Official implementation of 'Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings'

Requirements:

We recommend running these scripts using a virtual environment like Anaconda, which should have Tensorflow 2.4.1 and PyTorch 1.7.1 installed.

Install required python packages:

pip install -r requirements.txt

Install sox and libmediainfo in your system

sudo apt-get install sox
sudo apt-get install libmediainfo-dev

RAVDESS and IEMOCAP datasets need to be downloaded and placed at ~/Datasets with a folder structure like this:

├── IEMOCAP
│   ├── Documentation
│   ├── README.txt
│   ├── Session1
│   ├── Session2
│   ├── Session3
│   ├── Session4
│   └── Session5
└── RAVDESS
    └── RAVDESS
        ├── song
        └── speech

Replicating our experiments

In our paper we run many different experiments using 5 seeds for each one. If you want to replicate that procedure, run in a terminal:

./run_seeds.sh <output_path>

If you want to run just 1 seed:

./run_paper_experiments.sh <seed_number> <output_path>

If you don't want to run all the experiments performed in the paper, comment the unwanted experiments in the run_paper_experiments.sh script. For example, our best performing model is trained using the following lines:

#w2v2PT-fusion
errors=1
while (($errors!=0)); do
paiprun configs/main/w2v2-os-exps.yaml --output_path "${OUTPUT_PATH}/w2v2PT-fusion/${SEED}" --mods "${seed_mod}&global/wav2vec2_embedding_layer=enc_and_transformer&global/normalize=global"
errors=$?; done

The experiments outputs will be saved at <output_path>. A cache folder will be generated at the directory from which above line is called. Take into account that run_seeds.sh executes many experiments (all the presented in the paper), and repeats it 5 times (using different seeds for the random number generators), so it is expected that the process takes a very long time and drive space. We ran the experiments using multiple AWS P3.2x large instances, which have a Tesla V100 GPU.

Analyzing the outputs

The outputs saved at <output_path> can be examined from Python using joblib. For example, running:

import joblib
metrics = joblib.load('experiments/w2v2PT-fusion/0123/MainTask/DownstreamRavdess/RavdessMetrics/out')

will load the resulting metrics in the 'metrics' variable.

In this notebook, more examples of how the generated outputs can be analysed are given. Moreover, we provide the results from all our experiments in the experiments folder and the results.ipynb notebook will generate the tables of our paper.

🔥🔥🔥 Using pretrained models 🔥🔥🔥

⚠️WARNING: The models we trained, as most speech emotion recognition models, are very unlikely to generalize to datasets other than the used for training, which are recorded in clean conditions and with actors⚠️

Model	Dataset	Links
w2v2PT-fusion	IEMOCAP	Folds: 1 2 3 4 5
w2v2PT-fusion	RAVDESS	Model
w2v2PT-alllayers-global	IEMOCAP	Folds: 1 2 3 4 5
w2v2PT-alllayers-global	RAVDESS	Model
w2v2PT-alllayers	IEMOCAP	Folds: 1 2 3 4 5
w2v2PT-alllayers	RAVDESS	Model
Issa et al. eval setup	RAVDESS	Folds: 1 2 3 4 5

Cite as: Pepino, L., Riera, P., Ferrer, L. (2021) Emotion Recognition from Speech Using wav2vec 2.0 Embeddings. Proc. Interspeech 2021, 3400-3404, doi: 10.21437/Interspeech.2021-703

@inproceedings{pepino21_interspeech,
  author={Leonardo Pepino and Pablo Riera and Luciana Ferrer},
  title={{Emotion Recognition from Speech Using wav2vec 2.0 Embeddings}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={3400--3404},
  doi={10.21437/Interspeech.2021-703}
}