self):
+            ret = self[self._start]
+            self._start += 1
+            return ret
+        else:
+            raise StopIteration
\ No newline at end of file
diff --git a/paddlespeech/vector/utils/data_utils.py b/paddlespeech/vector/utils/data_utils.py
old mode 100644
new mode 100755
diff --git a/paddlespeech/vector/utils/utils.py b/paddlespeech/vector/utils/utils.py
old mode 100644
new mode 100755
index c46e42c2..a28cb526
--- a/paddlespeech/vector/utils/utils.py
+++ b/paddlespeech/vector/utils/utils.py
@@ -20,7 +20,7 @@ import sys
 import paddle
 import numpy as np
 
-from sidt import _logger as log
+from paddlespeech.vector import _logger as log
 
 
 def exit_if_not_exist(in_path):