Multilingual DistilWhisper
Collection
Multilingual Distilwhisper allows for better ASR performance in target languages by adding lightweight CLSR modules on top of whisper-small. • 4 items • Updated • 7
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("naver/multilingual-distilwhisper-3k", device_map="auto")Quick Links
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. More details in the ICASSP 2024 paper: arxiv.org/abs/2311.01070
Inference
Code for training and inference at: https://github.com/naver/multilingual-distilwhisper
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}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="naver/multilingual-distilwhisper-3k")