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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
import torch
import torch.nn.functional as F
class CTC(torch.nn.Module):
"""CTC module"""
def __init__(
self,
odim: int,
encoder_output_size: int,
dropout_rate: float = 0.0,
reduce: bool = True,
):
"""Construct CTC module
Args:
odim: dimension of outputs
encoder_output_size: number of encoder projection units
dropout_rate: dropout rate (0.0 ~ 1.0)
reduce: reduce the CTC loss into a scalar
"""
super().__init__()
eprojs = encoder_output_size
self.dropout_rate = dropout_rate
self.ctc_lo = torch.nn.Linear(eprojs, odim)
reduction_type = "sum" if reduce else "none"
self.ctc_loss = torch.nn.CTCLoss(reduction=reduction_type)
def forward(
self,
hs_pad: torch.Tensor,
hlens: torch.Tensor,
ys_pad: torch.Tensor,
ys_lens: torch.Tensor,
) -> torch.Tensor:
"""Calculate CTC loss.
Args:
hs_pad: batch of padded hidden state sequences (B, Tmax, D)
hlens: batch of lengths of hidden state sequences (B)
ys_pad: batch of padded character id sequence tensor (B, Lmax)
ys_lens: batch of lengths of character sequence (B)
"""
# hs_pad: (B, L, NProj) -> ys_hat: (B, L, Nvocab)
ys_hat = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate))
# ys_hat: (B, L, D) -> (L, B, D)
ys_hat = ys_hat.transpose(0, 1)
ys_hat = ys_hat.log_softmax(2)
loss = self.ctc_loss(ys_hat, ys_pad, hlens, ys_lens)
# Batch-size average
loss = loss / ys_hat.size(1)
return loss
def log_softmax(self, hs_pad: torch.Tensor) -> torch.Tensor:
"""log_softmax of frame activations
Args:
Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
Returns:
torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim)
"""
return F.log_softmax(self.ctc_lo(hs_pad), dim=2)
def argmax(self, hs_pad: torch.Tensor) -> torch.Tensor:
"""argmax of frame activations
Args:
torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
Returns:
torch.Tensor: argmax applied 2d tensor (B, Tmax)
"""
return torch.argmax(self.ctc_lo(hs_pad), dim=2)
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