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Sokuji – Open-source real-time speech translation for Microsoft Teams

Hacker News

Sokuji – Open-source real-time speech translation for Microsoft Teams

Hi HN! I'm excited to share Sokuji, an open-source project I've been working on that provides real-time speech translation, now supporting Microsoft Teams. The idea started from my own experience with international remote work and multilingual meetings, where existing solutions often fell short in terms of flexibility, platform support, or language coverage. Sokuji addresses these issues directly. You can use it as a Chrome extension to integrate seamlessly into web versions of Google Meet and Microsoft Teams, or run it as a Linux desktop app, acting as a virtual microphone to provide real-time translations in any app that accepts audio input. Some key points about Sokuji: Fully open-source on GitHub: https://github.com/kizuna-ai-lab/sokuji Uses OpenAI models, thus supporting virtually all major languages Easily integrates across multiple platforms: Microsoft Teams, Google Meet, Zoom, etc. We're particularly looking for contributors who can help develop a macOS version, as currently only Linux and browser extension support is available. You can try it right now without sign-up: Chrome Extension: Sokuji Chrome Extension: https://chromewebstore.google.com/detail/sokuji-ai-powered-l... GitHub for source code and contributions: https://github.com/kizuna-ai-lab/sokuji Any feedback, questions, or contributions are welcome! An interesting note: This project’s code was entirely created through vibe coding — no manual coding was involved, and most of the code has not been manually reviewed or read. Thanks for checking it out! Cheers, The Sokuji Team

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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