Instructions to use tarasabkar/wav2vec2-large-robust-finetuned-ie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarasabkar/wav2vec2-large-robust-finetuned-ie with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="tarasabkar/wav2vec2-large-robust-finetuned-ie")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("tarasabkar/wav2vec2-large-robust-finetuned-ie") model = AutoModelForAudioClassification.from_pretrained("tarasabkar/wav2vec2-large-robust-finetuned-ie") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72fb835c0f65af75f949b0123e73cbfac6d4e045dd249a5e4baa57caa4cb1f21
- Size of remote file:
- 4.79 kB
- SHA256:
- f93ee29a5dd953d9782f2dc768919c701b16d0472c98ed7b76f4e64f5a108960
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