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:

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.

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.

An external py_module_file=custom.py is used as an external Predictor class into this HF repos. We use foreign_class function from speechbrain.pretrained.interfaces that allow you to load you custom model.

The prediction tensor will contain a tuple of (embedding, id_class, label_name).

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

The model was trained with SpeechBrain (aa018540). To train it from scratch follows these steps:

cd speechbrain
pip install -r requirements.txt
pip install -e .

cd  recipes/IEMOCAP/emotion_recognition
python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here.

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Please, cite SpeechBrain if you use it for your research or business.