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metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: emotions_6_classes_small
    results: []

emotions_6_classes_small

This model is a fine-tuned version of facebook/wav2vec2-base on the 'Audio emotions' public dataset, available form https://www.kaggle.com/datasets/uldisvalainis/audio-emotions. 'Surprised' class was discarded due to lack of samples.

It achieves the following results on the evaluation set:

  • Loss: 0.9106
  • Accuracy: 0.7920

Model description

Classifies audios into 6 emotions:

  • Angry
  • Happy
  • Sad
  • Neutral
  • Fearful
  • Disgusted

Intended uses & limitations

This model was trained for educational purposes.

Training and evaluation data

  • Training: 80%
  • Test: 20%

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 512
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2009 0.99 19 0.6892 0.7891
0.2272 1.97 38 0.7235 0.7817
0.2196 2.96 57 0.7027 0.7809
0.2402 4.0 77 0.7953 0.7592
0.2301 4.99 96 0.7979 0.7699
0.1896 5.97 115 0.7533 0.7838
0.188 6.96 134 0.7483 0.7817
0.1573 8.0 154 0.8200 0.7756
0.1576 8.99 173 0.7623 0.7944
0.1452 9.97 192 0.7460 0.7944
0.1322 10.96 211 0.8031 0.7875
0.1353 12.0 231 0.7864 0.7883
0.1211 12.99 250 0.7934 0.7903
0.1165 13.97 269 0.7734 0.7936
0.0928 14.96 288 0.8743 0.7842
0.095 16.0 308 0.8483 0.7867
0.0824 16.99 327 0.8860 0.7850
0.0896 17.97 346 0.8314 0.7957
0.0874 18.96 365 0.8164 0.7936
0.081 20.0 385 0.8250 0.7993
0.0673 20.99 404 0.9118 0.7879
0.0716 21.97 423 0.8605 0.7912
0.0588 22.96 442 0.8470 0.7985
0.0579 24.0 462 0.8906 0.7920
0.0511 24.99 481 0.8853 0.7969
0.0488 25.97 500 0.8901 0.7973
0.0468 26.96 519 0.9083 0.7895
0.0505 28.0 539 0.9010 0.7903
0.0542 28.99 558 0.8924 0.7944
0.0542 29.61 570 0.9106 0.7920

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3