I work at Xiaomi AI lab as a senior engineer now, developing the Next-gen Kaldi under the leadership of Daniel Povey. Our team focus on the advanced and efficient open source E2E Automatic Speech Recognition. If you are interested in our Next-gen Kaldi project and want to know more about it, feel free to email me at wkang@pku.edu.cn.

I graduated from Peking University with a master’s degree majoring in Technology of Computer Application, advised by Tengjiao Wang(王腾蛟). I got my bachelor’s degree from School of Electronic Science and Engineering, Nanjing University.

I used to work for China Highway Engineering Consultants Corporation (CHECC) as a Big Data Engineer. At the spring of 2019, I changed my direction to Automatic Speech Recognition. Thanks to Mobvoi, it gave me a chance to start my ASR research as a layman. I worked there for more than 2 years focusing on intelligent cockpit. After that, at the summer of 2021, I joined Daniel’s team.

🔥 News

  • 2023.12:  🎉🎉 Two papers: Libriheavy pdf and PromptAsr pdf are accepted by ICASSP 2024.
  • 2023.06:  🎉🎉 I take part in the ICASSP 2023 conference at Greece.
  • 2023.05:  🎉🎉 We release a new version (stable version) of Zipformer code
  • 2023.05:  🎉🎉 Two papers: Blank skipping for transducer pdf and delay-penalized CTC pdf are accepted by Interspeech 2023.
  • 2023.02:  🎉🎉 Three papers: Fast decoding for transducer pdf, MVQ training pdf, Delay-penalized transducer pdf are accepted by ICASSP 2023.
  • 2022.11:  🎉🎉 We release the first version of Zipformer code.
  • 2022.06:  🎉🎉 We start the sherpa project, it is a ASR runtime based on libtorch.
  • 2022.06:  🎉🎉 Pruned rnnt paper is accepted by InterSpeech 2022 pdf.
  • 2022.05:  🎉🎉 We finish a reworked version of conformer, which converages faster and can train with fp16 stably, the performance is also slightly better code.
  • 2022.03:  🎉🎉 We finish our pruned rnnt loss (also in k2), which is much faster and memory efficient than RNNTLoss in torchaudio.
  • 2021.12:  🎉🎉 We decide to change our direction from CTC/MMI to RNN-T like models, because it is more suitable for efficient streaming ASR.
  • 2021.09:  🎉🎉 We release the first version of Icefall at the InterSpeech 2021.
  • 2021.06:  🎉🎉 I join Daniel’s team at Xiaomi.

🚀 Projects


k2: The core algorithm of the Next-gen Kaldi

  • Ragged Tensor running on both CPU and GPU.
  • Differentiable Finite State Acceptor.
  • Pruned RNN-T Loss.

Icefall: The recipes of the Next-gen Kaldi

  • Conformer CTC / MMI.
  • Xformer transducer.
  • MVQ training.
  • Pipeline to build an ASR.

📝 Publications

InterSpeech 2022

Pruned RNN-T for fast, memory-efficient ASR training

Fangjun Kuang, Liyong Guo, Wei Kang, Long Lin, Mingshuang Luo, Zengwei Yao, Daniel Povey

fast_rnnt k2

  • We introduce a method for faster and more memoryefficient RNN-T loss computation.
    • We first obtain pruning bounds for the RNN-T recursion using a simple joiner network that is linear in the encoder and decoder embeddings.
    • We then use those pruning bounds to evaluate the full, non-linear joiner network.

📖 Educations

  • 2014.09 - 2017.06 (Master), Computer Science (CS), Peking University (PKU).
    • Technology of Computer Application, Institute of Network Computing and Information Systems (NC&IS)
  • 2010.09 - 2014.06 (Bachelor), Electronics Engineering (EE), Nanjing University (NJU).
    • Information Electronic, School of Electronic Science and Engineering
  • 2007.09 - 2010.06, Changting No.1 Middle School, Fujian Province.

💬 Invited Talks

  • 2022.07, I give a presentation about the Next-gen Kaldi. | [video]

💻 Work Experiences

  • 2021.06 - Now, Xiaomi Corporation, Senior Engineer.
  • 2019.01 - 2021.05, Mobvoi Beijing, Speech Engineer.
  • 2017.07 - 2018.12, China Highway Engineering Consultants Corporation (CHECC), Big Data Engineer.
  • 2016.03 - 2016.12 (Internship), Mobvoi Beijing, Search Engineer.