This translation example is adapted from a pytorch tutorial. The s4d.py is adapted from state-spaces/s4.

All jupyter notebooks assume the usable of a GPU to train the model. Change the load_ckpt = False into load_ckpt = True (in second code cell) and then the checkpoints can be loaded from checkpoint. The comparison of training loss curve is provided in the /assets.

The following snippet is the code for environment construction and data preparation.

    conda create -n SSM_Examples python=3.10
    conda activate SSM_Examples
    pip install -r requirements.txt

    cd SSM_Examples
    wget https://download.pytorch.org/tutorial/data.zip
    unzip data.zip