Fair point. Looking back, the title probably promised more specifics than the post delivered.

A few things we've learned so far:

Running speech recognition, translation, and TTS locally is absolutely possible, but latency becomes one of the biggest challenges. Supporting multiple audio sources (microphones, meetings, browser tabs, system audio, etc.) often ends up being more complex than the translation itself. Self-hosting is a much stronger requirement than we initially expected for organizations with privacy, compliance, or data sovereignty concerns. Choosing models is a constant tradeoff between quality, speed, hardware requirements, and language coverage.

Regarding AI usage: the translation pipeline itself is AI-based. For the rest of the project, we've used AI tools where they were helpful, for example, coding assistance, drafting documentation, brainstorming, and editing content, but all code, documentation, testing, and releases are reviewed and validated by the team before becoming part of the project.

Thanks for the feedback. You're right that this post ended up being more of a project introduction than a lessons-learned write-up.

Thanks! We're still benchmarking different setups, so I don't want to give a misleading "minimum spec" number yet. In practice, the hardware requirements depend much more on the STT/translation/TTS models you choose than on PolyTalk itself. For a single-user setup, you don't necessarily need expensive hardware. As you push for lower latency, larger models, or multiple simultaneous streams, the requirements increase pretty quickly. Proper hardware benchmarks are something we plan to publish once we've tested a wider range of configurations.

Thanks! You're right that there are already excellent open-source STT, translation, and TTS projects. PolyTalk isn't trying to replace them, and we build on top of them. What we're focused on is creating a complete, self-hosted real-time communication platform that ties those components together and can handle different audio sources (microphones, meetings, browser tabs, system audio, etc.) through a single workflow.

Regarding latency, we're not targeting sub-200 ms. In our testing, we've intentionally favored translation quality and conversational flow over minimizing latency at all costs. Depending on the setup, end-to-end latency is typically around 2 seconds. One thing we've already improved is processing complete sentences rather than translating word-by-word. That gives the translation more context and generally produces much more natural results. We're also working on additional context-aware translation improvements, and tone adaptation is on our roadmap.