diff --git a/paddlespeech/vector/models/ecapa_tdnn.py b/paddlespeech/vector/models/ecapa_tdnn.py
new file mode 100644
index 000000000..5512f5097
--- /dev/null
+++ b/paddlespeech/vector/models/ecapa_tdnn.py
@@ -0,0 +1,417 @@
+# Copyright (c) 2022 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 math
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+def length_to_mask(length, max_len=None, dtype=None):
+    assert len(length.shape) == 1
+
+    if max_len is None:
+        max_len = length.max().astype(
+            'int').item()  # using arange to generate mask
+    mask = paddle.arange(
+        max_len, dtype=length.dtype).expand(
+            (len(length), max_len)) < length.unsqueeze(1)
+
+    if dtype is None:
+        dtype = length.dtype
+
+    mask = paddle.to_tensor(mask, dtype=dtype)
+    return mask
+
+
+class Conv1d(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            padding="same",
+            dilation=1,
+            groups=1,
+            bias=True,
+            padding_mode="reflect", ):
+        super(Conv1d, self).__init__()
+
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.dilation = dilation
+        self.padding = padding
+        self.padding_mode = padding_mode
+
+        self.conv = nn.Conv1D(
+            in_channels,
+            out_channels,
+            self.kernel_size,
+            stride=self.stride,
+            padding=0,
+            dilation=self.dilation,
+            groups=groups,
+            bias_attr=bias, )
+
+    def forward(self, x):
+        if self.padding == "same":
+            x = self._manage_padding(x, self.kernel_size, self.dilation,
+                                     self.stride)
+        else:
+            raise ValueError("Padding must be 'same'. Got {self.padding}")
+
+        return self.conv(x)
+
+    def _manage_padding(self, x, kernel_size: int, dilation: int, stride: int):
+        L_in = x.shape[-1]  # Detecting input shape
+        padding = self._get_padding_elem(L_in, stride, kernel_size,
+                                         dilation)  # Time padding
+        x = F.pad(
+            x, padding, mode=self.padding_mode,
+            data_format="NCL")  # Applying padding
+        return x
+
+    def _get_padding_elem(self,
+                          L_in: int,
+                          stride: int,
+                          kernel_size: int,
+                          dilation: int):
+        if stride > 1:
+            n_steps = math.ceil(((L_in - kernel_size * dilation) / stride) + 1)
+            L_out = stride * (n_steps - 1) + kernel_size * dilation
+            padding = [kernel_size // 2, kernel_size // 2]
+        else:
+            L_out = (L_in - dilation * (kernel_size - 1) - 1) // stride + 1
+
+            padding = [(L_in - L_out) // 2, (L_in - L_out) // 2]
+
+        return padding
+
+
+class BatchNorm1d(nn.Layer):
+    def __init__(
+            self,
+            input_size,
+            eps=1e-05,
+            momentum=0.9,
+            weight_attr=None,
+            bias_attr=None,
+            data_format='NCL',
+            use_global_stats=None, ):
+        super(BatchNorm1d, self).__init__()
+
+        self.norm = nn.BatchNorm1D(
+            input_size,
+            epsilon=eps,
+            momentum=momentum,
+            weight_attr=weight_attr,
+            bias_attr=bias_attr,
+            data_format=data_format,
+            use_global_stats=use_global_stats, )
+
+    def forward(self, x):
+        x_n = self.norm(x)
+        return x_n
+
+
