Instructions to use NbAiLab/nb-sami-asr-nemotron-3.5-streaming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use NbAiLab/nb-sami-asr-nemotron-3.5-streaming with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("NbAiLab/nb-sami-asr-nemotron-3.5-streaming") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Upload training_metadata.json with huggingface_hub
Browse files- training_metadata.json +6 -0
training_metadata.json
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{
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"run_name": "olivia_nemotron35_asr_streaming_sme_lr2e-4_steps3000_h200x1-nemo-main_1223143",
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"base_model": "nvidia/nemotron-3.5-asr-streaming-0.6b",
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"dataset_name": "nb-asr-parakeet/data_v5",
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"nemo_artifact": "/cluster/work/projects/nn30001k/versae/nb-sami-asr-nemotron-3.5-streaminn/outputs/olivia_nemotron35_asr_streaming_sme_lr2e-4_steps3000_h200x1-nemo-main_1223143/main_1223143/checkpoints/olivia_nemotron35_asr_streaming_sme_lr2e-4_steps3000_h200x1-nemo-main_1223143.nemo"
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}
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