pyannote.audio
PyTorch
TensorBoard
pyannote
pyannote-audio-model
audio
voice
speech
speaker
speaker-recognition
speaker-verification
speaker-identification
speaker-embedding
Instructions to use beclab/embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use beclab/embedding with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("beclab/embedding") 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
File size: 315 Bytes
9cbf575 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | - protocol=VoxCeleb.SpeakerVerification.VoxCeleb_X
- task=SpeakerEmbedding
- task.num_workers=20
- task.min_duration=2
- task.duration=5.
- task.num_classes_per_batch=64
- task.num_chunks_per_class=4
- task.margin=10.0
- task.scale=50.
- model=XVectorSincNet
- trainer.gpus=1
- +augmentation=background_then_reverb
|