Instructions to use qmeeus/whisper-large-multilingual-spoken-ner-pipeline-ft 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-ft with Transformers:
# Load model directly from transformers import WhisperSLU model = WhisperSLU.from_pretrained("qmeeus/whisper-large-multilingual-spoken-ner-pipeline-ft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 521eefe803942b6923317eb4c3f9b3331857ca73eab93ecf063ac33c55bc53c6
- Size of remote file:
- 1.23 GB
- SHA256:
- 906695aa30c3b409789a0d36eb46d4060446c746968f394cfc6e4f9fa564fac6
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