模型:
tawkit/phil-pyannote-speaker-diarization-endpoint
任务:
自动语音识别类库:
pyannote.audio数据集:
ami dihard voxconverse aishell repere voxceleb 3Avoxceleb 3Arepere 3Aaishell 3Avoxconverse 3Adihard 3Aami其他:
pyannote pyannote-audio-pipeline audio voice speech speaker speaker-diarization speaker-change-detection 语音活动检测 overlapped-speech-detection预印本库:
arxiv:2012.01477许可:
mitRelies on pyannote.audio 2.0: see installation instructions .
# 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)
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)
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.
This pipeline is benchmarked on a growing collection of datasets.
Processing is fully automatic:
... with the least forgiving diarization error rate (DER) setup (named "Full" in this paper ):
Benchmark | 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 v0.0.2 | 12.76 | 3.45 | 3.85 | 5.46 | RTTM | eval |
For commercial enquiries and scientific consulting, please contact me . For technical questions and bug reports , please check pyannote.audio Github repository.
@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}, }