pyannote / speaker-diarization

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automatic-speech-recognition

Introduction of speaker-diarization

Model Details of speaker-diarization

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🎹 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/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
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},
}

Runs of pyannote speaker-diarization on huggingface.co

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More Information About speaker-diarization huggingface.co Model

More speaker-diarization license Visit here:

https://choosealicense.com/licenses/mit

speaker-diarization huggingface.co

speaker-diarization huggingface.co is an AI model on huggingface.co that provides speaker-diarization's model effect (), which can be used instantly with this pyannote speaker-diarization model. huggingface.co supports a free trial of the speaker-diarization model, and also provides paid use of the speaker-diarization. Support call speaker-diarization model through api, including Node.js, Python, http.

speaker-diarization huggingface.co Url

https://huggingface.co/pyannote/speaker-diarization

pyannote speaker-diarization online free

speaker-diarization huggingface.co is an online trial and call api platform, which integrates speaker-diarization's modeling effects, including api services, and provides a free online trial of speaker-diarization, you can try speaker-diarization online for free by clicking the link below.

pyannote speaker-diarization online free url in huggingface.co:

https://huggingface.co/pyannote/speaker-diarization

speaker-diarization install

speaker-diarization is an open source model from GitHub that offers a free installation service, and any user can find speaker-diarization on GitHub to install. At the same time, huggingface.co provides the effect of speaker-diarization install, users can directly use speaker-diarization installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

speaker-diarization install url in huggingface.co:

https://huggingface.co/pyannote/speaker-diarization

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