Instructions to use SLPL/Sharif-wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLPL/Sharif-wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SLPL/Sharif-wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("SLPL/Sharif-wav2vec2") model = AutoModelForCTC.from_pretrained("SLPL/Sharif-wav2vec2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d9032c38e2055ae4eadc9c1b59b1b8c508b471113a0406c0c5c0e7cef3ec07e
- Size of remote file:
- 1.26 GB
- SHA256:
- 7b9404f6a4062df4a3c87e34982e6facb4d9840ccee2d210d8c2aec0155d1517
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