In our paper, we proposed HiFi-GAN: a GAN-based model capable of generating high fidelity speech efficiently.
We provide our implementation and pretrained models as open source in this repository.

Abstract : Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. Although such methods improve the sampling efficiency and memory usage, their sample quality has not yet reached that of autoregressive and flow-based generative models. In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. As speech audio consists of sinusoidal signals with various periods, we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality. A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9 times faster than real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen speakers and end-to-end speech synthesis. Finally, a small footprint version of HiFi-GAN generates samples 13.4 times faster than real-time on CPU with comparable quality to an autoregressive counterpart.

Visit our demo website for audio samples.

1. Python >= 3.6
2. Clone this repository.
3. Install python requirements. Please refer requirements.txt
4. Download and extract this multi-speaker/multi-lingual dataset. And move all wav files to /my/path/lb-de-fr-en-pt-12800-TTS-CORPUS/wavs
5. Download and extract PB2009 (ask Thomas Hueber)

We'll first train a model on our multi-speaker/multi-lingual dataset.

DATA_PATH=/my/path/lb-de-fr-en-pt-12800-TTS-CORPUS
# 1) Create train/val/test split
python split_data --data_path ${DATA_PATH}/wavs --test_prop 0.1 --val_prop 0.1
# 2) Extract mel-spectrograms 
python extract_mel.py --data_path ${DATA_PATH}/wavs \
   --out_path ${DATA_PATH}/mels \
   --config config_16k.json
# 3) Train HiFi-GAN
python train.py --input_wavs_dir ${DATA_PATH}/wavs --input_training_file ${DATA_PATH}/train.txt \
  --input_validation_file ${DATA_PATH}/val.txt --input_mels_dir ${DATA_PATH}/mels \
  --config config_16k.json

Checkpoints and copy of the configuration will be saved in cp_hifigan.

We'll fine-tune it on PB2009

DATA_PATH=/linkhome/rech/genscp01/uow84uh/agent/datasets/pb2009
# 1) Create train/val/test split
python split_data.py --data_path ${DATA_PATH}/wav --test_prop 0.1 --val_prop 0.1
# 2) Extract mel-spectrogram
# You can use the same instruction as above, but should already be generated using preprocess_datasets.py
# 2) Train HiFi-GAN
python train.py --input_wavs_dir ${DATA_PATH}/wav --input_training_file ${DATA_PATH}/train.txt \
  --input_validation_file ${DATA_PATH}/val.txt --input_mels_dir ${DATA_PATH}/mel \
  --config config_16k.json --fine_tuning True

1. Make test_files directory and copy wav files into the directory.
2. Run the following command.

   python inference.py --checkpoint_file [generator checkpoint file path]
   

Generated wav files are saved in generated_files by default.
You can change the path by adding --output_dir option.

1. Make test_mel_files directory and copy generated mel-spectrogram files into the directory.
   You can generate mel-spectrograms using Tacotron2, Glow-TTS and so forth.
2. Run the following command.

   python inference_e2e.py --checkpoint_file [generator checkpoint file path]
   

Generated wav files are saved in generated_files_from_mel by default.
You can change the path by adding --output_dir option.

We referred to WaveGlow, MelGAN and Tacotron2 to implement this.