pyannote.audio
ONNX
pyannote
pyannote-audio-model
audio
voice
speech
speaker
speaker-recognition
speaker-verification
speaker-identification
speaker-embedding
Instructions to use Library-Mutsumi/pyannote-embedding-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use Library-Mutsumi/pyannote-embedding-onnx with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("Library-Mutsumi/pyannote-embedding-onnx") 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
metadata
base_model: pyannote/embedding
datasets:
- voxceleb
extra_gated_fields:
Company/university: text
I plan to use this model for (task, type of audio data, etc): text
Website: text
extra_gated_prompt: >-
The collected information will help acquire a better knowledge of
pyannote.audio userbase and help its maintainers apply for grants to improve
it further. If you are an academic researcher, please cite the relevant papers
in your own publications using the model. If you work for a company, please
consider contributing back to pyannote.audio development (e.g. through
unrestricted gifts). We also provide scientific consulting services around
speaker diarization and machine listening.
inference: false
license: mit
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-recognition
- speaker-verification
- speaker-identification
- speaker-embedding
- onnx
This is the ONNX exported version of pyannote/embedding.