HiFiSinger

This code is an unofficial implementation of HiFiSinger. The algorithm is based on the following papers:

Chen, J., Tan, X., Luan, J., Qin, T., & Liu, T. Y. (2020). HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis. arXiv preprint arXiv:2009.01776.
Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. Y. (2019). Fastspeech: Fast, robust and controllable text to speech. Advances in Neural Information Processing Systems, 32, 3171-3180.
Yamamoto, R., Song, E., & Kim, J. M. (2020, May). Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6199-6203). IEEE.

Requirements

Please see the 'requirements.txt'.

Structure

Generator

• In training, length regulator use target duration.

Discriminator

• HiFiSinger uses Sub Frequency GAN(SF-GAN).
• The frequency range of sampling is fixed and length range is randomized.

Used dataset

• Code verification was conducted through a limited-sized, private Korean dataset.
  • Thus, current Pattern_Generator.py and Datasets.py are based on the Korean.
• Please report the information about any available open source dataset.
  • The set of midi files with syncronized lyric and high resolution vocal wave files

Hyper parameters

Before proceeding, please set the pattern, inference, and checkpoint paths in 'Hyper_Parameters.yaml' according to your environment.

• Sound

  • Setting basic sound parameters.

• Tokens

  • The number of Lyric token.

• Max_Note

  • The highest note value for embedding.

• Min/Max duration

  • Mel length which model use.
  • Min duration is used at pattern generating only.

• Encoder

  • Setting the encoder.

• Duration_Predictor

  • Setting for duration predictor

• Decoder

  • Setting for decoder.

• Discriminator

  • Setting for discriminator
  • In frequency range, frequency is the index of mel dimension.
    • The index must be equal or less than Sould.Mel_Dim.

• Vocoder_Path

  • Setting the traced vocoder path.
  • To generate this, please check Here

• Train

  • Setting the parameters of training.

• Use_Mixed_Precision

  • Setting mix precision usage.
  • Need a Nvidia-Apex.

• Inference_Batch_Size

  • Setting the batch size when inference

• Inference_Path

  • Setting the inference path

• Checkpoint_Path

  • Setting the checkpoint path

• Log_Path

  • Setting the tensorboard log path

• Device

  • Setting which GPU device is used in multi-GPU enviornment.
  • Or, if using only CPU, please set '-1'. (But, I don't recommend while training.)

Generate pattern

• There is no available open source dataset.

Inference file path while training for verification.

• Inference_for_Training
  • There are two examples for inference.
  • It is midi file based script.

Run

Command

python Train.py -s <int>

• -hp <path>

  • The hyper paramter file path
  • This is required.

• -s <int>

  • The resume step parameter.
  • Default is 0.