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.
292 lines
9.3 KiB
292 lines
9.3 KiB
# Copyright (c) 2021 PaddlePaddle Authors. 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 espnet(https://github.com/espnet/espnet)
|
|
# Conv2dSubsampling 测试通过
|
|
"""Subsampling layer definition."""
|
|
import paddle
|
|
|
|
from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
|
|
|
|
|
|
class TooShortUttError(Exception):
|
|
"""Raised when the utt is too short for subsampling.
|
|
Parameters
|
|
----------
|
|
message : str
|
|
Message for error catch
|
|
actual_size : int
|
|
the short size that cannot pass the subsampling
|
|
limit : int
|
|
the limit size for subsampling
|
|
"""
|
|
|
|
def __init__(self, message, actual_size, limit):
|
|
"""Construct a TooShortUttError for error handler."""
|
|
super().__init__(message)
|
|
self.actual_size = actual_size
|
|
self.limit = limit
|
|
|
|
|
|
def check_short_utt(ins, size):
|
|
"""Check if the utterance is too short for subsampling."""
|
|
if isinstance(ins, Conv2dSubsampling2) and size < 3:
|
|
return True, 3
|
|
if isinstance(ins, Conv2dSubsampling) and size < 7:
|
|
return True, 7
|
|
if isinstance(ins, Conv2dSubsampling6) and size < 11:
|
|
return True, 11
|
|
if isinstance(ins, Conv2dSubsampling8) and size < 15:
|
|
return True, 15
|
|
return False, -1
|
|
|
|
|
|
class Conv2dSubsampling(paddle.nn.Layer):
|
|
"""Convolutional 2D subsampling (to 1/4 length).
|
|
Parameters
|
|
----------
|
|
idim : int
|
|
Input dimension.
|
|
odim : int
|
|
Output dimension.
|
|
dropout_rate : float
|
|
Dropout rate.
|
|
pos_enc : paddle.nn.Layer
|
|
Custom position encoding layer.
|
|
"""
|
|
|
|
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
|
|
"""Construct an Conv2dSubsampling object."""
|
|
super(Conv2dSubsampling, self).__init__()
|
|
self.conv = paddle.nn.Sequential(
|
|
paddle.nn.Conv2D(1, odim, 3, 2),
|
|
paddle.nn.ReLU(),
|
|
paddle.nn.Conv2D(odim, odim, 3, 2),
|
|
paddle.nn.ReLU(), )
|
|
self.out = paddle.nn.Sequential(
|
|
paddle.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
|
|
pos_enc if pos_enc is not None else
|
|
PositionalEncoding(odim, dropout_rate), )
|
|
|
|
def forward(self, x, x_mask):
|
|
"""Subsample x.
|
|
Parameters
|
|
----------
|
|
x : paddle.Tensor
|
|
Input tensor (#batch, time, idim).
|
|
x_mask : paddle.Tensor
|
|
Input mask (#batch, 1, time).
|
|
Returns
|
|
----------
|
|
paddle.Tensor
|
|
Subsampled tensor (#batch, time', odim),
|
|
where time' = time // 4.
|
|
paddle.Tensor
|
|
Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 4.
|
|
"""
|
|
# (b, c, t, f)
|
|
x = x.unsqueeze(1)
|
|
x = self.conv(x)
|
|
b, c, t, f = x.shape
|
|
# x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
|
|
x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
if x_mask is None:
|
|
return x, None
|
|
return x, x_mask[:, :, :-2:2][:, :, :-2:2]
|
|
|
|
def __getitem__(self, key):
|
|
"""Get item.
|
|
When reset_parameters() is called, if use_scaled_pos_enc is used,
|
|
return the positioning encoding.
|
|
"""
|
|
if key != -1:
|
|
raise NotImplementedError(
|
|
"Support only `-1` (for `reset_parameters`).")
|
|
return self.out[key]
|
|
|
|
|
|
class Conv2dSubsampling2(paddle.nn.Layer):
|
|
"""Convolutional 2D subsampling (to 1/2 length).
|
|
Parameters
|
|
----------
|
|
idim : int
|
|
Input dimension.
|
|
odim : int
|
|
Output dimension.
|
|
dropout_rate : float
|
|
Dropout rate.
|
|
pos_enc : paddle.nn.Layer
|
|
Custom position encoding layer.
|
|
"""
|
|
|
|
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
|
|
"""Construct an Conv2dSubsampling2 object."""
|
|
super(Conv2dSubsampling2, self).__init__()
|
|
self.conv = paddle.nn.Sequential(
|
|
paddle.nn.Conv2D(1, odim, 3, 2),
|
|
paddle.nn.ReLU(),
|
|
paddle.nn.Conv2D(odim, odim, 3, 1),
|
|
paddle.nn.ReLU(), )
|
|
self.out = paddle.nn.Sequential(
|
|
paddle.nn.Linear(odim * (((idim - 1) // 2 - 2)), odim),
|
|
pos_enc if pos_enc is not None else
|
|
PositionalEncoding(odim, dropout_rate), )
|
|
|
|
def forward(self, x, x_mask):
|
|
"""Subsample x.
|
|
Parameters
|
|
----------
|
|
x : paddle.Tensor
|
|
Input tensor (#batch, time, idim).
