Recursions-Are-All-You-Need VS Real-Time-Voice-Cloning

The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance the efficiency of deep unfolding models. First, recursions are used to effectively eliminate the redundancies in deep unfolding networks. Secondly, we randomize the number of recursions during training to decrease the overall training time. Finally, to effectively utilize the power of recursions, we introduce a learnable unit to modulate the features of the model based on both the total number of iterations and the current iteration index. To evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST. Extensive testing shows that our proposed framework allows the network to cut down as much as 75% of its learnable parameters while mostly maintaining its performance, and at the same time, it cuts around 21% and 42% from the training time for ISTA-Net+ and COAST respectively. Moreover, when presented with a limited training dataset, the recursive models match or even outperform their respective non-recursive baseline. Codes and pretrained models are available at https://github.com/Rawwad-Alhejaili/Recursions-Are-All-You-Need .

Oh maybe it is available: https://github.com/CorentinJ/Real-Time-Voice-Cloning

I know this isn't the bulk of your argument, but if the concern is uploading, offline voice cloners have existed for years (albeit not as good as elevenlabs) but will presumably get far better in the years to come now that everyone has seen what's possible and as PC compute power continues to improve. https://github.com/CorentinJ/Real-Time-Voice-Cloning