Paper - Pytorch
Project description
Audio xLSTMs: Learning Self-supervised audio representations with xLSTMs
This is a community based approach to an implementation mostly for practice. I will implement the model architecture as defined in the paper but will leave someone else to implement the training script! So please create a training script if you have the time and energy
License
MIT
Citation
@article{xlstm,
title={xLSTM: Extended Long Short-Term Memory},
author={Beck, Maximilian and P{\"o}ppel, Korbinian and Spanring, Markus and Auer, Andreas and Prudnikova, Oleksandra and Kopp, Michael and Klambauer, G{\"u}nter and Brandstetter, Johannes and Hochreiter, Sepp},
journal={arXiv preprint arXiv:2405.04517},
year={2024}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
audio_xlstm-0.0.1.tar.gz
(8.6 kB
view hashes)
Built Distribution
Close
Hashes for audio_xlstm-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | db795ea07df6216eb7dc85852dd3ed57985b6a011b0e9dd52987ae0ef29a6fff |
|
MD5 | ceba1d3d5780efb550cb8333ae7c7ee9 |
|
BLAKE2b-256 | 59ceb690e0b28a1fc3d8ae3a24110b97064542fad6344705afbe7b1cc5c23eb0 |