You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
248 lines
9.2 KiB
248 lines
9.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# Modified from wenet(https://github.com/wenet-e2e/wenet)
|
|
"""Subsampling layer definition."""
|
|
from typing import Tuple
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
|
|
from paddlespeech.s2t.modules.embedding import PositionalEncoding
|
|
from paddlespeech.s2t.utils.log import Log
|
|
|
|
logger = Log(__name__).getlog()
|
|
|
|
__all__ = [
|
|
"LinearNoSubsampling", "Conv2dSubsampling4", "Conv2dSubsampling6",
|
|
"Conv2dSubsampling8"
|
|
]
|
|
|
|
|
|
class BaseSubsampling(nn.Layer):
|
|
def __init__(self, pos_enc_class: nn.Layer=PositionalEncoding):
|
|
super().__init__()
|
|
self.pos_enc = pos_enc_class
|
|
# window size = (1 + right_context) + (chunk_size -1) * subsampling_rate
|
|
self.right_context = 0
|
|
# stride = subsampling_rate * chunk_size
|
|
self.subsampling_rate = 1
|
|
|
|
def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
|
|
return self.pos_enc.position_encoding(offset, size)
|
|
|
|
|
|
class LinearNoSubsampling(BaseSubsampling):
|
|
"""Linear transform the input without subsampling."""
|
|
|
|
def __init__(self,
|
|
idim: int,
|
|
odim: int,
|
|
dropout_rate: float,
|
|
pos_enc_class: nn.Layer=PositionalEncoding):
|
|
"""Construct an linear object.
|
|
Args:
|
|
idim (int): Input dimension.
|
|
odim (int): Output dimension.
|
|
dropout_rate (float): Dropout rate.
|
|
pos_enc_class (PositionalEncoding): position encoding class
|
|
"""
|
|
super().__init__(pos_enc_class)
|
|
self.out = nn.Sequential(
|
|
nn.Linear(idim, odim),
|
|
nn.LayerNorm(odim, epsilon=1e-12),
|
|
nn.Dropout(dropout_rate),
|
|
nn.ReLU(), )
|
|
self.right_context = 0
|
|
self.subsampling_rate = 1
|
|
|
|
def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
|
|
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
|
|
"""Input x.
|
|
Args:
|
|
x (paddle.Tensor): Input tensor (#batch, time, idim).
|
|
x_mask (paddle.Tensor): Input mask (#batch, 1, time).
|
|
offset (int): position encoding offset.
|
|
Returns:
|
|
paddle.Tensor: linear input tensor (#batch, time', odim),
|
|
where time' = time .
|
|
paddle.Tensor: positional encoding
|
|
paddle.Tensor: linear input mask (#batch, 1, time'),
|
|
where time' = time .
|
|
"""
|
|
x = self.out(x)
|
|
x, pos_emb = self.pos_enc(x, offset)
|
|
return x, pos_emb, x_mask
|
|
|
|
|
|
class Conv2dSubsampling(BaseSubsampling):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
|
|
class Conv2dSubsampling4(Conv2dSubsampling):
|
|
"""Convolutional 2D subsampling (to 1/4 length)."""
|
|
|
|
def __init__(self,
|
|
idim: int,
|
|
odim: int,
|
|
dropout_rate: float,
|
|
pos_enc_class: nn.Layer=PositionalEncoding):
|
|
"""Construct an Conv2dSubsampling4 object.
|
|
|
|
Args:
|
|
idim (int): Input dimension.
|
|
odim (int): Output dimension.
|
|
dropout_rate (float): Dropout rate.
|
|
"""
|
|
super().__init__(pos_enc_class)
|
|
self.conv = nn.Sequential(
|
|
nn.Conv2D(1, odim, 3, 2),
|
|
nn.ReLU(),
|
|
nn.Conv2D(odim, odim, 3, 2),
|
|
nn.ReLU(), )
|
|
self.out = nn.Sequential(
|
|
nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim))
|
|
self.subsampling_rate = 4
|
|
# The right context for every conv layer is computed by:
|
|
# (kernel_size - 1) * frame_rate_of_this_layer
|
|
# 6 = (3 - 1) * 1 + (3 - 1) * 2
|
|
self.right_context = 6
|
|
|
|
def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
|
|
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
|
|
"""Subsample x.
|
|
Args:
|
|
x (paddle.Tensor): Input tensor (#batch, time, idim).
|
|
x_mask (paddle.Tensor): Input mask (#batch, 1, time).
|
|
offset (int): position encoding offset.
|
|
Returns:
|
|
paddle.Tensor: Subsampled tensor (#batch, time', odim),
|
|
where time' = time // 4.
|
|
paddle.Tensor: positional encoding
|
|
paddle.Tensor: Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 4.
