Instructions to use padmalcom/wav2vec2-large-emotion-detection-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmalcom/wav2vec2-large-emotion-detection-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="padmalcom/wav2vec2-large-emotion-detection-german")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("padmalcom/wav2vec2-large-emotion-detection-german") model = Wav2Vec2ForSpeechClassification.from_pretrained("padmalcom/wav2vec2-large-emotion-detection-german") - Notebooks
- Google Colab
- Kaggle
This wav2vec2 based emotion detection model is trained on the emo-DB dataset. Code for training can be found here.
Emotion classes are:
- 0: 'anger'
- 1: 'boredom'
- 2: 'disgust'
- 3: 'fear'
- 4: 'happiness'
- 5: 'sadness'
- 6: 'neutral'
inference.py shows, how the model can be used.
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