mirror of https://github.com/coqui-ai/TTS.git
12 lines
1.1 KiB
Markdown
12 lines
1.1 KiB
Markdown
### Speaker embedding (Experimental)
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This is an implementation of https://arxiv.org/abs/1710.10467. This model can be used for voice and speaker embedding. So you can generate d-vectors for multi-speaker TTS or prune bad samples from your TTS dataset. Below is an example showing embedding results of various speakers. You can generate the same plot with the provided notebook.
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To run the code, you need to follow the same flow as in TTS.
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- Define 'config.json' for your needs. Note that, audio parameters should match your TTS model.
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- Example training call ```python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360```
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- 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.
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- Watch training on Tensorboard as in TTS |