Instructions to use superb/wav2vec2-large-superb-er with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/wav2vec2-large-superb-er with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/wav2vec2-large-superb-er")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/wav2vec2-large-superb-er") model = AutoModelForAudioClassification.from_pretrained("superb/wav2vec2-large-superb-er", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6feb2d0967d9b07601de7641d191d3bc9c28b703c2d01906187ebaa85e1087ad
- Size of remote file:
- 1.26 GB
- SHA256:
- 7c86063fd342fbccaabc23963d6520be3d466c4a438c83260004ee3061583e31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.