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🎹 "Powerset" speaker segmentation
This model ingests 10 seconds of mono audio sampled at 16kHz and outputs speaker diarization as a (num_frames, num_classes) matrix where the 7 classes are
non-speech
,
speaker #1
,
speaker #2
,
speaker #3
,
speakers #1 and #2
,
speakers #1 and #3
, and
speakers #2 and #3
.
The various concepts behind this model are described in details in this
paper
.
It has been trained by Séverin Baroudi with
pyannote.audio
3.0.0
using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse.
# instantiate the modelfrom pyannote.audio import Model
model = Model.from_pretrained(
"pyannote/segmentation-3.0",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
Speaker diarization
This model cannot be used to perform speaker diarization of full recordings on its own (it only processes 10s chunks).
See
pyannote/speaker-diarization-3.0
pipeline that uses an additional speaker embedding model to perform full recording speaker diarization.
Voice activity detection
from pyannote.audio.pipelines import VoiceActivityDetection
pipeline = VoiceActivityDetection(segmentation=model)
HYPER_PARAMETERS = {
# 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)
HYPER_PARAMETERS = {
# remove overlapped speech regions shorter than that many seconds."min_duration_on": 0.0,
# fill non-overlapped speech regions shorter than that many seconds."min_duration_off": 0.0
}
pipeline.instantiate(HYPER_PARAMETERS)
osd = pipeline("audio.wav")
# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
Citations
@inproceedings{Plaquet23,
author={Alexis Plaquet and Hervé Bredin},
title={{Powerset multi-class cross entropy loss for neural speaker diarization}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
}
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