Multilingual DistilWhisper: Efficient Distillation of Multi-task Speech Models via Language-Specific Experts
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
摘要
Whisper is a multitask and multilingual speech model covering 99 languages.
It yields commendable automatic speech recognition (ASR) results in a subset of
its covered languages, but the model still underperforms on a non-negligible
number of under-represented languages, a problem exacerbated in smaller model
versions. In this work, we propose DistilWhisper, an approach able to bridge
the performance gap in ASR for these languages while retaining the advantages
of multitask and multilingual capabilities. Our approach involves two key
strategies: lightweight modular ASR fine-tuning of whisper-small using
language-specific experts, and knowledge distillation from whisper-large-v2.
This dual approach allows us to effectively boost ASR performance while keeping
the robustness inherited from the multitask and multilingual pre-training.
Results demonstrate that our approach is more effective than standard
fine-tuning or LoRA adapters, boosting performance in the targeted languages
for both in- and out-of-domain test sets, while introducing only a negligible
parameter overhead at inference.
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关键词
knowledge distillation,multitask speech processing,automatic speech recognition,multilingual speech processing,language experts
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