Automatic Speech Recognition
Transformers
PyTorch
Arabic
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use phantomcoder1996/wav2vec2-large-xls-r-300m-arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phantomcoder1996/wav2vec2-large-xls-r-300m-arabic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="phantomcoder1996/wav2vec2-large-xls-r-300m-arabic")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("phantomcoder1996/wav2vec2-large-xls-r-300m-arabic") model = AutoModelForCTC.from_pretrained("phantomcoder1996/wav2vec2-large-xls-r-300m-arabic") - Notebooks
- Google Colab
- Kaggle
- Downloads last month
- 8
Evaluation results
- Test WER on Common Voice 7.0self-reported57.800
- Test WER on Robust Speech Event - Dev Dataself-reported95.070
- Test WER on Robust Speech Event - Test Dataself-reported93.580