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GAN-TTS

A pytorch implementation of the GAN-TTS: HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL NETWORKS(https://arxiv.org/pdf/1909.11646.pdf)

Prepare dataset

  • Download dataset for training. This can be any wav files with sample rate 24000Hz.
  • Edit configuration in utils/audio.py (hop_length must remain unchanged)
  • Process data: python process.py --wav_dir="wavs" --output="data"

Train & Tensorboard

  • python train.py --input="data/train"
  • tensorboard --logdir logdir

Inference

  • python generate.py --input="data/test"

Result

  • You can find the results in the samples directory.

Attention

  • I did not use the loss function mentioned in the paper. I modified the loss function and learn from ParallelWaveGAN(https://arxiv.org/pdf/1910.11480.pdf).
  • I did not use linguistic features, I use mel spectrogram, so the model can be considered a vocoder.

Notes

  • This is not official implementation, some details are not necessarily correct.
  • In order to accelerate convergence, I modified some network structures and loss functions.

Reference

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A pytroch implementation of the GAN-TTS: HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL NETWORKS

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