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88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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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import torch
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import torch.nn as nn
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# We use a TDNN model as encoder, as it works very well with CTC training
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# for this tiny dataset.
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class Tdnn(nn.Module):
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def __init__(self, num_features: int, output_dim: int):
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"""
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Args:
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num_features:
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Model input dimension.
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ouput_dim:
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Model output dimension
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"""
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super().__init__()
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# Note: We don't use paddings inside conv layers
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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=32,
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kernel_size=3,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=32, affine=False),
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nn.Conv1d(
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in_channels=32,
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out_channels=32,
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kernel_size=5,
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dilation=2,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=32, affine=False),
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nn.Conv1d(
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in_channels=32,
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out_channels=32,
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kernel_size=5,
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dilation=4,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=32, affine=False),
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)
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self.output_linear = nn.Linear(in_features=32, out_features=output_dim)
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def forward(self, x: torch.Tensor, x_lens: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x:
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The input tensor with shape (N, T, C)
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x_lens:
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It contains the number of frames in each utterance in x
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before padding.
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Returns:
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Return a tuple with 2 tensors:
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- logits, a tensor of shape (N, T, C)
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- logit_lens, a tensor of shape (N,)
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"""
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x = x.permute(0, 2, 1) # (N, T, C) -> (N, C, T)
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x = self.tdnn(x)
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x = x.permute(0, 2, 1) # (N, C, T) -> (N, T, C)
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logits = self.output_linear(x)
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# the first conv layer reduces T by 3-1 frames
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# the second layer reduces T by (5-1)*2 frames
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# the second layer reduces T by (5-1)*4 frames
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# Number of output frames is 2 + 4*2 + 4*4 = 2 + 8 + 16 = 26
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x_lens = x_lens - 26
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return logits, x_lens
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