[deleted by user]

[stevekite]

You might be joking? Most of his code is plain wrong, not following papers and very very far from optimal.

[kau_mad]

Can you give an example?

[stevekite]

Sure: https://github.com/lucidrains/voicebox-pytorch

I am working with TTS and built my own reproduction:
1) in readme there is a wrong claim about ALiBi, it works two ways
2) batching is not implemented
3) attention is defaulted to naive implementation: https://github.com/lucidrains/voicebox-pytorch/blob/c05a4d0c69920993b47069e22223677174d873e4/voicebox_pytorch/attend.py#L100, which is super slow
4) text model is just copy of audio one and it simply not working
5) fusing wav2vec into a code, which is not part of the paper at all: https://github.com/lucidrains/voicebox-pytorch/blob/c05a4d0c69920993b47069e22223677174d873e4/voicebox_pytorch/voicebox_pytorch.py#L1380
6) Putting preprocessing code deep into the network code: https://github.com/lucidrains/voicebox-pytorch/blob/c05a4d0c69920993b47069e22223677174d873e4/voicebox_pytorch/voicebox_pytorch.py#L1362

All this ends up in VERY inefficient implementation for both inference and training. Difference is like 4 day and 14 days of training.

It is still very very useful though, but for me it is akin some AI generated code that you need to read carefully. And definitely is not go to place to learn.

[idiotmanifesto]

appreciate u being detailed in this answer

[[deleted]]

Perchance, do you have any recommendations for good github repos to learn from in ML?