Voice Activity Detection
NeMo
Safetensors
GGUF
Transformers
nemotron3_diarization
audio-frame-classification
speaker-diarization
streaming-sortformer
speaker-tagging
Instructions to use nvidia/Nemotron-3-Diarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/Nemotron-3-Diarization with NeMo:
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- Transformers
How to use nvidia/Nemotron-3-Diarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("nvidia/Nemotron-3-Diarization") model = AutoModelForAudioFrameClassification.from_pretrained("nvidia/Nemotron-3-Diarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download safety.md from nvidia/Nemotron-3-Diarization: direct link, hf CLI and curl.
- Browser
- Download file 489 Bytes
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https://huggingface.co/nvidia/Nemotron-3-Diarization/resolve/refs%2Fpr%2F3/safety.md
- Command line
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hf download hf://nvidia/Nemotron-3-Diarization@refs/pr/3/safety.md
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curl -L -o safety.md https://huggingface.co/nvidia/Nemotron-3-Diarization/resolve/refs%2Fpr%2F3/safety.md
489 Bytes
| Field | Response |
|---|---|
| Model Application Field(s) | Customer Service |
| Describe the life-critical impact (if present) | Not Applicable |
| Use Case Restrictions | Abide by OpenMDW License Agreement, version 1.1 |
| Model and Dataset Restrictions | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |