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:
- d6a36fa9d5d080663095f63044afb83e31605dd7317ae97eb12c4066feb33ad1
- Size of remote file:
- 1.26 GB
- SHA256:
- b3a2c9980533d643457fb4ca0b7e6552a59225987ab46941c53f2a481b0edd2a
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