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
Download hparams.yaml from beclab/embedding: direct link, hf CLI and curl.
- Browser
- Download file 93 Bytes
-
https://huggingface.co/beclab/embedding/resolve/main/hparams.yaml
- Command line
-
hf download hf://beclab/embedding/hparams.yaml
-
curl -L -o hparams.yaml https://huggingface.co/beclab/embedding/resolve/main/hparams.yaml
93 Bytes
| sample_rate: 16000 | |
| num_channels: 1 | |
| sincnet: | |
| stride: 10 | |
| sample_rate: 16000 | |
| dimension: 512 | |