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

• 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"

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

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

• You can find the results in the samples directory.

• 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.

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

• 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)