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Training paradigms that utilize large-scale, noisy, or loosely paired datasets to improve model robustness.
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This project is a speech recognition and translation engine that utilizes a sequence-to-sequence transformer architecture to convert audio into text. It is built upon a weakly supervised learning framework, which leverages large-scale, unlabelled audio-transcript data to create generalized speech representations capabl