Automatic Speech Recognition
Transformers
PyTorch
Arabic
wav2vec2
Arabic
MSA
Speech
Syllables
Wav2vec
ASR
Instructions to use IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2") model = AutoModelForCTC.from_pretrained("IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3834b67959f43054749afae2e48d1b7d9986c534cb950f5d2b85dbc835214c0
- Size of remote file:
- 1.27 GB
- SHA256:
- a77753fa0ee6d7ff035da5c16ef62494db3bca8073671cae8d3cbde8516c17a6
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