Instructions to use Devanshj7/whisper-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devanshj7/whisper-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Devanshj7/whisper-dev")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Devanshj7/whisper-dev") model = AutoModelForSpeechSeq2Seq.from_pretrained("Devanshj7/whisper-dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a42d09f1bfac32eb27e54cef0e29231b31056a1c4b85e13ce33a3a8667d358b0
- Size of remote file:
- 967 MB
- SHA256:
- 16a5ccf32a9ed5d1b0952d26b7f081b145717c9ef4d49065d47967f0cb393bfb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.