Multispeaker GlowTTS

• This code is a replication of official Glow TTS code. If you want to use Glow TTS model, I recommend that you refer to the official code.
• The following is the paper I referred:

  • Glow TTS

    • Kim, J., Kim, S., Kong, J., & Yoon, S. (2020). Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search. arXiv preprint arXiv:2005.11129.
    • Shaw, P., Uszkoreit, J., & Vaswani, A. (2018). Self-attention with relative position representations. arXiv preprint arXiv:1803.02155.

  • GE2E speaker embedding

    • Wan, L., Wang, Q., Papir, A., & Moreno, I. L. (2017). Generalized end-to-end loss for speaker verification. arXiv preprint arXiv:1710.10467.
    • Jia, Y., Zhang, Y., Weiss, R. J., Wang, Q., Shen, J., Ren, F., ... & Wu, Y. (2018). Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. arXiv preprint arXiv:1806.04558.
    • Qian, K., Zhang, Y., Chang, S., Yang, X., & Hasegawa-Johnson, M. (2019). Zero-shot voice style transfer with only autoencoder loss. arXiv preprint arXiv:1905.05879.

  • Prosody encoder (Global style token layer)

    • Wang, Y., Stanton, D., Zhang, Y., Skerry-Ryan, R. J., Battenberg, E., Shor, J., ... & Saurous, R. A. (2018). Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis. arXiv preprint arXiv:1803.09017.

  • Gradient reversal layer

    • Zhang, Y., Weiss, R. J., Zen, H., Wu, Y., Chen, Z., Skerry-Ryan, R. J., ... & Ramabhadran, B. (2019). Learning to speak fluently in a foreign language: Multilingual speech synthesis and cross-language voice cloning. arXiv preprint arXiv:1907.04448.
    • Jung, S., & Kim, H. (2020). Pitchtron: Towards audiobook generation from ordinary people's voices. arXiv preprint arXiv:2005.10456.

Requirements

• torch >= 1.5.1

• tensorboardX >= 2.0

• librosa >= 0.7.2

• matplotlib >= 3.1.3

• Optional for loss flow

  • tensorboard >= 2.2.2

Structure

Vanilla mode (Single speaker GlowTTS)

Training

Inference

Speaker embedding mode

Training

Inference

Prosody encoding mode (GST GlowTTS)

Training

Inference

Gradient reversal mode (Voice cloning GlowTTS - Failed)

Training

Inference

Used dataset

• Currently uploaded code is compatible with the following datasets.
• The O marks to the left of the dataset name are the dataset actually used in the uploaded result.

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.
  • Some paramters like pitch are not used in current code. These are for future works.

• Use_Cython_Alignment

  • Setting which implementation of Monotonic alignment search to use
  • If true, the cython implementation of official code will be used.
  • If false, the python implementation will be used.
  • I recommend to use cython implementation because of speed.
    • But, to use cython implementation, you must complie this before running.
    • Please refer following: https://github.com/jaywalnut310/glow-tts#2-pre-requisites

• Encoder

  • Setting the encoder parameters

• Decoder

  • Setting the glow decoder parameters.

• WaveNet

  • Setting the parameters of Vocoder.
  • This implementation uses a pre-trained Parallel WaveGAN model.
    • https://github.com/CODEJIN/PWGAN_Torch
  • If checkpoint path is null, model does not exports wav files.
  • If checkpoint path is not null, all parameters must be matched to pre-trained Parallel WaveGAN model.

• Speaker_Embedding

  • Setting the speaker embedding generating method
  • In Type, you can select null, 'LUT', 'GE2E'
    • null: No speaker embedding. Single speaker version
    • LUT: Model will generate a lookup table about the speakers.
    • GE2E: Model will use d-vectors which is generated by a pretrained GE2E model.
      • Pretrained GE2E model is from Speaker_Embedding_Torch

• Token path

  • Setting the token-to-index dict.
  • Pattern generator makes this file.

• Train

  • Setting the parameters of training.

• Inference_Batch_Size

  • Setting the batch size when inference.
  • If null, it will be same to Train/Batch_Size

• Inference_Path

  • Setting the inference path

• Checkpoint_Path

  • Setting the checkpoint path

• Log_Path

  • Setting the tensorboard log path

• Use_Mixed_Precision

  • Setting mixed precision.
  • To use, Nvidia apex must be installed in the environment.
  • In several preprocessing hyper parameters, loss overflow problem occurs.

• Device

  • Setting which GPU device is used in multi-GPU enviornment.
  • Or, if using only CPU, please set '-1'.

Generate pattern

Command

python Pattern_Generate.py [parameters]

Parameters

At least, one or more of datasets must be used.

• -lj
  • Set the path of LJSpeech. LJSpeech's patterns are generated.
• -bc2013
  • Set the path of Blizzard Challenge 2013. Blizzard Challenge 2013's patterns are generated.
• -cmua
  • Set the path of CMU arctic. CMU arctic's patterns are generated.
• -vctk
  • Set the path of VCTK. VCTK's patterns are generated.
• -libri
  • Set the path of LibriTTS. LibriTTS's patterns are generated.
• -vc1
  • Set the path of VoxCeleb1. Glow-TTS does not supports this because VoxCeleb datasets do not have text data.
• -vc2
  • Set the path of VoxCeleb2. Glow-TTS does not supports this because VoxCeleb datasets do not have text data.
• -vc1t
  • Set the path of VoxCeleb1 testset. Glow-TTS does not supports this because VoxCeleb datasets do not have text data.
• -text
  • Set whether the text information save or not.
  • This is for other model. To use in Glow TTS, this option must be set.
• -evalr
  • Set the evaluation pattern ratio.
  • Default is 0.001.
• -evalm
  • Set the evaluation pattern minimum of each speaker.
  • Default is 1.
• -mw
  • The number of threads used to create the pattern
  • Default is 10.

Run

Command

python Train.py -s <int>

• -s <int>
  • The resume step parameter.
  • Default is 0.
  • When this parameter is 0, model try to find the latest checkpoint in checkpoint path.

Inference

• Please check example files for the inference
  • Inference_Example.ipynb
  • Inference.py

Result

Please see at the demo site

Trained checkpoint

Future works

• Training with GE2E speaker embedding
• Gradient reversal model structure improvement
• Training additional steps