This is the PyTorch implementation of speaker embedding (d-vector) trained with GE2E loss.

The original paper about GE2E loss could be found here: Generalized End-to-End Loss for Speaker Verification

To use the script provided here, you have to organize your raw data in this way:

• all utterances from a speaker should be put under a directory (speaker directory)
• all speaker directories should be put under a directory (root directory)
• speaker directory can have subdirectories and utterances can be placed under subdirectories

You have to specify two things here:

• use -s or --save_dir to specify the directory for saving processed utterances
• use -c or --config_path to specify the path to the configuration for Audiotoolkit module

And you can specify the maximum amount of utterances to be extracted and preprocessed for a single speaker, e.g. -m 50.

And a good thing about this script is that you can extract utterances from multiple root directories. For example:

python prepare.py -s data-dir -c toolkit_config.yaml VCTK-Corpus/wav48 LibriSpeech/train-clean-360

Only DATA_DIR, MODEL_DIR and CONFIG_PATH have to be specified here. For example:

python train.py data-dir model-dir dvector_config.yaml

Note that the configuration needed here is different from the one for preprocessing. For more details, check the usage with python train.py -h. During training, event logs will be put under MODEL_DIR.

To continue the training from a saved checkpoint, just specify the checkpoint path with -c or --checkpoint_path. Note that you can still specify other optional arguments because they might be different from the ones in the previous training.

The dimension reduction result (using t-SNE) of some utterances from LibriSpeech.

The GE2E-Loss module is first borrowed from cvqluu/GE2E-Loss and then rewritten and optimized for speed by myself.