Update README.md

README.md

@@ -1,3 +1,54 @@
 ---
 license: apache-2.0
+language:
+- zh
+- en
+metrics:
+- wer
+tags:
+- ASR
+- onnx
 ---
+
+## Introduction
+
+
+This is a medium [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 68M (for ctc head), 73M (for transducer head).
+
+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        | 3.08 / 3.98 | 7.08 / 7.62 | 9.2  | 11.23| 3.01 / 6.06 | 11.22 | 15.28 | 10.38 |
+| Transducer | 2.67 / 3.67 | 6.79 / 7.33 | 8.97 | 10.67| 2.61 / 5.36 | 10.56 | 12.94 | 10.06 |
+
+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}
+}
+```