Instructions to use qmeeus/whisper-large-multilingual-spoken-ner-pipeline-step-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qmeeus/whisper-large-multilingual-spoken-ner-pipeline-step-1 with Transformers:
# Load model directly from transformers import WhisperSLU model = WhisperSLU.from_pretrained("qmeeus/whisper-large-multilingual-spoken-ner-pipeline-step-1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
whisper-large-multilingual-spoken-ner-pipeline-step-1 / checkpoint-2000 /model-00001-of-00002.safetensors
- Xet hash:
- 480ac2b64c6bde1c40bc2cc75a65104cfaef3da7ff409c7f8cd7bfdf397a66da
- Size of remote file:
- 4.99 GB
- SHA256:
- 74a9fc8ea3fb1810298d1ed34154ff295867d0a54570f38573c470bfdcf231a9
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