A ready-to-use hybrid Core ML conversion of Audio8-ASR-0.1B for fast, private, multilingual speech transcription on Apple Silicon.
This model is integrated into Hugging Mac, where you can run it locally and build macOS voice apps, transcription services, plugins, and agents.
Non-commercial license: the model is provided under CC-BY-NC-4.0 and may not be used commercially without separate permission from the upstream rights holder.
Architecture
This is an explicitly hybrid runtime rather than a fully Core ML autoregressive model:
The audio encoder and MLP tower run with Core ML on the Apple Neural Engine.
The cached Qwen2 decoder runs with PyTorch MPS on the GPU.
A multifunction ML Program provides optimized 5, 10, and 30-second audio buckets.
Audio-tower weights use INT8 linear quantization with FP16 compute.
The package includes the Core ML audio tower, decoder and projector weights, processor configuration, and reproducible conversion metadata. The tokenizer is downloaded from the pinned upstream model by Hugging Mac.
Inputs are 128-bin log-Mel features plus an additive attention mask. The default Core ML function is tower_30s; Hugging Mac automatically selects the smallest suitable bucket.
Use with Hugging Mac
Install the ASR dependencies and run the Hugging Mac application:
The converted model retains the upstream CC-BY-NC-4.0 license, attribution requirements, and non-commercial restriction. Hugging Mac is an independent open-source project and is not affiliated with or endorsed by Audio8 or AutoArk.
