#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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from funasr.register import tables
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from funasr.models.transformer.utils.nets_utils import get_activation
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@tables.register("joint_network_classes", "joint_network")
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class JointNetwork(torch.nn.Module):
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"""Transducer joint network module.
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Args:
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output_size: Output size.
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encoder_size: Encoder output size.
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decoder_size: Decoder output size..
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joint_space_size: Joint space size.
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joint_act_type: Type of activation for joint network.
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**activation_parameters: Parameters for the activation function.
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"""
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def __init__(
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self,
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output_size: int,
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encoder_size: int,
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decoder_size: int,
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joint_space_size: int = 256,
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joint_activation_type: str = "tanh",
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) -> None:
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"""Construct a JointNetwork object."""
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super().__init__()
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self.lin_enc = torch.nn.Linear(encoder_size, joint_space_size)
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self.lin_dec = torch.nn.Linear(decoder_size, joint_space_size, bias=False)
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self.lin_out = torch.nn.Linear(joint_space_size, output_size)
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self.joint_activation = get_activation(
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joint_activation_type
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)
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def forward(
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self,
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enc_out: torch.Tensor,
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dec_out: torch.Tensor,
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project_input: bool = True,
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) -> torch.Tensor:
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"""Joint computation of encoder and decoder hidden state sequences.
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Args:
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enc_out: Expanded encoder output state sequences (B, T, 1, D_enc)
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dec_out: Expanded decoder output state sequences (B, 1, U, D_dec)
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Returns:
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joint_out: Joint output state sequences. (B, T, U, D_out)
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"""
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if project_input:
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joint_out = self.joint_activation(self.lin_enc(enc_out) + self.lin_dec(dec_out))
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else:
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joint_out = self.joint_activation(enc_out + dec_out)
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return self.lin_out(joint_out)
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