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:
- 08a04f0ce1eb6c74169252e2431e10d1a314eeb3208e6adaef61d1c56c91ddd7
- Size of remote file:
- 1.26 GB
- SHA256:
- bf9a3e3b0e8fb79a9734f0568b96ca58a9b7312c9ab6294744b250d540292f3d
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