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109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (Author: Liyong Guo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from torch import nn, Tensor
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class Tdnn(nn.Module):
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"""
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Args:
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num_features (int): Number of input features
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num_classes (int): Number of output classes
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"""
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def __init__(self, num_features: int, num_classes: int) -> None:
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super().__init__()
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self.num_features = num_features
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self.num_classes = num_classes
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self.tdnn = nn.Sequential(
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nn.Conv1d(
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in_channels=num_features,
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out_channels=240,
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kernel_size=3,
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stride=1,
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padding=1,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=3, stride=1, padding=1
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240, out_channels=240, kernel_size=1, stride=1, padding=0
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=240, affine=False),
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nn.Conv1d(
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in_channels=240,
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out_channels=num_classes,
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kernel_size=1,
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stride=1,
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padding=0,
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),
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nn.LogSoftmax(1),
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)
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def forward(self, x: Tensor) -> Tensor:
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r"""
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Args:
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x (torch.Tensor): Tensor of dimension (N, T, C).
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Returns:
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Tensor: Predictor tensor of dimension (N, T, C).
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"""
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x = x.transpose(1, 2)
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x = self.tdnn(x)
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x = x.transpose(1, 2)
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return x
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