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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 numpy as np
import torch
def insert_blank(label, blank_id=0):
"""Insert blank token between every two label token."""
label = np.expand_dims(label, 1)
blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id
label = np.concatenate([blanks, label], axis=1)
label = label.reshape(-1)
label = np.append(label, label[0])
return label
def forced_align(ctc_probs: torch.Tensor, y: torch.Tensor, blank_id=0) -> list:
"""ctc forced alignment.
Args:
torch.Tensor ctc_probs: hidden state sequence, 2d tensor (T, D)
torch.Tensor y: id sequence tensor 1d tensor (L)
int blank_id: blank symbol index
Returns:
torch.Tensor: alignment result
"""
y_insert_blank = insert_blank(y, blank_id)
log_alpha = torch.zeros((ctc_probs.size(0), len(y_insert_blank)))
log_alpha = log_alpha - float("inf") # log of zero
state_path = (
torch.zeros((ctc_probs.size(0), len(y_insert_blank)), dtype=torch.int16) - 1
) # state path
# init start state
log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]]
log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]]
for t in range(1, ctc_probs.size(0)):
for s in range(len(y_insert_blank)):
if (
y_insert_blank[s] == blank_id
or s < 2
or y_insert_blank[s] == y_insert_blank[s - 2]
):
candidates = torch.tensor(
[log_alpha[t - 1, s], log_alpha[t - 1, s - 1]]
)
prev_state = [s, s - 1]
else:
candidates = torch.tensor(
[
log_alpha[t - 1, s],
log_alpha[t - 1, s - 1],
log_alpha[t - 1, s - 2],
]
)
prev_state = [s, s - 1, s - 2]
log_alpha[t, s] = torch.max(candidates) + ctc_probs[t][y_insert_blank[s]]
state_path[t, s] = prev_state[torch.argmax(candidates)]
state_seq = -1 * torch.ones((ctc_probs.size(0), 1), dtype=torch.int16)
candidates = torch.tensor(
[log_alpha[-1, len(y_insert_blank) - 1], log_alpha[-1, len(y_insert_blank) - 2]]
)
final_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2]
state_seq[-1] = final_state[torch.argmax(candidates)]
for t in range(ctc_probs.size(0) - 2, -1, -1):
state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]]
output_alignment = []
for t in range(0, ctc_probs.size(0)):
output_alignment.append(y_insert_blank[state_seq[t, 0]])
return output_alignment
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