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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Arabic syllables recognition with tashkeel
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**paper DOI** : https://doi.org/10.60161/2521-001-001-006
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This is fine tuned wav2vec2 model to recognize arabic syllables from speech.
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The model was trained on Modern standard arabic dataset .\
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5-gram language model is available with the model.
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- ar
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---
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# Arabic syllables recognition with tashkeel
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**paper DOI** : https://doi.org/10.60161/2521-001-001-006 \
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This is fine tuned wav2vec2 model to recognize arabic syllables from speech.
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| 10 |
The model was trained on Modern standard arabic dataset .\
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5-gram language model is available with the model.
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