Convert PyTorch checkpoints to CAIMAN-ASR programs

Release name: myrtle-asr-server-<version>/compile-model-checkpoint

This is a packaged version of the CAIMAN-ASR model compiler which can be used to convert PyTorch checkpoints to CAIMAN-ASR checkpoints. The CAIMAN-ASR checkpoint contains the instructions for the model to enable CAIMAN-ASR acceleration. These instructions depend on the weights of the model, so when the model is changed, the CAIMAN-ASR checkpoint needs to be recompiled.

The flow to deploy a trained CAIMAN-ASR model is:

  1. Convert the training checkpoint to a hardware checkpoint following the steps in the Exporting a checkpoint section. Hardware checkpoints can be used with the CAIMAN-ASR server directly if you specify --cpu-backend.
  2. Convert the hardware checkpoint to a CAIMAN-ASR checkpoint with the script in this directory. CAIMAN-ASR checkpoints can be used with the CAIMAN-ASR server with either of the CPU or FPGA backends.


The program can be run with docker or directly if you install the dependencies.


Install docker and run the following commands:

./ path/to/ output/path/to/

Without docker

Ensure that you are using Ubuntu 20.04 - there are libraries required by the CAIMAN-ASR assembler that may not be present on other distributions.

pip3 install -r ./requirements.txt
./ \
  --hardware-checkpoint path/to/ \
  --mau-checkpoint output/path/to/

These commands should be executed in the compile-model-checkpoint directory otherwise the python script won't be able to find the mau_model_compiler binary.