Instructions to use nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3") model = AutoModelForCTC.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11af051f5789b059cf31f3b55c2c268be5427c1ebd01ff8c2d3e41339d706ec3
- Size of remote file:
- 4.09 kB
- SHA256:
- 3797d0061098733731e5c5b65624ca5b6bd9d2d1a945010c38db24a2ce84b1d9
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