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 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 15…
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