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πΉ Speaker segmentation
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.
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