Suported ONNX runtime for pyannote.audio
Only Python 3.8+ is supported.
# for CPU
pip install onnxruntime
# for GPU, check version on: https://onnxruntime.ai/docs/build/eps.html#cuda
pip install onnxruntime-gpu
# install pyannote
pip install -e .# 1. Download pytorch model (.bin) from https://huggingface.co/pyannote/segmentation/blob/main/pytorch_model.bin
wget https://huggingface.co/pyannote/segmentation/blob/main/pytorch_model.bin -O pytorch_model/vad_model.bin
# 2. Export
python onnx/export_onnx.py -i pytorch_model/vad_model.bin -o onnx_model/
# use onnx model (2x faster)
python vad.py -m onnx_model/vad_model.onnx -i tests/data/test_vad.wav
# mean time cost = 5.32921104
# use pytorch model
python vad.py -m onnx_model/vad_model.bin -i tests/data/test_vad.wav
# mean time cost = 9.56711404Test file tests/data/test_vad.wav with duration 6m15s
- CPU Intel(R) Xeon(R) CPU E5-2683 v3 @ 2.00GHz
- GPU Nvidia GTX 1080Ti
Batch size 32
| Backend | CPU time (s) | GPU time (s) |
|---|---|---|
| PyTorch | 12.0 | 1.5 |
| ONNX | 4.33 | NA |
Batch size 64
| Backend | CPU time (s) | GPU time (s) |
|---|---|---|
| PyTorch | inf | 1.99 |
| ONNX | 4.02 | NA |
If you use pyannote.audio please use the following citations:
@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},
Year = {2020},
}@inproceedings{Bredin2021,
Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
Booktitle = {Proc. Interspeech 2021},
Year = {2021},
}