---
base_model: openai/whisper-large-v3-turbo
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
- whisper
- hf-asr-leaderboard
---

# Model Card for Lite-Whisper large-v3-turbo-acc

<!-- 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-large-v3](https://huggingface.co/openai/whisper-large-v3) | 10.1 | 635M | 907M |
| [lite-whisper-large-v3-acc](https://huggingface.co/efficient-speech/lite-whisper-large-v3-acc) | 10.1 | 429M | 907M |
| [lite-whisper-large-v3](https://huggingface.co/efficient-speech/lite-whisper-large-v3) | 10.2 | 377M | 907M |
| [lite-whisper-large-v3-fast](https://huggingface.co/efficient-speech/lite-whisper-large-v3-fast) | 11.3 | 308M | 907M |
| &nbsp; | &nbsp; | &nbsp; | &nbsp; |
| [whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) | 10.1 | 635M | 172M |
| [lite-whisper-large-v3-turbo-acc](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo-acc) | 10.2 | 421M | 172M |
| [lite-whisper-large-v3-turbo](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo) | 12.6 | 374M | 172M |
| [lite-whisper-large-v3-turbo-fast](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo-fast) | 20.1 | 313M | 172M |
| &nbsp; | &nbsp; | &nbsp; | &nbsp; |
| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 14.8 | 306M | 457M |

## Quick Start

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-large-v3-turbo", 
    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)
```

## 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}, 
}
```