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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 | |
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) | |