  Conformer-based models have become the dominant end-to-end architecture for
speech processing tasks. With the objective of enhancing the conformer
architecture for efficient training and inference, we carefully redesigned
Conformer with a novel downsampling schema. The proposed model, named Fast
Conformer(FC), is 2.8x faster than the original Conformer, supports scaling to
Billion parameters without any changes to the core architecture and also
achieves state-of-the-art accuracy on Automatic Speech Recognition benchmarks.
To enable transcription of long-form speech up to 11 hours, we replaced global
attention with limited context attention post-training, while also improving
accuracy through fine-tuning with the addition of a global token. Fast
Conformer, when combined with a Transformer decoder also outperforms the
original Conformer in accuracy and in speed for Speech Translation and Spoken
Language Understanding.

Audio and Speech Processing;Sound