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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 | |