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 |
|---|---|
 |
 |
- Dec.01, 2021: DiffSinger was accepted by AAAI-2022.
- Sep.29, 2021: Our recent work
PortaSpeech: Portable and High-Quality Generative Text-to-Speechwas 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.
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 xxx --reset
3. Inference Example
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/lj_ds_beta6.yaml --exp_name xxx --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
- WIP. We will provide a form to apply for PopCS dataset.
1. Data Preparation
- WIP. Similar to DiffSpeech.
2. Training Example
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_ds_beta6.yaml --exp_name xxx --reset
# or
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_ds_beta6_offline.yaml --exp_name xxx --reset
3. Inference Example
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config xxx --exp_name xxx --reset --infer
The pre-trained model for SVS will be provided recently.
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_1218.
(corresponding to the pre-trained model DiffSpeech)
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:
Also thanks Keon Lee for fast implementation of our work.
![size mismatch for model.encoder_embed_tokens.weight: copying a param with shape torch.Size([62, 256]) from checkpoint, the shape in current model is torch.Size([57, 256]).](https://avatars.githubusercontent.com/u/10137?v=4)
1 Feb 09, 2022
41 Dec 30, 2022
186 Dec 29, 2022
3 Jan 25, 2022
1 Nov 08, 2021
103 Dec 10, 2022
1.5k Dec 28, 2022
4 Aug 20, 2022
12 Feb 16, 2022
1 Oct 14, 2021
579 Jan 08, 2023
67 Dec 15, 2022
49 Dec 30, 2022
6.4k Jan 09, 2023
135 Dec 29, 2022
14 May 24, 2022
1.4k Jan 04, 2023
203 Dec 20, 2022
68 Jan 07, 2023
3 Aug 08, 2021