Whale, a 1.87B-parameter ASR model combining w2v-BERT and E-Branchformer, reports 2.4% WER on Librispeech test-clean and 3.4% CER on CSJ eval3, beating Whisper large-v3 and OWSM v3.1 on those benchmarks.
Next, acoustic features are extracted via SSL, w2v-BERT [18]
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Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data
Whale, a 1.87B-parameter ASR model combining w2v-BERT and E-Branchformer, reports 2.4% WER on Librispeech test-clean and 3.4% CER on CSJ eval3, beating Whisper large-v3 and OWSM v3.1 on those benchmarks.