Create README.md

README.md

@@ -0,0 +1,55 @@
+---
+license: apache-2.0
+language:
+- zh
+- en
+metrics:
+- wer
+tags:
+- ASR
+- onnx
+---
+
+
+## Introduction
+
+
+This is a large [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).
+
+| 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](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}
+}
+```