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  This model is fine tuned on the IEMOCAP dataset. We applied volume normalization and data augmentation (noise injection, pitch shift and audio stretching). Also, this is a speaker independent model: We use Ses05F in the IEMOCAP dataset as validation speaker and Ses05M as test speaker.
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- The initial pre-trained model is facebook/wav2vec2-base. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), *without frustration*. The audios are either padded or trimed to 8-sec-long before fine tuning.
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  After **10** epoches of training, the validation accuracy is around **67%**.
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  This model is fine tuned on the IEMOCAP dataset. We applied volume normalization and data augmentation (noise injection, pitch shift and audio stretching). Also, this is a speaker independent model: We use Ses05F in the IEMOCAP dataset as validation speaker and Ses05M as test speaker.
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+ The initial pre-trained model is **facebook/wav2vec2-base**. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), *without frustration*. The audios are either padded or trimed to 8-sec-long before fine tuning.
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  After **10** epoches of training, the validation accuracy is around **67%**.
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