# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.
import torch.nn as nn
from torch import Tensor
from typing import Tuple
from openspeech.modules.jasper_subblock import JasperSubBlock
[docs]class JasperBlock(nn.Module):
r"""
Jasper Block: The Jasper Block consists of R Jasper sub-block.
Args:
num_sub_blocks (int): number of sub block
in_channels (int): number of channels in the input feature
out_channels (int): number of channels produced by the convolution
kernel_size (int): size of the convolving kernel
stride (int): stride of the convolution. (default: 1)
dilation (int): spacing between kernel elements. (default: 1)
bias (bool): if True, adds a learnable bias to the output. (default: True)
dropout_p (float): probability of dropout
activation (str): activation function
Inputs: inputs, input_lengths, residual
- **inputs**: tensor contains input sequence vector
- **input_lengths**: tensor contains sequence lengths
- **residual**: tensor contains residual vector
Returns: output, output_lengths
(torch.FloatTensor, torch.LongTensor)
* output (torch.FloatTensor): tensor contains output sequence vector
* output_lengths (torch.LongTensor): tensor contains output sequence lengths
"""
def __init__(
self,
num_sub_blocks: int,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
bias: bool = True,
dropout_p: float = 0.2,
activation: str = 'relu',
) -> None:
super(JasperBlock, self).__init__()
padding = self._get_same_padding(kernel_size, stride, dilation)
self.layers = nn.ModuleList([
JasperSubBlock(
in_channels=in_channels if i == 0 else out_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
padding=padding,
bias=bias,
dropout_p=dropout_p,
activation=activation,
) for i in range(num_sub_blocks)
])
def _get_same_padding(self, kernel_size: int, stride: int, dilation: int):
if stride > 1 and dilation > 1:
raise ValueError("Only stride OR dilation may be greater than 1")
return (kernel_size // 2) * dilation
[docs] def forward(self, inputs: Tensor, input_lengths: Tensor, residual: Tensor) -> Tuple[Tensor, Tensor]:
r"""
Forward propagate of jasper block.
Inputs: inputs, input_lengths, residual
- **inputs**: tensor contains input sequence vector
- **input_lengths**: tensor contains sequence lengths
- **residual**: tensor contains residual vector
Returns: output, output_lengths
(torch.FloatTensor, torch.LongTensor)
* output (torch.FloatTensor): tensor contains output sequence vector
* output_lengths (torch.LongTensor): tensor contains output sequence lengths
"""
for layer in self.layers[:-1]:
inputs, input_lengths = layer(inputs, input_lengths)
outputs, output_lengths = self.layers[-1](inputs, input_lengths, residual)
return outputs, output_lengths