Instructions to use Rajaram1996/Hubert_emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rajaram1996/Hubert_emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Rajaram1996/Hubert_emotion")# Load model directly from transformers import AutoProcessor, HubertForSpeechClassification processor = AutoProcessor.from_pretrained("Rajaram1996/Hubert_emotion") model = HubertForSpeechClassification.from_pretrained("Rajaram1996/Hubert_emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9a0e9576f0289db1ba6c9b3d193a897547bf80f48bf61cfbc18ac0b84915d72
- Size of remote file:
- 726 MB
- SHA256:
- 2b11008acfee9f053503ebb538d0b7a1c5dd24497aae6f2eadf0f8a6097e0244
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.