Conformer

Conformer-2 is an advanced AI model designed for automatic speech recognition (ASR). It builds upon the success of Conformer-1 and has been trained on a vast dataset of 1.1 million hours of English audio. The primary focus of Conformer-2 is to improve the recognition of proper nouns, alphanumerics, and noise robustness. It follows the scaling laws proposed in DeepMind's Chinchilla paper and leverages a massive amount of training data. One notable feature of Conformer-2 is its use of model ensembling, which reduces variance and enhances performance. Despite its larger size, Conformer-2 exhibits improved speed and processing compared to its predecessor. In real-world applications, Conformer-2 demonstrates significant enhancements in user-oriented metrics, particularly in alphanumerics, proper noun error rate, and noise robustness. It is an invaluable component for AI pipelines focusing on generative AI applications using spoken data, providing accurate transcriptions with exceptional precision and reliability.