RVQ-VAE-GPT - Residual Vector Quantize VAE - GPT (wip)

My attempts at applying Soundstream design on learned tokenization of text and then applying a hierarchical transformer to text generation.

The Soundstream will be modified to use all local attention. Experiments will compare VQ, RVQ, and also multi-headed VQ

Was told by a researcher friend this will likely fail 😂😂 but I will try it anyways, yolo. In the case it does not work, maybe it can still be useful for genomics. Come to think of it, why shouldn't it be able to at least learn bigrams (for english) and codons (for genomics)? Why don't we have hierarchical predictive coding? We should

Update: Some live experiments

Todo

• add a diff in the autoencoder training between input and reconstructed, so one can examine the failure cases easily

Citations

@misc{https://doi.org/10.48550/arxiv.2107.03312,
  title  = {SoundStream: An End-to-End Neural Audio Codec},
  author = {Zeghidour, Neil and Luebs, Alejandro and Omran, Ahmed and Skoglund, Jan and Tagliasacchi, Marco},
  publisher = {arXiv},
  url    = {https://arxiv.org/abs/2107.03312},
  year   = {2021}
}

@unknown{unknown,
    author  = {Lee, Doyup and Kim, Chiheon and Kim, Saehoon and Cho, Minsu and Han, Wook-Shin},
    year    = {2022},
    month   = {03},
    title   = {Autoregressive Image Generation using Residual Quantization}
}

@article{Sunkara2022NoMS,
    title   = {No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects},
    author  = {Raja Sunkara and Tie Luo},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2208.03641}
}