Speaker 1: Hello, everybody. Welcome to a demo video of my real-time transcription library that should highlight the real-time capabilities. As you can see, the speech is transcribed almost instantly into text. Of course, there's always room for improvement, so maybe latency can be reduced even further. Ideally, the transcription would be perfectly synchronized with the spoken words. But nevertheless, given the circumstances around Whisper, I think this is a key achievement. Building this library was quite hard work, and I hope you recognize and appreciate the effort. It would be wonderful if you could support the project by giving a star on GitHub. The success of a library often depends on its visibility and user engagement, and if it goes unnoticed, bugs will too, and there will be less room for improvement. And I hope creating a stable, efficient, and user-friendly tool that everyone would love to use. So all the work I've put into this is for free, and my motivation is, of course, showcasing my skills as a developer and providing a valuable library to the community. So you can download and use this tool without any cost, and I just hope it finds its way into many applications and serves as the foundation for some innovative projects. Still, there is one issue with this library. I've noticed that sometimes at the end of a sentence or if I start speaking immediately after a sentence is detected, some words might get skipped or not detected as efficiently. I am unsure why this happens to the programming feels straightforward for me. It's quite challenging for me to pinpoint this issue right now, so I'm open to feedback and suggestions. If you have any ideas or insights on how this can be enhanced, please do share. Have a great day, everybody. Thank you for watching.