Instructions to use M-Quan/wav2vec2-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M-Quan/wav2vec2-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M-Quan/wav2vec2-demo")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("M-Quan/wav2vec2-demo") model = AutoModelForCTC.from_pretrained("M-Quan/wav2vec2-demo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 264cc1a949c5ae425163656c247d98f65b1bb24420e1aaaa16a88a9ce5548be1
- Size of remote file:
- 378 MB
- SHA256:
- 11274b6e3554753949891abe7b4b6052f6c1561608dd16fcae961f11c545ab4f
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