# MIT License
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# Copyright (c) 2021 Soohwan Kim
#
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# SOFTWARE.
import torch
import torch.nn as nn
from typing import Tuple
from openspeech.modules.pointwise_conv1d import PointwiseConv1d
from openspeech.modules.quartznet_subblock import QuartzNetSubBlock
[docs]class QuartzNetBlock(nn.Module):
r"""
QuartzNet’s design is based on the Jasper architecture, which is a convolutional model trained with
Connectionist Temporal Classification (CTC) loss. The main novelty in QuartzNet’s architecture is that QuartzNet
replaced the 1D convolutions with 1D time-channel separable convolutions, an implementation of depthwise separable
convolutions.
Inputs: inputs, input_lengths
inputs (torch.FloatTensor): tensor contains input sequence vector
input_lengths (torch.LongTensor): tensor contains sequence lengths
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
"""
supported_activations = {
'hardtanh': nn.Hardtanh(0, 20, inplace=True),
'relu': nn.ReLU(inplace=True),
'elu': nn.ELU(inplace=True),
'leaky_relu': nn.LeakyReLU(inplace=True),
'gelu': nn.GELU(),
}
def __init__(
self,
num_sub_blocks: int,
in_channels: int,
out_channels: int,
kernel_size: int,
bias: bool = True,
) -> None:
super(QuartzNetBlock, self).__init__()
padding = self._get_same_padding(kernel_size, stride=1, dilation=1)
self.layers = nn.ModuleList([
QuartzNetSubBlock(
in_channels=in_channels if i == 0 else out_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding=padding,
bias=bias,
) for i in range(num_sub_blocks)
])
self.conv1x1 = PointwiseConv1d(in_channels, out_channels)
self.batch_norm = nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.1)
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: torch.Tensor, input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Forward propagate of QuartzNet block.
Inputs: inputs, input_lengths
inputs (torch.FloatTensor): tensor contains input sequence vector
input_lengths (torch.LongTensor): tensor contains sequence lengths
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
"""
residual = self.batch_norm(self.conv1x1(inputs))
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