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on
Zero
Running
on
Zero
from typing import Optional | |
import torch | |
from torch import nn | |
from modules.wenet_extractor.utils.common import get_activation | |
class TransducerJoint(torch.nn.Module): | |
def __init__( | |
self, | |
voca_size: int, | |
enc_output_size: int, | |
pred_output_size: int, | |
join_dim: int, | |
prejoin_linear: bool = True, | |
postjoin_linear: bool = False, | |
joint_mode: str = "add", | |
activation: str = "tanh", | |
): | |
# TODO(Mddct): concat in future | |
assert joint_mode in ["add"] | |
super().__init__() | |
self.activatoin = get_activation(activation) | |
self.prejoin_linear = prejoin_linear | |
self.postjoin_linear = postjoin_linear | |
self.joint_mode = joint_mode | |
if not self.prejoin_linear and not self.postjoin_linear: | |
assert enc_output_size == pred_output_size == join_dim | |
# torchscript compatibility | |
self.enc_ffn: Optional[nn.Linear] = None | |
self.pred_ffn: Optional[nn.Linear] = None | |
if self.prejoin_linear: | |
self.enc_ffn = nn.Linear(enc_output_size, join_dim) | |
self.pred_ffn = nn.Linear(pred_output_size, join_dim) | |
# torchscript compatibility | |
self.post_ffn: Optional[nn.Linear] = None | |
if self.postjoin_linear: | |
self.post_ffn = nn.Linear(join_dim, join_dim) | |
self.ffn_out = nn.Linear(join_dim, voca_size) | |
def forward(self, enc_out: torch.Tensor, pred_out: torch.Tensor): | |
""" | |
Args: | |
enc_out (torch.Tensor): [B, T, E] | |
pred_out (torch.Tensor): [B, T, P] | |
Return: | |
[B,T,U,V] | |
""" | |
if ( | |
self.prejoin_linear | |
and self.enc_ffn is not None | |
and self.pred_ffn is not None | |
): | |
enc_out = self.enc_ffn(enc_out) # [B,T,E] -> [B,T,V] | |
pred_out = self.pred_ffn(pred_out) | |
enc_out = enc_out.unsqueeze(2) # [B,T,V] -> [B,T,1,V] | |
pred_out = pred_out.unsqueeze(1) # [B,U,V] -> [B,1 U, V] | |
# TODO(Mddct): concat joint | |
_ = self.joint_mode | |
out = enc_out + pred_out # [B,T,U,V] | |
if self.postjoin_linear and self.post_ffn is not None: | |
out = self.post_ffn(out) | |
out = self.activatoin(out) | |
out = self.ffn_out(out) | |
return out | |