- BREAKING: switch to new pyannote.pipeline package
- BREAKING: add unified FeatureExtraction base class
- feat: add support for on-the-fly data augmentation
- setup: switch to librosa 0.6
- fix: fix regression in Precomputed.call (#110, #105)
- chore: switch from keras to pytorch (with tensorboard support)
- improve: faster & better traning (
AutoLR
, advanced learning rate schedulers, improved batch generators) - feat: add tunable speaker diarization pipeline (with its own tutorial)
- chore: drop support for Python 2 (use Python 3.6 or later)
- feat: add python 3 support
- chore: rewrite neural speaker embedding using autograd
- feat: add new embedding architectures
- feat: add new embedding losses
- chore: switch to Keras 2
- doc: add tutorial for (MFCC) feature extraction
- doc: add tutorial for (LSTM-based) speech activity detection
- doc: add tutorial for (LSTM-based) speaker change detection
- doc: add tutorial for (TristouNet) neural speaker embedding
- feat: add LSTM-based speech activity detection
- feat: add LSTM-based speaker change detection
- improve: refactor LSTM-based speaker embedding
- feat: add librosa basic support
- feat: add SMORMS3 optimizer
- feat: add 'covariance_type' option to BIC segmentation
- chore: rename sequence generator in preparation of the release of TristouNet reproducible research package.
- first public version