Wespeaker Voxceleb Resnet34 LM

--- tags: - pyannote - pyannote-audio - pyannote-audio-model - wespeaker - audio - voice - speech - speaker - speaker-recognition - speaker-verification - speaker-identification - speaker-embedding datasets: - voxceleb license: cc-by-4.0 inference: false --- Using this open-source model in production? Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. # 🎹 Wrapper around wespeaker-voxceleb-resnet34-LM This model requires `pyannote.audio` version 3.1 or higher. This is a wrapper around [WeSpeaker](https://github.com/wenet-e2e/wespeaker) `wespeaker-voxceleb-resnet34-LM` pretrained speaker embedding model, for use in `pyannote.audio`. ## Basic usage ```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. ``` ## Advanced usage ### Running on GPU ```python import torch inference.to(torch.device("cuda")) embedding = inference("audio.wav") ``` ### Extract embedding from an excerpt ```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. ``` ### Extract embeddings using a sliding window ```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]. ``` ## License According to [this page](https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md): > The pretrained model in WeNet follows the license of it's corresponding dataset. For example, the pretrained model on VoxCeleb follows Creat...