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Nemotron-3-Diarization
mlx-community/Nemotron-3-Diarization
Nemotron-3-Diarization is a voice activity detection model from mlx-community. Use it for the voice activity detection task on the model card, and read the license before you ship it in a product. It is set up for mlx-audio. The card lists the license as openmdw-1.1.
Converted from nvidia/Nemotron-3-Diarization for use with mlx-audio.
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Model card
From the Hugging Face model README
Nemotron 3 Diarization (MLX)
Converted from nvidia/Nemotron-3-Diarization for use with mlx-audio.
It predicts up to eight speakers at 10 ms resolution from 16 kHz mono audio.
from mlx_audio.vad import load
model = load("mlx-community/Nemotron-3-Diarization", strict=True)
result = model.generate("meeting.wav")
print(result.text)
# Incremental results retain speaker identities through the AOSC and FIFO.
for result in model.generate_stream("meeting.wav"):
for segment in result.segments:
print(segment.start, segment.end, segment.speaker)
For live PCM input, call model.feed(chunk, state) with a state from
model.init_streaming_state() and 16 kHz mono chunks. Flush the final partial
chunk and lookahead with model.feed([], state, final=True). Timestamps are
absolute within the recording. Labels are generic arrival-order speaker IDs;
the model does not identify people. Overlapping speakers may be active together.
Consult the upstream model card for training data, evaluation and license information.
- Task
- Voice Activity Detection
- Library
- mlx-audio
- Type
- nemotron_diarization
- License
- openmdw-1.1
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026