Over the past few months, we’ve been working on a project called PolyTalk.
The original goal was pretty simple: make real-time multilingual communication possible without depending on external translation APIs or cloud-only services.
While testing existing solutions, we noticed that many of them required sending conversations through third-party infrastructure. That works for some use cases, but it wasn’t a great fit for organizations that care about privacy, deployment flexibility, or keeping communication workflows under their own control.
So we started building a self-hosted, open-source speech-to-speech translation platform instead.
A few things we’ve focused on:
Real-time speech translation Self-hosted deployment Open-source core No external translation APIs Live audio translation
The project is still evolving, but it’s been interesting exploring the challenges of multilingual communication, local AI infrastructure, and real-time translation workflows.
I’d be curious to hear how others here approach translation.
Are you using cloud-based services, self-hosted tools, or something in between?
GitHub: https://github.com/PolyTalkIO/polytalk
Website: https://polytalk.io/
It’s nice to you release as open source self hosted pj and also nice to see py and FastApi stack. There has multiple open source tts sst pipeline, what is your pj’s selling points? I can’t find in your src. Amd what exactly is near real-time? Sub 200 ms?
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.