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", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Discidius/Speech-Emotion-Classification") model = AutoModelForAudioClassification.from_pretrained("Discidius/Speech-Emotion-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ab462cf4f60d4b9df09e8b872282dded0e33ebda13f2b3274faade0a5e1b8fd
- Size of remote file:
- 378 MB
- SHA256:
- 7a5ead8de09ac38fb8483538c96e79fef83b590194696a439ef18b4c95592951
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