Instructions to use jadasdn/wav2vec2-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/wav2vec2-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/wav2vec2-3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/wav2vec2-3") model = AutoModelForCTC.from_pretrained("jadasdn/wav2vec2-3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e56646d9d87e1486ed9ff169bc646d72e0c72acc0d5fb5db3568a37d7f62eb3b
- Size of remote file:
- 4.54 kB
- SHA256:
- 0e57d0d7109d41584f80d353dd3108af1ddbbc5332855b8b300b56e6e8a5fc51
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