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:
- 4859eb73a1720a800159c547c864fdd8c19e769ea05dff382231ef6e5b084d30
- Size of remote file:
- 723 MB
- SHA256:
- 5ad4d084b1de650226a3ae204901aa83416cec45b5866341393a3c0c781f97af
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