Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
The model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english for a Speech Emotion Recognition (SER) task.
The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset. This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are:
emotions = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'neutral', 'sad', 'surprised']
It achieves the following results on the evaluation set:
- Loss: 0.5023
- Accuracy: 0.8223
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0752 | 0.21 | 30 | 2.0505 | 0.1359 |
| 2.0119 | 0.42 | 60 | 1.9340 | 0.2474 |
| 1.8073 | 0.63 | 90 | 1.5169 | 0.3902 |
| 1.5418 | 0.84 | 120 | 1.2373 | 0.5610 |
| 1.1432 | 1.05 | 150 | 1.1579 | 0.5610 |
| 0.9645 | 1.26 | 180 | 0.9610 | 0.6167 |
| 0.8811 | 1.47 | 210 | 0.8063 | 0.7178 |
| 0.8756 | 1.68 | 240 | 0.7379 | 0.7352 |
| 0.8208 | 1.89 | 270 | 0.6839 | 0.7596 |
| 0.7118 | 2.1 | 300 | 0.6664 | 0.7735 |
| 0.4261 | 2.31 | 330 | 0.6058 | 0.8014 |
| 0.4394 | 2.52 | 360 | 0.5754 | 0.8223 |
| 0.4581 | 2.72 | 390 | 0.4719 | 0.8467 |
| 0.3967 | 2.93 | 420 | 0.5023 | 0.8223 |
Contact
Any doubt, contact me on Twitter (GitHub repo soon).
Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
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