---
license: apache-2.0
language:
- zh
- en
metrics:
- wer
tags:
- ASR
- onnx
- streaming
---

## Introduction

This is a large streaming [zipformer](https://arxiv.org/pdf/2310.11230) 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).

> The chunk-size=16 and left-context-frames=128

| 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        | 3.78 / 4.71 | 8.65 / 10.54 | 11.8 | 15.35 | 3.74 / 8.5 | 12.32 | 19.7 | 10.92 |
| Transducer | 3.53 / 4.48 | 8.31 / 10.27 | 11.99| 14.83 | 3.26 / 7.51 | 11.77| 17.53| 10.82 |

Please refer to [zipformer in github](https://github.com/pkufool/zipformer) 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/](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}
}
```