+class TDNNBlock(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            dilation,
+            activation=nn.ReLU, ):
+        super(TDNNBlock, self).__init__()
+        self.conv = Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            dilation=dilation, )
+        self.activation = activation()
+        self.norm = BatchNorm1d(input_size=out_channels)
+
+    def forward(self, x):
+        return self.norm(self.activation(self.conv(x)))
+
+
+class Res2NetBlock(nn.Layer):
+    def __init__(self, in_channels, out_channels, scale=8, dilation=1):
+        super(Res2NetBlock, self).__init__()
+        assert in_channels % scale == 0
+        assert out_channels % scale == 0
+
+        in_channel = in_channels // scale
+        hidden_channel = out_channels // scale
+
+        self.blocks = nn.LayerList([
+            TDNNBlock(
+                in_channel, hidden_channel, kernel_size=3, dilation=dilation)
+            for i in range(scale - 1)
+        ])
+        self.scale = scale
+
+    def forward(self, x):
+        y = []
+        for i, x_i in enumerate(paddle.chunk(x, self.scale, axis=1)):
+            if i == 0:
+                y_i = x_i
+            elif i == 1:
+                y_i = self.blocks[i - 1](x_i)
+            else:
+                y_i = self.blocks[i - 1](x_i + y_i)
+            y.append(y_i)
+        y = paddle.concat(y, axis=1)
+        return y
+
+
+class SEBlock(nn.Layer):
+    def __init__(self, in_channels, se_channels, out_channels):
+        super(SEBlock, self).__init__()
+
+        self.conv1 = Conv1d(
+            in_channels=in_channels, out_channels=se_channels, kernel_size=1)
+        self.relu = paddle.nn.ReLU()
+        self.conv2 = Conv1d(
+            in_channels=se_channels, out_channels=out_channels, kernel_size=1)
+        self.sigmoid = paddle.nn.Sigmoid()
+
+    def forward(self, x, lengths=None):
+        L = x.shape[-1]
+        if lengths is not None:
+            mask = length_to_mask(lengths * L, max_len=L)
+            mask = mask.unsqueeze(1)
+            total = mask.sum(axis=2, keepdim=True)
+            s = (x * mask).sum(axis=2, keepdim=True) / total
+        else:
+            s = x.mean(axis=2, keepdim=True)
+
+        s = self.relu(self.conv1(s))
+        s = self.sigmoid(self.conv2(s))
+
+        return s * x
+
+
+class AttentiveStatisticsPooling(nn.Layer):
+    def __init__(self, channels, attention_channels=128, global_context=True):
+        super().__init__()
+
+        self.eps = 1e-12
+        self.global_context = global_context
+        if global_context:
+            self.tdnn = TDNNBlock(channels * 3, attention_channels, 1, 1)
+        else:
+            self.tdnn = TDNNBlock(channels, attention_channels, 1, 1)
+        self.tanh = nn.Tanh()
+        self.conv = Conv1d(
+            in_channels=attention_channels,
+            out_channels=channels,
+            kernel_size=1)
+
+    def forward(self, x, lengths=None):
+        C, L = x.shape[1], x.shape[2]  # KP: (N, C, L)
+
+        def _compute_statistics(x, m, axis=2, eps=self.eps):
+            mean = (m * x).sum(axis)
+            std = paddle.sqrt(
+                (m * (x - mean.unsqueeze(axis)).pow(2)).sum(axis).clip(eps))
+            return mean, std
+
+        if lengths is None:
+            lengths = paddle.ones([x.shape[0]])
+
+        # Make binary mask of shape [N, 1, L]
+        mask = length_to_mask(lengths * L, max_len=L)
+        mask = mask.unsqueeze(1)
+
+        # Expand the temporal context of the pooling layer by allowing the
+        # self-attention to look at global properties of the utterance.
