Instructions to use hts98/accent_classification4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/accent_classification4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hts98/accent_classification4")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hts98/accent_classification4") model = AutoModelForAudioClassification.from_pretrained("hts98/accent_classification4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b7a23c4e7d0f20c92862c0495996cac31e5213a2aa12ef87c3d82368acebe9a
- Size of remote file:
- 378 MB
- SHA256:
- 648eb8940a089daca9a9b8cf7bc72e13e6047c17f2a4d6e9edbcc19581e506ce
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