Skip to page content
SAVRN
Search Plan a project

wav2vec2-lg-xlsr-en-speech-emotion-recognition · Model Card

wav2vec2-lg-xlsr-en-speech-emotion-recognition: Model Card

Written by Enrique Hernández Calabrés, published under apache-2.0, revision b520c9c46a71, read 2026-10-21. Shown as written; SAVRN's own facts about this model are on its page.

Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0

The model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english for a Speech Emotion Recognition (SER) task.

The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset. This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are:

emotions = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'neutral', 'sad', 'surprised']

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

Citation

@misc {enrique_hernández_calabrés_2024,
    author       = { {Enrique Hernández Calabrés} },
    title        = { wav2vec2-lg-xlsr-en-speech-emotion-recognition (Revision 17cf17c) },
    year         = 2024,
    url          = { https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition },
    doi          = { 10.57967/hf/2045 },
    publisher    = { Hugging Face }
}

Contact

Any doubt, contact me on Twitter.

Framework versions

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