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wav2vec2-base-Toronto_emotional_speech_set

This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4925
  • Accuracy: 0.8804
  • Weighted f1: 0.8837
  • Micro f1: 0.8804
  • Macro f1: 0.8822
  • Weighted recall: 0.8804
  • Micro recall: 0.8804
  • Macro recall: 0.8757
  • Weighted precision: 0.9044
  • Micro precision: 0.8804
  • Macro precision: 0.9059

Model description

This model classifies the emotion when someone speaks in audio sample.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Audio-Projects/Emotion%20Detection/Toronto%20Emotional%20Speech%20Set%20(TESS)/Toronto%20Emotional%20Speech%20Set%20(TESS).ipynb

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/ejlok1/toronto-emotional-speech-set-tess

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: 15

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
1.9517 0.97 17 1.9432 0.2411 0.1338 0.2411 0.1201 0.2411 0.2411 0.2168 0.1161 0.2411 0.1049
1.9517 2.0 35 1.9036 0.3375 0.3037 0.3375 0.3082 0.3375 0.3375 0.3533 0.5364 0.3375 0.5379
1.9517 2.97 52 1.6629 0.4518 0.4020 0.4518 0.3936 0.4518 0.4518 0.4503 0.6751 0.4518 0.6555
1.9517 4.0 70 1.2026 0.7357 0.7121 0.7357 0.6989 0.7357 0.7357 0.7240 0.7903 0.7357 0.7848
1.9517 4.97 87 0.8458 0.8839 0.8796 0.8839 0.8767 0.8839 0.8839 0.8845 0.8874 0.8839 0.8807
1.9517 6.0 105 0.6493 0.8946 0.8939 0.8946 0.8914 0.8946 0.8946 0.8937 0.9049 0.8946 0.9014
1.9517 6.97 122 0.5149 0.9089 0.9046 0.9089 0.8989 0.9089 0.9089 0.8957 0.9275 0.9089 0.9327
1.9517 8.0 140 0.3814 0.9536 0.9531 0.9536 0.9501 0.9536 0.9536 0.9474 0.9577 0.9536 0.9583
1.9517 8.97 157 0.5627 0.85 0.8459 0.85 0.8402 0.85 0.85 0.8378 0.9100 0.85 0.9160
1.9517 10.0 175 0.4702 0.8911 0.8861 0.8911 0.8854 0.8911 0.8911 0.8938 0.9021 0.8911 0.8967
1.9517 10.97 192 0.3362 0.9393 0.9376 0.9393 0.9361 0.9393 0.9393 0.9399 0.9402 0.9393 0.9365
1.9517 12.0 210 0.3808 0.9179 0.9181 0.9179 0.9176 0.9179 0.9179 0.9180 0.9251 0.9179 0.9235
1.9517 12.97 227 0.4546 0.9036 0.9045 0.9036 0.9024 0.9036 0.9036 0.8988 0.9151 0.9036 0.9157
1.9517 14.0 245 0.5065 0.8786 0.8826 0.8786 0.8813 0.8786 0.8786 0.8742 0.9040 0.8786 0.9055
1.9517 14.57 255 0.4925 0.8804 0.8837 0.8804 0.8822 0.8804 0.8804 0.8757 0.9044 0.8804 0.9059

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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