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:
- 66b8dd40157fdfe9bc333de9e906d3b5605ee3ef48cfd7df93818aa6c0866e92
- Size of remote file:
- 967 MB
- SHA256:
- 864a4a2eb1493a177f0bfa13aea37dfec70f338435b452479219a6d0f47bc9b8
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