|
|
x_mask : paddle.Tensor
|
|
Input mask (#batch, 1, time).
|
|
Returns
|
|
----------
|
|
paddle.Tensor
|
|
ubsampled tensor (#batch, time', odim),
|
|
where time' = time // 2.
|
|
paddle.Tensor
|
|
Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 2.
|
|
"""
|
|
# (b, c, t, f)
|
|
x = x.unsqueeze(1)
|
|
x = self.conv(x)
|
|
b, c, t, f = x.shape
|
|
x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
if x_mask is None:
|
|
return x, None
|
|
return x, x_mask[:, :, :-2:2][:, :, :-2:1]
|
|
|
|
def __getitem__(self, key):
|
|
"""Get item.
|
|
When reset_parameters() is called, if use_scaled_pos_enc is used,
|
|
return the positioning encoding.
|
|
"""
|
|
if key != -1:
|
|
raise NotImplementedError(
|
|
"Support only `-1` (for `reset_parameters`).")
|
|
return self.out[key]
|
|
|
|
|
|
class Conv2dSubsampling6(paddle.nn.Layer):
|
|
"""Convolutional 2D subsampling (to 1/6 length).
|
|
Parameters
|
|
----------
|
|
idim : int
|
|
Input dimension.
|
|
odim : int
|
|
Output dimension.
|
|
dropout_rate : float
|
|
Dropout rate.
|
|
pos_enc : paddle.nn.Layer
|
|
Custom position encoding layer.
|
|
"""
|
|
|
|
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
|
|
"""Construct an Conv2dSubsampling6 object."""
|
|
super(Conv2dSubsampling6, self).__init__()
|
|
self.conv = paddle.nn.Sequential(
|
|
paddle.nn.Conv2D(1, odim, 3, 2),
|
|
paddle.nn.ReLU(),
|
|
paddle.nn.Conv2D(odim, odim, 5, 3),
|
|
paddle.nn.ReLU(), )
|
|
self.out = paddle.nn.Sequential(
|
|
paddle.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim),
|
|
pos_enc if pos_enc is not None else
|
|
PositionalEncoding(odim, dropout_rate), )
|
|
|
|
def forward(self, x, x_mask):
|
|
"""Subsample x.
|
|
Parameters
|
|
----------
|
|
x : paddle.Tensor
|
|
Input tensor (#batch, time, idim).
|
|
x_mask paddle.Tensor
|
|
Input mask (#batch, 1, time).
|
|
Returns
|
|
----------
|
|
paddle.Tensor
|
|
Subsampled tensor (#batch, time', odim),
|
|
where time' = time // 6.
|
|
paddle.Tensor
|
|
Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 6.
|
|
"""
|
|
# (b, c, t, f)
|
|
x = x.unsqueeze(1)
|
|
x = self.conv(x)
|
|
b, c, t, f = x.shape
|
|
x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
if x_mask is None:
|
|
return x, None
|
|
return x, x_mask[:, :, :-2:2][:, :, :-4:3]
|
|
|
|
|
|
class Conv2dSubsampling8(paddle.nn.Layer):
|
|
"""Convolutional 2D subsampling (to 1/8 length).
|
|
Parameters
|
|
----------
|
|
idim : int
|
|
Input dimension.
|
|
odim : int
|
|
Output dimension.
|
|
dropout_rate : float
|
|
Dropout rate.
|
|
pos_enc : paddle.nn.Layer
|
|
Custom position encoding layer.
|
|
"""
|
|
|
|
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
|
|
"""Construct an Conv2dSubsampling8 object."""
|
|
super(Conv2dSubsampling8, self).__init__()
|
|
self.conv = paddle.nn.Sequential(
|
|
paddle.nn.Conv2D(1, odim, 3, 2),
|
|
paddle.nn.ReLU(),
|
|
paddle.nn.Conv2D(odim, odim, 3, 2),
|
|
paddle.nn.ReLU(),
|
|
paddle.nn.Conv2D(odim, odim, 3, 2),
|
|
paddle.nn.ReLU(), )
|
|
self.out = paddle.nn.Sequential(
|
|
paddle.nn.Linear(odim * (((
|
|
(idim - 1) // 2 - 1) // 2 - 1) // 2), odim),
|
|
pos_enc if pos_enc is not None else
|
|
PositionalEncoding(odim, dropout_rate), )
|
|
|
|
def forward(self, x, x_mask):
|
|
"""Subsample x.
|
|
Parameters
|
|
----------
|
|
x : paddle.Tensor
|
|
Input tensor (#batch, time, idim).
|
|
x_mask : paddle.Tensor
|
|
Input mask (#batch, 1, time).
|
|
Returns
|
|
----------
|
|
paddle.Tensor
|
|
Subsampled tensor (#batch, time', odim),
|
|
where time' = time // 8.
|
|
paddle.Tensor
|
|
Subsampled mask (#batch, 1, time'),
|
|
where time' = time // 8.
|
|
"""
|
|
# (b, c, t, f)
|
|
x = x.unsqueeze(1)
|
|
x = self.conv(x)
|
|
b, c, t, f = x.shape
|
|
x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
|
|
if x_mask is None:
|
|
return x, None
|
|
return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2]
|