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README.md
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language:
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- ru
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pipeline_tag: audio-classification
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language:
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- ru
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pipeline_tag: audio-classification
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metrics:
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- accuracy
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---
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# Speech Emotion Recognition
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The model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for a Speech Emotion Recognition (SER) task.
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The dataset used to fine-tune the original pre-trained model is the [DUSHA dataset](https://huggingface.co/datasets/KELONMYOSA/dusha_emotion_audio). The dataset consists of about 125 000 audio recordings in Russian with four basic emotions that usually appear in a dialog with a virtual assistant: Happiness (Positive), Sadness, Anger and Neutral emotion.
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```python
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emotions = ['neutral', 'positive', 'angry', 'sad', 'other']
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```
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It achieves the following results:
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- Training Loss: 0.528700
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- Validation Loss: 0.349617
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- Accuracy: 0.901369
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