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:
# tag did not correspond to a valid NeMo domain.
- 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
Update ASR_INTEGRATION_GUIDE.md
Browse files- ASR_INTEGRATION_GUIDE.md +3 -3
ASR_INTEGRATION_GUIDE.md
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@@ -70,7 +70,7 @@ python examples/asr/asr_cache_aware_streaming/speech_to_text_multitalker_streami
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binary_diar_preds=true \
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att_context_size='[70,13]' \
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fifo_len=264 \
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spkcache_update_period=
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generate_realtime_scripts=false \
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output_path=./speaker_attributed_output.json
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```
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| `masked_asr=false` | Uses speaker activity as conditioning for Multitalker Parakeet rather than simply muting the mixed waveform outside one speaker's regions. |
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| `att_context_size='[70,13]'` | Uses 70 left-context frames and 13 right-context frames. With the model's current geometry, the streaming advance is about 1.12 seconds. |
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| `cache_gating=true` | Stabilizes speaker-cache updates during streaming. |
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`max_num_of_spks` is an upper bound, not a promise that every configured speaker
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will appear. The diarization model discovers active speaker identities from the
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masked_asr=False,
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cache_gating=True,
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binary_diar_preds=True,
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spkcache_update_period=
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fifo_len=264,
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)
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binary_diar_preds=true \
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att_context_size='[70,13]' \
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fifo_len=264 \
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spkcache_update_period=222 \
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generate_realtime_scripts=false \
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output_path=./speaker_attributed_output.json
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```
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| `masked_asr=false` | Uses speaker activity as conditioning for Multitalker Parakeet rather than simply muting the mixed waveform outside one speaker's regions. |
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| `att_context_size='[70,13]'` | Uses 70 left-context frames and 13 right-context frames. With the model's current geometry, the streaming advance is about 1.12 seconds. |
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| `cache_gating=true` | Stabilizes speaker-cache updates during streaming. |
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| `spkcache_update_period=222` | Refreshes the speaker cache frequently enough for the long, eight-speaker sessions used by this demo. Tune this for other workloads. |
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`max_num_of_spks` is an upper bound, not a promise that every configured speaker
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will appear. The diarization model discovers active speaker identities from the
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masked_asr=False,
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cache_gating=True,
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binary_diar_preds=True,
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spkcache_update_period=222,
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fifo_len=264,
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)
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