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I propose (paid) scientific consulting services to companies willing to make the most of their data and open-source speech processing toolkits (and pyannote in particular).
🎹 Voice activity detection
Relies on pyannote.audio 2.1: see installation instructions.
# 1. visit hf.co/pyannote/segmentation and accept user conditions
# 2. visit hf.co/settings/tokens to create an access token
# 3. instantiate pretrained voice activity detection pipeline
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/voice-activity-detection",
use_auth_token="ACCESS_TOKEN_GOES_HERE")
output = pipeline("audio.wav")
for speech in output.get_timeline().support():
# active speech between speech.start and speech.end
...
Citation
@inproceedings{Bredin2021,
Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
Booktitle = {Proc. Interspeech 2021},
Address = {Brno, Czech Republic},
Month = {August},
Year = {2021},
}
@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},
}
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