pyannote.audio // speaker segmentation

Model from End-to-end speaker segmentation for overlap-aware resegmentation,
by Hervé Bredin and Antoine Laurent.

Relies on pyannote.audio 2.0 currently in development: see installation instructions.

Support

For commercial enquiries and scientific consulting, please contact me.
For technical questions and bug reports, please check pyannote.audio Github repository.

Usage

Voice activity detection

from pyannote.audio.pipelines import VoiceActivityDetection
pipeline = VoiceActivityDetection(segmentation="pyannote/segmentation")
HYPER_PARAMETERS = {
  # onset/offset activation thresholds
  "onset": 0.5, "offset": 0.5,
  # remove speech regions shorter than that many seconds.
  "min_duration_on": 0.0,
  # fill non-speech regions shorter than that many seconds.
  "min_duration_off": 0.0
}
pipeline.instantiate(HYPER_PARAMETERS)
vad = pipeline("audio.wav")
# `vad` is a pyannote.core.Annotation instance containing speech regions

Overlapped speech detection

from pyannote.audio.pipelines import OverlappedSpeechDetection
pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation")
pipeline.instantiate(HYPER_PARAMETERS)
osd = pipeline("audio.wav")
# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions

Resegmentation

from pyannote.audio.pipelines import Resegmentation
pipeline = Resegmentation(segmentation="pyannote/segmentation", 
                          diarization="baseline")
pipeline.instantiate(HYPER_PARAMETERS)
resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
# where `baseline` should be provided as a pyannote.core.Annotation instance

Raw scores

from pyannote.audio import Inference
inference = Inference("pyannote/segmentation")
segmentation = inference("audio.wav")
# `segmentation` is a pyannote.core.SlidingWindowFeature
# instance containing raw segmentation scores like the 
# one pictured above (output)

Reproducible research

In order to reproduce the results of the paper "End-to-end speaker segmentation for overlap-aware resegmentation ", use the following hyper-parameters:

Voice activity detection	onset	offset	min_duration_on	min_duration_off
AMI Mix-Headset	0.684	0.577	0.181	0.037
DIHARD3	0.767	0.377	0.136	0.067
VoxConverse	0.767	0.713	0.182	0.501

Overlapped speech detection	onset	offset	min_duration_on	min_duration_off
AMI Mix-Headset	0.448	0.362	0.116	0.187
DIHARD3	0.430	0.320	0.091	0.144
VoxConverse	0.587	0.426	0.337	0.112

Resegmentation of VBx	onset	offset	min_duration_on	min_duration_off
AMI Mix-Headset	0.542	0.527	0.044	0.705
DIHARD3	0.592	0.489	0.163	0.182
VoxConverse	0.537	0.724	0.410	0.563

Expected outputs (and VBx baseline) are also provided in the /reproducible_research sub-directories.

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},
}