ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition is a forked repo from huggingface. License: apache-2-0
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README.md

license tags metrics model_index
apache-2.0
generated_from_trainer
accuracy
name
wav2vec2-lg-xlsr-en-speech-emotion-recognition

wav2vec2-lg-xlsr-en-speech-emotion-recognition

This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on an unkown dataset. 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

Framework versions

  • Transformers 4.8.2
  • Pytorch 1.9.0+cu102
  • Datasets 1.9.0
  • Tokenizers 0.10.3