+        if self.global_context:
+            total = mask.sum(axis=2, keepdim=True).astype('float32')
+            mean, std = _compute_statistics(x, mask / total)
+            mean = mean.unsqueeze(2).tile((1, 1, L))
+            std = std.unsqueeze(2).tile((1, 1, L))
+            attn = paddle.concat([x, mean, std], axis=1)
+        else:
+            attn = x
+
+        # Apply layers
+        attn = self.conv(self.tanh(self.tdnn(attn)))
+
+        # Filter out zero-paddings
+        attn = paddle.where(
+            mask.tile((1, C, 1)) == 0,
+            paddle.ones_like(attn) * float("-inf"), attn)
+
+        attn = F.softmax(attn, axis=2)
+        mean, std = _compute_statistics(x, attn)
+
+        # Append mean and std of the batch
+        pooled_stats = paddle.concat((mean, std), axis=1)
+        pooled_stats = pooled_stats.unsqueeze(2)
+
+        return pooled_stats
+
+
+class SERes2NetBlock(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            res2net_scale=8,
+            se_channels=128,
+            kernel_size=1,
+            dilation=1,
+            activation=nn.ReLU, ):
+        super(SERes2NetBlock, self).__init__()
+        self.out_channels = out_channels
+        self.tdnn1 = TDNNBlock(
+            in_channels,
+            out_channels,
+            kernel_size=1,
+            dilation=1,
+            activation=activation, )
+        self.res2net_block = Res2NetBlock(out_channels, out_channels,
+                                          res2net_scale, dilation)
+        self.tdnn2 = TDNNBlock(
+            out_channels,
+            out_channels,
+            kernel_size=1,
+            dilation=1,
+            activation=activation, )
+        self.se_block = SEBlock(out_channels, se_channels, out_channels)
+
+        self.shortcut = None
+        if in_channels != out_channels:
+            self.shortcut = Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1, )
+
+    def forward(self, x, lengths=None):
+        residual = x
+        if self.shortcut:
+            residual = self.shortcut(x)
+
+        x = self.tdnn1(x)
+        x = self.res2net_block(x)
+        x = self.tdnn2(x)
+        x = self.se_block(x, lengths)
+
+        return x + residual
+
+
+class ECAPA_TDNN(nn.Layer):
+    def __init__(
+            self,
+            input_size,
+            lin_neurons=192,
+            activation=nn.ReLU,
+            channels=[512, 512, 512, 512, 1536],
+            kernel_sizes=[5, 3, 3, 3, 1],
+            dilations=[1, 2, 3, 4, 1],
+            attention_channels=128,
+            res2net_scale=8,
+            se_channels=128,
+            global_context=True, ):
+
+        super(ECAPA_TDNN, self).__init__()
+        assert len(channels) == len(kernel_sizes)
+        assert len(channels) == len(dilations)
+        self.channels = channels
+        self.blocks = nn.LayerList()
+        self.emb_size = lin_neurons
+
+        # The initial TDNN layer
+        self.blocks.append(
+            TDNNBlock(
+                input_size,
+                channels[0],
+                kernel_sizes[0],
+                dilations[0],
+                activation, ))
+
+        # SE-Res2Net layers
+        for i in range(1, len(channels) - 1):
+            self.blocks.append(
+                SERes2NetBlock(
+                    channels[i - 1],
+                    channels[i],
+                    res2net_scale=res2net_scale,
+                    se_channels=se_channels,
+                    kernel_size=kernel_sizes[i],
+                    dilation=dilations[i],
+                    activation=activation, ))
+
+        # Multi-layer feature aggregation
+        self.mfa = TDNNBlock(
+            channels[-1],
+            channels[-1],
+            kernel_sizes[-1],
+            dilations[-1],
+            activation, )
+
+        # Attentive Statistical Pooling
+        self.asp = AttentiveStatisticsPooling(
+            channels[-1],
+            attention_channels=attention_channels,
+            global_context=global_context, )
+        self.asp_bn = BatchNorm1d(input_size=channels[-1] * 2)
+
+        # Final linear transformation
+        self.fc = Conv1d(
+            in_channels=channels[-1] * 2,
+            out_channels=self.emb_size,
+            kernel_size=1, )
+
+    def forward(self, x, lengths=None):
+        xl = []
+        for layer in self.blocks:
+            try:
+                x = layer(x, lengths=lengths)
+            except TypeError:
+                x = layer(x)
+            xl.append(x)
+
+        # Multi-layer feature aggregation
+        x = paddle.concat(xl[1:], axis=1)
+        x = self.mfa(x)
+
+        # Attentive Statistical Pooling
+        x = self.asp(x, lengths=lengths)
+        x = self.asp_bn(x)
+
+        # Final linear transformation
+        x = self.fc(x)
+
+        return x
+
+
+class Classifier(nn.Layer):
+    def __init__(self, backbone, num_class, dtype=paddle.float32):
+        super(Classifier, self).__init__()
+        self.backbone = backbone
+        self.params = nn.ParameterList([
+            paddle.create_parameter(
+                shape=[num_class, self.backbone.emb_size], dtype=dtype)
+        ])
+
+    def forward(self, x):
+        emb = self.backbone(x.transpose([0, 2, 1])).transpose([0, 2, 1])
+        logits = F.linear(
+            F.normalize(emb.squeeze(1)),
+            F.normalize(self.params[0]).transpose([1, 0]))
+
+        return logits