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README.md

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+---
+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
+---
+
+# 🎹 Speaker diarization
+
+Relies on pyannote.audio 2.0: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
+
+
+## TL;DR
+
+```python
+# load the pipeline from Hugginface Hub
+from pyannote.audio import Pipeline
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2022.07")
+
+# 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:
+
+```python
+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:
+
+```python
+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:
+
+```python
+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](https://doi.org/10.1016/j.csl.2021.101254)):
+
+* no forgiveness collar
+* evaluation of overlapped speech
+
+
+| Benchmark                                                                                                                          | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output                                                                            | File-level evaluation                                                                      |
+| ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ |
+| [AISHELL-4](http://www.openslr.org/111/)                                                                                           | 14.61                              | 3.31                        | 4.35                               | 6.95                                | [RTTM](reproducible_research/AISHELL.SpeakerDiarization.Full.test.rttm)                    | [eval](reproducible_research/AISHELL.SpeakerDiarization.Full.test.eval)                    |
+| [AMI *Mix-Headset*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 18.21                              | 3.28                        | 11.07                              | 3.87                                | [RTTM](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.rttm)          | [eval](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.eval)          |
+| [AMI *Array1-01*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup)   | 29.00                              | 2.71                        | 21.61                              | 4.68                                | [RTTM](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.rttm)      | [eval](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.eval)      |
+| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) [*Part2*](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1)         | 30.24                              | 3.71                        | 16.86                              | 9.66                                | [RTTM](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.rttm)       | [eval](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.eval)       |
+| [DIHARD 3 *Full*](https://arxiv.org/abs/2012.01477)                                                                                | 20.99                              | 4.25                        | 10.74                              | 6.00                                | [RTTM](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.rttm)             | [eval](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.eval)             |
+| [REPERE *Phase 2*](https://islrn.org/resources/360-758-359-485-0/)                                                                 | 12.62                              | 1.55                        | 3.30                               | 7.76                                | [RTTM](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.rttm)             | [eval](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.eval)             |
+| [VoxConverse *v0.0.2*](https://github.com/joonson/voxconverse)                                                                     | 12.76                              | 3.45                        | 3.85                               | 5.46                                | [RTTM](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.rttm) | [eval](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.eval) |
+
+
+## Support
+
+For commercial enquiries and scientific consulting, please contact [me](mailto:herve@niderb.fr).  
+For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/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},
+}
+```
+
+```bibtex
+@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},
+}
+```