metadata

tags:
  - pyannote
  - audio
  - voice
  - speech
  - speaker
  - speaker segmentation
  - voice activity detection
  - overlapped speech detection
  - resegmentation
datasets:
  - ami
  - dihard
  - voxconverse
license: mit
inference: false

Pretrained speaker segmentation model

This model relies on pyannote.audio 2.0 (which is still in development):

$ pip install https://github.com/pyannote/pyannote-audio/archive/develop.zip

Basic inference

>>> from pyannote.audio import Inference
>>> inference = Inference("pyannote/Segmentation")
>>> segmentation = inference("audio.wav")

Advanced pipelines

Voice activity detection

>>> from pyannote.audio.pipelines import VoiceActivityDetection
>>> HYPER_PARAMETERS = {"onset": 0.5, "offset": 0.5, "min_duration_on": 0.0, "min_duration_off": 0.0}
>>> pipeline = VoiceActivityDetection(segmentation="pyannote/Segmentation").instantiate(HYPER_PARAMETERS)
>>> vad = pipeline("audio.wav")

Dataset	`onset`	`offset`	`min_duration_on`	`min_duration_off`
AMI Mix-Headset	TODO	TODO	TODO	TODO
DIHARD3	TODO	TODO	TODO	TODO
VoxConverse	TODO	TODO	TODO	TODO

Overlapped speech detection

>>> from pyannote.audio.pipelines import OverlappedSpeechDetection
>>> pipeline = OverlappedSpeechDetection(segmentation="pyannote/Segmentation").instantiate(HYPER_PARAMETERS)
>>> osd = pipeline("audio.wav")

Dataset	`onset`	`offset`	`min_duration_on`	`min_duration_off`
AMI Mix-Headset	TODO	TODO	TODO	TODO
DIHARD3	TODO	TODO	TODO	TODO
VoxConverse	TODO	TODO	TODO	TODO

Segmentation

>>> from pyannote.audio.pipelines import Segmentation
>>> pipeline = Segmentation(segmentation="pyannote/Segmentation").instantiate(HYPER_PARAMETERS)
>>> seg = pipeline("audio.wav")

Dataset	`onset`	`offset`	`min_duration_on`	`min_duration_off`
AMI Mix-Headset	TODO	TODO	TODO	TODO
DIHARD3	TODO	TODO	TODO	TODO
VoxConverse	TODO	TODO	TODO	TODO

Resegmentation

>>> from pyannote.audio.pipelines import Resegmentation
>>> pipeline = Resegmentation(segmentation="pyannote/Segmentation", diarization="baseline")
>>> assert isinstance(baseline, pyannote.core.Annotation)
>>> resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})

Dataset	`onset`	`offset`	`min_duration_on`	`min_duration_off`
AMI Mix-Headset	TODO	TODO	TODO	TODO
DIHARD3	TODO	TODO	TODO	TODO
VoxConverse	TODO	TODO	TODO	TODO

Citations

@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},
}