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hicustomer/
pyannote-speaker-diarization

Name: pyannote-speaker-diarization
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🎹 Speaker diarization

Relies on pyannote.audio 2.1.1: see installation instructions.

TL;DR

python

# 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/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 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.

Support

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

Citations

bibtex

@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},
}