https://github.com/lakshmi-bashyam/neurallm2arpa
Implementation of conversion system : Neural Language models to backing off n-gram models for decoding in speech recognition systems
https://github.com/lakshmi-bashyam/neurallm2arpa
arpa lstm n-grams nlp speech-recognition
Last synced: 8 months ago
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Implementation of conversion system : Neural Language models to backing off n-gram models for decoding in speech recognition systems
- Host: GitHub
- URL: https://github.com/lakshmi-bashyam/neurallm2arpa
- Owner: Lakshmi-bashyam
- Created: 2021-10-18T15:05:33.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-08-04T09:27:06.000Z (about 3 years ago)
- Last Synced: 2023-12-06T17:35:31.234Z (almost 2 years ago)
- Topics: arpa, lstm, n-grams, nlp, speech-recognition
- Language: Python
- Homepage:
- Size: 300 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Convert Neural LM to backing off n-gram
## How to Run
To train RNNLM, add the corpus to data folder
1. Train an n-gram LM using SRILM tool to generate sentences from corpus
```
ngram-count -text corpus.txt -order 6 -lm new_lm.arpa -vocab new_vocab
ngram -lm new_lm.arpa -gen [no_of_sentences] > gen.txt
```
2. To train RNNLM using above generated corpus as training data, run
```
python train.py
```
Evaluation result - Test perpexity value is ~52
3. To extract RNNLM probabilities, run
```
python predict.py
```
## References
[1] Singh, Mittul & Oualil, Youssef & Klakow, Dietrich. (2017). \
Approximated and Domain-Adapted LSTM Language Models for First-Pass Decoding in Speech Recognition. \
2720-2724. 10.21437/Interspeech.2017-147. \
[2] H. Adel, K. Kirchhoff, N. T. Vu, D. Telaar, and T. Schultz, \
“Comparing approaches to convert recurrent neural networks into backoff language models for efficient decoding,” \
in INTER-SPEECH 2014, 15th Annual Conference of the International Speech Communication Association, Singapore, September 14-18, 2014, 2014, pp. 51–655.