Audio Classification
Transformers
Safetensors
English
wav2vec2
emotion
audio
classification
music
facebook
Instructions to use Discidius/Speech-Emotion-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Discidius/Speech-Emotion-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Discidius/Speech-Emotion-Classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Discidius/Speech-Emotion-Classification") model = AutoModelForAudioClassification.from_pretrained("Discidius/Speech-Emotion-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec39516a4d9bbca0a19a3582583a487e768e4b03d6436512a3ade4b4da0fac5b
- Size of remote file:
- 723 MB
- SHA256:
- 8f629a9e5fd9b8ee1d164de9a984d0ece89f2ce5151dc5ccbed956fa19979a27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.