LL

LLPlayer – The media player for language learning, with AI subtitles

Hacker News

LLPlayer – The media player for language learning, with AI subtitles

Hello HN! I've been developing a video player in C# and WPF for the last 8 months, and now that it reached a certain quality, I'd like to publish it. github (source, release build) http://github.com/umlx5h/LLPlayer website https://llplayer.com LLPlayer is not a normal media player like mpv or VLC, but a media player specialized for language learning. I originally created it for my own English learning, but I have made it more versatile by adding various features. It is completely free OSS under GPL license. The main feature is automatic AI subtitle generation using OpenAI Whisper. (Recently, VLC has been in the news for doing the same thing, It doesn't appear to be public yet.) Subtitles can be generated in real-time from any position in a video in 100 languages. It is fast because it supports CUDA and Vulkan. In addition, by linking with yt-dlp, subtitles can be generated in real time from any online videos. (By the way, Whisper is far more accurate than Youtube's subtitle generation.) Other unique features include a subtitle sidebar, OCR subtitles, dual subtitles, real-time translation, word translation, and more. I plan to add more language-specific features in the future, such as Japanese for anime fans. I have prepared a demo video on GitHub, and I would be very happy if you could take a look at it if you are interested. Currently, It only supports Windows, but I want to make it cross-platform in the future using Avalonia. Note that I did not make the core video player from scratch in C#. I used a .NET library called Flyleaf and modified it, which is a simple yet very high quality library. https://github.com/SuRGeoNix/Flyleaf It would take a tremendous amount of effort to create a video player in scratch by just one person. Without this library, LLPlayer would never have existed. Thanks for publishing it as OSS! Feedback and questions are welcome. Thanks for reading!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created · Missing: reddit linkedin, podcasting, latex
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Product HuntOn track for Day 1 leaderboard · Strong signals: new, openai, using · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, 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.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, way · Missing: mobile apps, ios, personal
48%48% 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
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: real time · 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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