🎙️🥁🚨🔊 Brouhaha
Joint voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation
TL;DR | Paper | Code | And Now for Something Completely Different
Installation
This model relies on pyannote.audio and brouhaha-vad.
pip install pyannote-audio
pip install https://github.com/marianne-m/brouhaha-vad/archive/main.zip
Usage
# 1. visit hf.co/pyannote/brouhaha and accept user conditions
# 2. visit hf.co/settings/tokens to create an access token
# 3. instantiate pretrained model
from pyannote.audio import Model
model = Model.from_pretrained("pyannote/brouhaha",
use_auth_token="ACCESS_TOKEN_GOES_HERE")
# apply model
from pyannote.audio import Inference
inference = Inference(model)
output = inference("audio.wav")
# iterate over each frame
for frame, (vad, snr, c50) in output:
t = frame.middle
print(f"{t:8.3f} vad={100*vad:.0f}% snr={snr:.0f} c50={c50:.0f}")
# ...
# 12.952 vad=100% snr=51 c50=17
# 12.968 vad=100% snr=52 c50=17
# 12.985 vad=100% snr=53 c50=17
# ...
Citation
@article{lavechin2022brouhaha,
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 = {2022},
Journal = {arXiv preprint arXiv: Arxiv-2210.13248}
}
```bibtex
@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},
}