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:
- 3823c4499216cec463cd89dd47dde6df0a64c36f51077002d71906d11e88b3ff
- Size of remote file:
- 5.05 kB
- SHA256:
- fc927c2a2ceb245910c810c9b5d1875d64952341f2de9238c870c2b1748e3869
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