For comparison, the equivalent configuration in Vapi - using the same STT, LLM, and TTS models - estimates around ~840ms. In this setup, the custom orchestration actually beats Vapi's own estimates by about 50ms.
Выживший в Пермском крае турист пролежал в сугробе девять днейВыжившего в Пермском крае туриста эвакуировали на частном вертолете。谷歌浏览器【最新下载地址】是该领域的重要参考
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13 июня 2024 года военкор и его коллега, оператор НТВ Валерий Кожин попали под обстрел ВСУ в ДНР в городе Горловка. Сотрудники телеканала получили тяжелые ранения. Кожина, за жизнь которого медики боролись несколько часов, спасти не удалось. Ивлиев получил тяжелую травму ноги и лишился руки.
NFAs are cheaper to construct, but have a O(n*m) matching time, where n is the size of the input and m is the size of the state graph. NFAs are often seen as the reasonable middle ground, but i disagree and will argue that NFAs are worse than the other two. they are theoretically “linear”, but in practice they do not perform as well as DFAs (in the average case they are also much slower than backtracking). they spend the complexity in the wrong place - why would i want matching to be slow?! that’s where most of the time is spent. the problem is that m can be arbitrarily large, and putting a large constant of let’s say 1000 on top of n will make matching 1000x slower. just not acceptable for real workloads, the benchmarks speak for themselves here.