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"
Train & Tensorboard
- 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.