https://github.com/gooofy/kaldi-adapt-lm

Adapt Kaldi-ASR nnet3 chain models from Zamia-Speech.org to a different language model
https://github.com/gooofy/kaldi-adapt-lm

Last synced: 2 months ago
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Adapt Kaldi-ASR nnet3 chain models from Zamia-Speech.org to a different language model

• Host: GitHub
• URL: https://github.com/gooofy/kaldi-adapt-lm
• Owner: gooofy
• License: apache-2.0
• Created: 2018-07-22T19:12:39.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-01-26T17:44:27.000Z (over 4 years ago)
• Last Synced: 2024-04-12T00:43:00.580Z (3 months ago)
• Language: Python
• Size: 17.6 KB
• Stars: 34
• Watchers: 6
• Forks: 12
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-kaldi - kaldi-adapt-lm - A tool that helps to adapt nnet3 chain models to a different language model. (Good Kaldi "production ready" examples )

README

        
# kaldi-adapt-lm

Adapt Kaldi-ASR nnet3 chain models from Zamia-Speech.org to a different

language model.

Constructive comments, patches and pull-requests are very welcome.

Tutorial

--------

To create the language model we would like to adapt our kaldi model to, we first

need to create a set of sentences. To get started, download and uncompress a generic set

of sentences for you language, e.g.

    wget 'http://goofy.zamia.org/zamia-speech/misc/sentences-en.txt.xz'

    unxz sentences-en.txt.xz

now suppose the file utts.txt contained the sentences you would like the model to

recognize with a higher probability than the rest. To achieve that, we add these

sentences five times in this examples to our text body:

    cat utts.txt utts.txt utts.txt utts.txt utts.txt sentences-en.txt >lm.txt

we also want to limit our language model to the vocabulary the audio model supports,

so let's extract the vocabulary next:

    MODEL="models/kaldi-generic-en-tdnn_sp-latest"

    cut -f 1 -d ' ' ${MODEL}/data/local/dict/lexicon.txt >vocab.txt

with those files in place we can now train our new language model using KenLM:

    lmplz -o 4 --prune 0 1 2 3 --limit_vocab_file vocab.txt --interpolate_unigrams 0 lm.arpa

Now we can start the kaldi model adaptation process:

    kaldi-adapt-lm ${MODEL} lm.arpa mymodel

You should now be able to find a tarball of the resulting model inside the work subdirectory.

If at the end of adaptation process you have a lot of messages like "cp: cannot stat

'exp/adapt/graph/HCLG.fst': No such file or directory", then highly likely you run out of memory

during adaptation process. (For example adapting kaldi-generic-en-tdnn_250 model consumes near 12Gb

of RAM)

Links

-----

-  [Kaldi ASR] 

-  [Zamia Speech] 

Requirements

------------

- Python 2

- Kaldi ASR

License

-------

My own code is Apache-2.0 licensed unless otherwise noted in the

script’s copyright headers.

Author

------

Guenter Bartsch \<\>