Instructions to use Cafet/whisper-tiny-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cafet/whisper-tiny-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cafet/whisper-tiny-final")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Cafet/whisper-tiny-final") model = AutoModelForSpeechSeq2Seq.from_pretrained("Cafet/whisper-tiny-final") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3d086b1769ae66a8da1d9b0730f2da72d91c936c3fe96d8406e1cd113c64a59
- Size of remote file:
- 5.11 kB
- SHA256:
- 1dd03404bb249ddaa1e11a8c85f4209b80b60349e3b7f2a599292c76c57115bd
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