• 更新
• Usage of Refactor Branch
• • Getting Started
  • • 0. Installation
    • 1. Preprocessing
    • 2. Training
    • 3. Inference
• DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
• • Environments
  • Documents
  • Tensorboard
  • Audio Demos
  • • TTS audio samples
    • SVS audio samples
  • Citation
  • Acknowledgements

更新

训练后的模型将自动保存到启智的 结果 里,更新多人

Usage of Refactor Branch

This is a cleaner version of Diffsinger, which provides:

• fewer code: scripts unused in the DiffSinger are marked *isolated*;
• better readability: many important functions are annotated (however, we assume the reader already knows how the neural networks work);
• abstract classes: the bass classes are filtered out into the "basics/" folder and are annotated. Other classes inherent from the base classes.
• better file structre: tts-related files are filtered out into the "tts/" folder, as they are not used in DiffSinger.
• (new) Much condensed version of the preprocessing, training, and inference pipeline. The preprocessing pipeline is at 'preprocessing/opencpop.py', the training pipeline is at 'training/diffsinger.py', the inference pipeline is at 'inference/ds_cascade.py' or 'inference/ds_e2e.py'.

Getting Started

0. Installation

# Install PyTorch manually (1.8.2 LTS recommended)
# See instructions at https://pytorch.org/get-started/locally/
# Below is an example for CUDA 11.1
pip3 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111

# Install other requirements
pip install -r requirements.txt

1. Preprocessing

export PYTHONPATH=.
CUDA_VISIBLE_DEVICES=0 python data_gen/binarize.py --config configs/acoustic/nomidi.yaml

2. Training

CUDA_VISIBLE_DEVICES=0 python run.py --config configs/acoustic/nomidi.yaml --exp_name $MY_DS_EXP_NAME --reset  

3. Inference

CUDA_VISIBLE_DEVICES=0 python run.py --exp_name $MY_DS_EXP_NAME --infer

Easy inference with Google Colab:

Version 1:

Version 2:

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

| Interactive🤗 TTS
| Interactive🤗 SVS

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).

DiffSinger/DiffSpeech at training	DiffSinger/DiffSpeech at inference

🎉 🎉 🎉 Updates:

• Sep.11, 2022: 🔌 DiffSinger-PN. Add plug-in PNDM, ICLR 2022 in our laboratory, to accelerate DiffSinger freely.
• Jul.27, 2022: Update documents for SVS. Add easy inference A & B; Add Interactive SVS running on HuggingFace🤗 SVS.
• Mar.2, 2022: MIDI-B-version.
• Mar.1, 2022: NeuralSVB, for singing voice beautifying, has been released.
• Feb.13, 2022: NATSpeech, the improved code framework, which contains the implementations of DiffSpeech and our NeurIPS-2021 work PortaSpeech has been released.
• Jan.29, 2022: support MIDI-A-version SVS.
• Jan.13, 2022: support SVS, release PopCS dataset.
• Dec.19, 2021: support TTS. HuggingFace🤗 TTS

🚀 News:

• Feb.24, 2022: Our new work, NeuralSVB was accepted by ACL-2022 . Demo Page.
• 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)

Documents

• Run DiffSpeech (TTS version).
• Run DiffSinger (SVS version).

Tensorboard

tensorboard --logdir_spec exp_name

Audio Demos

Old audio samples can be found in our demo page. Audio samples generated by this repository are listed here:

TTS audio samples

Speech samples (test set of LJSpeech) can be found in demos_1213.

SVS audio samples

Singing samples (test set of PopCS) can be found in demos_0112.

Citation

@article{liu2021diffsinger,
  title={Diffsinger: Singing voice synthesis via shallow diffusion mechanism},
  author={Liu, Jinglin and Li, Chengxi and Ren, Yi and Chen, Feiyang and Liu, Peng and Zhao, Zhou},
  journal={arXiv preprint arXiv:2105.02446},
  volume={2},
  year={2021}}

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.