Automatic Speech Recognition
Transformers
PyTorch
JAX
Arabic
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use mohammed/arabic-speech-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammed/arabic-speech-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mohammed/arabic-speech-recognition")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mohammed/arabic-speech-recognition") model = AutoModelForCTC.from_pretrained("mohammed/arabic-speech-recognition") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3f2504c719eab39505c848f28739d9a3ad8bc7e2cec7f1832c4fcc8bffa2fe7
- Size of remote file:
- 1.26 GB
- SHA256:
- ed7b9aacc29f4e5977ce49a6efa1b485d24aaa64d670f7ad2cd5e59f7361d4d4
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