Wespeaker Voxceleb Resnet34 LM is a speaker embedding model hosted on Hugging Face under the pyannote organization. It produces vector representations from audio that support speaker recognition, verification, and identification tasks.
The model is delivered as a PyTorch artifact that loads directly through the pyannote.audio library. Code examples show instantiation via Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM"), followed by an Inference wrapper that accepts a full audio file or a timed excerpt defined with a Segment object. It runs on CPU by default and can be moved to GPU. The underlying architecture is identified in the repository tags as a ResNet34 backbone trained on the VoxCeleb corpus.
It carries a cc-by-4.0 license. The repository provides a model card with usage instructions, Colab and Kaggle notebooks, and links for integration into local applications or inference providers. The page lists associated tags including voxceleb, pyannote-audio-model, wespeaker, audio, voice, speech, speaker-recognition, speaker-verification, speaker-identification, and speaker-embedding.
Developers and researchers incorporate the model into audio analysis pipelines that require speaker-level features. The Hugging Face page records 154 likes and belongs to an organization with 2.9k followers.
Wespeaker Voxceleb Resnet34 LM sits in PulseGate's Other AI category. Automatically identifying and verifying speakers in audio recordings. It is built as an open-source project for audio processing researchers and developers. Wespeaker Voxceleb Resnet34 LM is open source under the Apache-2.0 license. It runs on the web and the command line.
Behind Wespeaker Voxceleb Resnet34 LM is pyannote, and the product first shipped in 2021. Development happens publicly on GitHub with 1.4k stars and 8 commits in the last 90 days. Key capabilities include speaker recognition, speaker verification, and speaker identification.
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