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.