Conformer-2 is a cutting-edge automatic speech recognition AI model designed to enhance decoding accuracy and performance in challenging audio environments. Building on the strengths of its predecessor, Conformer-1, this model has undergone extensive training on 1.1 million hours of English audio data, resulting in significant improvements in identifying proper nouns and alphanumeric characters. Users can expect a more reliable experience, particularly in noisy settings, without any compromise on word error rates.

The advancements in Conformer-2 stem from a combination of increased training data, innovative training techniques, and a refined inference pipeline, which collectively reduce latency and improve overall performance. By utilizing a model ensembling approach, it generates labels from multiple 'teachers', enhancing versatility and robustness. These enhancements make Conformer-2 not only faster but also capable of leveraging larger model sizes effectively, delivering superior results without the drawbacks typically associated with larger models.