Instructions to use elgeish/wav2vec2-large-xlsr-53-levantine-arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elgeish/wav2vec2-large-xlsr-53-levantine-arabic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="elgeish/wav2vec2-large-xlsr-53-levantine-arabic")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("elgeish/wav2vec2-large-xlsr-53-levantine-arabic") model = AutoModelForCTC.from_pretrained("elgeish/wav2vec2-large-xlsr-53-levantine-arabic") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd5e57c5d7ae3d60313829af26249fbd1a1b7913095e0d60e271b14164114e47
- Size of remote file:
- 1.26 GB
- SHA256:
- 476a0065b62e7fecba64339f03be9968c8a855e1e7466c0f2512e9a39dfeedb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.