Instructions to use superb/wav2vec2-base-superb-ic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/wav2vec2-base-superb-ic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-ic")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/wav2vec2-base-superb-ic") model = AutoModelForAudioClassification.from_pretrained("superb/wav2vec2-base-superb-ic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f5a18ae7df67ad895f4d32683f751b5577b282d27bc268f6489866c2ff044a0
- Size of remote file:
- 378 MB
- SHA256:
- 5e1a902a3e157baabcc01424c49cf3958e41031139a88dd752a35bfbfc538242
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.