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:
- 5cf43ef06ee2bffb50394729c4cc77601b9c99b25e1a5fb6198e7c527467b5a8
- Size of remote file:
- 3.21 GB
- SHA256:
- 6e87c09b9814b3f458ca99a95a49e7ac66224ee052a8c0f47de5755b9010dbd1
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