pyannote.audio
PyTorch
pyannote
pyannote-audio-model
wespeaker
audio
voice
speech
speaker
speaker-recognition
speaker-verification
speaker-identification
speaker-embedding
Instructions to use befuturepl/embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use befuturepl/embedding with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("befuturepl/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
| model: | |
| _target_: pyannote.audio.models.embedding.WeSpeakerResNet34 | |
| sample_rate: 16000 | |
| num_channels: 1 | |
| num_mel_bins: 80 | |
| frame_length: 25 | |
| frame_shift: 10 | |
| dither: 0.0 | |
| window_type: hamming | |
| use_energy: false | |