Instructions to use nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD 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 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")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD") model = AutoModelForCTC.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68cfce2b9b7d00e081a0f1fa368f81833cefca707698adca6afd09d22244c32b
- Size of remote file:
- 1.26 GB
- SHA256:
- 2421eff14a7ae47eb80fb71e9a3d0c77b62ca2bf87f919083d9d3f5034c943d1
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