Lite-Whisper
Collection
https://github.com/efeslab/LiteASR β’ 7 items β’ Updated β’ 2
How to use efficient-speech/lite-whisper-large-v3-turbo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="efficient-speech/lite-whisper-large-v3-turbo", trust_remote_code=True) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("efficient-speech/lite-whisper-large-v3-turbo", trust_remote_code=True, dtype="auto")Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details.
Following is the average word error rate (WER) evaluated on the ESB datasets:
| Model | Average WER (β) | Encoder Size | Decoder Size |
|---|---|---|---|
| whisper-large-v3 | 10.1 | 635M | 907M |
| lite-whisper-large-v3-acc | 10.1 | 429M | 907M |
| lite-whisper-large-v3 | 10.2 | 377M | 907M |
| lite-whisper-large-v3-fast | 11.3 | 308M | 907M |
| whisper-large-v3-turbo | 10.1 | 635M | 172M |
| lite-whisper-large-v3-turbo-acc | 10.2 | 421M | 172M |
| lite-whisper-large-v3-turbo | 12.6 | 374M | 172M |
| lite-whisper-large-v3-turbo-fast | 20.1 | 313M | 172M |
| whisper-medium | 14.8 | 306M | 457M |
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},
}