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:
- 1fe0de1ebdc86b4daeea4da8c1cd16a55ede7436d80176d628c28d061d2d7b00
- Size of remote file:
- 4.99 GB
- SHA256:
- f78da36e569e7cce1ad608c44077f98f31f73c8ad8977fd6c60334787ba5ee33
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