aoiandroid/

mirror-FluidInference-kokoro-82m-coreml

Based on the original kokoro model, see https://github.com/FluidInference/FluidAudio for inference

We generated the same strings with to gerneate audio between 1s to ~300s in order to test the speed across a range of varying inputs on Pytorch CPU, MPS, and MLX pipeline, and compared it against the native Swift version with Core ML models.

Each pipeline warmed up the models by running through it once with pesudo inputs, and then comparing the raw inference time with the model already loaded. You can see that for the Core ML model, we traded lower memory and very slightly faster inference for longer initial warm-up.

Note that the Pytorch kokoro model in Pytorch has a memory leak issue: https://github.com/hexgrad/kokoro/issues/152

The following tests were ran on M4 Pro, 48GB RAM, Macbook Pro. If you have another device, please do try replicating it as well!

I wasn't able to run the MPS model for longer durations, even with PYTORCH_ENABLE_MPS_FALLBACK=1 enabled, it kept crashing for the longer strings.

Note that it does take ~15s to compile the model on the first run, subsequent runs are shorter, we expect ~2s to load.

special thanks to @laishere for making kokoro ANE possible his source: https://github.com/laishere/kokoro-coreml