Instructions to use mitchelldehaven/whisper-large-v2-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitchelldehaven/whisper-large-v2-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mitchelldehaven/whisper-large-v2-uk")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mitchelldehaven/whisper-large-v2-uk") model = AutoModelForSpeechSeq2Seq.from_pretrained("mitchelldehaven/whisper-large-v2-uk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8fef75cba402c1c7467529e90b887e5cef3184db61b54aa4de3caff69a34aab
- Size of remote file:
- 6.17 GB
- SHA256:
- 809b25309399e2dbb41b444734459581604b734042e1d4208f9071d261ac5be7
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