2023-01-15 17:22:28 +08:00

96 lines
2.9 KiB
Python

# Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Tuple
import torch
import torch.nn as nn
from encoder_interface import EncoderInterface
class CTCAttentionModel(nn.Module):
"""Hybrid CTC & Attention decoder model."""
def __init__(
self,
encoder: EncoderInterface,
decoder: nn.Module,
encoder_dim: int,
vocab_size: int,
):
"""
Args:
encoder:
It is the Zipformer encoder model. Its accepts
two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
It returns two tensors: `logits` of shape (N, T, encoder_dm) and
`logit_lens` of shape (N,).
decoder:
It is the attention decoder.
encoder_dim:
The embedding dimension of encoder.
vocab_size:
The vocabulary size.
"""
super().__init__()
assert isinstance(encoder, EncoderInterface), type(encoder)
self.encoder = encoder
self.ctc_output = nn.Sequential(
nn.Dropout(p=0.1),
nn.Linear(encoder_dim, vocab_size),
nn.LogSoftmax(dim=-1),
)
# Attention decoder
self.decoder = decoder
def forward(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
token_ids: List[List[int]],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
A 3-D tensor of shape (N, T, C).
x_lens:
A 1-D tensor of shape (N,). It contains the number of frames in `x`
before padding.
token_ids:
A list of token id list.
Returns:
- ctc_output, ctc log-probs
- att_loss, attention decoder loss
"""
assert x.ndim == 3, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert x.size(0) == x_lens.size(0) == len(token_ids)
# encoder forward
encoder_out, x_lens = self.encoder(x, x_lens)
assert torch.all(x_lens > 0)
# compute ctc log-probs
ctc_output = self.ctc_output(encoder_out)
# compute attention decoder loss
att_loss = self.decoder.calc_att_loss(encoder_out, x_lens, token_ids)
return ctc_output, att_loss