Hugging Face's logo

anilbs
/
pipeline

Automatic Speech Recognition
pyannote.audio
pyannote
pyannote-audio-pipeline
audio
voice
speech
speaker
speaker-diarization
speaker-change-detection
voice-activity-detection
overlapped-speech-detection
Model card Files Community
pipeline / README.md
Anil Battalahalli Sreenath
changed README.md
97217d6
preview code
|
raw
history blame contribute delete
9.56 kB
metadata
tags:
  - pyannote
  - pyannote-audio
  - pyannote-audio-pipeline
  - audio
  - voice
  - speech
  - speaker
  - speaker-diarization
  - speaker-change-detection
  - voice-activity-detection
  - overlapped-speech-detection
  - automatic-speech-recognition
datasets:
  - ami
  - dihard
  - voxconverse
  - aishell
  - repere
  - voxceleb
license: mit
extra_gated_prompt: >-
  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.
extra_gated_fields:
  Company/university: text
  Website: text
  I plan to use this model for (task, type of audio data, etc): text

🎹 Speaker diarization

Relies on pyannote.audio 2.0.1: see installation instructions.

TL;DR 

# load the pipeline from Hugginface Hub
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("anilbs/pipeline")

# 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)

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
Benchmark (2.0.1) DER% FA% Miss% Conf% Expected output File-level evaluation
AISHELL-4 14.61 3.31 4.35 6.95 RTTM eval
AMI Mix-Headset only_words 18.21 3.28 11.07 3.87 RTTM eval
AMI Array1-01 only_words 29.00 2.71 21.61 4.68 RTTM eval
CALLHOME Part2 30.24 3.71 16.86 9.66 RTTM eval
DIHARD 3 Full 20.99 4.25 10.74 6.00 RTTM eval
REPERE Phase 2 12.62 1.55 3.30 7.76 RTTM eval
VoxConverse v0.3 12.61 3.45 3.85 5.31 RTTM eval

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},
}