#speaker #embedding #diarization #identify #macos #timestamp #pyannote

pyannote-rs

Speaker diarization using pyannote in Rust

18 releases

0.2.7 Aug 18, 2024
0.2.6 Aug 12, 2024
0.1.9 Aug 7, 2024

#132 in Audio

Download history 948/week @ 2024-08-05 378/week @ 2024-08-12 97/week @ 2024-08-19 47/week @ 2024-08-26

1,470 downloads per month
Used in sherpa-rs

MIT license

15KB
211 lines

pyannote-rs

Crates License

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

How it works

pyannote-rs uses 2 models for speaker diarization:

  1. Segmentation: segmentation-3.0 identifies when speech occurs.
  2. 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

Dependencies

~3–11MB
~117K SLoC