Vaghenu, a meter aware sloka-to-chant, TTS for Sanskrit

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A 15-year-old dream has come true today. I started a PhD with the dream of creating a system that chants any Sanskrit shloka perfectly. And here I am opening sourcing Vaghenu, a meter aware sloka-to-chant, TTS for Sanskrit . This is the world's first vrutta-aware, open-source TTS for Sanskrit Chanting. I am making the model weights, training scripts, and even data (that I meticulously collected) public - https://prathosh.in/vagdhenu/ No large AI lab. No big engineering team. No venture-scale budget. Just a professor's conviction that one of humanity's oldest knowledge traditions deserves modern, open infrastructure. The name comes from the Upanishadic phrase: "Vācaṃ dhenum upāsīta" - Like the mythical wish-fulfilling cow, Vāgdhenu is intended to make Sanskrit texts more accessible to students, teachers, researchers, and devotees everywhere. Test out the live demo here and let me know your comments - https://prathosh.in/vagdhenu/ The entire system, from data collection to model building and demos, is built by a single person (your truly) using the powerful harness that we are building at LatentForce. I have attached a sample audio file generated by the system. P.S: Posting on behalf of my friend, their aren't on HN.

Visit product View on Hacker NewsJun 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, single, using · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

14%14% 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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