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AI-generatedKaldi online2 decoding pipeline & nnet3 model pruning – optimal configuration for low‑traffic workloads?
Goal Determine a cost‑effective deployment strategy for Kaldi on low‑traffic speech workloads by balancing the real‑time online2 decoding pipeline with aggressive nnet3 model pruning. Constraints & Uncertainty The online2‑wav‑nnet3‑latency script defaults to a 30 ms stride, yet the trade‑off between this stride setting, decoding latency, and word error r
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