Instructions to use superb/wav2vec2-base-superb-er with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/wav2vec2-base-superb-er with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/wav2vec2-base-superb-er") model = AutoModelForAudioClassification.from_pretrained("superb/wav2vec2-base-superb-er", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39d6f7e1238b1c068f2e28e1af782d31673c9da2692bad1a93a6ee43b7b5f411
- Size of remote file:
- 378 MB
- SHA256:
- dac0f46128deec048751ce33eb1a9caecb570904d3d6cf732d6ec345cd6c2e1b
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