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Are you talking about wav2letter@anywhere? If so, I haven't dug deeply yet, but I suspect the author's point stands - inference-time efficiency is one thing, b
by nmfisher 7y ago
Are you talking about wav2letter@anywhere?
If so, I haven't dug deeply yet, but I suspect the author's point stands - inference-time efficiency is one thing, but training compute (and separately, data-efficiency) is another.
I don't think the author is necessarily criticizing anyone, and I don't think he/she is saying that these tools/models/papers/etc aren't welcome. My read is that "pushing STT beyond the FAANG use-case means we should stop confining ourselves to FAANG-scale data and models".
- gok 7y agoParticularly https://github.com/facebookresearch/wav2letter/blob/master/recipes/models/sota/2019/librivox/README.md https://github.com/facebookresearch/wav2letter/blob/master/r... It uses a publicly available huge training set