pyannote-rs
Pyannote audio diarization in Rust
Features
- Compute 1 hour of audio in less than a minute on CPU.
- Faster performance with DirectML on Windows and CoreML on macOS.
- Accurate timestamps with Pyannote segmentation.
- Identify speakers with wespeaker embeddings.
Install
cargo add pyannote-rs
Usage
See Building
Examples
See examples
pyannote-rs uses 2 models for speaker diarization:
-
Segmentation: segmentation-3.0 identifies when speech occurs.
-
Speaker Identification: wespeaker-voxceleb-resnet34-LM identifies who is speaking.
Inference is powered by onnxruntime.
- The segmentation model processes up to 10s of audio, using a sliding window approach (iterating in chunks).
- The embedding model processes filter banks (audio features) extracted with knf-rs.
Speaker comparison (e.g., determining if Alice spoke again) is done using cosine similarity.
Credits
Big thanks to pyannote-onnx and kaldi-native-fbank