zipformer 0.1.1

zipformer: A faster and better encoder for ASR

中文版本

zipformer

A faster and better encoder for automatic speech recognition

Overview

zipformer is a speech encoder that achieves both high performance and efficiency. It is specifically optimized for speech recognition tasks and is the only model that outperforms Google's Conformer under fair comparison.

Features

• Efficient model architecture: UNet-style multi-scale encoder with module innovations (BiasNorm, Swoosh, Balancer, Whitener).
• New optimizer: ScaledAdam.
• State-of-the-art performance with 50% fewer FLOPs than Conformer.
• Supports CTC, Transducer, and AED modeling.
• CR-CTC: Consistency regularization for stronger CTC models.

Models

zipformer ASR models are available in xlarge, large, medium, and small variants, with both streaming and non-streaming versions. The table below provides download links. For more details, please refer to the documentation.

Model	Parameters	ModelScope	Huggingface	Languages	Architectures
zipformer-xlarge	300M	link	link	Chinese, English	CTC
zipformer-large	150M	link	link	Chinese, English	CTC, Transducer
zipformer-large-streaming	150M	link	link	Chinese, English	CTC, Transducer
zipformer-medium	65M	link	link	Chinese, English	CTC, Transducer
zipformer-medium-streaming	65M	link	link	Chinese, English	CTC, Transducer
zipformer-small	25M	link	link	Chinese, English	CTC, Transducer
zipformer-small-streaming	25M	link	link	Chinese, English	CTC, Transducer

News

2026/06/22: Created standalone zipformer repository from icefall, and released xlarge, large, medium, and small Chinese/English models.

Installation

pip install zipformer

Usage

[!TIP] The examples below use the non-streaming medium model. For more models, please refer to the documentation.

Command Line

# Use jit scripted model
# Transducer
zipformer inference --hf-model pkufool/zipformer-medium --model-type jit --ctc 0 en.wav zh.wav

# CTC
zipformer inference --hf-model pkufool/zipformer-medium --model-type jit --ctc 1 en.wav zh.wav

# Use onnx model
# Transducer
zipformer inference --hf-model pkufool/zipformer-medium --model-type onnx --ctc 0 en.wav zh.wav

# CTC
zipformer inference --hf-model pkufool/zipformer-medium --model-type onnx --ctc 1 en.wav zh.wav

Python API

from zipformer import inference

# jit scripted model
result = inference([en.wav, zh.wav], hf_model='pkufool/zipformer-medium', model_type='jit', ctc=False)

result = inference([en.wav, zh.wav], hf_model='pkufool/zipformer-medium', model_type='jit', ctc=True)

# onnx model
result = inference([en.wav, zh.wav], hf_model='pkufool/zipformer-medium', model_type='onnx', ctc=False)

result = inference([en.wav, zh.wav], hf_model='pkufool/zipformer-medium', model_type='onnx', ctc=True)

# fp16 model
result = inference([en.wav, zh.wav], hf_model='pkufool/zipformer-medium', model_type='onnx', ctc=False, dtype='fp16')

result = inference([en.wav, zh.wav], hf_model='pkufool/zipformer-medium', model_type='onnx', ctc=True, dtype='fp16')

Documentation

For more information about model training, evaluation, and deployment, please refer to the documentation.

Discussion & Contact

For task-related issues, please open an issue on GitHub Issues.

You can also scan the QR code below to join our developer WeChat group or follow our WeChat official account.

Developer Group Admin	WeChat Official Account

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}
}