Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use EYEDOL/whisper-small-arbyeg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EYEDOL/whisper-small-arbyeg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-small-arbyeg")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EYEDOL/whisper-small-arbyeg") model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-small-arbyeg") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e83814b65b1b689e4c67a385747826fb73ab099a2b38ae8b2441b9200da236c4
- Size of remote file:
- 5.33 kB
- SHA256:
- 4b42f11996354ecf1938f8af5813a450b5aa8c090558c5d3b0bf4db0babc71c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.