Audio Classification
Transformers
Safetensors
English
wav2vec2
emotion
audio
classification
music
facebook
Instructions to use prithivMLmods/Speech-Emotion-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Speech-Emotion-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="prithivMLmods/Speech-Emotion-Classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Speech-Emotion-Classification") model = AutoModelForAudioClassification.from_pretrained("prithivMLmods/Speech-Emotion-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adf61c3e71b3cc7bc570f2b335798670f31d646e94016639e57afe06e39f1d6e
- Size of remote file:
- 5.3 kB
- SHA256:
- 2a868b38cbd73376b16ac8e3d8306db2a42a29f39b8f6d41ef6bd11bcdc6b19c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.