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:
- db489661463c6acfb9bdccd9f3e432991711597921e5e6f8577a1dca24c0ff9d
- Size of remote file:
- 988 Bytes
- SHA256:
- c357e3fcd5a4026c6fbfc21f6d0286251977d70579d35af48fb93121ca019e2d
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