Instructions to use aadel4/Wav2vec_Classroom_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/Wav2vec_Classroom_FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aadel4/Wav2vec_Classroom_FT")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aadel4/Wav2vec_Classroom_FT") model = AutoModelForCTC.from_pretrained("aadel4/Wav2vec_Classroom_FT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0952d8acb96b4e60fbe91e844f9916da1b809da5082517b5a8f5f351e4d02c8
- Size of remote file:
- 1.26 GB
- SHA256:
- 9eaa5ff4701b546c698d3067c82ccdb9b1bdff7f9017ed687a90a7100675cef6
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