Kaldi beam & max‑active tuning for low‑traffic cost

For low‑traffic speech‑recognition services using Kaldi’s online decoder (online2-wav-nnet3-latgen-faster), the objective is to limit the real‑time factor (RTF) per utterance to a predefined cost threshold—say ≤0.04—while ensuring that the word error rate (WER) does not rise more than 2 % relative to the baseline configuration (beam = 13.0, max‑active = 5000).

Although the decoder exposes independent --beam and --max-active knobs, there is no published guideline for choosing them together to hit both the RTF and WER targets. Lowering the beam reduces computation but may prune correct paths; decreasing max‑active caps worst‑case latency but can cause search failures if set too low. The trade‑off surface between the two parameters remains unmapped for specific cost‑accuracy pairs.

What beam and max‑active values achieve an RTF of 0.04 with ≤2 % relative WER increase on a representative test set? How does the interaction change when the traffic pattern shifts from bursty to uniformly sparse? Which combination yields the lowest latency variance without triggering empty‑lattice errors?