metadata

tags:
  - pyannote
  - pyannote-audio
  - pyannote-audio-model
  - audio
  - voice
  - speech
  - speaker
  - speaker-segmentation
  - voice-activity-detection
  - overlapped-speech-detection
  - resegmentation
license: mit
inference: false
extra_gated_prompt: >-
  The collected information will help acquire a better knowledge of
  pyannote.audio userbase and help its maintainers apply for grants to improve
  it further. If you are an academic researcher, please cite the relevant papers
  in your own publications using the model. If you work for a company, please
  consider contributing back to pyannote.audio development (e.g. through
  unrestricted gifts). We also provide scientific consulting services around
  speaker diarization and machine listening.
extra_gated_fields:
  Company/university: text
  Website: text
  I plan to use this model for (task, type of audio data, etc): text

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🎹 Speaker segmentation

Paper | Demo | Blog post

Usage

Relies on pyannote.audio 2.1.1: 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 model
from pyannote.audio import Model
model = Model.from_pretrained("pyannote/segmentation", 
                              use_auth_token="ACCESS_TOKEN_GOES_HERE")

Voice activity detection

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

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},
}

Reproducible research

In order to reproduce the results of the paper "End-to-end speaker segmentation for overlap-aware resegmentation ", use pyannote/segmentation@Interspeech2021 with 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.