Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation (2023)
Here's the companion repository of Brouhaha. You'll find the instructions to install and run our pretrained model. Given an audio segment, Brouhaha extracts:
- speech/non-speech segments.
- Speech-to-Noise Ratio (SNR) , that measures the speech level compared to the noise level..
- C50, that measures to which extent the environment is reverberant
You can listen to some audio samples we generated to train the model here.
If you want to dig further, you'll also find the instructions to run the audio contamination pipeline, and retrain a model from scratch.
Installation
git clone https://github.com/marianne-m/brouhaha-vad.git
cd brouhaha-vad
conda env create -f environment.yml
conda activate brouhaha-vad
pip install git+ssh://git@gitlab.cognitive-ml.fr:1022/htiteux/pyannote-brouhaha-db.git
conda install -c conda-forge libsndfile
Extracting predictions
python main.py path/to/predictions apply \
--model_path models/best/checkpoints/best.ckpt \
--classes brouhaha \
--data_dir path/to/data \
--ext "wav"