wespeaker-voxceleb-resnet34-LM EU AI Act Compliance Profile

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🎹 Wrapper around wespeaker-voxceleb-resnet34-LM

This model requires pyannote.audio version 3.1 or higher.

This is a wrapper around WeSpeaker wespeaker-voxceleb-resnet34-LM pretrained speaker embedding model, for use in pyannote.audio.

Basic usage

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

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.

Advanced usage

Running on GPU

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

Extract embedding from an excerpt

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.

Extract embeddings using a sliding window

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].

License

According to [this page](https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.m

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