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🎹 Speaker embedding

Relies on 2.1: see installation instructions.

This model is based on the canonical x-vector TDNN-based architecture, but with filter banks replaced with trainable SincNet features. See XVectorSincNet architecture for implementation details.

Basic usage

# 1. visit and accept user conditions
# 2. visit to create an access token
# 3. instantiate pretrained model
from import Model
model = Model.from_pretrained("pyannote/embedding", 
from 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.

Using cosine distance directly, this model reaches 2.8% equal error rate (EER) on VoxCeleb 1 test set.
This is without voice activity detection (VAD) nor probabilistic linear discriminant analysis (PLDA). Expect even better results when adding one of those.

Advanced usage

Running on GPU

inference = Inference(model, window="whole", device="cuda")
embedding = inference("audio.wav")

Extract embedding from an excerpt

from import Inference, 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.

Extract embeddings using a sliding window

from 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].


For commercial enquiries and scientific consulting, please contact me.
For technical questions and bug reports, please check Github repository.


  Title = {{ 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},
    author="Coria, Juan M. and Bredin, Herv{\'e} and Ghannay, Sahar and Rosset, Sophie",
    editor="Espinosa-Anke, Luis and Mart{\'i}n-Vide, Carlos and Spasi{\'{c}}, Irena",
    title="{A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification}",
    booktitle="Statistical Language and Speech Processing",
    publisher="Springer International Publishing",
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