| ### Speaker Encoder | |
| This is an implementation of https://arxiv.org/abs/1710.10467. This model can be used for voice and speaker embedding. | |
| With the code here you can generate d-vectors for both multi-speaker and single-speaker TTS datasets, then visualise and explore them along with the associated audio files in an interactive chart. | |
| Below is an example showing embedding results of various speakers. You can generate the same plot with the provided notebook as demonstrated in [this video](https://youtu.be/KW3oO7JVa7Q). | |
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| Download a pretrained model from [Released Models](https://github.com/mozilla/TTS/wiki/Released-Models) page. | |
| To run the code, you need to follow the same flow as in TTS. | |
| - Define 'config.json' for your needs. Note that, audio parameters should match your TTS model. | |
| - Example training call ```python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360``` | |
| - Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth model/config/path/config.json dataset/path/ output_path``` . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files. | |
| - Watch training on Tensorboard as in TTS | |