|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
from typing import Tuple |
|
|
|
import torch |
|
import torch.nn as nn |
|
|
|
|
|
class EncoderInterface(nn.Module): |
|
def forward( |
|
self, x: torch.Tensor, x_lens: torch.Tensor |
|
) -> Tuple[torch.Tensor, torch.Tensor]: |
|
""" |
|
Args: |
|
x: |
|
A tensor of shape (batch_size, input_seq_len, num_features) |
|
containing the input features. |
|
x_lens: |
|
A tensor of shape (batch_size,) containing the number of frames |
|
in `x` before padding. |
|
Returns: |
|
Return a tuple containing two tensors: |
|
- encoder_out, a tensor of (batch_size, out_seq_len, output_dim) |
|
containing unnormalized probabilities, i.e., the output of a |
|
linear layer. |
|
- encoder_out_lens, a tensor of shape (batch_size,) containing |
|
the number of frames in `encoder_out` before padding. |
|
""" |
|
raise NotImplementedError("Please implement it in a subclass") |
|
|