# 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 pipelinefrom pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
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 formatwithopen("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:
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
):
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},
}
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