Design of the IBM Granite 5.0 TurboCTC ASR Model

Title:Design of the IBM Granite 5.0 TurboCTC ASR Model

Abstract:We describe the architecture, training methodology and inference speedups of Granite 5.0 Turbo CTC, a 470 million parameter encoder-only model with an excellent speed-accuracy tradeoff. The architecture uses pyramidal temporal subsampling within Conformer blocks using strided depthwise convolutions, block-diagonal (chunk-wise) self-attention, and conditioning on intermediate predictions from the middle layer. Training highlights are the use of only publicly available data, the novel use of a Muon optimizer, and balanced data sampling. Inference speedups include replacing 1 x 1 convolutions with linear layers and optimizing the attention computation in the Conformer blocks. Collectively, these 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. The model can be used under a permissive license and downloaded from this https URL.

Computation and Language (cs.CL)

arXiv:2609.20104 [cs.CL]

(or arXiv:2609.20104v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.20104 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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