pyannote-rs
| Crates.io | pyannote-rs |
| lib.rs | pyannote-rs |
| version | |
| source | src |
| created_at | 2024-08-06 15:57:20.209334 |
| updated_at | 2024-11-30 01:47:44.305686 |
| description | Speaker diarization using pyannote in Rust |
| homepage | |
| repository | |
| max_upload_size | |
| id | 1327464 |
| Cargo.toml error: | TOML parse error at line 17, column 1 | 17 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include` |
| size | 0 |
(thewh1teagle)
documentation
README
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
How it works
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