"""Transducer joint network implementation."""
import torch
from espnet2.asr_transducer.activation import get_activation
[docs]class JointNetwork(torch.nn.Module):
"""Transducer joint network module.
Args:
output_size: Output size.
encoder_size: Encoder output size.
decoder_size: Decoder output size.
joint_space_size: Joint space size.
joint_act_type: Type of activation for joint network.
**activation_parameters: Parameters for the activation function.
"""
def __init__(
self,
output_size: int,
encoder_size: int,
decoder_size: int,
joint_space_size: int = 256,
joint_activation_type: str = "tanh",
lin_dec_bias: bool = True,
**activation_parameters,
) -> None:
"""Construct a JointNetwork object."""
super().__init__()
self.lin_enc = torch.nn.Linear(encoder_size, joint_space_size)
self.lin_dec = torch.nn.Linear(
decoder_size, joint_space_size, bias=lin_dec_bias
)
self.lin_out = torch.nn.Linear(joint_space_size, output_size)
self.joint_activation = get_activation(
joint_activation_type, **activation_parameters
)
[docs] def forward(
self,
enc_out: torch.Tensor,
dec_out: torch.Tensor,
no_projection: bool = False,
) -> torch.Tensor:
"""Joint computation of encoder and decoder hidden state sequences.
Args:
enc_out: Expanded encoder output state sequences.
(B, T, s_range, D_enc) or (B, T, 1, D_enc)
dec_out: Expanded decoder output state sequences.
(B, T, s_range, D_dec) or (B, 1, U, D_dec)
Returns:
joint_out: Joint output state sequences.
(B, T, U, D_out) or (B, T, s_range, D_out)
"""
if no_projection:
joint_out = self.joint_activation(enc_out + dec_out)
else:
joint_out = self.joint_activation(
self.lin_enc(enc_out) + self.lin_dec(dec_out)
)
return self.lin_out(joint_out)