Textless NLP -- Zero Resource Challenge with Low Resource Compute
It addresses resource constraints for low-resource compute settings in NLP, particularly for Indian languages, but is incremental in its optimizations.
This work tackles the problem of high training time and GPU resource requirements for Textless NLP models by optimizing learning rate schedulers, hop length, and interpolation scale factors, achieving clear reconstructed audio and significantly reduced training time across English, Tamil, and Bengali datasets.
This work addresses the persistent challenges of substantial training time and GPU resource requirements even when training lightweight encoder-vocoder models for Textless NLP. We reduce training steps significantly while improving performance by a) leveraging learning rate schedulers for efficient and faster convergence b) optimizing hop length and c) tuning the interpolation scale factors for better audio quality. Additionally, we explore the latent space representation for Indian languages such as Tamil and Bengali for the acoustic unit discovery and voice conversion task. Our approach leverages a quantized encoder architecture, in conjunction with a vocoder which utilizes the proposed mixture of optimized hop length, tuned interpolation scale factors and a cyclic learning rate scheduler. We obtain consistently good results across English, Tamil and Bengali datasets. The proposed method excels in capturing complex linguistic patterns, resulting in clear reconstructed audio during voice conversion with significantly reduced training time.