efficient-speech/lite-whisper-medium-acc

Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details.

Benchmark Results

Following is the average word error rate (WER) evaluated on the ESB datasets:

Model	Average WER (↓)	Encoder Size	Decoder Size
whisper-tiny	22.01	7.63M	29.55M
lite-whisper-tiny-acc	22.97	7.41M	29.55M
lite-whisper-tiny	23.95	7.00M	29.55M
lite-whisper-tiny-fast	27.09	6.48M	29.55M
whisper-base	17.67	19.82M	52.00M
lite-whisper-base-acc	19.07	18.64M	52.00M
lite-whisper-base	19.71	17.44M	52.00M
lite-whisper-base-fast	23.05	16.07M	52.00M
whisper-small	15.89	87.00M	153.58M
lite-whisper-small-acc	15.37	76.99M	153.58M
lite-whisper-small	14.96	70.16M	153.58M
lite-whisper-small-fast	14.92	63.11M	153.58M
whisper-medium	15.12	305.68M	456.64M
lite-whisper-medium-acc	13.46	269.93M	456.64M
lite-whisper-medium	14.50	239.99M	456.64M
lite-whisper-medium-fast	14.52	215.31M	456.64M

Citation

If you use LiteASR in your research, please cite the following paper:

@misc{kamahori2025liteasrefficientautomaticspeech,
      title={LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation}, 
      author={Keisuke Kamahori and Jungo Kasai and Noriyuki Kojima and Baris Kasikci},
      year={2025},
      eprint={2502.20583},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.20583}, 
}