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wav2vec2-base-Speech_Emotion_Recognition

This model is a fine-tuned version of facebook/wav2vec2-base.

It achieves the following results on the evaluation set:

  • Loss: 0.7264
  • Accuracy: 0.7539
  • F1
    • Weighted: 0.7514
    • Micro: 0.7539
    • Macro: 0.7529
  • Recall
    • Weighted: 0.7539
    • Micro: 0.7539
    • Macro: 0.7577
  • Precision
    • Weighted: 0.7565
    • Micro: 0.7539
    • Macro: 0.7558

Model description

This model predicts the emotion of the person speaking in the audio sample.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/tree/main/Audio-Projects/Emotion%20Detection/Speech%20Emotion%20Detection

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Weighted F1 Micro F1 Macro F1 Weighted Recall Micro Recall Macro Recall Weighted Precision Micro Precision Macro Precision
1.5581 0.98 43 1.4046 0.4653 0.4080 0.4653 0.4174 0.4653 0.4653 0.4793 0.5008 0.4653 0.4974
1.5581 1.98 86 1.1566 0.5997 0.5836 0.5997 0.5871 0.5997 0.5997 0.6093 0.6248 0.5997 0.6209
1.5581 2.98 129 0.9733 0.6883 0.6845 0.6883 0.6860 0.6883 0.6883 0.6923 0.7012 0.6883 0.7009
1.5581 3.98 172 0.8313 0.7399 0.7392 0.7399 0.7409 0.7399 0.7399 0.7417 0.7415 0.7399 0.7432
1.5581 4.98 215 0.8708 0.7028 0.6963 0.7028 0.6970 0.7028 0.7028 0.7081 0.7148 0.7028 0.7114
1.5581 5.98 258 0.7969 0.7297 0.7267 0.7297 0.7277 0.7297 0.7297 0.7333 0.7393 0.7297 0.7382
1.5581 6.98 301 0.7349 0.7603 0.7613 0.7603 0.7631 0.7603 0.7603 0.7635 0.7699 0.7603 0.7702
1.5581 7.98 344 0.7714 0.7469 0.7444 0.7469 0.7456 0.7469 0.7469 0.7485 0.7554 0.7469 0.7563
1.5581 8.98 387 0.7183 0.7630 0.7615 0.7630 0.7631 0.7630 0.7630 0.7652 0.7626 0.7630 0.7637
1.5581 9.98 430 0.7264 0.7539 0.7514 0.7539 0.7529 0.7539 0.7539 0.7577 0.7565 0.7539 0.7558

Framework versions

  • Transformers 4.26.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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