KNN-CTC: Enhancing ASR via Retrieval of CTC Pseudo Labels

This is the offical implemenet of the paper KNN-CTC: ENHANCING ASR VIA RETRIEVAL OF CTC PSEUDO LABELS

To get started, follow these steps:

1. Use the provided recipe from Wenet to download and prepare AISHELL-1 in the .list format. Alternatively, you can manually download the AISHELL-1 dataset from OpenSLR. The format should adhere to the following structure:

{"key": "BAC009S0764W0121", "wav": "/mnt/sda/jiaming_space/datasets/aishell/data_aishell/wav/test/S0764/BAC009S0764W0121.wav", "txt": "甚至出现交易几乎停滞的情况"}
{"key": "BAC009S0764W0122", "wav": "/mnt/sda/jiaming_space/datasets/aishell/data_aishell/wav/test/S0764/BAC009S0764W0122.wav", "txt": "一二线城市虽然也处于调整中"}
{"key": "BAC009S0764W0123", "wav": "/mnt/sda/jiaming_space/datasets/aishell/data_aishell/wav/test/S0764/BAC009S0764W0123.wav", "txt": "但因为聚集了过多公共资源"}

2. Download the AISHELL-1 checkpoint from wenet_pretrain_models

Once the data is prepared, you can run the in-domain KNN-CTC on AISHELL-1 using the following script:

# for knn-CTC (pruned)
bash knn_run_aishell-1.sh --stage 1 --stop-stage 2 --lmbda 0.5 --use_null_mask True --decode_skip_blank True --dstore_size 1798000

# for knn-CTC (full)
bash knn_run_aishell-1.sh --stage 1 --stop-stage 2 --lmbda 0.4 --use_null_mask Fasle --decode_skip_blank False --dstore_size 13000000

Dependencies

This project relies on the following open-source frameworks:

• Wenet: Link
• knn-transformers: Link

Acknowledgements

Special thanks to the developers and contributors of Wenet and knn-transformers for their incredible work and dedication.

📚 Cite me

@INPROCEEDINGS{10447075,
  author={Zhou, Jiaming and Zhao, Shiwan and Liu, Yaqi and Zeng, Wenjia and Chen, Yong and Qin, Yong},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={KNN-CTC: Enhancing ASR via Retrieval of CTC Pseudo Labels}, 
  year={2024},
  volume={},
  number={},
  pages={11006-11010},
  keywords={Codes;Signal processing;Natural language processing;Acoustics;Speech processing;Task analysis;Automatic speech recognition;speech recognition;CTC;retrieval-augmented method;datastore construction},
  doi={10.1109/ICASSP48485.2024.10447075}}