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#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
import torch
from funasr_detach.register import tables
from funasr_detach.models.transformer.utils.nets_utils import get_activation
@tables.register("joint_network_classes", "joint_network")
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",
) -> 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=False)
self.lin_out = torch.nn.Linear(joint_space_size, output_size)
self.joint_activation = get_activation(joint_activation_type)
def forward(
self,
enc_out: torch.Tensor,
dec_out: torch.Tensor,
project_input: bool = True,
) -> 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)
"""
if project_input:
joint_out = self.joint_activation(
self.lin_enc(enc_out) + self.lin_dec(dec_out)
)
else:
joint_out = self.joint_activation(enc_out + dec_out)
return self.lin_out(joint_out)
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