efficient-speech/lite-whisper-large-v3-turbo

Model

12

stars

10

commits

4

repos using this model

1

linked in READMEs

Apr 3, 2025

updated

audio
automatic-speech-recognition
custom_code
feature-extraction
hf-asr-leaderboard
lite-whisper
safetensors
transformers
whisper
Browse cluster: Speech Recognition and Audio Processing

README

Model Card for Lite-Whisper large-v3-turbo

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:

ModelAverage WER (↓)Encoder SizeDecoder Size
whisper-large-v310.1635M907M
lite-whisper-large-v3-acc10.1429M907M
lite-whisper-large-v310.2377M907M
lite-whisper-large-v3-fast11.3308M907M
    
whisper-large-v3-turbo10.1635M172M
lite-whisper-large-v3-turbo-acc10.2421M172M
lite-whisper-large-v3-turbo12.6374M172M
lite-whisper-large-v3-turbo-fast20.1313M172M
    
whisper-medium14.8306M457M

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}, 
}

Contributors

kamahori

6 commits

eyoel-gebre

2 commits

nielsr

1 commits

Xenova

1 commits

efficient-speech/lite-whisper-large-v3-turbo

Model

12

stars

10

commits

4

repos using this model

1

linked in READMEs

Apr 3, 2025

updated

audio
automatic-speech-recognition
custom_code
feature-extraction
hf-asr-leaderboard
lite-whisper
safetensors
transformers
whisper
Browse cluster: Speech Recognition and Audio Processing

README

Model Card for Lite-Whisper large-v3-turbo

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:

ModelAverage WER (↓)Encoder SizeDecoder Size
whisper-large-v310.1635M907M
lite-whisper-large-v3-acc10.1429M907M
lite-whisper-large-v310.2377M907M
lite-whisper-large-v3-fast11.3308M907M
    
whisper-large-v3-turbo10.1635M172M
lite-whisper-large-v3-turbo-acc10.2421M172M
lite-whisper-large-v3-turbo12.6374M172M
lite-whisper-large-v3-turbo-fast20.1313M172M
    
whisper-medium14.8306M457M

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}, 
}

Contributors

kamahori

6 commits

eyoel-gebre

2 commits

nielsr

1 commits

Xenova

1 commits