tdnn model

egs/himia/wuw/ctc_tdnn/tdnn.py

@@ -0,0 +1,108 @@
+#!/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.
+
+from torch import nn, Tensor
+
+
+class Tdnn(nn.Module):
+    """
+    Args:
+        num_features (int): Number of input features
+        num_classes (int): Number of output classes
+    """
+
+    def __init__(self, num_features: int, num_classes: int) -> None:
+        super().__init__()
+        self.num_features = num_features
+        self.num_classes = num_classes
+        self.tdnn = nn.Sequential(
+            nn.Conv1d(
+                in_channels=num_features,
+                out_channels=240,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240, out_channels=240, kernel_size=1, stride=1, padding=0
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=240, affine=False),
+            nn.Conv1d(
+                in_channels=240,
+                out_channels=num_classes,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.LogSoftmax(1),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""
+        Args:
+            x (torch.Tensor): Tensor of dimension (N, T, C).
+        Returns:
+            Tensor: Predictor tensor of dimension (N, T, C).
+        """
+
+        x = x.transpose(1, 2)
+        x = self.tdnn(x)
+        x = x.transpose(1, 2)
+        return x

egs/himia/wuw/shared

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+../../../icefall/shared