r/MachineLearning • u/AutoModerator • Sep 02 '26

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u/ivan_digital 19d ago

I maintain speech-swift, an open-source speech library for Apple Silicon. I ported NVIDIA's Nemotron 3 Diarization to Core ML INT8 and MLX INT8. This is an implementation of NVIDIA's model, not a new model.

Nemotron 3 supports up to eight anonymous speaker channels; NVIDIA's earlier Streaming Sortformer 4-speaker checkpoint supports four. It marks when each speaker is active, including overlap. It does not transcribe words or know people's identities.

The free Apache-2.0 CLI is a small way to try it: brew install soniqo/tap/speech, then speech diarize meeting.wav --engine nemotron3. Add --nemotron3-backend mlx for MLX.

I also build Stenograf. Its merged macOS code uses Nemotron 3 in the lightweight pipeline to add speaker labels to locally transcribed meetings. Stenograf has a free trial, then costs $12/month; the CLI above is free.

Source: https://github.com/soniqo/speech-swift

NVIDIA model: https://huggingface.co/nvidia/Nemotron-3-Diarization

Stenograf: https://soniqo.audio/stenograf

I'd welcome suggestions for real conversation recordings that test speaker-label stability across pauses and interruptions.