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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model_index: |
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name: wav2vec-english-speech-emotion-recognition |
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--- |
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# Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0 |
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The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) for a Speech Emotion Recognition (SER) task. |
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Several datasets were used the fine-tune the original model: |
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- Surrey Audio-Visual Expressed Emotion [(SAVEE)](http://kahlan.eps.surrey.ac.uk/savee/Database.html) - 480 audio files from 4 male actors |
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- Ryerson Audio-Visual Database of Emotional Speech and Song [(RAVDESS)](https://zenodo.org/record/1188976) - 1440 audio files from 24 professional actors (12 female, 12 male) |
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- Toronto emotional speech set [(TESS)](https://tspace.library.utoronto.ca/handle/1807/24487) - 2800 audio files from 2 female actors |
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7 labels/emotions were used as classification labels |
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```python |
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emotions = ['angry' 'disgust' 'fear' 'happy' 'neutral' 'sad' 'surprise'] |
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``` |
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It achieves the following results on the evaluation set: |
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- Loss: 0.104075 |
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- Accuracy: 0.97463 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- eval_steps: 500 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- num_epochs: 4 |
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- max_steps=7500 |
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- save_steps: 1500 |
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### Training results |
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| Step | Training Loss | Validation Loss | Accuracy | |
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| ---- | ------------- | --------------- | -------- | |
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| 500 | 1.8124 | 1.365212 | 0.486258 | |
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| 1000 | 0.8872 | 0.773145 | 0.79704 | |
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| 1500 | 0.7035 | 0.574954 | 0.852008 | |
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| 2000 | 0.6879 | 1.286738 | 0.775899 | |
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| 2500 | 0.6498 | 0.697455 | 0.832981 | |
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| 3000 | 0.5696 | 0.33724 | 0.892178 | |
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| 3500 | 0.4218 | 0.307072 | 0.911205 | |
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| 4000 | 0.3088 | 0.374443 | 0.930233 | |
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| 4500 | 0.2688 | 0.260444 | 0.936575 | |
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| 5000 | 0.2973 | 0.302985 | 0.92389 | |
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| 5500 | 0.1765 | 0.165439 | 0.961945 | |
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| 6000 | 0.1475 | 0.170199 | 0.961945 | |
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| 6500 | 0.1274 | 0.15531 | 0.966173 | |
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| 7000 | 0.0699 | 0.103882 | 0.976744 | |
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| 7500 | 0.083 | 0.104075 | 0.97463 | |
