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

bibtex

@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