pyannote.audio: neural building blocks for speaker diarization
Hervé Bredin, Ruiqing Yin, Juan Manuel Coria, Gregory Gelly, Pavel Korshunov, Marvin Lavechin, Diego Fustes, Hadrien Titeux, Wassim Bouaziz, Marie-Philippe Gill
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ReproduceCode
- github.com/pyannote/pyannote-audioOfficialIn paperpytorch★ 9,388
- github.com/MarvinLvn/voice-type-classifiernone★ 50
- github.com/muskang48/Speaker-Diarizationtf★ 0
Abstract
We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. pyannote.audio also comes with pre-trained models covering a wide range of domains for voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding -- reaching state-of-the-art performance for most of them.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| AMI | pyannote (waveform) | DER(%) | 6 | — | Unverified |
| AMI | pyannote (MFCC) | DER(%) | 6.3 | — | Unverified |
| DIHARD | pyannote (MFCC) | DER(%) | 10.5 | — | Unverified |
| DIHARD | pyannote (waveform) | DER(%) | 9.9 | — | Unverified |
| DIHARD | Baseline (the best result in the literature as of Oct.2019) | DER(%) | 11.2 | — | Unverified |
| ETAPE | pyannote (MFCC) | DER(%) | 5.6 | — | Unverified |
| ETAPE | Baseline | DER(%) | 7.7 | — | Unverified |
| ETAPE | pyannote (waveform) | DER(%) | 4.9 | — | Unverified |