2e0f255ada
* add timit recipe for icefall * add shared file * update the docs for timit recipe * Delete shared * update the timit recipe and check style * Update model.py * Do some changes * Update model.py * Update model.py * Add README.md and RESULTS.md * Update RESULTS.md * Update README.md * update the docs for timit recipe
111 lines
3.7 KiB
Python
111 lines
3.7 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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class TdnnLstm(nn.Module):
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def __init__(
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self, num_features: int, num_classes: int, subsampling_factor: int = 3
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) -> None:
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"""
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Args:
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num_features:
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The input dimension of the model.
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num_classes:
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The output dimension of the model.
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subsampling_factor:
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It reduces the number of output frames by this factor.
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"""
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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.subsampling_factor = subsampling_factor
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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=512,
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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=512, affine=False),
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nn.Conv1d(
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in_channels=512,
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out_channels=512,
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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=512, affine=False),
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nn.Conv1d(
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in_channels=512,
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out_channels=512,
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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=512, affine=False),
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nn.Conv1d(
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in_channels=512,
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out_channels=512,
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kernel_size=3,
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stride=self.subsampling_factor, # stride: subsampling_factor!
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=512, affine=False),
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)
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self.lstms = nn.ModuleList(
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[
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nn.LSTM(input_size=512, hidden_size=512, num_layers=1)
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for _ in range(4)
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]
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)
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self.lstm_bnorms = nn.ModuleList(
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[nn.BatchNorm1d(num_features=512, affine=False) for _ in range(5)]
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)
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self.dropout = nn.Dropout(0.2)
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self.linear = nn.Linear(in_features=512, out_features=self.num_classes)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x:
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Its shape is [N, C, T]
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Returns:
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The output tensor has shape [N, T, C]
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"""
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x = self.tdnn(x)
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x = x.permute(2, 0, 1) # (N, C, T) -> (T, N, C) -> how LSTM expects it
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for lstm, bnorm in zip(self.lstms, self.lstm_bnorms):
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x_new, _ = lstm(x)
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x_new = bnorm(x_new.permute(1, 2, 0)).permute(
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2, 0, 1
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) # (T, N, C) -> (N, C, T) -> (T, N, C)
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x_new = self.dropout(x_new)
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x = x_new + x # skip connections
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x = x.transpose(
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1, 0
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) # (T, N, C) -> (N, T, C) -> linear expects "features" in the last dim
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x = self.linear(x)
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x = nn.functional.log_softmax(x, dim=-1)
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return x
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