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:
- 715e84e3dd48d51cc83cea39b95006a6f817ee5a8f1a12d1dbfd200428e058bb
- Size of remote file:
- 1.51 MB
- SHA256:
- 83276340bb06e24aacdffff35790d12809875ec3859de613ac7ee0b27e857ea7
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