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

kan-bayashi/ParallelWaveGAN(https://github.com/kan-bayashi/ParallelWaveGAN)
Parallel WaveGAN(https://arxiv.org/pdf/1910.11480.pdf)
GAN-TTS: HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL(https://arxiv.org/pdf/1909.11646.pdf)