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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