Pyannote Speaker Diarization Endpoint

🚀 🎹 Speaker diarization

This project provides a speaker diarization solution relying on pyannote.audio 2.0, enabling automatic processing of audio files to distinguish different speakers.

🚀 Quick Start

Relies on pyannote.audio 2.0: see installation instructions.

💻 Usage Examples

Basic Usage

# load the pipeline from Hugginface Hub
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2022.07")

# apply the pipeline to an audio file
diarization = pipeline("audio.wav")

# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
    diarization.write_rttm(rttm)

Advanced Usage

In case the number of speakers is known in advance, one can use the num_speakers option:

diarization = pipeline("audio.wav", num_speakers=2)

One can also provide lower and/or upper bounds on the number of speakers using min_speakers and max_speakers options:

diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5)

If you feel adventurous, you can try and play with the various pipeline hyper-parameters.
For instance, one can use a more aggressive voice activity detection by increasing the value of segmentation_onset threshold:

hparams = pipeline.parameters(instantiated=True)
hparams["segmentation_onset"] += 0.1
pipeline.instantiate(hparams)

📚 Documentation

Benchmark

Real-time factor

Real-time factor is around 5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part).

In other words, it takes approximately 3 minutes to process a one hour conversation.

Accuracy

This pipeline is benchmarked on a growing collection of datasets.

Processing is fully automatic:

• no manual voice activity detection (as is sometimes the case in the literature)
• no manual number of speakers (though it is possible to provide it to the pipeline)
• no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset

... with the least forgiving diarization error rate (DER) setup (named "Full" in this paper):

• no forgiveness collar
• evaluation of overlapped speech

📄 Support

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

📚 Citations

@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},
}