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The architecture, training methodology and inference speedups of Granite 5.0 Turbo CTC result in a model that is on the speed-accuracy Pareto frontier of the Open ASR leaderboard for English short-form ASR while being twice as fast as the fastest competitor.
This paper presents models that add timestamps and speaker information to a given transcript using a non-autoregressive LLM-based architecture and achieves state-of-the-art timestamp accuracy and the best cpWER for speaker attribution.
