Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.
8点多不见客人多起来,Maggie姐决定上台唱首歌。她挑了邓丽君的《漫步人生路》,唱得竟有八分像。一个长一圈胡子的中年男人闻声从包厢里出来,是她认识10年的老客,某公司高层。他仍在捧她的场,挑她手下的小姐,只不过以前他一周来3次,现在一个月只来1次,也不喝酒,只喝茶。他不爱去卡拉OK,就喜欢站在夜总会的投影幕布前唱得掏心掏肺。贴面拥抱、寒暄一阵后,两人手拉手甜蜜地唱起《明天你是否依然爱我》。
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auto result = t.transcribe("audio.wav", parakeet::Decoder::CTC); // fast greedy
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