zlacker

>>tablet+(OP)
Machine translation and speech recognition. The state of the art for these is a multi-modal language model. I'm hearing impaired veering on deaf, and I use this technology all day every day. I wanted to watch an old TV series from the 1980s. There are no subtitles available. So I fed the show into a language model (Whisper) and now I have passable subtitles that allow me to watch the show.

Am I the only one who remembers when that was the stuff of science fiction? It was not so long ago an open question if machines would ever be able to transcribe speech in a useful way. How quickly we become numb to the magic.

>>retrac+J
Definitely not. I took this same basic idea of feeding videos into Whisper to get SRT subtitles and took it a step further to make automatic Anki flashcards for listening practice in foreign languages [1]. I literally feel like I'm living in the future every time I run across one of those cards from whatever silly Finnish video I found on YouTube pops up in my queue.

These models have made it possible to robustly practice all 4 quadrants of language learning for most common languages using nothing but a computer, not just passive reading. Whisper is directly responsible for 2 of those quadrants, listening and speaking. LLMs are responsible for writing [2]. We absolutely live in the future.

[1]: https://github.com/hiandrewquinn/audio2anki

[2]: https://hiandrewquinn.github.io/til-site/posts/llm-tutored-w...

>>hiAndr+U9
Hi Andrew, I've been trying to get a similar audio language support app hacked together in a podcast player format (I started with Anytime Player) using some of the same principles in your project (transcript generation, chunking, level & obscurity aware timestamped hints and translations).

I really think support for native content is the ideal way to learn for someone like me, especially with listening.

Thanks for posting and good luck.