pyannote.audio
PyTorch
ONNX
pyannote
pyannote-audio-model
wespeaker
audio
voice
speech
speaker
speaker-recognition
speaker-verification
speaker-identification
speaker-embedding
Instructions to use eek/wespeaker-voxceleb-resnet293-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use eek/wespeaker-voxceleb-resnet293-LM with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("eek/wespeaker-voxceleb-resnet293-LM") inference = Inference(model) # inference on the whole file inference("file.wav") # inference on an excerpt from pyannote.core import Segment excerpt = Segment(start=2.0, end=5.0) inference.crop("file.wav", excerpt) - Notebooks
- Google Colab
- Kaggle
Download speaker-embedding.onnx from eek/wespeaker-voxceleb-resnet293-LM: direct link, hf CLI and curl.
- Browser
- Download file 114 MB
-
https://huggingface.co/eek/wespeaker-voxceleb-resnet293-LM/resolve/main/speaker-embedding.onnx
- Command line
-
hf download hf://eek/wespeaker-voxceleb-resnet293-LM/speaker-embedding.onnx
-
curl -L -o speaker-embedding.onnx https://huggingface.co/eek/wespeaker-voxceleb-resnet293-LM/resolve/main/speaker-embedding.onnx
114 MB
- Xet hash:
- 941163d29c460f7c6e4bb7abee7f683706abd65285e7a992deec4bf57df5914f
- Size of remote file:
- 114 MB
- SHA256:
- dbb1ccc7754caff552ebc46347a51aaee2669bb24efc740e665d1a1133d20e98
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