Instructions to use devkyle/whisper-tiny-10dp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/whisper-tiny-10dp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/whisper-tiny-10dp")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/whisper-tiny-10dp") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/whisper-tiny-10dp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f7291486cea21c5c902a129ba36011e3d07313bc1724b8c6e4c300ade0ded84
- Size of remote file:
- 151 MB
- SHA256:
- e6d97108c3e50941afb18600cf13ca65a60523613ee8ae726a72ee60d6c94553
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