You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening.

Log in or Sign Up to review the conditions and access this model content.

I propose (paid) scientific consulting services to companies willing to make the most of their data and open-source speech processing toolkits (and pyannote in particular).

🎹 Speaker diarization

Relies on pyannote.audio 2.1.1: see installation instructions.

TL;DR

# 1. visit hf.co/pyannote/speaker-diarization and accept user conditions
# 2. visit hf.co/pyannote/segmentation and accept user conditions
# 3. visit hf.co/settings/tokens to create an access token
# 4. instantiate pretrained speaker diarization pipeline
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/[email protected]",
                                    use_auth_token="ACCESS_TOKEN_GOES_HERE")


# 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)

Benchmark

Real-time factor

Real-time factor is around 2.5% 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 1.5 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
Benchmark DER% FA% Miss% Conf% Expected output File-level evaluation
AISHELL-4 14.09 5.17 3.27 5.65 RTTM eval
Albayzin (RTVE 2022) 25.60 5.58 6.84 13.18 RTTM eval
AliMeeting (channel 1) 27.42 4.84 14.00 8.58 RTTM eval
AMI (headset mix, only_words) 18.91 4.48 9.51 4.91 RTTM eval
AMI (array1, channel 1, only_words) 27.12 4.11 17.78 5.23 RTTM eval
CALLHOME (part2) 32.37 6.30 13.72 12.35 RTTM eval
DIHARD 3 (Full) 26.94 10.50 8.41 8.03 RTTM eval
Ego4D v1 (validation) 63.99 3.91 44.42 15.67 RTTM eval
REPERE (phase 2) 8.17 2.23 2.49 3.45 RTTM eval
This American Life 20.82 2.03 11.89 6.90 RTTM eval
VoxConverse (v0.3) 11.24 4.42 2.88 3.94 RTTM eval

Technical report

This report describes the main principles behind version 2.1 of pyannote.audio speaker diarization pipeline.
It also provides recipes explaining how to adapt the pipeline to your own set of annotated data. In particular, those are applied to the above benchmark and consistently leads to significant performance improvement over the above out-of-the-box performance.

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},
}
Downloads last month
1,733,815
Hosted inference API
or
This model can be loaded on the Inference API on-demand.

Dataset used to train pyannote/speaker-diarization

Spaces using pyannote/speaker-diarization 70