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.
167 lines
6.0 KiB
167 lines
6.0 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)
|
|
"""ConvolutionModule definition."""
|
|
from typing import Tuple
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
from typeguard import check_argument_types
|
|
|
|
from paddlespeech.s2t.modules.align import BatchNorm1D
|
|
from paddlespeech.s2t.modules.align import Conv1D
|
|
from paddlespeech.s2t.modules.align import LayerNorm
|
|
from paddlespeech.s2t.utils.log import Log
|
|
|
|
logger = Log(__name__).getlog()
|
|
|
|
__all__ = ['ConvolutionModule']
|
|
|
|
|
|
class ConvolutionModule(nn.Layer):
|
|
"""ConvolutionModule in Conformer model."""
|
|
|
|
def __init__(self,
|
|
channels: int,
|
|
kernel_size: int=15,
|
|
activation: nn.Layer=nn.ReLU(),
|
|
norm: str="batch_norm",
|
|
causal: bool=False,
|
|
bias: bool=True):
|
|
"""Construct an ConvolutionModule object.
|
|
Args:
|
|
channels (int): The number of channels of conv layers.
|
|
kernel_size (int): Kernel size of conv layers.
|
|
activation (nn.Layer): Activation Layer.
|
|
norm (str): Normalization type, 'batch_norm' or 'layer_norm'
|
|
causal (bool): Whether use causal convolution or not
|
|
bias (bool): Whether Conv with bias or not
|
|
"""
|
|
assert check_argument_types()
|
|
super().__init__()
|
|
self.pointwise_conv1 = Conv1D(
|
|
channels,
|
|
2 * channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias_attr=None
|
|
if bias else False, # None for True, using bias as default config
|
|
)
|
|
|
|
# self.lorder is used to distinguish if it's a causal convolution,
|
|
# if self.lorder > 0:
|
|
# it's a causal convolution, the input will be padded with
|
|
# `self.lorder` frames on the left in forward (causal conv impl).
|
|
# else: it's a symmetrical convolution
|
|
if causal:
|
|
padding = 0
|
|
self.lorder = kernel_size - 1
|
|
else:
|
|
# kernel_size should be an odd number for none causal convolution
|
|
assert (kernel_size - 1) % 2 == 0
|
|
padding = (kernel_size - 1) // 2
|
|
self.lorder = 0
|
|
|
|
self.depthwise_conv = Conv1D(
|
|
channels,
|
|
channels,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=padding,
|
|
groups=channels,
|
|
bias_attr=None
|
|
if bias else False, # None for True, using bias as default config
|
|
)
|
|
|
|
assert norm in ['batch_norm', 'layer_norm']
|
|
if norm == "batch_norm":
|
|
self.use_layer_norm = False
|
|
self.norm = BatchNorm1D(channels)
|
|
else:
|
|
self.use_layer_norm = True
|
|
self.norm = LayerNorm(channels)
|
|
|
|
self.pointwise_conv2 = Conv1D(
|
|
channels,
|
|
channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias_attr=None
|
|
if bias else False, # None for True, using bias as default config
|
|
)
|
|
self.activation = activation
|
|
|
|
def forward(self,
|
|
x: paddle.Tensor,
|
|
mask_pad: paddle.Tensor,
|
|
cache: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
|
|
"""Compute convolution module.
|
|
Args:
|
|
x (paddle.Tensor): Input tensor (#batch, time, channels).
|
|
mask_pad (paddle.Tensor): used for batch padding (#batch, 1, time),
|
|
(0, 0, 0) means fake mask.
|
|
cache (paddle.Tensor): left context cache, it is only
|
|
used in causal convolution (#batch, channels, cache_t),
|
|
(0, 0, 0) meas fake cache.
|
|
Returns:
|
|
paddle.Tensor: Output tensor (#batch, time, channels).
|
|
paddle.Tensor: Output cache tensor (#batch, channels, time')
|
|
"""
|
|
# exchange the temporal dimension and the feature dimension
|
|
x = x.transpose([0, 2, 1]) # [B, C, T]
|
|
|
|
# mask batch padding
|
|
if paddle.shape(mask_pad)[2] > 0: # time > 0
|
|
x = x.masked_fill(mask_pad, 0.0)
|
|
|
|
if self.lorder > 0:
|
|
if paddle.shape(cache)[2] == 0: # cache_t == 0
|
|
x = nn.functional.pad(
|
|
x, [self.lorder, 0], 'constant', 0.0, data_format='NCL')
|
|
else:
|
|
assert cache.shape[0] == x.shape[0] # B
|
|
assert cache.shape[1] == x.shape[1] # C
|
|
x = paddle.concat((cache, x), axis=2)
|
|
|
|
assert (x.shape[2] > self.lorder)
|
|
new_cache = x[:, :, -self.lorder:] #[B, C, T]
|
|
else:
|
|
# It's better we just return None if no cache is requried,
|
|
# However, for JIT export, here we just fake one tensor instead of
|
|
# None.
|
|
new_cache = paddle.zeros([0, 0, 0], dtype=x.dtype)
|
|
|
|
# GLU mechanism
|
|
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
|
|
x = nn.functional.glu(x, axis=1) # (batch, channel, dim)
|
|
|
|
# 1D Depthwise Conv
|
|
x = self.depthwise_conv(x)
|
|
if self.use_layer_norm:
|
|
x = x.transpose([0, 2, 1]) # [B, T, C]
|
|
x = self.activation(self.norm(x))
|
|
if self.use_layer_norm:
|
|
x = x.transpose([0, 2, 1]) # [B, C, T]
|
|
x = self.pointwise_conv2(x)
|
|
|
|
# mask batch padding
|
|
if paddle.shape(mask_pad)[2] > 0: # time > 0
|
|
x = x.masked_fill(mask_pad, 0.0)
|
|
|
|
x = x.transpose([0, 2, 1]) # [B, T, C]
|
|
return x, new_cache
|