Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data
This is an incremental improvement in speech recognition for multilingual applications, offering competitive performance on benchmarks.
The paper tackled large-scale multilingual automatic speech recognition by developing Whale, a model that achieved a 2.4% word error rate on Librispeech test-clean and a 3.4% character error rate on CSJ eval3, outperforming Whisper and OWSM.
This paper reports on the development of a large-scale speech recognition model, Whale. Similar to models such as Whisper and OWSM, Whale leverages both a large model size and a diverse, extensive dataset. Whale's architecture integrates w2v-BERT self-supervised model, an encoder-decoder backbone built on E-Branchformer, and a joint CTC-attention decoding strategy. The training corpus comprises varied speech data, of not only public corpora but also in-house data, thereby enhancing the model's robustness to different speaking styles and acoustic conditions. Through evaluations on multiple benchmarks, Whale achieved comparable performance to existing models. In particular, it achieves a word error rate of 2.4% on the Librispeech test-clean set and a character error rate of 3.4% on the CSJ eval3 set, outperforming Whisper large-v3 and OWSM v3.1.