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:
- 305f9661bc1ee6ad6f5b3fd746cae44260965bffd28c64e72e0f6fa5779391d9
- Size of remote file:
- 1.06 kB
- SHA256:
- 5e0ae568730eb2b95ddf61a6c4c85dc560a09630f44147b146379fc5a94fc219
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