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FastSpeech-Pytorch

The Implementation of FastSpeech Based on Pytorch.

Update (2020/07/20)

  1. Optimize the training process.
  2. Optimize the implementation of length regulator.
  3. Use the same hyper parameter as FastSpeech2.
  4. The measures of the 1, 2 and 3 make the training process 3 times faster than before.
  5. Better speech quality.

Model

<div style="text-align: center"> <img src="img/fastspeech_structure.png" style="max-width:100%;"> </div>

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Prepare Dataset

  1. Download and extract LJSpeech dataset.
  2. Put LJSpeech dataset in data.
  3. Unzip alignments.zip.
  4. Put Nvidia pretrained waveglow model in the waveglow/pretrained_model and rename as waveglow_256channels.pt;
  5. Run python3 preprocess.py.

Training

Run python3 train.py.

Evaluation

Run python3 eval.py.

Notes

Reference

Repository

Paper