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114 lines
4.0 KiB
Python
114 lines
4.0 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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from typing import Tuple
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import torch
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import torch.nn as nn
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from encoder_interface import EncoderInterface
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from subsampling import Conv2dSubsampling, VggSubsampling
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from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
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class LstmEncoder(EncoderInterface):
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def __init__(
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self,
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num_features: int,
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hidden_size: int,
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output_dim: int,
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subsampling_factor: int = 4,
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num_encoder_layers: int = 12,
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dropout: float = 0.1,
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vgg_frontend: bool = False,
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proj_size: int = 0,
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):
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super().__init__()
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real_hidden_size = proj_size if proj_size > 0 else hidden_size
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assert (
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subsampling_factor == 4
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), "Only subsampling_factor==4 is supported at present"
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# self.encoder_embed converts the input of shape (N, T, num_features)
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# to the shape (N, T//subsampling_factor, d_model).
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# That is, it does two things simultaneously:
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# (1) subsampling: T -> T//subsampling_factor
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# (2) embedding: num_features -> d_model
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if vgg_frontend:
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self.encoder_embed = VggSubsampling(num_features, real_hidden_size)
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else:
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self.encoder_embed = Conv2dSubsampling(num_features, real_hidden_size)
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self.rnn = nn.LSTM(
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input_size=hidden_size,
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hidden_size=hidden_size,
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num_layers=num_encoder_layers,
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bias=True,
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proj_size=proj_size,
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batch_first=True,
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dropout=dropout,
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bidirectional=False,
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)
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self.encoder_output_layer = nn.Sequential(
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nn.Dropout(p=dropout),
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nn.Linear(real_hidden_size, output_dim),
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)
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def forward(
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self, x: torch.Tensor, x_lens: torch.Tensor
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) -> Tuple[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. Its shape is (batch_size, seq_len, feature_dim).
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x_lens:
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A tensor of shape (batch_size,) containing the number of frames in
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`x` before padding.
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Returns:
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Return a tuple containing 2 tensors:
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- logits, its shape is (batch_size, output_seq_len, output_dim)
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- logit_lens, a tensor of shape (batch_size,) containing the number
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of frames in `logits` before padding.
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"""
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x = self.encoder_embed(x)
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# Caution: We assume the subsampling factor is 4!
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lengths = ((x_lens - 1) // 2 - 1) // 2
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assert x.size(1) == lengths.max().item(), (
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x.size(1),
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lengths.max(),
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)
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if False:
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# It is commented out as DDP complains that not all parameters are
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# used. Need more checks later for the reason.
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#
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# Caution: We assume the dataloader returns utterances with
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# duration being sorted in decreasing order
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packed_x = pack_padded_sequence(
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input=x,
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lengths=lengths.cpu(),
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batch_first=True,
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enforce_sorted=True,
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)
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packed_rnn_out, _ = self.rnn(packed_x)
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rnn_out, _ = pad_packed_sequence(packed_rnn_out, batch_first=True)
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else:
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rnn_out, _ = self.rnn(x)
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logits = self.encoder_output_layer(rnn_out)
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return logits, lengths
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