Voice Activity Detection
Safetensors
MLX
mlx-audio
nemotron_diarization
speaker-diarization
streaming-sortformer
8-bit precision
Instructions to use mlx-community/Nemotron-3-Diarization-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Nemotron-3-Diarization-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Nemotron-3-Diarization-8bit mlx-community/Nemotron-3-Diarization-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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.
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Model size
99.3M params
Tensor type
BF16
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U32 路
F32 路
Hardware compatibility
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8-bit
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Model tree for mlx-community/Nemotron-3-Diarization-8bit
Base model
nvidia/Nemotron-3-Diarization