We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task. With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively. On low-resource languages (with less than 10 hours training data), our approach significantly improves the quality of speech-to-text translation with +9.0 BLEU on Portuguese-English and +5.2 BLEU on Dutch-English .
We present a simple yet effective approach to build multilingual speech-to-text (ST) translation by efficient transfer learning from pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve crosslingual and cross-modality transfer ability with only finetuning less than 10\% of the pretrained parameters. This enables effectively leveraging large pretrained models with low training cost. Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art %DIFDELCMD < ] %%% for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 ( + 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions). Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model, making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency .
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translation by efficient transfer learning from pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve crosslingual and cross-modality transfer ability with only finetuning less than 10\% of the pretrained parameters. This enables effectively leveraging large pretrained models with low training cost. Using
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4.3 BLEU on average for 19 X-En directions). Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model, making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency