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.

翻译：Whisper是一个覆盖99种语言的多任务多语言语音模型。该模型在其覆盖的部分语言中取得了可观的自动语音识别（ASR）效果，但在相当数量的低资源语言上仍表现欠佳，这一问题在较小规模的模型版本中更为突出。本研究提出DistilWhisper方法，能够在保留多任务与多语言优势的同时，缩小这些语言在ASR任务中的性能差距。我们的方法包含两大关键策略：基于语言特定专家对whisper-small进行轻量级模块化ASR微调，以及从whisper-large-v2中进行知识蒸馏。这种双重策略既能有效提升ASR性能，又能保持多任务多语言预训练所赋予的鲁棒性。实验结果表明，该方法优于标准微调或LoRA适配器，在目标语言的域内与域外测试集上均提升了性能，且推理时仅引入可忽略不计的参数开销。