Segmentation

🚀 pyannote.audio // speaker segmentation

This project offers a model for speaker segmentation, which can be used for voice activity detection, overlapped speech detection, and resegmentation. It is based on the research by Hervé Bredin and Antoine Laurent, and relies on the in - development pyannote.audio 2.0.

Model from End-to-end speaker segmentation for overlap-aware resegmentation,
by Hervé Bredin and Antoine Laurent.

Relies on pyannote.audio 2.0 currently in development: see installation instructions.

🚀 Quick Start

This model can be used for various audio processing tasks such as voice activity detection, overlapped speech detection, and resegmentation. For detailed usage, please refer to the "Usage" section below.

✨ Features

• Voice activity detection
• Overlapped speech detection
• Resegmentation
• Raw scores extraction

📦 Installation

Relies on pyannote.audio 2.0 currently in development: see installation instructions.

💻 Usage Examples

Basic Usage

Voice activity detection

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

Advanced Usage

In order to reproduce the results of the paper "End-to-end speaker segmentation for overlap-aware resegmentation", use the following hyper - parameters:

Expected outputs (and VBx baseline) are also provided in the /reproducible_research sub - directories.

📚 Documentation

Support

For commercial enquiries and scientific consulting, please contact me.
For technical questions and bug reports, please check pyannote.audio Github repository.

📄 License

This project is licensed under the MIT license.

📚 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},
}