pyannote.audio
PyTorch
TensorBoard
pyannote
pyannote-audio-model
audio
voice
speech
speaker
speaker-recognition
speaker-verification
speaker-identification
speaker-embedding
Instructions to use dereklvlv/embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use dereklvlv/embedding with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("dereklvlv/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
| - 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 | |