pan-yangxu/brouhaha-vad

Predicts the level of noise and reverberation on your audiofiles

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

# clone brouhaha
git clone https://github.com/marianne-m/brouhaha-vad.git
cd brouhaha-vad

# creating a conda environment
conda create -n brouhaha python=3.8
conda activate brouhaha

# install brouhaha
pip install .

Depending on the environment you're running the model in, it may be necessary to install libsndfile with the following command:

conda install -c conda-forge libsndfile

Extract predictions

python brouhaha/main.py apply \
      --data_dir path/to/data \
      --out_dir path/to/predictions \
      --model_path models/best/checkpoints/best.ckpt \
      --ext wav

Going further

1. Run the audio contamination pipeline
2. Train your own model

Citation

@article{lavechin2023brouhaha,
  Title   = {{Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation}},
  Author  = {Marvin Lavechin and Marianne Métais and Hadrien Titeux and Alodie Boissonnet and Jade Copet and Morgane Rivière and Elika Bergelson and Alejandrina Cristia and Emmanuel Dupoux and Hervé Bredin},
  Year    = {2023},
  Journal = {ASRU}
}

@inproceedings{Bredin2020,
  Title = {{pyannote.audio: neural building blocks for speaker diarization}},
  Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
  Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
  Address = {Barcelona, Spain},
  Month = {May},
  Year = {2020},
}