Segmentation 3.0

🚀 "Powerset" Speaker Segmentation

This open - source model focuses on speaker segmentation. It takes in 10 - second mono audio sampled at 16kHz and outputs speaker diarization as a matrix. It offers a practical solution for audio processing tasks such as speaker identification and speech analysis.

Using this open - source model in production?
Consider switching to pyannoteAI for better and faster options.

🚀 Quick Start

Prerequisites

1. Install [pyannote.audio](https://github.com/pyannote/pyannote - audio) 3.0 with pip install pyannote.audio
2. Accept [pyannote/segmentation - 3.0](https://hf.co/pyannote/segmentation - 3.0) user conditions
3. Create access token at hf.co/settings/tokens.

Example of Initializing the Model

# instantiate the model
from pyannote.audio import Model
model = Model.from_pretrained(
  "pyannote/segmentation-3.0", 
  use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")

✨ Features

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.

💻 Usage Examples

Basic Usage

# waveform (first row)
duration, sample_rate, num_channels = 10, 16000, 1
waveform = torch.randn(batch_size, num_channels, duration * sample_rate) 

# powerset multi-class encoding (second row)
powerset_encoding = model(waveform)

# multi-label encoding (third row)
from pyannote.audio.utils.powerset import Powerset
max_speakers_per_chunk, max_speakers_per_frame = 3, 2
to_multilabel = Powerset(
    max_speakers_per_chunk, 
    max_speakers_per_frame).to_multilabel
multilabel_encoding = to_multilabel(powerset_encoding)

Advanced Usage - 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](https://hf.co/pyannote/speaker - diarization - 3.0) pipeline that uses an additional speaker embedding model to perform full recording speaker diarization.

Advanced Usage - 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

Advanced Usage - 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

📚 Documentation

The various concepts behind this model are described in details in this [paper](https://www.isca - speech.org/archive/interspeech_2023/plaquet23_interspeech.html).

It has been trained by Séverin Baroudi with [pyannote.audio](https://github.com/pyannote/pyannote - audio) 3.0.0 using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA - AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse.

This [companion repository](https://github.com/FrenchKrab/IS2023 - powerset - diarization/) by Alexis Plaquet also provides instructions on how to train or finetune such a model on your own data.

📄 License

This project is licensed under the MIT license.

📚 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},
}

@inproceedings{Bredin23,
  author={Hervé Bredin},
  title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
}

📋 Metadata