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wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-babycry-v2

This model is a fine-tuned version of ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8522
  • Accuracy: {'accuracy': 0.8043478260869565}
  • F1: 0.7171
  • Precision: 0.6470
  • Recall: 0.8043

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: 8
  • 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
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.6078 0.4854 25 0.8682 {'accuracy': 0.8043478260869565} 0.7171 0.6470 0.8043
0.7269 0.9709 50 0.8559 {'accuracy': 0.8043478260869565} 0.7171 0.6470 0.8043
0.6815 1.4563 75 0.8204 {'accuracy': 0.8043478260869565} 0.7171 0.6470 0.8043
0.6144 1.9417 100 0.8417 {'accuracy': 0.8043478260869565} 0.7171 0.6470 0.8043
0.6246 2.4272 125 0.8454 {'accuracy': 0.8043478260869565} 0.7171 0.6470 0.8043
0.5687 2.9126 150 0.8527 {'accuracy': 0.8043478260869565} 0.7171 0.6470 0.8043

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.19.1
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Evaluation results