Pyannote ONNX
Project description
Speaker Diarization
pyannote-audio is an open-source toolkit written in Python for speaker diarization.
pyannote-onnx is used to convert the pretrained model defined in PyTorch into the ONNX format and then run it with ONNX Runtime (in C++ or Python).
Only Python 3.8+ is supported.
Usage
- Download the pretrained model from Hugging Face pyannote/segmentation-3.0.
- Export the pretrained model to ONNX model.
- Run the ONNX model with ONNX Runtime in C++ or Python.
$ pip install torch onnx https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip
$ python export_onnx.py pytorch_model.bin segmentation-3.0.onnx
$ pip install pyannote-onnx
$ diarize data/test_16k.wav --plot
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyannote-onnx-0.1.1.tar.gz
(8.7 kB
view details)
File details
Details for the file pyannote-onnx-0.1.1.tar.gz.
File metadata
- Download URL: pyannote-onnx-0.1.1.tar.gz
- Upload date:
- Size: 8.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.10.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 615d613e4d539eae3f6689950599be69d66bd6eefa812321b0f7c09606b310ca |
|
| MD5 | b05fce90df63df1a4e0542ff0372ae74 |
|
| BLAKE2b-256 | 0919fa432b95cc6fa89be8ad44d0a8d7230d374892404e44882b06534a0d07f4 |
