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1# waveform (first row)
2duration, sample_rate, num_channels = 10, 16000, 1
3waveform = torch.randn(batch_size, num_channels, duration * sample_rate)
4
5# powerset multi-class encoding (second row)
6powerset_encoding = model(waveform)
7
8# multi-label encoding (third row)
9from pyannote.audio.utils.powerset import Powerset
10max_speakers_per_chunk, max_speakers_per_frame = 3, 2
11to_multilabel = Powerset(
12 max_speakers_per_chunk,
13 max_speakers_per_frame).to_multilabel
14multilabel_encoding = to_multilabel(powerset_encoding)3.0.0 using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse.pyannote.audio 3.0 with pip install pyannote.audiopyannote/segmentation-3.0 user conditionshf.co/settings/tokens.1# instantiate the model
2from pyannote.audio import Model
3model = Model.from_pretrained(
4 "pyannote/segmentation-3.0",
5 use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")1from pyannote.audio.pipelines import VoiceActivityDetection
2pipeline = VoiceActivityDetection(segmentation=model)
3HYPER_PARAMETERS = {
4 # remove speech regions shorter than that many seconds.
5 "min_duration_on": 0.0,
6 # fill non-speech regions shorter than that many seconds.
7 "min_duration_off": 0.0
8}
9pipeline.instantiate(HYPER_PARAMETERS)
10vad = pipeline("audio.wav")
11# `vad` is a pyannote.core.Annotation instance containing speech regions1from pyannote.audio.pipelines import OverlappedSpeechDetection
2pipeline = OverlappedSpeechDetection(segmentation=model)
3HYPER_PARAMETERS = {
4 # remove overlapped speech regions shorter than that many seconds.
5 "min_duration_on": 0.0,
6 # fill non-overlapped speech regions shorter than that many seconds.
7 "min_duration_off": 0.0
8}
9pipeline.instantiate(HYPER_PARAMETERS)
10osd = pipeline("audio.wav")
11# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions1@inproceedings{Plaquet23,
2 author={Alexis Plaquet and Hervé Bredin},
3 title={{Powerset multi-class cross entropy loss for neural speaker diarization}},
4 year=2023,
5 booktitle={Proc. INTERSPEECH 2023},
6}1@inproceedings{Bredin23,
2 author={Hervé Bredin},
3 title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
4 year=2023,
5 booktitle={Proc. INTERSPEECH 2023},
6}