Summary of Textless Nlp — Zero Resource Challenge with Low Resource Compute, by Krithiga Ramadass et al.
Textless NLP – Zero Resource Challenge with Low Resource Compute
by Krithiga Ramadass, Abrit Pal Singh, Srihari J, Sheetal Kalyani
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents innovative techniques to improve the efficiency of training lightweight models for textless natural language processing (NLP) tasks, while maintaining performance and audio quality. The authors develop a quantized encoder architecture combined with a vocoder that utilizes learning rate schedulers, optimized hop lengths, and tuned interpolation scale factors to accelerate convergence and reduce GPU resource requirements. The proposed method is evaluated on English, Tamil, and Bengali datasets, achieving consistently good results in tasks like acoustic unit discovery and voice conversion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to train lightweight models for textless NLP quickly and efficiently, without sacrificing performance or audio quality. The authors use special techniques to speed up the training process and make it easier on computer resources. They test their method on English and Indian languages like Tamil and Bengali, showing that it works well across different languages and tasks. |
Keywords
* Artificial intelligence * Encoder * Natural language processing * Nlp
