Zipformer Medium
This is a medium 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 68M (for...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 8,59–335 MB
- Versión
- vRevision f3f88cd
- Actualizado
- 4 oct 2026
- Publicador
- @pkufool
- Descargas
- 0
- Tareas
- Audio
- Fuente
- Hugging Face
Descripción de la fuente
Introduction
This is a medium 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 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 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}
}
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 4 oct
Artefactos del modelo
9 artefactos
Artefactos del modelo
9 artefactosExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- 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} } ```
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