Design of the IBM Granite 5.0 TurboCTC ASR Model
IBM details Granite 5.0 TurboCTC, a 470M-parameter Conformer-based English ASR model on the Open ASR speed-accuracy Pareto frontier, released on Hugging Face.
IBM describes Granite 5.0 Turbo CTC, a 470 million parameter encoder-only ASR model built on Conformer blocks with pyramidal temporal subsampling, strided depthwise convolutions, chunk-wise self-attention, and conditioning on middle-layer predictions. Training uses only publicly available data, a novel Muon optimizer, and balanced data sampling. Inference optimizations make it twice as fast as the fastest competitor while sitting on the Open ASR leaderboard Pareto frontier for English short-form ASR. Weights are released on Hugging Face under a permissive license.
- 470M-parameter encoder-only Conformer model with strided depthwise convolutions and chunk-wise self-attention
- Trained only on publicly available data using a novel Muon optimizer and balanced sampling
- Twice as fast as the fastest competitor on the Open ASR leaderboard Pareto frontier
- Released on Hugging Face (ibm-granite/granite-speech-5.0-470m-turboctc) under a permissive license
- Products
- Granite 5.0 TurboCTC
- Organizations
- IBM
- AI models
- Granite 5.0 TurboCTC
From the original article
1 min read at arxiv.orgWe 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.
By Brian Kingsbury · arxiv.org
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