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240 lines
9.0 KiB
240 lines
9.0 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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"""Subsampling layer definition."""
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from typing import Tuple
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import paddle
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from paddle import nn
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from deepspeech.modules.embedding import PositionalEncoding
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = [
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"LinearNoSubsampling", "Conv2dSubsampling4", "Conv2dSubsampling6",
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"Conv2dSubsampling8"
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]
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class BaseSubsampling(nn.Layer):
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def __init__(self, pos_enc_class: nn.Layer=PositionalEncoding):
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super().__init__()
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self.pos_enc = pos_enc_class
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# window size = (1 + right_context) + (chunk_size -1) * subsampling_rate
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self.right_context = 0
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# stride = subsampling_rate * chunk_size
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self.subsampling_rate = 1
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def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
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return self.pos_enc.position_encoding(offset, size)
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class LinearNoSubsampling(BaseSubsampling):
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"""Linear transform the input without subsampling."""
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def __init__(self,
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idim: int,
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odim: int,
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dropout_rate: float,
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pos_enc_class: nn.Layer=PositionalEncoding):
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"""Construct an linear object.
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc_class (PositionalEncoding): position encoding class
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"""
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super().__init__(pos_enc_class)
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self.out = nn.Sequential(
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nn.Linear(idim, odim),
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nn.LayerNorm(odim, epsilon=1e-12),
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nn.Dropout(dropout_rate), )
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self.right_context = 0
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self.subsampling_rate = 1
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def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Input x.
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Args:
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x (paddle.Tensor): Input tensor (#batch, time, idim).
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x_mask (paddle.Tensor): Input mask (#batch, 1, time).
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offset (int): position encoding offset.
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Returns:
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paddle.Tensor: linear input tensor (#batch, time', odim),
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where time' = time .
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paddle.Tensor: positional encoding
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paddle.Tensor: linear input mask (#batch, 1, time'),
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where time' = time .
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"""
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x = self.out(x)
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x, pos_emb = self.pos_enc(x, offset)
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return x, pos_emb, x_mask
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class Conv2dSubsampling4(BaseSubsampling):
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"""Convolutional 2D subsampling (to 1/4 length)."""
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def __init__(self,
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idim: int,
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odim: int,
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dropout_rate: float,
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pos_enc_class: nn.Layer=PositionalEncoding):
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"""Construct an Conv2dSubsampling4 object.
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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"""
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super().__init__(pos_enc_class)
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self.conv = nn.Sequential(
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nn.Conv2D(1, odim, 3, 2),
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nn.ReLU(),
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nn.Conv2D(odim, odim, 3, 2),
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nn.ReLU(), )
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self.out = nn.Sequential(
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nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim))
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self.subsampling_rate = 4
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# The right context for every conv layer is computed by:
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# (kernel_size - 1) / 2 * stride * frame_rate_of_this_layer
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# 6 = (3 - 1) / 2 * 2 * 1 + (3 - 1) / 2 * 2 * 2
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self.right_context = 6
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def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Subsample x.
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Args:
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x (paddle.Tensor): Input tensor (#batch, time, idim).
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x_mask (paddle.Tensor): Input mask (#batch, 1, time).
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offset (int): position encoding offset.
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Returns:
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paddle.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 4.
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paddle.Tensor: positional encoding
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paddle.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 4.
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"""
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x = x.unsqueeze(1) # (b, c=1, t, f)
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x = self.conv(x)
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b, c, t, f = paddle.shape(x)
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x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
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x, pos_emb = self.pos_enc(x, offset)
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return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2]
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class Conv2dSubsampling6(BaseSubsampling):
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"""Convolutional 2D subsampling (to 1/6 length)."""
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def __init__(self,
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idim: int,
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odim: int,
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dropout_rate: float,
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pos_enc_class: nn.Layer=PositionalEncoding):
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"""Construct an Conv2dSubsampling6 object.
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (PositionalEncoding): Custom position encoding layer.
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"""
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super().__init__(pos_enc_class)
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self.conv = nn.Sequential(
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nn.Conv2D(1, odim, 3, 2),
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nn.ReLU(),
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nn.Conv2D(odim, odim, 5, 3),
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nn.ReLU(), )
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# O = (I - F + Pstart + Pend) // S + 1
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# when Padding == 0, O = (I - F - S) // S
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self.linear = nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim)
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# The right context for every conv layer is computed by:
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# (kernel_size - 1) / 2 * stride * frame_rate_of_this_layer
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# 14 = (3 - 1) / 2 * 2 * 1 + (5 - 1) / 2 * 3 * 2
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self.subsampling_rate = 6
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self.right_context = 14
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def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Subsample x.
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Args:
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x (paddle.Tensor): Input tensor (#batch, time, idim).
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x_mask (paddle.Tensor): Input mask (#batch, 1, time).
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offset (int): position encoding offset.
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Returns:
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paddle.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 6.
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paddle.Tensor: positional encoding
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paddle.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 6.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = paddle.shape(x)
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x = self.linear(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
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x, pos_emb = self.pos_enc(x, offset)
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return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-4:3]
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class Conv2dSubsampling8(BaseSubsampling):
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"""Convolutional 2D subsampling (to 1/8 length)."""
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def __init__(self,
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idim: int,
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odim: int,
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dropout_rate: float,
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pos_enc_class: nn.Layer=PositionalEncoding):
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"""Construct an Conv2dSubsampling8 object.
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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"""
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super().__init__(pos_enc_class)
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self.conv = nn.Sequential(
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nn.Conv2D(1, odim, 3, 2),
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nn.ReLU(),
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nn.Conv2D(odim, odim, 3, 2),
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nn.ReLU(),
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nn.Conv2D(odim, odim, 3, 2),
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nn.ReLU(), )
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self.linear = nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2),
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odim)
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self.subsampling_rate = 8
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# The right context for every conv layer is computed by:
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# (kernel_size - 1) / 2 * stride * frame_rate_of_this_layer
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# 14 = (3 - 1) / 2 * 2 * 1 + (3 - 1) / 2 * 2 * 2 + (3 - 1) / 2 * 2 * 4
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self.right_context = 14
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def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Subsample x.
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Args:
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x (paddle.Tensor): Input tensor (#batch, time, idim).
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x_mask (paddle.Tensor): Input mask (#batch, 1, time).
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offset (int): position encoding offset.
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Returns:
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paddle.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 8.
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paddle.Tensor: positional encoding
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paddle.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 8.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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x = self.linear(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
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x, pos_emb = self.pos_enc(x, offset)
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return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2]
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