Re: [Kaldi-users] Adapting LibriSpeech model to Blizzard2013 corpus

[Mate A. <ele...@gm...>]

I am using the nnet_a_online model. Does this model require iVectors?

Also, regarding the left and right contexts for get_egs2.sh, would I have
to use the values found in nnet_a_online/conf/splice.conf (--left-context=3,
--right-context=3) ?

Thank you for the advice. I am tackling a 19-hour subset of Blizzard before
moving on to the full, 300-hour dataset.

On Mon, Jun 29, 2015 at 2:42 PM, Daniel Povey <dp...@gm...> wrote:

> Actually, you should probably be using train_more2.sh.  It looks like
> the update_nnet.sh script is deprecated.
> train_more2.sh requires egs dumped by get_egs2.sh.  [the "2" format of
> the egs is more compact.]
> In your scenario you would be dumping egs for the blizzard data.  You
> would need alignments for the blizzard data.  Be careful with the
> get_egs2.sh script because like other get_egs scripts, it will dump
> egs with the left-context and right context you specify, and the
> features you give it, but it can't check that it's correct.   If you
> are using one of the "online" models that uses ivectors you would have
> to provide dumped ivectors, and these need to be computed with the
> same iVector extractor as the model that you are starting from.
>
> You might want to run on a small subset first; make sure that the
> training objective (e.g. in compute_train_prob.*.sh) is in the normal
> range, otherwise it may mean that you did something wrong.
>
> To get the alignments  you would need to align using the same model as
> was used to align the data for training the original nnet- you can
> download that from kaldi-asr.org.
> Dan
>
>
> On Mon, Jun 29, 2015 at 7:03 AM, Mate Andre <ele...@gm...> wrote:
> > The train_more.sh script requires an egs directory, which seems to be
> > created by update_nnet.sh. However, update_nnet.sh requires an alignments
> > directory.
> >
> > If I'm planning to run update_nnet.sh with data/train_960, does that
> mean I
> > have to find alignments for train_960 before running update_nnet.sh? Is
> > there a faster way to generate the egs directory without having to update
> > the neural net?
> >
> > On Thu, Jun 25, 2015 at 2:26 PM, Daniel Povey <dp...@gm...> wrote:
> >>
> >> I think the script train_more.sh might be useful here.
> >> If you only have 1 GPU it might take as long as a week, but
> >> downloading the trained models might be a better idea.
> >> Dan
> >>
> >>
> >>
> >> > l am going to train a deep neural net model with "multi-splice" using
> >> > the
> >> > LibriSpeech dataset with the local/online/run_nnet2_ms.sh script
> >> > included in
> >> > Kaldi's repository, which I think will give the best resulting WER.
> The
> >> > end
> >> > goal is to use the trained model in this phase for initializing a next
> >> > model
> >> > to train and do forced alignment on Blizzard2013 dataset, specifically
> >> > the
> >> > 2013-EH2 subset including 1 female speaker, 19 hours of speech and
> >> > sentence-level alignments.
> >> > I don't have much of experience with Kaldi and my questions are:
> >> >
> >> > 1. How long does it take to train on all (960hrs) of Librispeech on a
> >> > GPU
> >> > (say GTX TITAN X or K6000)? Even a rough estimate could be useful.
> >> > 2. Is there anything to take into account before training on
> >> > Librispeech?
> >> > 3. And more importantly, how should I initialize/train the next model
> >> > for
> >> > the Blizzard2013 dataset? I managed to go through data preparation for
> >> > that
> >> > and created the necessary files.
> >> >
> >> >
> >> >
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