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:
- 3302404fbf0259b3bf727b901d73a823ff730ca11c9a07f75812ff43f864f81d
- Size of remote file:
- 4.98 kB
- SHA256:
- 3c266c3165471e8b53400c397a85f0c3129ace36125800845f0224a7cbfbfdbf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.