Glimpse-Dictation / Nemotron-3-Diarization-gguf

Nemotron-3 Diarization (GGUF)

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.

Results

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 real time):

Fidelity to the reference

On a 12-second two-speaker clip, every stage (mel, embedder, encoder layers, projection, upsampler, speaker probabilities) matches the Transformers reference within 2e-4. The final probabilities match within 6e-6 for every latency preset, including runs that force speaker-cache compression. Over the 9 AMI hours, the Metal build and the reference disagree on 0.03% of 10 ms decisions. These come from floating-point order flipping a near-tie in the discrete cache compression, and DER is unchanged.

Usage

transcribe-cli -m nemotron-3-diarization-Q8_0.gguf meeting.wav

Speaker turns are read through transcribe_n_speaker_segments / transcribe_get_speaker_segment . Speaker ids are 1-based, in order of first appearance. The latency preset comes from the Sortformer run extension ( transcribe/sortformer.h ):

In Rust, through Glimpse-Speech :

let turns = glimpse_speech::diarization::diarize(&model_path, &samples, sample_rate, true)?;

Input is 16 kHz mono; other rates are resampled.

Provenance

Converted from nvidia/Nemotron-3-Diarization at revision a435e9867d79e789e90053f9b6d6834053af564a ( model.safetensors , fp32) with scripts/convert-nemotron3_diar.py , then quantized with transcribe-quantize . The learned speaker-cache silence embedding stays F32 in every file.

Glimpse

This model labels speakers in Glimpse , a free, open-source dictation app for Mac and Windows, entirely on your device. The source is on GitHub .

License

The model and its weights are NVIDIA's, released under the OpenMDW License Agreement 1.1 , which permits commercial use. This repository only converts the original checkpoint. It is not an official NVIDIA release. See NVIDIA's model card for training data, intended use, and bias, safety and privacy notes.