The CoreML backend is WIP in Axiom and will roll over to parakeet.cpp when it's ready, the same with CUDA. FluidAudio is a great option for those building Mac-only apps, but the goal with Axiom and Parakeet.cpp is to be very portable and embeddable into almost any app. I will write C and Swift wrappers shortly, then if it's really wanted, a Python wrapper.

On macbook pro - parakeet.cpp is very low latency, under 100ms (76ms) for 60s audio.

I built a C++ inference engine for NVIDIA's Parakeet speech recognition models using Axiom(https://github.com/Frikallo/axiom) my tensor library.

What it does: - Runs 7 model families: offline transcription (CTC, RNNT, TDT, TDT-CTC), streaming (EOU, Nemotron), and speaker diarization (Sortformer) - Word-level timestamps - Streaming transcription from microphone input - Speaker diarization detecting up to 4 speakers

I see a number of references to macOS support in your docs for Axiom. Can this run on iOS?

Theoretically, yes? This hasent been tested but xcode has great c++ interop and the goal with Axiom and now parakeet.cpp is to be used for portable deployments so making that process easier is definitely on the roadmap.

Oh hey I just implemented this in golang. Mine implementation heavily optimized for cpu.

can you share your repo.

I appreciate the advice. Right now, numerical coverage, absolute performance, and DX are my biggest priorities. Looking to get traction from OSS so scope creep doesn't catch up to me and some passionate devs can jump on board, autograd and CUDA are the next really big milestones for Axiom.