|
|
"""
|
|
x = x.unsqueeze(1) # (b, c=1, t, f)
|
|
x = self.conv(x)
|
|
b, c, t, f = paddle.shape(x)
|
|
x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
x, pos_emb = self.pos_enc(x, offset)
|
|
return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2]
|
|
|
|
|
|
class Conv2dSubsampling6(Conv2dSubsampling):
|
|
"""Convolutional 2D subsampling (to 1/6 length)."""
|
|
|
|
def __init__(self,
|
|
idim: int,
|
|
odim: int,
|
|
dropout_rate: float,
|
|
pos_enc_class: nn.Layer=PositionalEncoding):
|
|
"""Construct an Conv2dSubsampling6 object.
|
|
|
|
Args:
|
|
idim (int): Input dimension.
|
|
odim (int): Output dimension.
|
|
dropout_rate (float): Dropout rate.
|
|
pos_enc (PositionalEncoding): Custom position encoding layer.
|
|
"""
|
|
super().__init__(pos_enc_class)
|
|
self.conv = nn.Sequential(
|
|
nn.Conv2D(1, odim, 3, 2),
|
|
nn.ReLU(),
|
|
nn.Conv2D(odim, odim, 5, 3),
|
|
nn.ReLU(), )
|
|
# O = (I - F + Pstart + Pend) // S + 1
|
|
# when Padding == 0, O = (I - F - S) // S
|
|
self.linear = nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim)
|
|
# The right context for every conv layer is computed by:
|
|
# (kernel_size - 1) * frame_rate_of_this_layer
|
|
# 10 = (3 - 1) * 1 + (5 - 1) * 2
|
|
self.subsampling_rate = 6
|
|
self.right_context = 10
|
|
|
|
def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
|
|
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
|
|
"""Subsample x.
|
|
Args:
|
|
x (paddle.Tensor): Input tensor (#batch, time, idim).
|
|
x_mask (paddle.Tensor): Input mask (#batch, 1, time).
|
|
offset (int): position encoding offset.
|
|
Returns:
|
|
paddle.Tensor: Subsampled tensor (#batch, time', odim),
|
|
where time' = time // 6.
|
|
paddle.Tensor: positional encoding
|
|
paddle.Tensor: Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 6.
|
|
"""
|
|
x = x.unsqueeze(1) # (b, c, t, f)
|
|
x = self.conv(x)
|
|
b, c, t, f = paddle.shape(x)
|
|
x = self.linear(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
x, pos_emb = self.pos_enc(x, offset)
|
|
return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-4:3]
|
|
|
|
|
|
class Conv2dSubsampling8(Conv2dSubsampling):
|
|
"""Convolutional 2D subsampling (to 1/8 length)."""
|
|
|
|
def __init__(self,
|
|
idim: int,
|
|
odim: int,
|
|
dropout_rate: float,
|
|
pos_enc_class: nn.Layer=PositionalEncoding):
|
|
"""Construct an Conv2dSubsampling8 object.
|
|
|
|
Args:
|
|
idim (int): Input dimension.
|
|
odim (int): Output dimension.
|
|
dropout_rate (float): Dropout rate.
|
|
"""
|
|
super().__init__(pos_enc_class)
|
|
self.conv = nn.Sequential(
|
|
nn.Conv2D(1, odim, 3, 2),
|
|
nn.ReLU(),
|
|
nn.Conv2D(odim, odim, 3, 2),
|
|
nn.ReLU(),
|
|
nn.Conv2D(odim, odim, 3, 2),
|
|
nn.ReLU(), )
|
|
self.linear = nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2),
|
|
odim)
|
|
self.subsampling_rate = 8
|
|
# The right context for every conv layer is computed by:
|
|
# (kernel_size - 1) * frame_rate_of_this_layer
|
|
# 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4
|
|
self.right_context = 14
|
|
|
|
def forward(self, x: paddle.Tensor, x_mask: paddle.Tensor, offset: int=0
|
|
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
|
|
"""Subsample x.
|
|
Args:
|
|
x (paddle.Tensor): Input tensor (#batch, time, idim).
|
|
x_mask (paddle.Tensor): Input mask (#batch, 1, time).
|
|
offset (int): position encoding offset.
|
|
Returns:
|
|
paddle.Tensor: Subsampled tensor (#batch, time', odim),
|
|
where time' = time // 8.
|
|
paddle.Tensor: positional encoding
|
|
paddle.Tensor: Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 8.
|
|
"""
|
|
x = x.unsqueeze(1) # (b, c, t, f)
|
|
x = self.conv(x)
|
|
x = self.linear(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
x, pos_emb = self.pos_enc(x, offset)
|
|
return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2]
|