
## About

Multilingual Distilwhisper allows for better ASR performance in target languages by adding lightweight CLSR modules on top of whisper-small. 
These modules are trained on a mix of cross-entropy (ASR) and knowledge distillation losses, where whisper-large-v2 is used as teacher. 

## Inference

Loader will be made available soon at https://github.com/naver

## Citation
```
@inproceedings{ferraz2024distilwhisper,
  title={Multilingual DistilWhisper: Efficient Distillation of Multi-task Speech Models via Language-Specific Experts},
  author={Ferraz, Thomas Palmeira and Boito, Marcely Zanon and Brun, Caroline and Nikoulina, Vassilina},
  booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2024},
  organization={IEEE}
}
```
{"language": ["ca", "ta", "th"], "license": "mit", "tags": ["automatic-speech-recognition"], "datasets": ["mozilla-foundation/common_voice_13_0"], "inference": false, "pipeline_tag": "automatic-speech-recognition"}
transformers pytorch distilwhisper text2text-generation automatic-speech-recognition ca ta th dataset:mozilla-foundation/common_voice_13_0 license:mit autotrain_compatible region:us