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
- Xet hash:
- 8426d6a43a958edd7bed4f4ad5525ab7c8c29c4b80d4c37e43204aabec751d59
- Size of remote file:
- 1.23 GB
- SHA256:
- 3c9320bfcc32b9fb251f7b179ba841b985e9d209a1a86d946218a0805f59d005
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