ehcalabres

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: It achieves the following results on the evaluation set: - Loss: 0.5023 - Accuracy: 0.8223 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - seed: 42 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lrschedulertype: linear - numepochs: 3 - mixedprecisiontraining: Native AMP | 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 | - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3