pkufool/zipformer-large

Introduction

This is a large zipformer model developed by Xiaomi AI Lab Next-gen-Kaldi team. The model was trained on around 20,0000 hours of open-sourced Chinese and English datasets. The number of parameters is around 150M.

The performance on some popular test sets (CER for Chinese, WER for English).

Head	aishell test 1 / 2	wenetspeech test-net/meetting	Common Voice zh	kespeech test	librispeech test-clean / other	gigaspeech test	Common voice en	tedium test
CTC	2.51 / 3.51	6.23 / 6.67	7.96	8.95	2.62 / 5.17	10.73	12.99	10.11
Transducer	2.42 / 3.55	6.7 / 7.81	7.92	8.88	2.27 / 4.64	10.08	11.27	9.82

Please refer to zipformer in github for model details.

Training set list: Librispeech, Gigaspeech, Commonvoice-2022(zh + en), Libriheavy, Emilia (zh+en), AIshell 2, Wenetspeech, Wenetspeech4tts, Kespeech, AIshell, aidatatang, aishell4, alimeeting, magicdata, primewords, stcmds, thchs30.

Documentation

Please refer to https://pkufool.github.io/zipformer/en/models/

Citation

@inproceedings{yao2024zipformer,
  title={Zipformer: A faster and better encoder for automatic speech recognition},
  author={Yao, Zengwei and Guo, Liyong and Yang, Xiaoyu and Kang, Wei and Kuang, Fangjun and Yang, Yifan and Jin, Zengrui and Lin, Long and Povey, Daniel},
  booktitle={International Conference on Learning Representations},
  volume={2024},
  pages={44440--44455},
  year={2024}
}
@inproceedings{yao2025cr,
  title={Cr-ctc: Consistency regularization on ctc for improved speech recognition},
  author={Yao, Zengwei and Kang, Wei and Yang, Xiaoyu and Kuang, Fangjun and Guo, Liyong and Zhu, Han and Jin, Zengrui and Li, Zhaoqing and Lin, Long and Povey, Daniel},
  booktitle={International Conference on Learning Representations},
  volume={2025},
  pages={26850--26868},
  year={2025}
}