Instructions to use rjac/whisper-medium-BTCv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/whisper-medium-BTCv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rjac/whisper-medium-BTCv2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rjac/whisper-medium-BTCv2") model = AutoModelForSpeechSeq2Seq.from_pretrained("rjac/whisper-medium-BTCv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e474277c726393d54858c0c7ee1a45511b8d43e8c8bc521f928b3f23cc3e140
- Size of remote file:
- 3.06 GB
- SHA256:
- a30e200ed8a9758bd0535911ca131fb6c708d6cc02ee42ebebb2a167d8367e5e
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