Emotion Recognition with wav2vec2 base on IEMOCAP This repository provides all the necessary tools to perform emotion recognition with a fine tuned wav2vec2 (base) model using SpeechBrain. It is trained on IEMOCAP training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on IEMOCAP test set is: Release Accuracy(%) : : : : 19 10 21 78.7 (Avg: 75.3) Pipeline description This system is composed of an wav2vec2 model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classify file if needed. Install SpeechBrain First of all, please install the development version of SpeechBrain with the following command: Please notice that we encourage you to read our tutorials and learn more about SpeechBrain. Perform Emotion recognition An external py module fi…
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