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Glimpse-Dictation/Qwen3-ASR-0.6B-coreml
Qwen3-ASR-0.6B-coreml is a automatic speech recognition model from Glimpse-Dictation. Use it when you need speech turned into text. It is set up for coreml. The card lists the license as apache-2.0.
This repository contains the optional Apple Silicon encoder for Qwen3-ASR 0.6B (Q80) in Glimpse. It is a companion to Handy's Q80 GGUF, not a replacement for it: the GGUF still provides the decoder, tokenizer and mode…
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.gguf640 MB · 65%
From the Hugging Face model README
This repository contains the optional Apple Silicon encoder for Qwen3-ASR 0.6B (Q8_0) in Glimpse. It is a companion to Handy's Q8_0 GGUF, not a replacement for it: the GGUF still provides the decoder, tokenizer and model metadata, and this package moves the audio encoder onto Core ML.
| File | Download size | Purpose |
|---|---|---|
Qwen3-ASR-0.6B-Q8_0-encoder.mlmodelc.zip | 340.3 MB | Compiled Core ML encoder |
On Windows, Intel Macs, or anywhere without Core ML, use the GGUF on its own. The Core ML package requires Apple Silicon.
The model originates from Qwen3-ASR 0.6B,
licensed Apache 2.0. The encoder was converted from Handy's Qwen3-ASR-0.6B-Q8_0.gguf with
transcribe.cpp's scripts/convert-qwen3-asr-gguf-to-coreml.py, then zipped with
ditto -c -k --keepParent.
| Artifact | SHA-256 |
|---|---|
| Source GGUF | f081b2d5e23bd669d92cc331d722a8a0681943b8e6f34b48996fd5c319b5acd8 |
| Encoder ZIP | cfd2e37a30d0da1b685da0f96e82f05ef6fc208136c85efa5368ddd2ee18ad5d |
Extract the ZIP next to the GGUF, keeping the Qwen3-ASR-0.6B-Q8_0-encoder.mlmodelc
directory name. Glimpse-Speech looks for the companion beside the GGUF and falls back to the
GGUF's own encoder when it isn't there.
The encoder holds up to 15 seconds of audio. Glimpse splits longer recordings into chunks.
Qwen3-ASR is an experimental model in Glimpse. Core ML may run some operations on the CPU; this is not a guarantee of exclusive Neural Engine execution.
This encoder speeds up on-device dictation on Apple Silicon in Glimpse, a free, open-source dictation app for Mac and Windows. The source is on GitHub.