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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e5f64798f40011a7a3dc5b762e2610877b5f2390765292442ae4c8908e99c0d
- Size of remote file:
- 1.26 GB
- SHA256:
- 918299adc626e323a062ae50f60a3aa2666879795285e66bbdd55635efaf18bf
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