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

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