Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use arbml/whisper-medium-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbml/whisper-medium-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arbml/whisper-medium-ar")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arbml/whisper-medium-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("arbml/whisper-medium-ar") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63ea7bc279d33c763a933f31bf6749596270bdd88cb74700f317ccb56058acde
- Size of remote file:
- 3.06 GB
- SHA256:
- 3e692eb6043acda8671ca15866b5800d1926740b4f023fd9fb15ee413f9d6e5c
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