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

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

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

Yes it does.  You would have to extract them for your data- see the
commands used in the script that trained the nnet_a_online model.
You'd have to download the iVector extractor.  You may also have to
dump 40-dim features.  That will be in the script to- also see the
_common.sh script which it sources near the beginning.  Some parts are
in there.

> 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) ?

No, I don't think so; you have to use the left and right contexts of
the nnet model itself.  These are printed out by nnet-am-info.

Dan


>
> 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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