Fair pushback. On end to end: we measure those too, same methodology: https://benchmarks.speko.ai/s2s. If the single models win, we route to them the same way, so we do not care which architecture (s2s or cascaded) wins. For now, what we see in production so far is that most teams still want to control each piece: swap the STT for medical vocabulary, keep the LLM, keep the voice.

On "promptfoo of voice models": that is closer to how it started. At my last company we ran these evals manually, we would even hire native-speaking raters, benchmark, switch if it wins. The evals are the value, agreed. The routing is what makes them actionable: teams told us swapping always looked like an R&D project, so scores alone did not change what ran in production.

On prompt-based voice gen and reference-audio cloning: agreed, that is what we see too. It makes continuous measurement more important: the same style prompt behaves differently per language and per content type, so we rank the voices themselves, tagged by use case: https://benchmarks.speko.ai/tts-voices

> The industry is very much moving towards one-model-does-all end to end trained

I've worked with hundreds of enterprises on voice AI and voice agent solutions. In my experience, this isn't true. Or rather I should say, the people actually paying for voice agents (i.e enterprises) are not moving towards STS solutions in a meaningful way. The composability, observability, and reliability profile of STS systems is not amenable to enterprise criteria. Not to mention costs.

This is not true in my experience. By and large, everyone uses the cascading voice stack.

totally agree with this