Re: [Kaldi-developers] Kaldi comparison with Hydra?

[Daniel P. <dp...@gm...>]

Interestingly, Hydra is what I wanted to call the Kaldi project (I was
outvoted).

It's not really possible to compare the two.  Hydra is a closed-source
decoder, and it's only a decoder, it doesn't have a system for
building models like Kaldi does.

I would imagine that the Hydra decoder is faster, since they've
obviously put a lot of effort into it, but the online-nnet2 decoder is
sufficiently fast, in that you can get it to decode in real-time
fairly easily, without much loss in accuracy by using suitable beams
and a large enough chunk-size (e.g. 20 frames), and by configuring
your matrix library (ATLAS, OpenBlas, MKL) to use, say, 2 threads.
Although it would be very easy to use GPUs for the neural net part of
the computation, there hasn't been much demand for it because if you
can decode in real-time using a couple of cores of CPU, it'll
generally be more efficient in terms of hardware cost than using one
core of CPU, plus a GPU.

Note that the online-nnet2 decoder is not really a decoder per se, it
just calls the standard decoding code in lattice-faster-decoder.h,
which isn't that complicated; but the online-nnet2 code takes care of
various online feature estimation issues and of batching up the
features into suitable size chunks so that matrix operations in the
neural net code will be fast.

Dan


On Tue, Jan 6, 2015 at 4:02 PM,  <Dan...@pa...> wrote:
> CMU’s Hydra ASR decoder made a splash out here.  From the references below
> (or any other info you can find), does anyone have a feeling for how this
> compares with the Kaldi nnet2 online decoder in speed and accuracy?
>
>
>
> http://www.cs.cmu.edu/~ianlane/publications/2012_Kim_Interspeech.pdf
>
> http://on-demand.gputechconf.com/gtc/2013/presentations/S3406-HYDRA-Hybrid-CPU-GPU-Speech-Recognition-Engine.pdf
>
> http://www.cs.cmu.edu/~ianlane/publications/SLT_JungsukKim.pdf
>
> http://www.nvidia.com/content/cuda/spotlights/ian-lane-cmu.html
>
>
>
> Dan
>
>
>
>
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