This repository contains the optional Apple Silicon package for Parakeet TDT 0.6B V3 (GGUF) in Glimpse. It requires the transcribe.cpp Parakeet Core ML adapter with decoder-only GGUF support. The application dependency integration is under development; these are not standalone transcription executables. The combined download is 1.11 GB, without duplicating the encoder weights in the GGUF. For CPU/GPU transcription on Windows or Mac, use instead. The Core ML package requires Apple Silicon. The model originates from NVIDIA Parakeet TDT 0.6B V3, licensed CC BY 4.0. Both artifacts derive from Handy's parakeet-tdt-0.6b-v3-Q80.gguf using transcribe.cpp's scripts/convert-parakeet-gguf-to-coreml.py…
Open weights
cc-by-4.0
coreml
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 model metadata, and this package moves the audio encoder onto Core ML. 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-Q80.gguf with transcribe.cpp's scripts/convert-qwen3-asr-gguf-to-coreml.py, then zipped with ditto -c -k --keepParent. Extract the ZIP next to the GGUF, keeping the…
Open weights
apache-2.0
coreml
GGUF conversions of NVIDIA's Nemotron-3 Diarization (Streaming Sortformer v3) for transcribe.cpp, the runtime behind speaker labels in Glimpse. The model answers "who spoke when" for up to 8 speakers at 10 ms resolution. It produces no text. AMI meeting corpus, IHM test set: 16 meetings, 9.06 hours. Scored against NVIDIA's forced-alignment RTTMs with a 0 s collar, overlapped speech included, and a plain 0.5 threshold at 10 ms with no post-processing. Every row went through the same pipeline. Compared with Sortformer v2.1, errors drop by 42% and speaker confusion by 76%, and the 1-second streaming mode beats v2.1's offline mode. Throughput on an Apple M2 Pro (offline mode, times faster than…
Open weights
other
