Instructions to use alphaedge-ai/whisper-large-v3-turbo-mri-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-large-v3-turbo-mri-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-large-v3-turbo-mri-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f67b68bdfeb7b9ac2c16baef25edfd138fce0fa79387916e8913df65aff769bd
- Size of remote file:
- 1.57 GB
- SHA256:
- d77767ec1e42821572e89a0cceb7fb9eff3c9017de0422110797570c52a2e043
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