"""Transducer joint network implementation."""
import torch
from espnet.nets.pytorch_backend.nets_utils import get_activation
[docs]class JointNetwork(torch.nn.Module):
"""Transducer joint network module.
Args:
joint_output_size: Joint network output dimension
encoder_output_size: Encoder output dimension.
decoder_output_size: Decoder output dimension.
joint_space_size: Dimension of joint space.
joint_activation_type: Type of activation for joint network.
"""
def __init__(
self,
joint_output_size: int,
encoder_output_size: int,
decoder_output_size: int,
joint_space_size: int = 256,
joint_activation_type: str = "tanh",
):
"""Joint network initializer."""
super().__init__()
self.lin_enc = torch.nn.Linear(encoder_output_size, joint_space_size)
self.lin_dec = torch.nn.Linear(decoder_output_size, joint_space_size)
self.lin_out = torch.nn.Linear(joint_space_size, joint_output_size)
self.joint_activation = get_activation(joint_activation_type)
[docs] def forward(
self,
enc_out: torch.Tensor,
dec_out: torch.Tensor,
) -> torch.Tensor:
"""Joint computation of encoder and decoder hidden state sequences.
Args:
enc_out: Expanded encoder output state sequences (B, T, 1, D_enc)
dec_out: Expanded decoder output state sequences (B, 1, U, D_dec)
Returns:
joint_out: Joint output state sequences. (B, T, U, D_out)
"""
joint_out = self.joint_activation(self.lin_enc(enc_out) + self.lin_dec(dec_out))
return self.lin_out(joint_out)