Instructions to use dmmagdal/whisper-large-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmmagdal/whisper-large-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmmagdal/whisper-large-onnx")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dmmagdal/whisper-large-onnx") model = AutoModelForSpeechSeq2Seq.from_pretrained("dmmagdal/whisper-large-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db639bb362e281743287c26e0514ae9a030244a7d3f3f8e3ded32640e98ebaa0
- Size of remote file:
- 3.63 GB
- SHA256:
- 2427b10399f1bb74e46bee7da0d487273313be00eb2c7c49a3f9fe348869f3d0
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