We

We built an open-source tool for real-time speech-to-speech translation

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We built an open-source tool for real-time speech-to-speech translation

For the past few days, we have been working on an open-source, self-hosted real-time speech-to-speech translation tool called PolyTalk. The goal was that there are people and organisations who need privacy around the tool they are using, and for the speech-to-speech translation, we haven't had many options. We built the tool with Ollama, Faster Whisper, and Piper. The tool is not limited to speech-to-speech translation only, but you can also share any of your tabs, whether you're watching a YouTube video in another language, the tool will give you audio output in your target language. We are aware of how often context and tone get lost in translation, so we ensured translation quality by processing complete sentences instead of individual words. Now we are focused on context support and tone adaptation. If you want to see the project, here is the GitHub repo: https://github.com/PolyTalkIO/polytalk

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, open · Missing: mac, agents, macos
83%83% 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.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, pipe · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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.

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