Instructions to use smcproject/Malwhisper-v1-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smcproject/Malwhisper-v1-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="smcproject/Malwhisper-v1-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("smcproject/Malwhisper-v1-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("smcproject/Malwhisper-v1-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d506765a4b97e8f99a361cf2438a18285aacc5a83558946b30ec76ed526f782
- Size of remote file:
- 4.22 kB
- SHA256:
- f09bf30501a0349d4ba6cd2f5655f212d909d140b1fe19742c88d2c8c1113e7c
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