Instructions to use aadel4/Wav2vec_Classroom_WSP_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/Wav2vec_Classroom_WSP_FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aadel4/Wav2vec_Classroom_WSP_FT")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aadel4/Wav2vec_Classroom_WSP_FT") model = AutoModelForCTC.from_pretrained("aadel4/Wav2vec_Classroom_WSP_FT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model Card: Wav2vec_Classroom_WSP_FT
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### Model Overview
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**Model Name:** Wav2vec_Classroom_WSP_FT
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**Version:** 1.0
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**Developed By:** Ahmed Adel Attia (University of Maryland & Stanford University)
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**Date:** 2025
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## Model Card: Wav2vec_Classroom_WSP_FT
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### Model Overview
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**Model Name:** Wav2vec_Classroom_WSP_FT
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**Version:** 1.0
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**Developed By:** Ahmed Adel Attia (University of Maryland & Stanford University)
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**Date:** 2025
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