Instructions to use optimum/whisper-tiny.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum/whisper-tiny.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="optimum/whisper-tiny.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("optimum/whisper-tiny.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("optimum/whisper-tiny.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42c5ca479a9e49c06c110fcd83cab587a395ccbf5af930a882cb7636885a4914
- Size of remote file:
- 198 MB
- SHA256:
- 664eeb243a6cdd836fe18bedd5c7dd1413f7ddbf31550bc8171c0e898e49c5cb
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