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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f29af7fca643b7e0d9dfb40581ae6720cf39dc209945d73a7f657e46fbd7d24e
- Size of remote file:
- 1.06 kB
- SHA256:
- 47b14229da988503083d2571709a86a8077268c1850af987a7c46f851943718c
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