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Update README.md

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Eren Gölge 2019-11-01 12:52:12 +01:00 committed by GitHub
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@ -4,9 +4,11 @@ This is an implementation of https://arxiv.org/abs/1710.10467. This model can be
![](https://user-images.githubusercontent.com/1402048/64603079-7fa5c100-d3c8-11e9-88e7-88a00d0e37d1.png) ![](https://user-images.githubusercontent.com/1402048/64603079-7fa5c100-d3c8-11e9-88e7-88a00d0e37d1.png)
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. 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. - 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``` - 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.tar 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. - Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth.tar 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 - Watch training on Tensorboard as in TTS