Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="efficient-speech/lite-whisper-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: openai/whisper-base | |
| library_name: transformers | |
| license: apache-2.0 | |
| pipeline_tag: automatic-speech-recognition | |
| tags: | |
| - audio | |
| - automatic-speech-recognition | |
| - whisper | |
| - hf-asr-leaderboard | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our [GitHub repository](https://github.com/efeslab/LiteASR) and [paper](https://arxiv.org/abs/2502.20583) for details. | |
| ## Benchmark Results | |
| Following is the average word error rate (WER) evaluated on the [ESB datasets](https://huggingface.co/datasets/hf-audio/esb-datasets-test-only-sorted): | |
| | Model | Average WER (↓) | Encoder Size | Decoder Size | | |
| |-------|----------------|--------------|--------------| | |
| | [whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 22.01 | 7.63M | 29.55M | | |
| | [lite-whisper-tiny-acc](https://huggingface.co/efficient-speech/lite-whisper-tiny-acc) | 22.97 | 7.41M | 29.55M | | |
| | [lite-whisper-tiny](https://huggingface.co/efficient-speech/lite-whisper-tiny) | 23.95 | 7.00M | 29.55M | | |
| | [lite-whisper-tiny-fast](https://huggingface.co/efficient-speech/lite-whisper-tiny-fast) | 27.09 | 6.48M | 29.55M | | |
| | | | | | | |
| | [whisper-base](https://huggingface.co/openai/whisper-base) | 17.67 | 19.82M | 52.00M | | |
| | [lite-whisper-base-acc](https://huggingface.co/efficient-speech/lite-whisper-base-acc) | 19.07 | 18.64M | 52.00M | | |
| | [lite-whisper-base](https://huggingface.co/efficient-speech/lite-whisper-base) | 19.71 | 17.44M | 52.00M | | |
| | [lite-whisper-base-fast](https://huggingface.co/efficient-speech/lite-whisper-base-fast) | 23.05 | 16.07M | 52.00M | | |
| | | | | | | |
| | [whisper-small](https://huggingface.co/openai/whisper-small) | 15.89 | 87.00M | 153.58M | | |
| | [lite-whisper-small-acc](https://huggingface.co/efficient-speech/lite-whisper-small-acc) | 15.37 | 76.99M | 153.58M | | |
| | [lite-whisper-small](https://huggingface.co/efficient-speech/lite-whisper-small) | 14.96 | 70.16M | 153.58M | | |
| | [lite-whisper-small-fast](https://huggingface.co/efficient-speech/lite-whisper-small-fast) | 14.92 | 63.11M | 153.58M | | |
| | | | | | | |
| | [whisper-medium](https://huggingface.co/openai/whisper-medium) | 15.12 | 305.68M | 456.64M | | |
| | [lite-whisper-medium-acc](https://huggingface.co/efficient-speech/lite-whisper-medium-acc) | 13.46 | 269.93M | 456.64M | | |
| | [lite-whisper-medium](https://huggingface.co/efficient-speech/lite-whisper-medium) | 14.50 | 239.99M | 456.64M | | |
| | [lite-whisper-medium-fast](https://huggingface.co/efficient-speech/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}, | |
| } | |
| ``` |