python
instantiate pretrained model
from pyannote.audio import Model
model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM")

python
from pyannote.audio import Inference
inference = Inference(model, window="whole")
embedding1 = inference("speaker1.wav")
embedding2 = inference("speaker2.wav")
embeddingX is (1 x D) numpy array extracted from the file as a whole.


from scipy.spatial.distance import cdist
distance = cdist(embedding1, embedding2, metric="cosine")[0,0]
distance is a float describing how dissimilar speakers 1 and 2 are.

python
import torch
inference.to(torch.device("cuda"))
embedding = inference("audio.wav")

python
from pyannote.audio import Inference
from pyannote.core import Segment
inference = Inference(model, window="whole")
excerpt = Segment(13.37, 19.81)
embedding = inference.crop("audio.wav", excerpt)
embedding is (1 x D) numpy array extracted from the file excerpt.

python
from pyannote.audio import Inference
inference = Inference(model, window="sliding",
                      duration=3.0, step=1.0)
embeddings = inference("audio.wav")
embeddings is a (N x D) pyannote.core.SlidingWindowFeature
embeddings[i] is the embedding of the ith position of the
sliding window, i.e. from [i * step, i * step + duration].

bibtex
@inproceedings{Wang2023,
  title={Wespeaker: A research and production oriented speaker embedding learning toolkit},
  author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin},
  booktitle={ICASSP 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

bibtex
@inproceedings{Bredin23,
  author={Hervé Bredin},
  title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={1983--1987},
  doi={10.21437/Interspeech.2023-105}
}