#!/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)