DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

This repository is the official PyTorch implementation of our AAAI-2022 paper, in which we propose DiffSinger (for Singing-Voice-Synthesis) and DiffSpeech (for Text-to-Speech).

Besides, more detailed & improved code framework, which contains the implementations of FastSpeech 2, DiffSpeech and our NeurIPS-2021 work PortaSpeech is coming soon .

DiffSinger/DiffSpeech at training	DiffSinger/DiffSpeech at inference

News:

• Dec.01, 2021: DiffSinger was accepted by AAAI-2022.
• Sep.29, 2021: Our recent work PortaSpeech: Portable and High-Quality Generative Text-to-Speech was accepted by NeurIPS-2021 .
• May.06, 2021: We submitted DiffSinger to Arxiv .

Environments

conda create -n your_env_name python=3.8
source activate your_env_name 
pip install -r requirements_2080.txt   (GPU 2080Ti, CUDA 10.2)
or pip install -r requirements_3090.txt   (GPU 3090, CUDA 11.4)

DiffSpeech (TTS version)

1. Data Preparation

a) Download and extract the LJ Speech dataset, then create a link to the dataset folder: ln -s /xxx/LJSpeech-1.1/ data/raw/

b) Download and Unzip the ground-truth duration extracted by MFA: tar -xvf mfa_outputs.tar; mv mfa_outputs data/processed/ljspeech/

c) Run the following scripts to pack the dataset for training/inference.

export PYTHONPATH=.
CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config configs/tts/lj/fs2.yaml

# `data/binary/ljspeech` will be generated.

2. Training Example

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/lj_ds_beta6.yaml --exp_name lj_exp1 --reset

3. Inference Example

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/lj_ds_beta6.yaml --exp_name lj_exp1 --reset --infer

We also provide:

• the pre-trained model of DiffSpeech;
• the pre-trained model of HifiGAN vocoder;
• the individual pre-trained model of FastSpeech 2 for the shallow diffusion mechanism in DiffSpeech;

Remember to put the pre-trained models in checkpoints directory.

About the determination of 'k' in shallow diffusion: We recommend the trick introduced in Appendix B. We have already provided the proper 'k' for Ljspeech dataset in the config files.

DiffSinger (SVS version)

0. Data Acquirement

• See in apply_form.
• Dataset preview.

1. Data Preparation

a) Download and extract PopCS, then create a link to the dataset folder: ln -s /xxx/popcs/ data/processed/

b) Run the following scripts to pack the dataset for training/inference.

export PYTHONPATH=.
CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config usr/configs/popcs_ds_beta6.yaml
# `data/binary/popcs-pmf0` will be generated.

2. Training Example

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_ds_beta6_offline.yaml --exp_name popcs_exp2 --reset

3. Inference Example

# first run fs2 infer;
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_fs2.yaml --exp_name popcs_fs2_pmf0_1230 --reset --infer 
# second run ds infer;
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_ds_beta6_offline.yaml --exp_name popcs_exp2 --reset --infer

We also provide:

• the pre-trained model of DiffSinger;
• the pre-trained model of FFT-Singer for the shallow diffusion mechanism in DiffSinger;
• the pre-trained model of HifiGAN-Singing which is specially designed for SVS with NSF mechanism.

Note that:

• the original PWG version vocoder in the paper we used has been put into commercial use, so we provide this HifiGAN version vocoder as a substitute.
• we assume the ground-truth F0 to be given as the pitch information following [1][2][3] (because we do not have manually labeled MIDI). If you want to conduct experiments on MIDI data (with external F0 predictor or joint prediction with spectrograms), you may turn on the pe_enable option. Otherwise, the vocoder with NSF could not work well.

[1] Adversarially trained multi-singer sequence-to-sequence singing synthesizer. Interspeech 2020.

[2] SEQUENCE-TO-SEQUENCE SINGING SYNTHESIS USING THE FEED-FORWARD TRANSFORMER. ICASSP 2020.

[3] DeepSinger : Singing Voice Synthesis with Data Mined From the Web. KDD 2020.

Tensorboard

tensorboard --logdir_spec exp_name

Mel Visualization

Along vertical axis, DiffSpeech: [0-80]; FastSpeech2: [80-160].

DiffSpeech vs. FastSpeech 2

Audio Demos

Audio samples can be found in our demo page.

We also put part of the audio samples generated by DiffSpeech+HifiGAN (marked as [P]) and GTmel+HifiGAN (marked as [G]) of test set in resources/demos_1213.

(corresponding to the pre-trained model DiffSpeech)

Update:

New singing samples can be found in resources/demos_0112.

Citation

@misc{liu2021diffsinger,
  title={DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism}, 
  author={Jinglin Liu and Chengxi Li and Yi Ren and Feiyang Chen and Zhou Zhao},
  year={2021},
  eprint={2105.02446},
  archivePrefix={arXiv},}

Acknowledgements

Our codes are based on the following repos:

• denoising-diffusion-pytorch
• PyTorch Lightning
• ParallelWaveGAN
• HifiGAN
• espnet
• DiffWave

Also thanks Keon Lee for fast implementation of our work.