| | --- |
| | tags: |
| | - pyannote |
| | - pyannote-audio |
| | - pyannote-audio-pipeline |
| | - audio |
| | - voice |
| | - speech |
| | - speaker |
| | - speaker-diarization |
| | - speaker-change-detection |
| | - voice-activity-detection |
| | - overlapped-speech-detection |
| | 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}, |
| | } |
| | ``` |
| |
|
