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:
- 6d8d0f6104fa7f24b6767a7c70253895ef441e68dfe7dbace02d3a184d20765f
- Size of remote file:
- 3.9 kB
- SHA256:
- 3b0db90bd01fcff1cff57127b8f82991f95907a2e847406d684ebab875a1c5bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.