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.
Runs of mlx-community Nemotron-3-Diarization on huggingface.co
635
Total runs
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24-hour runs
129
3-day runs
513
7-day runs
513
30-day runs
More Information About Nemotron-3-Diarization huggingface.co Model
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