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:
- 48dcd6de84b443c2b31b18773edba52925cbe0d9a84c3d9ff152d18c8102e17e
- Size of remote file:
- 1.26 GB
- SHA256:
- fc4c37ab6f1846dc42fe7092d12ce73c0e487202f6710a42fe0f188080938c87
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