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:
- 5065e1ceebe9472165b7ac204e844f168153b33bab8d459afa07598f39bcd81a
- Size of remote file:
- 2.55 GB
- SHA256:
- d2ae107dc377193bcef7fa208a0d5a08a4ce397c38acba115fdfb5a58223a6b5
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