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--- |
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tags: |
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- pyannote |
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- pyannote-audio |
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- pyannote-audio-model |
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- audio |
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- voice |
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- speech |
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- speaker |
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- speaker-segmentation |
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- voice-activity-detection |
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- overlapped-speech-detection |
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- resegmentation |
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datasets: |
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- ami |
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- dihard |
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- voxconverse |
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license: mit |
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inference: false |
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--- |
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# 🎹 Speaker segmentation |
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Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045)*, |
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by Hervé Bredin and Antoine Laurent. |
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[Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space. |
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## Support |
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For commercial enquiries and scientific consulting, please contact [me](mailto:herve@niderb.fr). |
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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. |
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## Usage |
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Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation). |
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### Voice activity detection |
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```python |
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from pyannote.audio.pipelines import VoiceActivityDetection |
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pipeline = VoiceActivityDetection(segmentation="anilbs/segmentation") |
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HYPER_PARAMETERS = { |
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# onset/offset activation thresholds |
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"onset": 0.5, "offset": 0.5, |
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# remove speech regions shorter than that many seconds. |
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"min_duration_on": 0.0, |
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# fill non-speech regions shorter than that many seconds. |
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"min_duration_off": 0.0 |
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} |
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pipeline.instantiate(HYPER_PARAMETERS) |
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vad = pipeline("audio.wav") |
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# `vad` is a pyannote.core.Annotation instance containing speech regions |
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``` |
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### Overlapped speech detection |
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```python |
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from pyannote.audio.pipelines import OverlappedSpeechDetection |
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pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation") |
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pipeline.instantiate(HYPER_PARAMETERS) |
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osd = pipeline("audio.wav") |
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# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions |
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``` |
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### Resegmentation |
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```python |
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from pyannote.audio.pipelines import Resegmentation |
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pipeline = Resegmentation(segmentation="pyannote/segmentation", |
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diarization="baseline") |
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pipeline.instantiate(HYPER_PARAMETERS) |
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resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline}) |
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# where `baseline` should be provided as a pyannote.core.Annotation instance |
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``` |
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### Raw scores |
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```python |
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from pyannote.audio import Inference |
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inference = Inference("pyannote/segmentation") |
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segmentation = inference("audio.wav") |
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# `segmentation` is a pyannote.core.SlidingWindowFeature |
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# instance containing raw segmentation scores like the |
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# one pictured above (output) |
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``` |
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## Reproducible research |
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In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation |
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"](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters: |
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| Voice activity detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` | |
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| ------------------------ | ------- | -------- | ----------------- | ------------------ | |
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| AMI Mix-Headset | 0.684 | 0.577 | 0.181 | 0.037 | |
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| DIHARD3 | 0.767 | 0.377 | 0.136 | 0.067 | |
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| VoxConverse | 0.767 | 0.713 | 0.182 | 0.501 | |
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| Overlapped speech detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` | |
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| --------------------------- | ------- | -------- | ----------------- | ------------------ | |
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| AMI Mix-Headset | 0.448 | 0.362 | 0.116 | 0.187 | |
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| DIHARD3 | 0.430 | 0.320 | 0.091 | 0.144 | |
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| VoxConverse | 0.587 | 0.426 | 0.337 | 0.112 | |
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| Resegmentation of VBx | `onset` | `offset` | `min_duration_on` | `min_duration_off` | |
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| --------------------- | ------- | -------- | ----------------- | ------------------ | |
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| AMI Mix-Headset | 0.542 | 0.527 | 0.044 | 0.705 | |
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| DIHARD3 | 0.592 | 0.489 | 0.163 | 0.182 | |
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| VoxConverse | 0.537 | 0.724 | 0.410 | 0.563 | |
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Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories. |
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## Citation |
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```bibtex |
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@inproceedings{Bredin2021, |
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Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}}, |
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Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine}, |
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Booktitle = {Proc. Interspeech 2021}, |
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Address = {Brno, Czech Republic}, |
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Month = {August}, |
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Year = {2021}, |
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``` |
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```bibtex |
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@inproceedings{Bredin2020, |
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Title = {{pyannote.audio: neural building blocks for speaker diarization}}, |
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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}, |
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Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, |
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Address = {Barcelona, Spain}, |
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Month = {May}, |
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Year = {2020}, |
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} |
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``` |
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