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:
- 5a314eff8d803bbb425078f24c9c6f3c895ceb3b47859e806c652b1d5800b575
- Size of remote file:
- 2.8 kB
- SHA256:
- 1d0881a3f1bb7dc8c84a331a6611724b83dc28d1f6611ec8d5c69d99d965ac05
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