Instructions to use Baselhany/Graduation_Project_Distil_Whisper_base3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Baselhany/Graduation_Project_Distil_Whisper_base3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Baselhany/Graduation_Project_Distil_Whisper_base3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Baselhany/Graduation_Project_Distil_Whisper_base3") model = AutoModelForSpeechSeq2Seq.from_pretrained("Baselhany/Graduation_Project_Distil_Whisper_base3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13151af64466b02c43713d799c99bced741012dc7e6e0dedd32a89f65dcf76b9
- Size of remote file:
- 223 MB
- SHA256:
- 295a98c04e292f237592ab3ab451af1bff5373bc2118fe5cc1790552c35587a7
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