README.md

---
base_model: openai/whisper-small
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.

## Abstract

Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in reduced dimensionality. Evaluation results show that our method can compress Whisper large-v3's encoder size by over 50%, matching Whisper medium's size with better transcription accuracy, thereby establishing a new Pareto frontier of accuracy and efficiency.

## Sample Usage

The easiest way to run our model is to use our integration with HuggingFace Transformers library.
We provide model weights for the compressed version of OpenAI Whisper series [here](https://huggingface.co/efficient-speech).

```python
import librosa
import torch
from transformers import AutoProcessor, AutoModel

device = "cuda:0"
dtype = torch.float16

# load the compressed Whisper model
model = AutoModel.from_pretrained(
    "efficient-speech/lite-whisper-small",
    trust_remote_code=True,
)
model.to(dtype).to(device)

# we use the same processor as the original model
processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")

# set the path to your audio file
path = "path/to/audio.wav"
audio, _ = librosa.load(path, sr=16000)

input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
input_features = input_features.to(dtype).to(device)

predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(
    predicted_ids,
    skip_special_tokens=True
)[0]

print(transcription)
```

## 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 |
| &nbsp; | &nbsp; | &nbsp; | &nbsp; |
| [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 |
| &nbsp; | &nbsp; | &nbsp; | &nbsp; |
| [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 |
| &nbsp; | &nbsp; | &nbsp; | &nbsp; |
| [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},
}
```