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:
- b1960ddcd893c5125d35e6c60bd4dd99a0d7cd55772b94a7bb3b91eaeca1b113
- Size of remote file:
- 3.9 kB
- SHA256:
- 9bc292b27569a46b05ea04458ebcaf113802280054b1fee60b36598922e4a131
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