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--- |
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license: apache-2.0 |
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datasets: |
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- KELONMYOSA/dusha_emotion_audio |
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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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widget: |
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- example_title: Neurtal |
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src: https://huggingface.co/KELONMYOSA/wav2vec2-xls-r-300m-emotion-ru/resolve/main/neutral.mp3 |
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- example_title: Positive |
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src: https://huggingface.co/KELONMYOSA/wav2vec2-xls-r-300m-emotion-ru/resolve/main/positive.mp3 |
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- example_title: Angry |
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src: https://huggingface.co/KELONMYOSA/wav2vec2-xls-r-300m-emotion-ru/resolve/main/angry.mp3 |
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- example_title: Sad |
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src: https://huggingface.co/KELONMYOSA/wav2vec2-xls-r-300m-emotion-ru/resolve/main/sad.mp3 |
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- example_title: Other |
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src: https://huggingface.co/KELONMYOSA/wav2vec2-xls-r-300m-emotion-ru/resolve/main/other.mp3 |
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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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