Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use arbml/whisper-largev2-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbml/whisper-largev2-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arbml/whisper-largev2-ar")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arbml/whisper-largev2-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("arbml/whisper-largev2-ar") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83995c2ed1cea9f679b463d2de65779e66a9de032770ef3b7f48e2819dd88863
- Size of remote file:
- 3.09 GB
- SHA256:
- d258c7dd5330d596932c221d7c63839aa60863cb18568ec540cf474bd0a459ce
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