Instructions to use hts98/accent_classification_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/accent_classification_ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hts98/accent_classification_")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hts98/accent_classification_") model = AutoModelForAudioClassification.from_pretrained("hts98/accent_classification_", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6c9a48b9c016926a2becc66d8f5a35a74bf0e1ac680907847712062f9248079
- Size of remote file:
- 378 MB
- SHA256:
- f4a2c390d1016c5b590b0b7939b562f7abc60b1447fe67ffda8362eca1620317
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