Pyannote Segmentation

🚀 "Powerset" Speaker Segmentation

This open-source model is designed for speaker segmentation. It takes in 10 seconds of mono audio sampled at 16kHz and outputs speaker diarization as a matrix. It can be used in various audio analysis tasks such as speaker diarization, voice activity detection, and overlapped speech detection.

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

✨ Features

• Speaker Segmentation: Ingests 10 seconds of 16kHz mono audio and outputs speaker diarization as a (num_frames, num_classes) matrix with 7 classes including non - speech and different speaker combinations.
• Multi - Encoding: Supports powerset multi - class encoding and can be converted to multi - label encoding.
• Multiple Applications: Can be used for voice activity detection and overlapped speech detection through pipelines.

📦 Installation

Requirements

1. Install pyannote.audio 3.0 with pip install pyannote.audio.
2. Accept pyannote/segmentation-3.0 user conditions.
3. Create access token at hf.co/settings/tokens.

💻 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 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.

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.

This companion repository by Alexis Plaquet also provides instructions on how to train or finetune such a model on your own data.

📄 License

This model 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},
}

📋 Model Information

⚠️ Important Note

The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers improve it further. Though this model uses MIT license and will always remain open - source, we will occasionnally email you about premium models and paid services around pyannote.

💡 Usage Tip

If you want to perform speaker diarization of full recordings, use pyannote/speaker-diarization-3.0 pipeline instead of this model alone.